Doctor of Philosophy Mamdouh Ayad Gadalla Prof. Robin ...

RETROFIT DESIGN OF HEAT-INTEGRATED CRUDE OIL

DISTILLATION SYSTEMS

A thesis submitted to the

University of Manchester Institute of Science and Technology

for the degree of

Doctor of Philosophy

by

Mamdouh Ayad Gadalla

Under the direction of

Prof. Robin Smith

Dr. Megan Jobson

Department of Process Integration

University of Manchester Institute of Science and Technology

P.O. Box 88, Sackville Street

Manchester M60 1QD

January 2003

i

Declaration

No portion of the work referred to in this thesis has been submitted in support of an

application for another degree or qualification of this or any other university or other

institution of learning.

Mamdouh Gadalla

ii

To my Mum ‘Neamat’ and my Brother ‘Nabil’, who

continuously gave me the courage, inspiration and the love in

doing this work, but did not live to see it. Now, your memories

will last forever through this work. To Susana Arnal, for her

being with me, encouraging and supporting me. To my dad,

brothers and my whole family, for being always supportive.

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ACKNOWLEDGMENTS

Although words and space are not enough, I would like to deeply express my gratitude

to both my supervisors, Prof. Robin Smith and Dr. Megan Jobson, for their guidance,

encouragement and support throughout my research and study. I found invaluable their

contribution and inspiration which put me in the right direction towards accomplishing

my research. I specially thank Dr. Megan Jobson for her great cooperation during the

stage of writing-up.

Special thanks to the Department of Process Integration for giving me the opportunity

and the sponsorship to study at UMIST and the chance to meet many people.

Throughout my study, I spent a pleasant time with so many wonderful people from

different parts of the globe and of many different cultures; I found one common thing

they all have and share that is kindness.

My sincere thanks go to all staffs at the department of Process Integration, for their help

and support whenever needed. I would like to thank Chris for his help in the

programming part of my research; he really deserves a very big thank. Special thanks to

Steve for his valuable discussion and explanations during my research work.

I am also grateful to all students at the department. Thanks to them for creating a

friendly atmosphere and making a great time in which I spent and worked. Many

colleagues and friends left and new ones came, but they will always stay in my memory.

I cannot name them all because they are too many; I can only say that they are those

people whom I met since I arrived at the department.

I would like lastly to thank all people unmentioned and no longer exist who were at the

background of my life but their effects reach so deep up to the front. I would like to

continuously send deep thanks and love to my mum, Neamat and my brother, Nabil; I

wish they could be here to share and touch their dream. Special thanks go to Susana

Arnal, for being with me when I needed. I also thank my father, my brothers and the

whole family for their support and remembrance.

Last but not least, I give unlimited thanks to the Lord who provides all I need and ask.

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ABSTRACT

Heat-integrated crude oil distillation systems are energy and capital intensive, and have a very complex structure with strong interactions between the individual units. Retrofit of these systems is of major interest to petroleum refiners. Retrofit objectives are various and preferably achieved with minimum capital expenditure, while equipment constraints are met.

Traditional approaches to retrofit design of crude oil distillation systems identify promising modifications based on experience or pinch analysis. Later, sequential approaches to retrofit design were developed, in which distillation and heat recovery units are modified individually. Recent approaches considered simultaneously the distillation column and heat integration targets, rather than the existing heat recovery system. That shortcut models for retrofit design of distillation columns are not available is an additional limitation of established methodologies.

In this thesis, a new approach is presented for retrofit design of heat-integrated crude oil distillation systems. Shortcut models are developed for distillation retrofit, including reboiled and steam-stripped columns. These models are based on the Underwood equation and are appropriate for retrofit design of simple columns and various complex column arrangements. Models are also proposed for exchanger network retrofit, addition of new columns to the existing distillation unit, modifying column internals, enhancing heat transfer in exchanger tubes and for evaluating CO2 emissions in existing crude oil distillation units.

The retrofit design methodology is optimisation-based, and considers the existing distillation process simultaneously with the details of the associated heat recovery system. Existing equipment limitations, such as the hydraulic capacity of the distillation column, exchanger network pressure drop and bottlenecked exchangers, are accounted for. The approach considers various structural modifications and design options resulting in significant benefits. Examples of these are the installation of preflash and prefractionator units to the existing column configuration, replacement of column internals with packing, enhancement of exchanger heat transfer and integration of a gas turbine with an existing furnace.

The optimisation framework comprises column and exchanger network retrofit models, cost models and suitable objective functions. The approach optimises all operating conditions of the existing distillation process and any new columns to minimise or maximise a specified objective function, while satisfying existing constraints. The objective function is flexible and varies according to retrofit objectives. Several objectives are taken into account, such as reducing energy consumption and overall cost, increasing capacity, improving profit and reducing CO2 emissions. The approach allows these objectives to be met by considering several design alternatives.

The new retrofit approach is applied to different industrial cases of crude oil distillation units, for energy and total cost savings, throughput enhancement, product yield changes, profit increase and emissions reduction. Typical results conclude that retrofit goals can be achieved with substantial savings in energy and total cost, and improved profit with minimal capital investment.

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Table of contents Chapter 1: Introduction 1

1.1. Retrofit of heat-integrated crude oil distillation systems 2

1.2. Motivation and objective of the work 4

1.3. Overview of the thesis 5

Chapter 2: Literature review 7

2.1. Introduction 7

2.2. Grassroots design of heat-integrated crude oil distillation systems 8

2.3. Shortcut models for design of distillation columns 10

2.4. Retrofit design of heat-integrated crude oil distillation systems 16

2.4.1. Modifications-based retrofit methods 16

2.4.2. Pinch analysis-based retrofit approaches 19

2.5. Retrofit design of crude oil distillation systems 22

2.6. Retrofit design of heat recovery systems 24

2.7. Concluding remarks 27

Chapter 3: Shortcut models for retrofit design of distillation columns 30

3.1. Introduction 30

3.2. Retrofit models for design of reboiled distillation columns 31

3.2.1. Retrofit shortcut model for simple reboiled distillation

columns 31

3.2.2. Retrofit shortcut model for complex configurations of

reboiled distillation columns 41

3.2.3. Summary 53

3.3. Retrofit models for design of steam-stripped distillation columns 54

3.3.1. Retrofit shortcut model for simple steam-stripped

distillation columns 55

3.3.2. Retrofit shortcut model for complex configurations of

steam-stripped distillation columns 62

3.3.3. Summary 67

3.4. Retrofit modelling for design of refinery distillation columns 68

3.4.1. Illustrative example – an atmospheric crude oil

distillation column 69

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3.5. Summary and conclusions 79

Chapter 4: Modelling for retrofit of heat-integrated crude oil distillation

systems 81

4.1. Modelling existing crude oil distillation columns 81

4.1.1. Hydraulic analysis for existing crude oil distillation

columns 82

4.2. Modelling existing heat exchanger networks 84

4.2.1. Retrofit curve for an existing heat exchanger network 85

4.3. Modelling the installation of preflash units to existing crude oil

distillation units 91

4.3.1. Illustrative example: an existing crude unit with a preflash

drum 95

4.4. Modelling the installation of prefractionator columns to existing

crude oil distillation units 96

4.4.1. Illustrative example: an existing crude unit with a

prefractionator 98

4.5. Modelling carbon dioxide emissions from an existing refinery

distillation for reducing environmental impact 99

4.5.1. Introduction 99

4.5.2. Model for CO2 emissions 101

4.6. Modelling heat transfer enhancement in existing heat exchanger

networks 107

4.6.1. Retrofit model for enhanced heat exchanger networks 107

4.6.2. Pressure drop considerations in enhanced exchanger

networks 110

4.7. Modelling packed sections in existing crude oil distillation

columns 114


Chapter 5: Retrofit design of heat-integrated crude oil distillation systems 122

5.1. Features of heat-integrated crude oil distillation systems 122

5.2. Retrofit design philosophy in refinery distillation systems 125

5.3. Interactions between distillation operating conditions, heat recovery

potential and column hydraulics 126

5.4. New approach for retrofit of heat-integrated crude oil distillation

vii

systems 129

5.4.1. Retrofit design strategies for heat-integrated crude oil

distillation systems 130

5.4.2. Objective functions for optimisation of heat-integrated crude

oil distillation systems 132

5.4.3. Optimisation procedure for existing heat-integrated crude oil

distillation systems 136

5.5. Implementation of retrofit models and applications of retrofit

approach 139

5.5.1. Retrofit for energy reduction 141

5.5.2. Retrofit for throughput enhancement 142

5.5.3. Retrofit for profit increase 143

5.5.4. Retrofit for CO2 emissions reduction 143

5.5.5. Retrofit for product/feed specification changes 143


Chapter 6: Case studies 145

6.1. Reducing energy consumption of an existing atmospheric crude oil

distillation tower 145

6.1.1. Base case problem data 145

6.1.2. Retrofit objective and approach results 149

6.1.3. Comparison of new retrofit approach with previous work 154

6.2. Increasing profit of an existing atmospheric crude oil distillation

tower 157

6.3. Increasing throughput of an existing atmospheric crude oil


6.3.1. Increasing current throughput by 20% over base case

capacity 161

6.3.2. Increasing current throughput to maximum capacity 167

6.4. Reducing CO2 emissions from an existing crude oil distillation

unit 171

6.5. Changing product yields of an existing crude oil distillation unit 175

6.6. Installing preflash drum or prefractionator column in existing crude

oil distillation unit 179

6.6.1. Installing a preflash drum for reducing energy consumption

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and increasing capacity 179

6.6.2. Installing prefractionator column for reducing energy

consumption and increasing capacity 189

6.7. Heat transfer enhancement in preheat train for retrofit of crude oil


6.8. Case studies comparison 202


Chapter 7: Conclusions and future work 206

7.1. Conclusions 206

7.1.1. Shortcut models for retrofit design of distillation columns 206

7.1.2. Retrofit modelling for heat-integrated crude oil distillation

systems 208

7.1.3. Retrofit design methodology and its application 210

7.2. Future work 212

7.3. Closing remarks 213

References 214

Appendix A: Pressure drop correlations 221

A.1. Parameters for pressure drop correlations of Polley et al. (1990) 221

A.2. Pressure drop for enhanced HEN 221

A.3. Pressure drop correlation for packed beds 224

Appendix B: Heat exchanger costs 225

Appendix C: Data for case study 6.1 227

C.1. Problem data 227

C.2. Retrofit curve data for existing HEN 229

C.3. Data of retrofit results for optimum unit 231

C.4. Comparison of new retrofit approach with previous work 233

Appendix D: Data for case study 6.2 236

Appendix E: Data for case study 6.3 239

E.1. Problem data 239

E.2. Data of retrofit results for 20% capacity increase on base case 241

E.3. Retrofit curve data for existing unit with 20% capacity increase 243

E.4. Data of retrofit results for optimum unit with 20% capacity

increase 246

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Appendix F: Data for case study 6.4 249

F.1. Problem data 249

F.2. Results for retrofit with integrated gas turbine 249

F.3. Results for retrofit without integrated gas turbine 252

Appendix G: Data for case study 6.5 254

Appendix H: Data for case study 6.6.1 257

Appendix I: Data for case study 6.6.2 262

Appendix J: Data for case study 6.7 273

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List of figures Figure 3.1: Simple distillation column with reboiler 32

Figure 3.2: Sequences of two simple reboiled distillation columns 39

Figure 3.3: Decomposition of reboiled complex distillation columns

(with full thermal coupling), showing distribution of existing

number of stages 42

Figure 3.4: Simple uncoupled sequences versus thermally coupled sequences 43

Figure 3.5: Retrofit algorithm for complex column configurations 44

Figure 3.6: Thermal coupled complex column configurations (direct and

indirect sequences) 47

Figure 3.7: Different configurations of complex columns with prefractionators 50

Figure 3.8: Modelling of column with a prefractionator and Petlyuk column 51

Figure 3.9: Simple distillation column using stripping steam 56

Figure 3.10: Retrofit algorithm for stripping sections 58

Figure 3.11: Sequences of direct and indirect simple steam-stripped

distillation columns 59

Figure 3.12: Decompositions of steam-stripped complex columns,

showing distribution of existing number of stages (full thermal

coupling) 63

Figure 3.13: Thermal coupled complex columns with steam (direct and

indirect sequences) 66

Figure 3.14: Atmospheric crude oil distillation column, showing the

equivalent sequence of simple columns 73

Figure 3.15: Light naphtha composition for shortcut and rigorous models 75

Figure 3.16: Heavy naphtha composition for shortcut and rigorous models 75

Figure 3.17: Light distillate composition for shortcut and rigorous models 76

Figure 3.18: Heavy distillate composition for shortcut and rigorous models 76

Figure 3.19: Residue composition for shortcut and rigorous models 77

Figure 3.20: True boiling curves of light naphtha for shortcut and rigorous

models 77

Figure 3.21: True boiling curves of heavy naphtha for shortcut and rigorous

models 78

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Figure 3.22: True boiling curves of light distillate for shortcut and rigorous

models 78

Figure 3.23: True boiling curves of heavy distillate for shortcut and rigorous

models 79

Figure 3.24: True boiling curves of residue for shortcut and rigorous models 79

Figure 4.1: Existing heat exchanger network of a crude oil distillation unit 85

Figure 4.2: Retrofit curve of zero and one modification to the HEN topology 88

Figure 4.3: Retrofit curve of an existing HEN 89

Figure 4.4: Procedure of obtaining retrofit model of an existing HEN 90

Figure 4.5: A crude oil distillation column with a preflash unit 91

Figure 4.6: Sketch for preflash calculation variables 92

Figure 4.7: Preflash vapour mixing scheme 93

Figure 4.8: An atmospheric crude oil tower with a prefractionator column 97

Figure 4.9: Sources of CO2 emissions from a crude oil distillation unit 102

Figure 4.10: Retrofit curve for enhanced heat exchanger networks 110

Figure 4.11: Regression of flooding data from Seader and Henley (1998) 116

Figure 4.12: Regression of density data from Seader and Henley (1998) 117

Figure 4.13: Regression of viscosity data from Seader and Henley (1998) 118

Figure 5.1: Heat-integrated crude oil distillation system including atmospheric

and vacuum towers and naphtha splitter 124

Figure 5.2: Simultaneous retrofit strategy for heat-integrated distillation

systems 132

Figure 5.3: CO2 emissions reduction strategy for refinery distillation systems 136

Figure 5.4: Simultaneous retrofit strategy for heat-integrated distillation

systems 137

Figure 5.5: Overall procedure for heat-integrated crude oil distillation systems 141

Figure 6.1: Atmospheric crude oil distillation column, showing the

equivalent sequence of simple columns (numbers in (a) refer to

section numbers, while in (b) refer to column numbers) 147

Figure 6.2: Structure of existing heat exchanger network (streams data are

given in Table C.1.5, Appendix C) (light shaded exchangers

indicate coolers; dark shaded exchangers indicate heaters) 147

Figure 6.3: Modifications to existing heat exchanger network 152

Figure 6.4: Relocating exchanger number 4 in the existing exchanger network 153

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Figure 6.5: Existing crude oil distillation column (numbers refer to section

numbers) 168

Figure 6.6: Preheat train structure of existing crude oil distillation unit 169

Figure 6.7: Simulated stage diameter for current operation (stages from

top of column to bottom) (tray sizing using HYSYS) 170

Figure 6.8: FUA plot for existing distillation column (column stages:

top-bottom) 170

Figure 6.9: Simulated stage diameter for maximum capacity increase (stages

from column top-down) (tray sizing using HYSYS) 171

Figure 6.10: Temperature profile for existing unit with preflash 172

Figure 6.11: Crude oil stream routing in existing HEN with preflash 183

Figure 6.12: Location of preflash vapour to enter main column 184

Figure 6.13: Temperature profile for optimum unit with preflash 185

Figure 6.14: Column diameter for base case and unit with preflash (stages from

top to bottom of the column) (tray sizing using HYSYS) 187

Figure 6.15: Column diameter for 38% capacity increase of unit with

preflash, compared with existing unit and base case (stages:

top-bottom) 188

Figure 6.16: Reconfiguration of existing crude oil distillation tower with

installed prefractionator 190

Figure 6.17: Location of installed prefractionator in existing preheat train 191

Figure 6.18: FUA curve for unit with prefractionator (stages from column

top-down) 197

Figure 6.19: Simulated column diameter for 59% increased throughput to unit

with prefractionator (number of stages is top-down) (tray sizing

using HYSYS) 198

Figure B.1: Exchanger cost data from various sources (45 and 35% represent

the ratios of the installation cost of exchangers to their purchase

costs as recommended by Peters and Timmerhaus, 1980) 225

Figure B.2: Exchanger cost curve, showing model regression parameters

(data used for regressions are in Table B.1) 226

Figure C.2.1: Retrofit curve and data regression for existing exchanger network

(Aret: area obtained from retrofit study, Amodel: area obtained

from regressed model) 230

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Figure C.2.2: Relocation of exchanger 4 in existing HEN (points 2, 2′) 231



Figure C.2.5: Introduction of new exchanger in existing HEN (points 5, 5′) 231

Figure C.4.1: Relocation of exchanger 4 for network pinch approach results 234

Figure D.1: Modifications to existing heat exchanger network 236

Figure E.2.1: Modifications to existing heat exchanger network for optimum unit

with 20% capacity increase 241

Figure E.3.1: Retrofit curve and data regression (Aret: area obtained from

retrofit study, Amodel: area obtained from regressed model) 244

Figure E.3.2: Relocation of exchanger 1 in existing HEN (points 2, 2′) 245


Figure E.3.4: Introduction of new exchanger in existing HEN (points 4, 4′) 245


Figure E.3.6: Comparison of retrofit model of unit with 20% increased

throughput with existing throughput (vertical dotted line

represents existing energy demand) 246

Figure E.4.1: Modifications to existing heat exchanger network for optimum

unit with 20% capacity increase 247

Figure F.2.1: Relocation of exchanger 4 for optimum network (with integrated

gas turbine) 251

Figure F.3.2: Relocation of exchanger 4 for optimum network 252

Figure H.1: Relocation of exchanger 8 for optimum unit with preflash 261

Figure H.2: FUA curve for existing unit with preflash (stages from column

top to bottom) 261

Figure I.1: Redistribution of stages into sections of decomposed column

sequence of main distillation column 262

Figure I.2: Heat exchanger network for unit with prefractionator (see Table I.2,

for stream references) 264

Figure I.3: Relocation of exchanger 7 in existing for optimum unit

with prefractionator 270

Figure I.4: Stage diameter of unit with prefractionator (stages from column

top to bottom) 270

Figure J.1: Existing exchangers to be enhanced, crude oil is split into 2

xiv

branches 273

Figure J.2: Retrofit curve and data regression for existing HEN with heat

transfer enhancement (HTE) (horizontal dotted line represents

the existing exchanger total area) 276

Figure J.3: Retrofit modifications to existing preheat train, showing types

of exchanger additional area 278

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List of tables Table 3.1: Typical degrees of freedom for design of reboiled distillation

columns 39

Table 3.2: Feed data and column specifications 40

Table 3.3: Retrofit shortcut and rigorous model results (HYSYS) 41

Table 3.4: Typical degrees of freedom for design of fully thermally

coupled sequences of reboiled distillation columns 45

Table 3.5: Column specifications for thermally coupled 46

Table 3.6: Retrofit shortcut and rigorous model results 46


Table 3.8: Data for column with a prefractionator 53

Table 3.9: Results for column with prefractionator 54

Table 3.10: Typical degrees of freedom for steam-stripped distillation columns 60

Table 3.11: Feed mixture data and column specifications 61

Table 3.12: Retrofit shortcut and rigorous simulation (HYSYS) results 61

Table 3.13: Column and product specifications 65



Table 3.16: Typical degrees of freedom for atmospheric crude oil tower 69

Table 3.17: Crude oil assay data 71

Table 3.18: Key components for the separation of each pair of products 72

Table 3.19: Feed composition of crude oil mixture (derived from assay data) 72

Table 3.20: Specifications of atmospheric crude oil distillation column 73

Table 3.21: Results of atmospheric crude oil distillation column 74

Table 4.1: Preflash column results 95

Table 4.2: Product flow rates of an existing crude oil unit 99

Table 6.1: Crude oil assay data 148

Table 6.2: Key components for separation of crude oil products

(recoveries are presented in Table 6.4) 148

Table 6.3: Number of stages and existing diameters of distillation tower

sections 148

Table 6.4: Operating conditions of base case for crude oil distillation tower 148

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Table 6.5: Optimisation variables for crude oil distillation tower 150

Table 6.6: Optimum values of operating conditions 151

Table 6.7: Energy consumption and operating costs for optimum unit 152

Table 6.8: Comparison of new approach results with previous work 156

Table 6.9: Comparison of additional area to existing exchanger 156

Table 6.10: Products income and operating costs of existing unit 158

Table 6.11: Retrofit results of optimum unit with maximum profit 159

Table 6.12: Optimum values of operating conditions for maximum profit 159

Table 6.13: Comparison of energy reduction and increasing profit optimum

cases 160

Table 6.14: Energy consumption, operating costs and profit for base case 162

Table 6.15: Column diameter required for base case and actual diameter 162

Table 6.16: Column diameter for 20% increase in throughput on base case 163

Table 6.17: Process requirements for 20% increase in throughput 163

Table 6.18: Optimisation results for optimum unit with 20% increase in

capacity 165

Table 6.19: Optimum values of operating conditions for 20% capacity increase 166

Table 6.20: Column diameter for optimum unit with 20% capacity increase 166

Table 6.21: Number of stages and existing diameters of distillation tower

sections 168

Table 6.22: CO2 emissions from base case of crude oil distillation tower 172

Table 6.23: CO2 emissions from optimised unit with integrated gas turbine 173

Table 6.24: CO2 emissions from optimum unit without integrated gas turbine 175

Table 6.25: Product yields of base case 176

Table 6.26: Key components of separation for product yield changes 176

Table 6.27: Product yields of new unit with yield changes 177

Table 6.28: Energy consumption and operating costs for new product yield

units 177

Table 6.29: Retrofit results for base case with preflash 180

Table 6.30: Energy consumption and operating costs for optimum unit with

preflash 181

Table 6.31: Energy consumption and operating costs for optimum unit with

preflash 184

Table 6.32: Comparison of results of unit with different locations of preflash

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vapours 185

Table 6.33: Retrofit results for existing unit with prefractionator 192

Table 6.34: Optimum results of unit with prefractionator 194

Table 6.35: Optimum results of unit with prefractionator (all operating

conditions) 195

Table 6.36: Energy and cost requirement and profit for maximum capacity

increase 198

Table 6.37: Optimisation results of optimum unit with maximum capacity

increase 199

Table 6.38: Retrofit approach results for optimum unit with heat

transfer enhancement (HTE) 201

Table 6.39: Case studies comparison 202

Table B.1: Exchanger area and cost data 226

Table C.1.1: Feed composition of crude oil mixture 227

Table C.1.2: Utility, stripping steam and exchanger unit costs 227

Table C.1.3: Energy consumption and operating costs for existing unit 228

Table C.1.4: Heat exchanger data 228

Table C.1.5: Process and utility stream data for existing unit 229

Table C.2.1: Retrofit data for 229

Table C.3.1: Product flow rates of optimum unit 232

Table C.3.2: Key component recoveries of products for optimum unit 232

Table C.3.3: Process stream data for optimum unit 232

Table C.3.4: Additional area and cost for case study 6.1 233

Table C.4.1: Additional area and cost for network pinch approach (no

modifications to column operating conditions) 234

Table C.4.2: Comparison of optimisation results with minimum energy approach

of Bagajewicz (1998) 234

Table C.4.3: Additional area and cost for energy-based approach 235

Table D.1: Product value and crude oil price 236

Table D.2: Additional area and capital cost 237

Table D.3: Process stream data for optimum unit of maximum profit 237

Table D.4: Key component recoveries of products for maximum profit 238

Table E.1.1: Operating conditions of base case 239

Table E.1.2: Heat exchanger data 239

xviii

Table E.1.3: Process and utility stream data for base case 240

Table E.2.1: Operating conditions of 20% capacity increase on base case 241

Table E.2.2: Additional area and cost for 20% increase on base case 242

Table E.2.3: Process and utility stream data for 20% increase of base case

capacity 243

Table E.3.1: Retrofit data 243

Table E.4.1: Operating conditions of optimum unit with 20% capacity increase 246

Table E.4.2: Additional area and cost for optimum unit with 20% capacity

increase 247

Table E.4.3: Process stream data for optimum unit with 20% capacity increase 248

Table F.1.1: Data of hot utilities for case study 249

Table F.1.2: Data for heating fuels in CO2 emissions 249

Table F.2.1: CO2 emissions from optimum unit with integrated gas turbine 249

Table F.2.2: Cost and economic parameters for CO2 emissions calculation 250

Table F.2.3: Optimum values of operating conditions 250

Table F.2.4: Additional area and cost for optimum unit 251

Table F.2.5: Process stream data for optimum unit 251

Table F.3.1: Optimum values of operating conditions 252

Table F.3.2: Additional area and cost for optimum unit 252

Table F.3.3: Process stream data for optimum unit 253

Table G.1: Operating conditions for new product yields 254

Table G.2: Additional area and cost for unit with new product yields 254

Table G.3: Process stream data for unit with new product yields 255

Table G.4: Optimum operating conditions for unit with new product yields 255

Table G.5: Additional area and cost for optimum unit with new product yields 256

Table G.6: Process stream data for optimum unit with new product yields 256

Table H.1: Process stream data for base case with preflash 257

Table H.2: Additional area and cost for base case with preflash 257

Table H.3: Optimum operating conditions for unit with preflash drum 258

Table H.4: Process stream data for optimum unit with preflash 258

Table H.5: Additional area and cost for optimum unit with preflash 259

Table H.6: Optimum operating conditions for unit with preflash drum 259

Table H.7: Product flow rates of optimum unit with preflash 259

Table H.8: Additional area and cost for optimum unit with preflash 260

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Table H.9: Process stream data for optimum unit with preflash 260

Table I.1: Number of stages and of main distillation tower sections 262

Table I.2: Design specifications of prefractionator column 262

Table I.3: Operating conditions of main column of unit with prefractionator 263

Table I.4: Product flow rates of unit with prefractionator 263

Table I.5: Key component recoveries of products for unit with

prefractionator 263

Table I.6: Process streams for base case and unit with prefractionator 264

Table I.7: Process stream data for unit with prefractionator 265

Table I.8: Additional area and cost for unit with prefractionator 265

Table I.9: Operating conditions of main column of optimum

unit with prefractionator 266

Table I.10: Product flow rates of optimum with prefractionator 266

Table I.11: Key component recoveries for optimum unit with prefractionator 266

Table I.12: Process stream data for optimum unit with prefractionator 267

Table I.13: Additional area and cost for optimum unit with prefractionator 267

Table I.14: Optimum operating conditions for unit with prefractionator 268

Table I.15: Product flow rates of optimum with prefractionator 268

Table I.16: Key component recoveries for optimum unit with prefractionator 268

Table I.17: Process stream data for optimum unit with prefractionator 269

Table I.18: Additional area and cost for optimum unit with prefractionator 269

Table I.19: Optimum operating conditions for unit with prefractionator

(59% increase) 270

Table I.20: Optimum design specifications of unit with prefractionator

(59% increase) 271

Table I.21: Product flow rates of optimum with prefractionator (59% increase) 271

Table I.22: Recoveries of products for unit with prefractionator (59% increase) 271

Table I.23: Additional area for optimum unit with prefractionator

(59% increase) 272

Table I.24: Process stream data for optimum unit with prefractionator

(59% increase) 272

Table J.1: Data for exchanger units 273

Table J.2: Maximum area for heat transfer enhancement 273

Table J.3: Calculations of model parameters for retrofit with enhancement 274

xx

Table J.4: Calculations of model parameters for retrofit with enhancement 274

Table J.5: Optimum operating conditions for unit with heat transfer

enhancement 276

Table J.6: Product flow rates of optimum unit with enhancement 277

Table J.7: Key component recoveries of products for optimum unit

with enhancement 277

Table J.8: Additional and enhancement area for optimum unit with

enhancement 277

Table J.9: Process stream data for optimum unit with heat transfer

enhancement 278

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Nomenclature and abbreviation a1, a2 Constant parameters for equation 4.51 A Exchanger area (m2) Aen Enhanced exchanger area (m2) Aex, Bex, Cex Cost parameters in equation 5.1 ($, $/m2, dimensionless) Aexist Existing area of heat exchanger network (m2) Ao, Bo, Co Constant parameters for flooding limits in equation 4.1 Areq Additional exchanger area for retrofit (m2) Aret Retrofit exchanger area given by equation 4.9 (m2) b0, b1, b2, b3 Retrofit model parameters of polynomial form in equation 4.8 B Molar flow rate of bottom product (kmol/h) c1, c2 Cost parameters in equation 5.2 ($, $/m2) C% Carbon mass percent in fuel CC Composite curve CGCC Column grand composite curve CO2 Emiss Carbon dioxide emissions (kg/h) CostFInst Installed cost of preflash ($) CostGT Capital cost of gas turbines (k$) Costpack Installed cost of packed section ($) CostPower

GT Cost of power generated by gas turbines ($/h) CostPrShell Shell installed cost of prefractionator column ($) CostPrStages Stages installed cost of prefractionator column ($) CP Specific heat capacity of flue gases (kJ/kg oC) CSB Souder and Brown flooding constant (capacity factor) (ft/s) d Tube diameter in Appendix A (m) D Molar flow rate of top product (kmol/h) DC Ratio of down-comer area to cross sectional area of stage (≈12%) de Tube bundle equivalent diameter in Appendix A (m) DFlash Diameter of preflash (m) di Molar flow rate of component i in top product (kmol/h) Dpack Diameter of packing (ft) DPrColumn Diameter of prefractionator column (m) (DR)HK Distribution ratio of heavy key component (DR)i Distribution ratio of component i DT Stage diameter (ft) E Energy consumption of existing HEN (MW) EnCostex Cost of enhancement material ($) f Fraction of flooding velocity (0.5 to 0.7) F Molar flow rate of feed (kmol/h) FLV Flow parameter Fm Correction factor for column material of construction in equation

4.20 Fp Correction factor for column pressure in equation 4.20 Fpack Packing factor (ft2/ft3) Fpd Dry-bed packing factor (ft-1) FUA Fractional of utilised area FuelFact Fuel factor defined in equation 4.26 f{f×uo} Actual velocity function defined (calculated from equation 4.64)

xxii

f{uo} Flooding velocity function defined in equation 4.64 f{µL} Liquid viscosity correction factor given by equation 4.67 f{ρL} Liquid density correction factor given by equation 4.66 g0, g1, g2, g3, g4 Regression parameters in equation 4.66 g Acceleration due to gravity (32.2 ft/s2) GT Gas turbine h0, h1, h2, h3, h4 Regression parameters in equation 4.64 HColumn Enthalpy of crude oil before entering distillation tower (kW) HD Heavy distillate product HETP Height equivalent to a theoretical plate (ft) HF Enthalpy of crude oil before preflash exchanger (kW) HFI Enthalpy of crude oil before entering preflash column (kW) HFlash Height of preflash (m) HK Light key component HL Enthalpy of bottom liquid from preflash (kW) HV Enthalpy of top vapour from preflash (kW) Hmix Enthalpy of vapour mixture (kW) HN Heavy naphtha product Hn Enthalpy of vapour from column n (kW) HP High pressure HPrColumn Height of prefractionator column (m) hProc Enthalpy of steam delivered to process (kJ/kg) hshell Shell side heat transfer coefficient (W/ m2 oC) hshell

exist Shell side heat transfer coefficient in parallel arrangement (W/ m2

oC) HTE Heat transfer enhancement htube

exist Tube side heat transfer coefficient in parallel arrangement (W/ m2

oC) htube

en Enhanced tube side heat transfer coefficient (W/ m2 oC) HXCostNex Capital cost of heat exchanger ($) K Scaling parameter for penalty function in equation 5.3 K1, K2 Parameters for pressure drops (equations 4.49, 4.50) k1, k2, k3 Parameters for pressure drop correlation in Appendix A k′1, k′2, k′3 Parameters for pressure drop correlation in Appendix A L Flow rate of liquid (kmol/h) (only in equation 4.2, units are kg/h) LD Light distillate product LF Gas loading factor in Robbins correlation (lb/h ft2) LFlash Molar flow rate of bottom liquid from preflash (kmol/h) Lflux Mass flow rate of liquid flux (lb/h ft2) LK Light key component LN Light naphtha product m0, m1 Retrofit model parameters in equation 5.4 (m2/MW,

m2.h/MW.kmol) m, c Retrofit model parameters of power form in equation 4.7

(m2/MW, -) m', c' Retrofit model parameters of power form in equation 4.48

(m2/MW, -) MFG Flow rate of flue gas (kg/s) MINLP Mixed integer non-linear programming

xxiii

MSIndex Marshall and Swift index for equipment costs scaling up (1094.3 for the year 2001)

n0, n1 Retrofit model parameters in equation 5.4 (-, h/kmol) N Total number of theoretical stages NActual Actual total number of stages in distillation column NHV Fuel net heating value (kJ/kg) NLP Non-linear programming Nmin Minimum number of stages at total reflux NOVLDTray Number of overloaded (bottlenecked) trays NR Number of theoretical stages in rectifying section NRActual Number of actual stages in rectifying section NS Number of theoretical stages in stripping section NSActual Number of actual stages in stripping section NSpacing Stage spacing (m) (≈ 0.60) NTP Number of theoretical plates P Pressure (Pa) PA Pump-around PCKVcost Cost of packing per unit volume ($/ft3) Penalty Value of penalty function Pmix Pressure of vapour mixture (Pa) Pn Pressure of vapour from column n (Pa) Pr Prandtl Number in Appendix A Pt Tube pinch in Appendix A (m) PUnitCost Unit cost of power produced in gas turbines ($/kW h) p, z Pressure drop model constant parameters (Pa/MW, -) q1, q2 Constants in Robbins correlation given by equation 4.70, 4.71 q Liquid fraction of feed at feed stage conditions Q Heat load of heat exchanger (kW) QFuel Amount of fuel burnt in utility devices (kW) QPreflash Heat duty of required for preflash column (kW) QPreheat Heat duty of furnace (kW) QProc Heat duty required by process (kJ/kg) QFuel

Furn Amount of fuel consumed in a furnace (kW) QFuel

GT Amount of fuel consumed in a gas turbine (MW) QFuel

PS Reduction in fuel consumption at a power station (kW) QFurn Process heat duty provided by a furnace (kW) QGT Process heat duty provided by a gas turbine (kW) r Split ratio for the new to existing shell branches R Reflux ratio RES Residue product RFlash Ratio of height to diameter of preflash RHK Recovery of heavy key component to bottom product RLK Recovery of light key component to top product Rmin Minimum reflux ratio SS Side-stripper TBP True boiling point (oC) TExhaust Exhaust gas temperature from a gas turbine (oC) TFTB Flue gases flame temperature in boilers (oC) TFTF Flue gases flame temperature in furnaces (oC)

xxiv

Tinlet Inlet temperature to the gas turbine (combustion temperature) (oC)

To Ambient temperature (oC) Toutlet Outlet temperature from the gas turbine (flue gas temperature)

(oC) TStack Stack temperature (oC) U Overall heat transfer coefficient for existing exchanger (W/ m2

oC) Udes Design velocity “actual velocity” (ft/s) Uen Overall heat transfer coefficient for enhanced exchanger (W/ m2

oC) Umax Flooding velocity (ft/s) uo Flooding velocity in a packed column (ft/s) Uratio Ratio of design velocity to flooding velocity (70-80%) V Flow rate of vapour (kmol/h) (only in equation 4.2, units are

kg/h) VF Gas loading factor in Robbins correlation (lb/h ft2) VFlash Molar flow rate of top vapour from preflash (kmol/h) Vflux Mass flow rate of liquid flux (lb/h ft2) VL Volumetric flow rate of flashed liquid (m3/s) Vmin Minimum molar vapour flow rate in top section (kmol/h) V′min Minimum molar vapour flow rate at bottom pinch (kmol/h) Vmin,top: Minimum molar vapour flow rate in top section (kmol/h) Vmix Flow rate of vapour mixture (kmol/h) Vn Flow rate of vapour from column n (kmol/h) VS Volumetric flow rate in shell side in Appendix A (m3/s) VT Volumetric flow rate in tube side in Appendix A (m3/s) VV Volumetric flow rate of vapour (ft3/s)) WGT Power produced in a gas turbine (kW) X Molar composition of bottom liquid from preflash xbLK Mole fraction of light key component in bottom product xdHK Mole fraction of heavy key component in top product xdLK Mole fraction of light key component in top product xf,i Mole fraction of component i in feed xfHK Mole fraction of heavy key component in feed xfLK Mole fraction of light key component in feed xHKFS Liquid mole fraction of heavy key component on feed stage xi Dependent variable in optimisation, equation 5.3 xLKFS Liquid mole fraction of light key component on feed stage xi,lim Constrained value for variable in optimisation, equation 5.3 x, y Number of carbon and hydrogen atoms respectively in equation

4.24 XX Location of entering vapours from preflash to distillation column Y Molar composition of top vapour from preflash Ymix Molar composition of vapour mixture Yn Molar composition of vapour from column n Z Molar composition of feed to preflash

xxv

Greek letters

α Ratio of molar masses of carbon dioxide and carbon (=3.67) αHK Volatility of heavy key component relative to a reference

component αi Volatility of component i relative to a reference component αLK Volatility of light key component relative to a reference

component ∆A Additional area to an existing exchanger unit (m2) ∆Amax Maximum area that can be achieved by enhancing an exchanger

(m2) ∆Atotal

max Total maximum area that can be achieved by enhancing a HEN (m2)

∆HPrColumn Extra height added to prefractionator column (m) ∆P Pressure drop for a plain tube (Pa) ∆PC.O. Pressure drop for the crude oil stream in preheat train (Pa) ∆Pen Pressure drop for an enhanced exchanger (Pa) ∆Pshell Pressure drop on shell side (Pa) ∆Pshell

exist Pressure drop of existing shell (Pa) ∆Pshell

new Pressure drop of new shell (Pa) ∆Pshell

total Total pressure drop of existing and new shells (Pa) ∆Pflood Pressure drop in a packed bed at flooding (in. H2O/ft height) ∆Ppack Pressure drop in a packed bed (in. H2O/ft height) ∆Ptube Pressure drop in tube sides (Pa) ∆T Temperature drop through pump-around (oC) ∆TLM Logarithmic mean temperature difference (oC) ∆Tmin Minimum difference temperature approach (oC) φ Roots of Underwood equation φFensk Fenske term given by equation 3.6 to simplify equation 3.5 φKirk Kirkbride term given by equation 3.17 to simplify equation 3.16 Φ Friction factor in Appendix A ηC Carnot Factor of gas turbine ηFurn Furnace efficiency ηGT Gas turbine heat recovery efficiency ηOP Overall plate efficiency ηOTray Overall tray efficiency of distillation column ηRTray Tray efficiency of rectifying section ηSTray Tray efficiency of stripping section κ, υ Constants in Coker correlation for pressure drop in Appendix A λ Thermal conductivity in Appendix A (W/m2 K) λProc Latent heat of steam delivered to process in Appendix A (kJ/kg) µ Viscosity (kg/m s) µF,avg Average viscosity of feed at section temperature and pressure

(cP) µL Liquid viscosity (cP) θR Residence time in preflash (s) ρ Density in Appendix A (kg/m3)

OHρ 2 Density of water (62.4 lb/ft3)

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ρL Mass density of liquid (kg/m3) (in equations 4.74, 4.75 only, units are lb/ft3)

ρV Mass density of vapour (kg/m3) (in equations 4.72, 4.73 only, units are lb/ft3)

ϕSteam Stripping steam term given by equation 3.31 to simplify equation 3.30

σ Surface tension (dyne/cm) ξ Term defined by equations 2.8, 3.9 and 3.34 ψGill Gilliland term in equations 3.7, 3.32

Subscripts and superscripts

C Carnot factor C.O. Crude oil stream des Design en Heat transfer enhanced exist Existing unit F,avg Feed average property FG Flue gases Fensk Fenske equation flood Flooding condition Furn Furnace Gill Gilliland equation GT Gas turbine HK Heavy key component Kirk Kirkbride correlation lim Limiting value for a variable LK Light key component LM Logarithmic mean max Maximum condition min Minimum condition mix Mixture property OP Overall plate property OTray Overall tray property OVLDTray Overloaded or bottlenecked trays Pack Packed section Proc Distillation process PS Central power station R Residence time, Rectifying section RActual Rectifying actual trays req Required ret Retrofit design RTray Rectifying tray section S Stripping section SActual Stripping actual trays SB Souder and Brown constant shell Shell-side Steam Stripping steam STray Stripping tray section tube Tube-side

Chapter 1 Introduction

1

Chapter 1: Introduction

Crude oil distillation is a process of major importance in the refining industry. This

process is energy and capital intensive. An existing crude oil distillation unit is a

complex structure with highly interlinked columns. The crude oil distillation column has

strong connections with the associated heat recovery system, i.e. the distillation column

is integrated with the heat recovery system. The operating variables of the distillation

process (e.g. feed preheating temperature, steam flow rates, pump-around duties, reflux

ratio, etc.) and the existing hardware of the heat recovery system affect opportunities for

heat recovery and throughput enhancement. Retrofit of crude oil distillation systems for

energy savings, capacity increase, emissions reductions and other objectives is of major

concern to the refining industry. Retrofit objectives can be achieved by changing the

operating conditions of the distillation column and increasing heat recovery or by

modifying the structure of the existing distillation and heat recovery units.

Few new crude oil distillation units are built; on the other hand, projects revamping

existing equipment are common. Rather than install new equipment, refinery managers

prefer to exploit existing units for more profit. Retrofit objectives are conventionally

energy saving and increasing production capacity; however, economic and

environmental incentives drive other objectives, such as improving process profit,

reducing greenhouse emissions, processing new feedstock or changing product yields.

Many retrofit objectives are closely related; for example, energy retrofit projects are

accompanied by reducing emissions; the associated reduction in vapour loads may

allow both the column and the preheat train to process more crude oil feed.

Retrofit goals are best achieved by reusing existing equipment more efficiently, with

minor modifications to the existing equipment, rather than installing new units and

incurring greater capital investment. While achieving these objectives, the existing

equipment and process constraints, such as distillation hydraulic capacity and crude oil

feed stream pressure drop, must be met.

The interactions between the existing distillation process and heat recovery system have

a critical effect on the retrofit of the overall process. These interactions are the operating

conditions of the distillation column, including feed preheating temperature, steam flow

rate, pump-around duties and flow rates and reflux ratio, in addition to the existing


2

exchanger matches and areas of the heat recovery system. Changing the operating

conditions may benefit the heat recovery in the exchanger network and enhance the

processing capacity of the distillation column. On the other hand, existing exchanger

matches and hardware of the heat recovery system affect opportunities for heat

recovery. Furthermore, modifying the column structure by adding preflash or

prefractionator units, changing the column internals, or integrating a gas turbine with

the furnace can increase the heat recovery, allow more capacity to be processed and

help the overall system reduce the combustion emissions.

This thesis presents a retrofit design methodology and modelling and optimisation

framework that aims to identify quantitatively the operating conditions and structural

changes, such as adding a preflash drum or a prefractionator unit, integrating a gas

turbine, etc., that can best achieve a specified retrofit objective. The approach addresses

both the distillation column and the heat exchanger network to maximise use of existing

equipment.

1.1. Retrofit of heat-integrated crude oil distillation systems

For retrofitting a crude oil distillation system (i.e. the distillation column, side-strippers

and associated heat exchanger network), the existing system has to be modified. Which

modifications will be beneficial is a key design issue that changes according to the

existing system together with the retrofit goal. Modifications might be made to the

distillation process or to the heat recovery system. Modifications that can be made have

different cost implications, ranging from zero cost to high capital investment. Examples

of these modifications and their investment requirements can be summarised as follows

(Fraser and Sloley, 2000):

1. Changing the operating conditions of the distillation process. These modifications

do not necessarily require any fixed cost.

2. Repiping the existing exchangers to improve the heat recovery of the process

without the purchase of new exchanger units. The investment for repiping is

moderate.

3. Existing equipment can be modified in various ways. For example, internals can be

changed in the heat exchangers (e.g. tube bundles) or distillation columns (e.g.


3

packing) and the external size may be modified (e.g. add to the height of a column).

Clearly, these modifications require some capital investment.

4. The purchase of new equipment is normally the most costly retrofit decision,

although adding a new shell to an existing heat exchanger is much cheaper than

purchasing a new distillation column or a new compressor. Installing new

equipment may be combined with modifying piping to change the configuration of

the process.

The above modifications are more beneficial for retrofit when they are applied in

parallel with the exploitation of the interactions between the distillation process and the

associated heat recovery system. Many efforts have been devoted to solve the retrofit

design problem of heat-integrated crude oil distillation systems. Early research

concentrated on applying engineering experience and heuristics to propose

modifications separately to the distillation column and the heat exchanger network in

order to reduce energy consumption. These modifications include the installation of new

column internals with higher efficiency and the use of intermediate reboilers (e.g. Sittig,

1978), the installation of pump-arounds (e.g. Bannon and Marple, 1978), the addition of

preflash drums and prefractionator columns (e.g. Harbert, 1978), and changing

operating conditions (e.g. Fraser and Sloley, 2000; Liu, 2000).

Other researchers used pinch analysis principles to identify modifications to distillation

columns for reducing energy consumption and improving the performance of the

system. (e.g. Linnhoff et al., 1983; Dhole and Linnhoff, 1993a; Dhole and Buckingham,

1994). Liebmann (1996) proposed a two-step (sequential) approach for improving the

performance of refinery distillation columns, based on insights derived from pinch

analysis. Bagajewicz (1998) extended the approach to optimise existing refinery

distillation columns by incorporating pinch analysis principles within a rigorous-based

simulation framework. Recent work was carried out by one research group (Bagajewicz

and Ji, 2001; Bagajewicz and Soto, 2001; Ji and Bagajewicz, 2002a; b; c) for grassroots

design of heat-integrated crude oil distillation units. The objective was to design energy-

efficient distillation units and heat exchanger networks that can handle different types of

crude oils. The design was applied to conventional atmospheric towers, stripping-type

columns (crude oil is heated to a relative low temperature, 150 oC, and fed at the top of

the column, crude oil goes down the column and heated consecutively in three heaters)


4

and vacuum distillation units. Different design options were considered, such as

preflash drums or prefractionator columns.

1.2. Motivation and objective of the work

Existing retrofit design methodologies (research and practice) do not consider the

distillation process and the heat recovery system at the same time. Research in this area

considers individual units (i.e. distillation column and heat recovery system) separately

and heat integration targets (area and energy targets provided by pinch analysis), instead

of the details of the existing heat recovery system. The proposed modifications do not

systematically account for the capital cost requirements of the retrofitted distillation

column and heat recovery system. Published approaches to retrofit design do not take

the existing system constraints, such as hydraulic capacity limitations of the distillation

column or pressure drop constraints for the crude oil feed stream, into account.

Therefore, the modified distillation and heat recovery units or the optimum solutions

from a number of the previous approaches may have infeasible operation. Thus, no

systematic approach for retrofit of heat-integrated crude oil distillation systems is

published. Retrofit objectives considered in published research are restricted to energy

savings or reduction of emissions to the atmosphere.

Furthermore, design methodologies developed for grassroots design cannot be applied

directly to retrofit design of heat-integrated crude oil distillation systems. No shortcut

models are available for retrofit design of distillation columns. Shortcut models would

be valuable for column optimisation and can be combined with heat exchanger network

model for retrofit studies.

In conclusion, it is difficult to apply systematically engineering experience, heuristics

and sequential approaches to retrofit design. Therefore, there is a need for shortcut

models for retrofit design of distillation columns and heat exchanger networks, and for a

simultaneous optimisation strategy for retrofit design of heat-integrated crude oil

distillation systems. The shortcomings of existing approaches to retrofit design motivate

the present work. The main objective of this work is to develop a retrofit design

methodology and modelling framework for heat-integrated crude oil distillation

systems. This approach considers the existing distillation process simultaneously with

the heat recovery system, together with their constraints. It also considers the process


5

conditions of the distillation column in a simultaneous approach. The new methodology

provides a system for considering and exploring structural modifications to the existing

flowsheet and heat exchanger network. The trade-offs between the capital investment

required by retrofit modifications and the cost savings need to be accounted for. New

retrofit objectives, as well as conventional goals, need to be tackled; these objectives

may include energy saving, reducing operating costs, increasing production capacity,

decreasing combustion emissions, and processing new feedstock or changing product

yields. Retrofit design options may be explored to result in better performances and

achieve objectives with minimum fixed costs and larger cost savings.

Another objective is to develop shortcut models for retrofit design of distillation

columns. These models should consider different types of vapour generation

mechanisms, such as reboilers and stripping steam, and need to be valid for crude oil

distillation units. Various column arrangements need to be addressed. The models are

required to allow optimisation of the distillation process.

1.3. Overview of the thesis

In the next chapter, published approaches on grassroots and retrofit design of heat-

integrated crude oil distillation systems are reviewed.

In Chapter 3, shortcut models for design of distillation columns are presented. The

models are developed specifically for retrofit design of reboiled and steam-stripped

distillation columns. Several column configurations, in addition to simple columns, will

be considered. Illustrative examples will also be provided for design model applications.

Chapter 4 proposes retrofit modelling of heat-integrated crude oil distillation systems. In

this chapter, various design options for retrofit of crude oil distillation systems will be

modelled, such as modifying the existing structure of the crude oil distillation unit and

retrofit design options for combustion-related emissions. This chapter develops models

for characterising the existing heat exchanger networks and the hydraulic performance

of existing distillation columns.

In Chapter 5, a new approach for retrofit of heat-integrated crude oil distillation systems

is presented. This chapter also provides a design procedure for applying the new retrofit

approach. Different industrially-related cases of crude oil distillation systems will be


6

considered in Chapter 6 to apply the new approach to achieve various retrofit goals.

Results will be analysed and conclusions will be drawn.

Chapter 7 summarises the work, discuss the limitations of the approach and recommend

some future work.

Chapter 2 Literature review

7

Chapter 2: Literature review

Retrofit of heat-integrated crude oil distillation systems has received significant

attention from many researchers and refining industries due to the economic and

environmental significance of these systems. This chapter reviews and evaluates the

relevant research and industrial efforts and published results of the retrofit of heat-

integrated crude oil distillation systems.

Previous research on grassroots and retrofit design will be reviewed for crude oil

distillation units with possible opportunities for heat integration and for retrofit design

of heat recovery systems separately. Previous work on shortcut models for retrofit

design of distillation columns will also be presented.

2.1. Introduction

Design of chemical processes can be classified generally into two tasks, grassroots

design and retrofit design. Grassroots design involves designing the process equipment

to meet required specifications and certain economic criteria (objectives). Retrofit

design, on the other hand, focuses on redesigning and reusing the existing process

equipment for achieving certain economical or environmental objectives.

In crude oil distillation processes, while grassroots design aims to find the most energy-

efficient and least capital cost column and exchanger network designs to separate crude

oil feed into specific products, retrofit design deals with an existing crude oil distillation

unit connected to the associated heat exchanger network. The two individual units, i.e.

the distillation column and heat exchanger network, have existing equipment design and

configuration together with physical constraints. These physical constraints include

column actual diameter, pump-arounds and side-columns locations, exchanger matches

and areas, pressure drop for crude oil feed stream, exchanger units operate at maximum

performance, etc. It is clear that in grassroots design, there are more degrees of

freedoms to exploit. However, in retrofit design situation, more physical and process

operation constraints exist and must be met. Therefore, retrofit design of crude oil

distillation processes is more challenged than grassroots design. The major challenges

for retrofit design are: an existing crude oil distillation unit with its associated

exchanger network are to be efficiently reused and modified to achieve a specific


8

objective or to accommodate changes in the market demand. Another challenge of

major importance in retrofit design is the modelling aspect of crude oil distillation

processes. While grassroots design models are widely available, retrofit shortcut design

models and methodologies do not exist. The typical objectives for retrofit design of

crude oil distillation processes are energy saving, reducing operating cost, increasing

capacity, improving profit, reducing CO2 emissions, changing product yields and

processing new crude oil feedstocks. These objectives are preferably achieved with

minimum operating costs and capital investment and low payback time.

The next sections overview the existing approaches and methodologies for grassroots

and retrofit design of crude oil distillation and heat recovery systems.

2.2. Grassroots design of heat-integrated crude oil distillation systems

Grassroots design of crude oil distillation processes was carried out earlier based on

using engineering experience, empirical correlations and hand calculations. Watkins

(1979) suggested a design procedure for crude oil distillation columns and

recommended that, for example, the number of stages required in the steam stripping

section is about 4 to 7 trays.

Liebmann et al. (1998) proposed an integrated design procedure for crude oil distillation

columns. The design procedure starts with a sequence of simple columns; the number of

trays in each column is calculated based on the assumption that the reflux ratio (ratio to

the minimum reflux ratio) is approximately the same for all columns. Thermal coupling

and reboilers are then introduced to the column design to reduce the energy

consumption. The process grand composite curve is used to propose column

modifications for reducing the utility consumption further. After exploring all design

options for improving the energy efficiency, the simple columns are merged into a

complex column. This design procedure has the advantage of coupling the column

design and the heat recovery aspects.

Suphanit (1999) developed an integrated approach for grassroots design of crude oil

distillation systems; it is an improvement to the integrated design proposed by

Liebmann et al. (1998). He used and developed shortcut distillation models, pinch

analysis and an optimisation framework, to generate energy-efficient column designs. It


9

was the first method that systematically allowed the degrees of freedom (e.g. feed

preheating temperature, stripping steam flow rate, reflux ratio, pump-around

temperature drop, etc.) in new column design to be exploited to maximise the energy

efficiency of a refinery distillation process, based on heat integration targets (i.e. area

and energy targets obtained from pinch analysis). It was found in this work that

optimising the operating conditions of crude oil distillation column reduces the energy

target significantly for given minimum temperature approach. The shortcut models

developed in this work for grassroots design of crude oil distillation columns will be

presented in Section 2.3.

Very recently, work was carried out by Bagajewicz and co-workers on grassroots design

of crude oil distillation units, addressing various design options and extending the

design methodology and insights of Liebmann et al. (1998). Bagajewicz and Ji (2001)

present a rigorous targeting methodology to design conventional atmospheric crude

fractionation units for processing different types of crude oil (from heavy to light crude

oil) at optimal conditions. This work uses heat demand-supply diagrams, which account

for the contribution of each process stream or pump-around to the utility consumption,

as a tool guiding the design to result in the optimal condenser and pump-around duties.

Bagajewicz and Soto (2001) proposed a model to design a multipurpose heat exchanger

network to process various crude oils. This exchanger network is capable of providing

the process heat requirements for the different types of crude oils processed in the

design obtained by the method of Bagajewicz and Ji (2001).

Ji and Bagajewicz (2002a) presented a rigorous procedure to obtain cooling and heating

energy targets for grassroots design of crude oil fractionation with preflash and

prefractionator columns. This procedure is based on rigorous calculations and combined

with heat integration. The work pointed out that the optimum temperature of the flash

drum, the location of the flash vapour to the distillation column and the optimum

prefractionator feed temperature need to be studied. The work concluded that the

introduction of a preflash drum into a heat exchanger network design reduces the

number of exchanger units and reduce the utility consumption.

Ji and Bagajewicz (2002b; c) extended their previous work (2002a) to design both

conventional crude oil atmospheric towers and stripping-type distillation columns plants

with vacuum units and multipurpose heat exchanger networks. In stripping-type


10

columns, the crude oil is heated to a relative low temperature (about 150 oC) and fed at

the top of the column, and then the crude oil goes down the column and heated

consecutively in three heaters. Two design options were considered upstream of the

atmospheric tower in the design, design with a preflash drum and design with a

prefractionator column. It was found that the introduction of a vacuum tower affects the

conventional and preflash designs, and hence changes the heat distribution among the

pump-around circuits. The work results in energy targets for the different types of

column designs considered. Then, these energy targets obtained for different types of

crude oils are used to develop a multipurpose heat exchanger network that can handle

the process requirements for a range of crude oils.

The presented grassroots design methodologies for crude oil distillation systems provide

basis and insights for retrofit design situations; however, they cannot be applied directly

for such a problem. The reason is as mentioned above that the challenges and problem

nature of retrofit design are conceptually different from those of grassroots design. In

addition, most these design methods use rigorous simulations which cannot be applied

in retrofit situations to account for the complexities of an existing crude oil distillation

unit and the associated heat exchanger network. In this case, shortcut models would be

valuable. The next section summaries the existing shortcut models for grassroots design

of distillation columns, since they are relevant to the work of this thesis.

2.3. Shortcut models for design of distillation columns

Shortcut models for grassroots design of distillation columns are well established and

have been widely applied. The best known shortcut method for distillation design is the

Underwood method (Underwood, 1948). This method is based on two limiting

assumptions, constant molar overflow within each column section, and constant relative

volatilities throughout the column. It is easy to use, with only recoveries of two key

components and thermal condition of feed needing to be specified. This method is

applicable to simple conventional distillation columns, i.e. single-feed two-product

columns with a single condenser and a single reboiler. The minimum condenser and

reboiler duties and also the minimum vapour flow rates in column sections are obtained

from the calculations. The method gives good results for distillation systems with

relatively ideal mixtures. However, for multicomponent mixtures and for systems with

non-ideal vapour-liquid equilibrium behaviour, the molar overflow is not constant and


11

the relative volatilities change through the column (Seader and Henley, 1998; Suphanit,

1999). Where the underlying assumptions are not valid, the accuracy of the results will

be compromised. Even in these cases, however, the estimation of minimum vapour flow

rates is good in regions of constant composition (pinch zones) (King, 1980: p. 418;

Kister, 1992: p. 313). Some improvements (King, 1980; Nandakumar et al., 1981; Rev,

1990; Suphanit, 1999) to the Underwood method have been suggested to extend its

applicability.

Based on the Underwood equation, many researchers have proposed shortcut models for

grassroots design of non-conventional distillation columns. The Fenske-Underwood-

Gilliland (FUG) model is the most popular shortcut model for design. Petlyuk et al.

(1965) and Stupin and Lockhart (1972) used this model to analyse the energy

consumption of fully thermally coupled distillation arrangements, compared with

conventional columns. Cerda and Westerberg (1981) developed shortcut models for the

design of various complex distillation configurations. Glinos and Malone (1985)

developed a shortcut procedure for design of a distillation column with a side-stripper.

Carlberg and Westerberg (1989b) and Triantafyllou and Smith (1992) applied the

Underwood equation to a three-column model for the design and analysis of fully

thermally coupled distillation columns. Suphanit (1999) developed shortcut models for

grassroots design of distillation columns using reboilers and stripping steam. Various

complex distillation column configurations, including side-strippers and side-rectifiers

are considered. The model of Suphanit (1999) is applicable for crude oil distillation

columns.

The design model of Suphanit (1999) is presented in some details, as it is highly

relevant to the work of this thesis. This model is an improvement to the standard FUG

method. The improvements concentrate on the limiting assumptions of the FUG

method, i.e. constant molar overflow within each column section and constant relative

volatilities throughout the column. The relative volatility of each component is taken to

be the geometric mean of that at different locations in the column, i.e. top section,

bottom section and feed stage. Enthalpy balances around column sections are carried

out to accommodate the changes in vapour flow rates at both minimum and actual reflux

conditions. The modified shortcut design procedure at the minimum reflux conditions is

as follows:


12

1. Use the Underwood equation to estimate the minimum vapour flow rates at the top

and bottom pinch zones.

2. An enthalpy balance around the top section is performed to calculate the minimum

condenser duty and the minimum vapour flow rate at the top of the column. Then,

the corresponding reboiler duty is calculated by an overall enthalpy balance.

3. The minimum vapour flow rate at the bottom of the column is calculated by

enthalpy balance around the reboiler.

At the actual reflux condition, the vapour flow rate in the top section is calculated by

material balance around the condenser, assuming a reasonable value for the reflux ratio.

Then, the vapour flow rate in the bottom section is calculated by an enthalpy balance

around the reboiler.

Based on these improvements to the FUG method, Suphanit (1999) developed a

shortcut model for grassroots design of reboiled distillation columns, including different

column configurations, such as columns with side-strippers, side-rectifiers and side-

exchangers. For a given feed and required product specifications, the model calculates

the number of theoretical stages in each section, assuming a value for the reflux ratio.

The basic model equations of the shortcut model are presented for simple distillation

columns with reboilers, as follows:

The Underwood equation at the feed stage can be written as:

qx

i

ifi −=−

Σ 1,

φαα

(2.1)

The minimum vapour flow rate (Vmin) and distribution of components with volatilities

between those of the key components at the top pinch location is given by:

minVd

i

ii =−

Σφα

α (2.2)

The minimum vapour flow rate at the bottom pinch (V'min) location is then calculated as

follows:

FqVV )1(minmin −−=′ (2.3)


13

The minimum reflux (Rmin) ratio is then calculated:

1)/( min,min −= DVR top (2.4)

The Fenske equation (Fenske, 1932) is used to determine the minimum number of

stages (Nmin) at total reflux (Cited in Seader and Henley, 1998; Chapter 9):

[ ]HKLK

HKHK

LKLK

RRRR

Nαα /ln

/)1()1/(ln

min

−

−

= (2.5)

To achieve a specified separation between two key components, the actual reflux ratio

and the number of stages must be greater than their minimum values. The actual reflux

ratio is generally chosen, by economic considerations, as some multiple of minimum

reflux. The corresponding number of stages is then determined by suitable graphical

methods or by an empirical correlation. The most successful and simplest graphical

correlation for the number of stages was developed by Gilliland (1940) and slightly

modified later by Robinson and Gilliland (1950). Seader and Henley (1998) reviewed

the various equations for the Gilliland correlation and presented a comparison of

rigorous calculations with the Gilliland correlation. The Gilliland correlation relates the

number of stages to the minimum number of stages, and minimum and actual reflux

ratios. The data of this correlation are based on plant data. Molokanov et al. (1972)

represented these data by a line with the following equation:

−

++

−=+

−5.0

min 12.11711

4.541exp11 ξ

ξξξ

NNN

(2.6)

where,

1min

+−

=R

RRξ (2.7)

After calculating the total number of theoretical stages (N) inside the column, the

location of the feed stage can be identified using the empirical equation of Kirkbride

(1944) (Cited in Seader and Henley, 1998; Chapter 9):

206.02

=

dHK

bLK

fLK

fHK

S

R

xx

xx

DB

NN (2.8)


14

Following the calculations of the total number of stages and the feed location, an energy

balance is carried out to calculate the condenser and reboiler duties (Seader and Henley,

1998).

For distillation columns using stripping steam, the separation characteristics, such as

temperature profile and vaporisation mechanism, are different from those of reboiled

columns. The stage temperature in reboiled columns decreases continuously from the

reboiler to the condenser as the vapour is created in the reboiler, where the temperature

is the highest in the column. In steam-stripped columns, the vapour phase in the column

is generated in the stripping section by the reduction in the partial pressure of the liquid

caused by the stripping steam; the liquid itself supplies the heat of vaporisation. The

liquid temperature reduces from the feed stage towards the bottom of the column.

Therefore, the temperature profile of the steam-stripped columns shows a peak value at

the feed stage. Suphanit (1999) developed a shortcut model for grassroots design of

steam-stripped distillation columns. This model is applicable for simple and complex

configurations of steam-stripped distillation columns, including columns with side-

strippers, side-rectifiers, and side-exchangers. In this model, the Underwood equation

and enthalpy balance are applied to estimate the minimum vapour flow rate in each

section, and the minimum reflux ratio and the condenser duty. At actual reflux, the

vapour flow rate in the top section is calculated by material balance around the

condenser, assuming a reasonable value for the reflux ratio. An enthalpy balance around

the top section calculates the condenser duty; while an overall enthalpy balance finds

the enthalpy of the bottom product. The dew point temperature is calculated for the top

product for a partial condenser; the bubble point temperature is calculated for a total

condenser. The temperature of the bottom product is calculated by bubble point

calculation with known enthalpy. Then, the vapour flow rate at the bottom of the

column is calculated by an overall enthalpy balance.

The number of stages is calculated separately for each section of the column. In the

rectifying section, the Fenske equation is applied to determine the minimum number of

stages at total reflux condition:

[ ]HKLK

HKFSLKFS

dHKdLK

xxxx

Nαα /ln

//

ln

min

= (2.9)


15

Then, the Gilliland correlation determines the number of stages in this section.

In the stripping section, the vapour flow rate profile is non-linear. The changes in

vapour flow rate are restricted by enthalpy balance. Thus, to obtain a good prediction of

the number of stages, consecutive flash calculations are performed from the bottom

stage towards the feed stage. The number of stages is counted from the bottom stage

until the stage vapour flow rate reaches or exceeds the vapour flow rate below the feed

stage. The vapour flow rate below the feed stage is assumed to be the minimum vapour

flow rate at the bottom pinch of the column.

Although much attention has been paid to the development of shortcut models for

grassroots design of distillation columns, no shortcut models are published for retrofit

design; only rigorous models are applied in the commercial simulation packages. In all

the above design models, the number of theoretical stages, reflux ratio, heating and

cooling duties, etc. are calculated for a given set of operating conditions (e.g. column

pressure, reflux ratio, stripping steam flow rate, etc.), feed data and product

specifications. However, to carry out a retrofit study on heat-integrated distillation

systems (e.g. crude oil distillation), retrofit models are necessary to fix the existing

distillation design, i.e. column configuration, number of real stages, locations of

reboilers, condensers, side-exchangers and side-strippers.

Established rigorous models do not provide multivariable optimisation. Shortcut models

are quicker to solve, do not have convergence problems and are more robust than

rigorous models for column optimisation. In particular, shortcut models for retrofit

would be valuable for evaluating retrofit design options, to improve the performance of

an existing heat-integrated distillation system and can be combined with detailed heat

exchanger network models for optimising the overall distillation system.

Nevertheless, these grassroots design models are valuable and can be modified to be

applicable for retrofit design. In particular, the shortcut models developed by Suphanit

(1999) are significantly useful, since they overcome the underlying limitations of most

conventional models and considered both reboiled and steam-stripped distillation

columns, as is the case in crude oil distillation units.


16

2.4. Retrofit design of heat-integrated crude oil distillation systems

Research work, reports and industrial experience mainly focused on heat-integrated

crude oil distillation systems, not necessarily accounting for the existing heat recovery

systems. The heat recovery system, however, are approximated by heat integration

targets (area and energy targets) provided by pinch analysis. The opportunities for heat

recovery that may result from the consideration of the existing heat recovery system are

not analysed. Interactions between the two individual units, i.e. distillation column and

heat recovery system, are not exploited.

The previous work on heat-integrated crude oil distillation systems and their evaluation

are summarised as follows:

2.4.1. Modifications-based retrofit methods

Retrofit design of crude oil distillation columns was initially carried out based on

modifications suggested by research studies or engineering experience. These

modifications may include structural modifications to the distillation column or the heat

exchanger network and changes to the process conditions of the distillation column.

Sittig (1978) suggested a number of modifications in order to improve the energy

efficiency of distillation systems, such as:

1. Installation of new internals with higher efficiency.

2. Use of intermediate reboilers on crude oil distillation towers.

3. Selection of optimum balance between number of stages and reflux.

4. Use of tower side draws.

5. Minimisation of entrainment and optimisation of tray efficiency.

6. Use of optimum column control.

7. Reduction of column pressure.

8. Choice of best feed location and condition.

The recommendations to improve the energy efficiency are given without a proposed


17

procedure on which order and how these modifications can be applied.

Bannon and Marple (1978) recommended other column modifications for retrofit of

crude oil distillation columns. These modifications include the installation of pump-

arounds at the right location in the tower and adjusting the cooling duty for each pump-

around. The energy consumption in the associated heat recovery system is expected to

reduce. However, these modifications incur large capital investment, do not account for

the hydraulic capacity limitations of distillation columns and may require major changes

to the existing design of the exchanger network.

Harbert (1978) advised that the installation of preflash units or prefractionator columns

to existing crude oil distillation unit saves energy and allows the increase of the

throughput of the distillation unit. The location of the additional units in the preheat

train was not addressed, as well as the consequences of changing the preflash vapour

feed to the distillation column.

The increase in the production capacity of distillation unit is achieved by the installation

of additional equipment together with changing the existing distillation equipment and

the heat exchanger networks (McConnel and Royer, 1988). No detailed

recommendations or systematic design procedure were given on how the capacity can

be increased or how the exchanger network is to be modified and the capital cost

requirements.

Rivero and Anaya (1990) suggested the use of more trays in existing distillation towers

and strippers as well as the installation of a reboiler in stripping columns are suggested

for improving the performance of crude oil distillation towers. These recommendations

did not account for the physical constraints of the existing distillation towers, such as

existing diameter limitations and column space, and the large capital investment

incurred by these modifications.

Golden (1997) concluded in his research that preflash drums should be used to

debottleneck an existing crude oil distillation unit. This work concentrates on the

operational problems and troubleshooting of preflash drums. He advised that if a

preflash drum is undersized, and entrains flashed crude oil to the crude distillation

column, severe problems will occur, such as black distillate products (product

contaminants). Increasing the flash drum operating temperature can reduce the heat


18

input to the atmospheric column. Two important issues were not considered, the

optimum temperature of the preflash drum and the location of the preflash vapour to the

distillation column. However, the proposal of the proper sizing of the preflash drum is

valuable.

Although the above retrofit methods recommended process modifications for energy

saving and throughput increase, they have not provided a systematic procedure of

applying the proposed modifications or have not accounted for the capital requirements.

The capital costs of process modifications are very large (e.g. additional columns) and

impractical (e.g. more trays) in some cases without the efficient reuse of the existing

equipment. In addition, the modified distillation process may have infeasible operation

because the hydraulic capacity limitations have not been taken into account during the

retrofit design procedure. Methods that considered retrofit with preflash drums have not

addressed the issues of the location of the flash drum in the preheat train or the location

of flash vapour to the distillation column. Also, the optimum preflash operating

temperature has not been studied. None of these retrofit methods addressed the potential

of changing the operating conditions of the distillation process (e.g. feed preheating

temperature, stripping steam flow rate, pump-around flow rate and duty) for reducing

energy demand and increasing processing capacity or for other objectives, such as

reducing CO2 emissions.

Among the above retrofit design methodologies, changing the existing structure of

crude oil distillation unit has not been dealt with systematically. When a preflash drum

or a prefractionator column is added, the optimum operating temperature, the location of

the unit in the preheat train and the location of the flash drum vapour to the distillation

column need to be addressed.

However, some insights and design options can be taken into account and studied

further in retrofit design of heat-integrated crude oil distillation systems. These insights

and design options include adding a preflash drum or a prefractionator column upstream

of an existing crude oil distillation column for energy saving and throughput

enhancement. The location of preflash vapour feed and the optimum preflash

temperature can be explored for better performance. Also, the preflash drum needs to be

properly sized to avoid operational problems.


19

2.4.2. Pinch analysis-based retrofit approaches

The concept of distillation column targeting (Dhole and Linnhoff, 1993a) provides

column modifications relevant to reduction in energy demand. The column targets (Heat

loads and temperature) are based on the “Column Grand Composite Curve” (CGCC),

which determines the scope for appropriate modifications and sets the temperature

targets for these modifications. The CGCC is obtained from converged simulations.

This CGCC is then used to identify beneficial column modifications; appropriate

column process modifications are those which result in reducing energy demand and

improving the temperature-quality of heat sources. These modifications include

appropriate feed location, reflux ratio modifications, feed conditioning and using side

condensing or reboiling. The difficulty in using this design procedure is that it is based

on graphical representations and it considers only two key components as a basis for the

calculations. The proposed modifications do not account for the diameter constraints of

the distillation column or the existing stages; a large number of stages is required to

accommodate some particular modifications.

Dhole and Buckingham (1994) published a methodology for retrofit of refinery

distillation columns. In this methodology, energy saving and debottlenecking objectives

were considered. The approach uses three stages, column modifications using CGCC,

design modifications for energy saving and design modifications for debottlenecking.

Several techniques for saving energy and debottlenecking were involved in this

approach, such as (1) increasing the heat recovery in the heat exchanger network (HEN)

in order to reduce the utility consumption, which consequently may provide opportunity

for debottlenecking, and (2) revamping the utility system for higher duty and

correspondingly modify the column and the HEN for throughput increase. The

limitation of this approach is that it is a sequential approach, i.e. the column is modified,

and then the implications on the heat exchanger network are determined. When

modifications are suggested, the details of the existing exchanger network are not

considered. The hydraulic limitations of the distillation column are not accounted for;

therefore, the modified column may have bottlenecks.

Liebmann (1996) proposed a two-step (sequential) approach for grassroots and retrofit

designs of crude oil distillation systems. This approach uses pinch analysis and rigorous

simulation to analyse the existing column configuration and to propose column


20

modifications that result in reduced energy consumption and enhanced throughput

capabilities. The retrofit procedure identifies the interactions between the column

modifications and the energy targets. The retrofit procedure is divided into three levels.

Simple and inexpensive modifications are proposed first. If these modifications do not

achieve the retrofit objectives, more complex modifications with higher investment are

considered. The shortcoming of this approach is that it decomposes the problem into

two stages, i.e. the distillation column and the heat exchanger network are not

considered at the same time. In addition, the details of the heat exchanger network,

including, exchanger matches, area, heat loads, and existing structure, are not accounted

for. Therefore, the capital costs required by the HEN are not evaluated. The proposed

changes to the operating conditions are sequential and are not considered

simultaneously. The modified distillation column is different from the existing design;

this requires extensive structure modifications and huge capital costs. Some column

modifications are impractical, such as redistributing the existing number of stages and

changing the locations of the side-draws and pump-arounds. Furthermore, the modified

distillation column may have infeasible operation, because the hydraulic capacity

limitations are not taken into account.

Bagajewicz (1998) adopted and extended the approach of Liebmann (1996) for

optimising an existing crude oil distillation column based on pinch analysis principles

and rigorous model-based simulation. This approach applies for reducing energy

consumption and atmospheric emissions. Pinch analysis is used to calculate energy

target for the process conditions of the distillation column. The approach changes the

operating parameters of the distillation process to minimise energy consumption or

atmospheric emissions. This work concluded that the simultaneous consideration of the

process conditions of an existing crude oil distillation unit can reduce the minimum

utility consumption. This approach overcomes two shortcomings involved in the work

by Liebmann (1996); the approach changes the operating conditions of the distillation

column at the same time and considers the energy target simultaneously with the

distillation process. However, no capital-energy trade-off is considered in the

optimisation. In addition, the approach does not account for the details and the structure

of the existing heat exchanger network and the hydraulic capacity constraints of the

distillation column. This may lead to bottlenecked distillation column and result in

modified heat exchanger network with significant structure changes (e.g. change


21

exchanger matches, large additional areas, bottlenecked exchanger units). In addition,

the approach does not explore the benefits of changing the structure of the existing

distillation column (e.g. adding a preflash or a prefractionator) or the heat exchanger

network (e.g. integrating a gas turbine with process furnace, enhancing heat transfer) for

better energy and hydraulic performances. Only two objectives for retrofit design are

considered in this work; however, more objectives need to be addressed, such as

processing new crude oil feedstocks and changing product yields.

Group of researchers have dealt with particular retrofit problems; they proposed

modifications and applied previous approaches to specific problems to achieve certain

retrofit objective. Briones et al. (1999) considered revamping the crude oil distillation

unit of a Mexican refinery for reducing the energy consumption. A number of

modifications and suggestions, based on pinch analysis and the approach of Liebmann

(1996), were proposed, such as integrating the atmospheric and vacuum distillation

units, adding pump-arounds and using stripping steam instead of reboilers in the side-

strippers. Fraser and Sloley (2000) considered retrofit of a crude oil distillation unit to

increase the vacuum gas oil production. The revamp modifications were concentrated in

the vacuum unit and its feed preheating system. These modifications include adding

pump-arounds and reducing the operating pressure, increasing the preflash overhead

vapour, modifying the vacuum stripping section trays and optimising the flow rate of

stripping steam. These researchers (i.e. Briones et al., 1999; Fraser and Sloley, 2000)

have not provided general procedures for proposing or applying the retrofit

modifications; they rather applied previous approaches. These modifications are strictly

suitable for particular cases to which they were applied. They recommended expensive

structural modifications without exploiting the potential of changing the operating

conditions of the overall system for better performance.

It can be seen that the previous retrofit approaches have not considered the existing

distillation column and the details of the associated heat recovery system at the same

time, but in a sequential manner. Most methods suggested column modifications, which

possibly will require large capital costs without full exploitation of the existing

distillation equipment. Practical constraints, such as hydraulic limitations of the existing

distillation column or allowable pressure drop for the crude oil feed stream, have not

been taken into account. This may lead to unfeasible operational designs, i.e. hydraulic

bottleneck in the distillation column or bottleneck in the exchanger network. Many


22

approaches used rigorous simulations, which are time consuming and do not provide

multivariable optimisation. Retrofit approaches that dealt with particular cases of crude

oil distillation units cannot be generalised since they do not provide systematic

procedure for applying the proposed modifications to other cases.

2.5. Retrofit design of crude oil distillation systems

This section includes previous work on crude oil distillation systems, not necessarily

accounting for the heat integration aspects; this includes increasing capacity of

distillation columns, reducing atmospheric emissions and using heat transfer

enhancement. Liu (2000) proposed a graphical method to analyse the hydraulic

performance of an existing simple distillation column (i.e. single column with single

feed, and single reboiler and condenser) for throughput enhancement. The FUA

(fraction of utilised area) is obtained for an existing distillation column to determine the

potential for increasing the production capacity and identify modifications for

debottlenecking. This method is accompanied by guidelines to identify modifications to

increase capacity. The proposed modifications include changing the operating

conditions, changing the feed condition and using side condensers or reboilers. The

objective in this work was capacity-related and no opportunities for heat recovery were

considered. Although the retrofit tool presented by Liu (2000) is valid for simple

distillation columns and did not consider the opportunities for heat recovery, it can

systematically be extended to crude oil distillation columns, where there are varied

column diameters. Moreover, the retrofit modifications and insights given in this work

for increasing capacity and identifying column bottlenecks can still be used for crude oil

distillation units.

Smith and Delaby (1991) presented an approach for targeting flue gas emissions (i.e.

relating minimum energy consumption to emissions) from utility systems for a given

process with fixed conditions. They introduced a new term for the global accounting of

the CO2 emissions; these emissions are the sum of the emissions from the utilities on a

site together with those from outside the factory boundary. Furthermore, Delaby and

Smith (1995) proposed a design methodology to minimise the flue gas emissions.

Design options include changing fuels, changing the utility system design, process

changes, improved heat recovery and chemical treatment of flue gases. They also

presented a method which identifies the best combination of these techniques to achieve


23

a target for emissions reduction with minimum cost. The models proposed by Delaby

and co-workers are valuable to consider the atmospheric emissions aspect in retrofit of

heat-integrated crude oil distillation systems, although they did not consider any

existing design details (e.g. distillation column and HEN).

Manninen and Zhu (1999) presented a methodology for integrating a gas turbine with an

existing site to cut down the flue gas emissions and reduce the operational costs. This

methodology involves operational optimisation to achieve the best economic

performance. Also, Townsend and Linnhoff (1983a, b) and Dhole and Linnhoff (1993b)

developed graphical tools to analyse the integration of gas turbine to an existing process

and estimate the size of the gas turbine required for process requirements. On the other

hand, Chew (2001) extended previous work developed on overall refinery optimisation

to account for reducing the CO2 emissions from refineries. The work aimed at

exploiting the interactions between the major networks in a refinery, i.e. material

processing units, hydrogen network and utility system (H2, heat, power, fuel, steam), to

reduce the CO2 emissions using optimisation. These works on CO2 emissions did not

consider the details of the existing designs of the crude oil distillation column and heat

recovery system, as well as the operating conditions of the distillation column.

However, the models proposed for integrating a gas turbine by Manninen and Zhu

(1999) can be extended to retrofit design of crude oil distillation column and heat

exchanger network, where the two individual units can be considered at the same time

together with their practical constraints.

Heat transfer enhancement has proved to be useful for retrofit design of heat exchanger

networks and process integration (Polley et al., 1992). Zhu et al. (2000) developed a

targeting procedure for using heat transfer enhancement in retrofit of heat exchanger

networks This procedure determines which exchangers are suitable for using heat

transfer enhancement, what level of enhancement is required and which enhancement

technique is most suitable. By applying heat transfer enhancement to an existing

exchanger unit, additional exchanger area will become available for heat transfer; this

additional area can be estimated as follows:

existtube

shell Ahh

A

−=∆ 1

21

max (2.10)


24

Where ∆Amax is the maximum area that can be achieved by using enhancement, hshell and

htube are the shell and tube-side film heat transfer coefficients, and Aexist is the existing

exchanger area. Nie and Zhu (1999) extended the work of Zhu et al. (2000) by

considering pressure drop limitations in retrofit with heat transfer enhancement. The

motivation is that heat transfer enhancement techniques have a significant impact on

pressure drops. They developed models, based on those proposed by Polley et al.

(1990), to account for pressure drop when applying heat transfer enhancement. The

models for heat transfer enhancement can be used to consider heat transfer enhancement

as a design option in retrofit of heat-integrated crude oil distillation systems.

As seen, the design methodologies presented in this section apply only for specific

issues in retrofit design of crude oil distillation systems, including debottlenecking an

existing distillation column, reducing CO2 emissions and using heat transfer

enhancement. These methodologies do not consider the process conditions of the

distillation column simultaneously while considering the different retrofit issues or the

existing distillation process and the associated heat exchanger network simultaneously.

Nevertheless, the proposed models and retrofit insights (e.g. Polley et al., 1990; Smith

and Delaby, 1991; Nie and Zhu, 1999; Zhu et al., 2000; Liu, 2000) can be extended to

be applicable for retrofit design of the crude oil distillation unit and the heat recovery

systems simultaneously for debottlenecking existing distillation columns, reducing CO2

emissions and using heat transfer enhancement.

2.6. Retrofit design of heat recovery systems

Another group of researchers concentrated on improving the performance of the heat

recovery systems, irrespective of the background process, in this case the distillation

process. The literature is rich in methods and approaches for retrofit design of heat

exchanger networks (e.g. Linnhoff and Hindmarsh, 1983; Tjoe and Linnhoff, 1986;

Silangwa, 1986; Ciric and Floudas, 1989; Yee and Grossmann, 1991; Shokoya, 1992;

Carlsson et al., 1993; Asante and Zhu, 1997; Jos et al., 1998; Sharma et al., 1999;

Varbanov and Klemes, 2000; Zhu et al., 2000). Asante (1996) provided a

comprehensive review for most of the retrofit methods and approaches of heat

exchanger networks (HEN). We only review some of these approaches.


25

Pinch analysis principles, which were initially developed for grassroots design, were

extensively used to redesign existing heat exchanger networks for energy savings. The

most important rule of the pinch analysis is that there should not be heating (using hot

utilities) below the process pinch and no cooling (using cold utilities) above the process

pinch. A large number of researchers extended the application of pinch analysis to

retrofit situations. Tjoe and Linnhoff (1986) developed a graphical tool (i.e. graph that

relate the minimum exchanger area required for retrofit to the minimum energy

consumption) to illustrate the constant area efficiency targeting method, which is one of

the earlier retrofit approaches. Area efficiency is defined as the ratio between the

minimum area required and that actually used to achieve a specific heat recovery. This

retrofit method used the rules and guidelines defined in the pinch design method

(Linnhoff and Hindmarsh, 1983), together with some additional rules, especially those

applicable to the retrofit design task, such as it is not good retrofit design to add more

exchanger area and the associated energy demand increases or to waste the purchased

area for reducing energy demand (Tjoe, 1986). This method assumes that the area

efficiency of the retrofit network is equal to that of the existing exchanger network. The

retrofit design procedure decomposes the exchanger network retrofit problem around

the pinch. Any exchanger that lies on both sides of the pinch is removed as it conflicts

with the fundamental pinch analysis rule. The design is conducted separately for regions

above and below the pinch. Finally, introducing new units into the network as required

completes the retrofit design. These retrofit approaches (e.g. Tjoe and co-workers,

Linnhoff and co-workers) do not consider the distillation process in the retrofit design.

The process conditions of the distillation column (e.g. feed preheating temperature,

reflux ratio, pump-around flow rates, etc.) are also not exploited.

Silangwa (1986) proposed that the incremental area efficiency (i.e. the retrofit area

efficiency is greater than the original network efficiency) targeting procedure should be

used instead of constant area efficiency method in retrofit cases where the area

efficiency of the original network is very low (less than 0.6). This method assumes that

any retrofit area added to the existing network has an efficiency greater than that of the

original HEN. Similar limitations as for the above approaches are involved in this

retrofit approach. The only advantage over the previous approaches (e.g. Tjoe and co-

workers, Linnhoff and co-workers) is that the additional area has an efficiency of new

area which is rather realistic than the efficiency of the original network.


26

Recently, with advances in computers, a number of researchers directed their retrofit

research to mathematical programming methods. Retrofit design of the heat exchanger

network is modelled mathematically as an optimisation problem; the objective is to

identify the lowest retrofit design costs.

In 1989, Ciric and Floudas decomposed the HEN retrofit design problem into a two-

stage approach consisting of a match selection stage and an optimisation stage. The

match selection stage selects process stream matches, and matches exchangers for the

retrofit problem, while the optimisation stage optimises the exchanger matches. Then,

the two stages are combined into a single stage approach. The limitation of this

approach is that it is assumed that the heat recovery to be achieved is fixed by

experience and hence there is no method proposed to initialise the heat recovery to be

achieved. In addition, the repiping cost is independent of the stream involved. There is

no user interaction to select from the proposed modifications or to impose practical

constraints.

Asante and Zhu (1997) introduced a new powerful approach for retrofit design of heat

exchanger networks. This approach combines the strength of both the mathematical

optimisation techniques with thermodynamic analysis and practical engineering. This

retrofit approach is built based on the observation that certain exchanger matches

thermodynamically limit the heat recovery in an existing exchanger network. The new

method uses a two-stage approach to HEN retrofit, a diagnosis stage and an

optimisation stage. The first stage selects a minimum number of promising HEN

topology modifications, which achieve the required heat recovery target. In the second

stage, the modified network is optimised (i.e. redistributing heat loads) using non-linear

optimisation techniques to minimise the cost of additional exchanger area.

The approach of Asante and Zhu (1997) defines concepts in retrofit of heat exchanger

network, which are network pinch and pinching matches. The pinching matches are

exchanger matches for which the temperature approach between the hot and cold

streams tends towards a limiting value as the heat recovery in the HEN increases; these

matches act as a bottleneck to the heat recovery. The pinching matches identify the

locations of the network pinch or the bottlenecks of the existing heat exchanger

network. To increase the heat recovery beyond the limits caused by the pinching


27

matches, the network topology must be changed by relocating existing exchanger units,

adding new matches or creating stream splits.

Network pinch analysis can be applied to retrofit a heat exchanger network for energy

saving or capacity increase. The application starts with fixing the structure of the

existing heat exchanger network. Then, the pinching matches are identified and the

network pinch is located. The retrofit approach then proposes a set of structural

modifications which overcome the bottlenecks caused by pinching matches. These

modifications incur minimum capital expenditure for maximum energy savings.

The significance of network pinch approach is that it provides alternative design options

for the same level of energy saving. These modifications can be screened according to

any practical limitations or constraints involved in the existing HEN. It also allows a

user interaction, so that any retrofit decision can be examined.

Other researchers worked on retrofit of particular heat exchanger networks associated

with existing crude oil distillation columns by applying previous approaches and using

new techniques, such as heat transfer enhancement. Examples of these research results

are given by Jos et al. (1998), Sharma et al. (1999), Klemes and Varbanov (2000), and

Zhu et al. (2000).

The common feature between most retrofit methods of heat recovery systems is that

these methods did not consider the background process (distillation process) in the

overall retrofit procedure, and assumed fixed process conditions for the distillation unit.

Therefore, the interactions between the distillation process and the associated heat

recovery systems are not exploited. These interactions and process conditions have

proven useful for increasing heat recovery opportunities in previous research on heat-

integrated crude oil distillation systems. Nevertheless, some of the reviewed

approaches, in particular network pinch (Asante and Zhu (1997) can be used in the

overall retrofit of heat-integrated crude oil distillation systems.

2.7. Concluding remarks

It can be seen that although various approaches and design methodologies have been

proposed for retrofit design of crude oil distillation units and heat recovery systems,

there is still no simultaneous approach that can address the two individual unit


28

operations. The literature shows that exploiting the operating conditions of the existing

crude oil distillation columns can improve heat recovery opportunities. Modifying the

existing structure (e.g. adding a preflash or a prefractionator, integrating a gas turbine,

etc.) of the crude oil distillation units can reduce energy demand, allow processing more

throughout and reduce atmospheric emissions. Also, many shortcut models have been

proposed for grassroots design of distillation columns. The limitations of the presented

approaches and design models can be then summarised as follows:

1. No shortcut models are published for retrofit design of distillation columns.

2. The details of the existing heat recovery system are not considered simultaneously

with the crude oil distillation column.

3. The existing equipment constraints and process limitations, such as distillation

hydraulic capacity, pressure drop in heat recovery system and bottlenecked

exchanger units, are not accounted for during optimisation in the simultaneous

retrofit approaches.

4. The process conditions (operating parameters) of the crude oil distillation column

and the design options (e.g. adding preflash, prefractionator and gas turbine units,

modifying existing column and heat exchanger network internals) are not considered

at the same time.

5. Retrofit column modifications (e.g. more trays, new pump-arounds, new columns,

etc.) require large capital expenditure and may require severe changes to the existing

column design.

6. Optimum results from retrofit approaches (e.g. Liebmann and co-workers,

Bagajewicz and co-workers) may lead to an infeasible (hydraulically bottlenecked)

column operation.

7. Only a few retrofit objectives are tackled. However, many other retrofit purposes are

industrially relevant, including improving profit, changing product yields and

processing new crude oil feedstocks.

8. No systematic method is available for retrofit with structural modifications to

existing crude oil distillation units. These modifications may include adding preflash


29

drums or prefractionator columns, replacing column internals with packing,

integrating a gas turbine and enhancing exchanger tubes.

The above shortcomings of established retrofit design methods and the perceived needs

of industry motivate the present work and form the fundamental goal of this study.

Consequently, the principal aim is to develop a new approach for retrofit design of heat-

integrated crude oil distillation systems. The need for shortcut models for retrofit design

of distillation columns is addressed; these models need to be applicable for crude oil

distillation units. The retrofit approach needs to consider the distillation column

simultaneously with the details of the existing heat recovery system. It is essential to

consider all operating conditions of distillation unit simultaneously. Furthermore, the

design methodology has to account for existing equipment and process limitations.

Various retrofit objectives need to be addressed and preferably achieved with reduced

operating costs and minimum capital expenses. The retrofit approach needs to consider

structural modifications and design options in a systematic way.

Chapter 3 Shortcut models for retrofit design of distillation columns

30

Chapter 3: Shortcut models for retrofit design of distillation columns

In this chapter, new shortcut models are developed for retrofit design of distillation

columns, including both reboiled and steam-stripped columns. The models are primarily

based on those models developed by Suphanit (1999), for grassroots design of

distillation columns. The retrofit shortcut models will be presented firstly for simple

distillation columns, and then followed by models for sequences of simple columns and

various configurations of complex distillation columns, such as columns with side-

strippers, side-rectifiers, and prefractionators. Illustrative examples are presented for

various distillation configurations, showing a comparison of the results with those of

existing rigorous models

3.1. Introduction

Distillation is certainly the most widely used separation technology. However, it is

energy and capital intensive. Distillation generally requires heat to carry out separation.

Reboilers provide this heat to distillation processes in most industrial applications, such

as petrochemicals, naphtha fractionation, etc. Other distillation applications, such as

crude oil distillation, use live steam to supply the required heat.

Industrial distillation applications use various column configurations. These

configurations vary from simple to complex column sequences. For crude oil

distillation, the conventional complex column configuration that is extensively used is a

main tower with a set of side-strippers and pump-arounds. Other column configurations

also exist, such as a progressive distillation sequence. Progressive distillation is a recent

energy-efficient development, and was first installed at Mider refinery, Leuna,

Germany. It consists of a main tower with a side-stripper, and uses two successive

prefractionators (Rhode, 1997).

Retrofit projects of these distillation column configurations aim to reuse the existing

equipment more efficiently in order to increase the profit. To carry out a retrofit study

on a given distillation column, retrofit models are necessary to fix the existing

distillation design. Shortcut models are well established for grassroots design of

distillation columns and have been broadly applied. However, no shortcut models are


31

published for retrofit; only rigorous models are applied in the commercial simulation

packages. Established rigorous models are time consuming and have convergence

problems. Furthermore, they fail to consider all design parameters simultaneously in the

optimisation of heat integrated distillation columns. In contrast, shortcut models are

quicker to solve, do not have significant convergence problems, and are more robust

than rigorous models for column optimisation. In particular, shortcut models for retrofit

would be valuable for evaluating retrofit design options, for improving the performance

of existing distillation systems (distillation columns and heat recovery systems).

Furthermore, shortcut models can be combined with detailed heat exchanger network

models for optimising existing heat-integrated distillation processes, as will be seen in

Chapters 4 and 5.

3.2. Retrofit models for design of reboiled distillation columns

In this section, shortcut models for retrofit design of reboiled distillation columns are

developed. These models are an extension of those for grassroots design (Suphanit,

1999) that are presented in Chapter 2. The retrofit models account for the change in the

molar overflow and relative volatilities throughout the distillation column. The relative

volatility of each component is calculated as the geometric mean of that at different

locations in the column, while the constant molar overflow is corrected by enthalpy

balances around the column sections. Initially, a shortcut model is developed for retrofit

design of simple distillation columns. Thereafter, a retrofit model is proposed for design

of complex distillation configurations. The section also presents a number of examples

to illustrate the applications of the retrofit models, and to validate these models for

applications in the design of distillation columns using reboilers.

3.2.1. Retrofit shortcut model for simple reboiled distillation columns

For retrofit design of simple distillation columns with reboilers, a shortcut model is

developed. The basic model equations for grassroots design are rewritten for existing

simple distillation columns with reboilers. Then, these equations are solved

simultaneously with the material balance equations in order to calculate the product

compositions and flow rates for a fixed number of existing stages and given operating

conditions.


32

Figure 3.1 shows an existing simple distillation column with a reboiler; the number of

stages in the top and bottom sections is NR and NS respectively.

Figure 3.1: Simple distillation column with reboiler

Material balances are carried out around the column for the light and heavy key

components. Light key (LK) component material balance results in:

( ) BxRFx bLKLKfLK =−1 (3.1)

( )B

RFxx LKfLK

bLK

−=

1 (3.2)

Heavy key (HK) component material balance gives the following equations:

( ) DxRFx dHKHKfHK =−1 (3.3)

( )D

RFxx HKfHK

dHK

−=

1 (3.4)

For an existing simple distillation column with a reboiler, the Fenske equation is

rewritten in a new form in order to give the recovery of the key components as a

function of the minimum number of stages, as follows:

FenskHK

HK

LK

LK

RR

RR

φ=

−

− 11

(3.5)

where

Feed

Top product

Bottom product

NR

NS


33

minN

HK

LKFensk

=

αα

φ (3.6)

The relative volatilities of the key components, which are substituted in equation 3.6,

are calculated as the geometric mean of the values at the top and bottom of the column

and the feed stage. The compositions of these streams are given as a function of the

component recoveries.

From the existing total number of stages inside the distillation column and for given

operating conditions, the minimum number of stages Nmin, that is required to calculate

the term φFensk is calculated from a new form of the Gilliland correlation, as follows:

( ) GillGillNN ψψ −−= 1min (3.7)

where

−

++

−= 5.0

12.11711

4.541exp1ξξ

ξξψ Gill (3.8)

1min

+−

=R

RRξ (3.9)

The number of theoretical stages N in equation 3.7 is related to the actual total number

of stages in the distillation column NActual by the overall tray efficiency, as given by:

ActualOTray NN η= (3.10)

The tray efficiency is provided from the design data available for the distillation

column. However, when no data are available the efficiency can be calculated from

correlations found in many textbooks such as Seader and Henley (1998), Peters and

Timmerhaus (1980), and Kister (1992). Note that in the examples presented in this

chapter, the tray efficiency is set to 100%.

In the calculation of the minimum reflux, the change in the vapour flow rate in the

column is accounted for by applying the procedure of Suphanit (1999). So, the

Underwood equation estimates the minimum vapour flow rates at the top and bottom

pinch zones (King, 1980: p. 418; Kister, 1992: p. 313). Then, by enthalpy balances


34

around the column sections, the minimum vapour flow rates at the top and the bottom of

the column are obtained.

Equations 3.7, 3.8 and 3.9 were developed by Molokanov et al. (1972) to represent the

data of the Gilliland correlation. Although Molokanov equation is not the only equation

that describes the Gilliland correlation, it is a good representation of the Gilliland data

including very low values of ξ (King, 1980; p. 430). However, any other suitable

equations can be substituted. These equations may include those of Eduljee (King,

1980; p. 428), Erbar and Maddox (King, 1980; p. 430) and Robinson and Gilliland

(1950; p. 347-350).

Eduljee (1975) proposed a similar equation to represent the Gilliland correlation, as

follows:

5668.0min

1min75.075.0

1

+−

−=+

−R

RRN

NN (3.11)

The latter equation is not an accurate representation of the Gilliland correlation for all

operating conditions, as it does not fulfil the minimum condition since it results in N >

Nmin, as R ≈ Rmin (King, 1980).

The Kirkbride correlation is rearranged for an existing simple distillation column, with

known number of existing stages in each column section. The new form gives the ratio

of the product compositions of key components, as a function of the number of existing

stages in each section.

854.42

=

S

R

fLK

fHK

dHK

bLK

NN

xx

DB

xx

(3.12)

Similarly, the numbers of theoretical stages in the rectifying and stripping sections are

related to the existing stages in each section, as follows:

RActualRTrayR NN η= (3.13)

SActualSTrayS NN η= (3.14)

Dividing equation 3.2 by equation 3.4, the ratio of the key component mole fractions

can be obtained:


35

−−

=

HK

LK

fHK

fLK

dHK

bLK

RR

BD

xx

xx

11

(3.15)

By substituting equation 3.15 into equation 3.12, the recovery of the key components

can be related to the number of existing stages in each section:

KirkHK

LK

RR

φ=−−

11 (3.16)

where

427.22/1

=

S

R

fLK

fHKKirk N

Nxx

DBφ (3.17)

Equation 3.16 fixes the split of the two key components for existing distillation columns

with given number of stages in each section. The term, φKirk can be calculated for the

existing distillation column, where the number of existing stages in each section is given

and the top and bottom product molar flow rates and the mole fractions of the key

components are known.

Equation 3.16 is rearranged in order to be solved with equation 3.5:

( ) KirkHKLK RR φ−=− 11 (3.18)

( ) KirkHKLK RR φ−−= 11 (3.19)

( )( ) KirkHK

KirkHK

LK

LK

RR

RR

φφ

−−−

=− 1

111

(3.20)

Then, equation 3.5 is rearranged as follows:

−

=−

FenskLK

LK

HK

HK

RR

RR

φ1

11 (3.21)

Equations 3.20 and 3.21 are solved simultaneously to calculate the recovery of the

heavy key component as follows:

( )( ) FenskHKKirk

HKKirk

HK

HK

RR

RR

φφφ 1

1111−−−

=− (3.22)


36

( ) ( ) 01111 2 =−−+−FenskKirk

HK

FenskHKHKHK

RRRRφφφ

(3.23)

( ) ( ) 01111111 2 =−

+−+

−−

KirkFenskKirkFenskFenskHK

FenskHK RR

φφφφφφ (3.24)

Equation 3.24 is quadratic, in one unknown, RHK, as a function of various constants. The

equation is rearranged to the form: aX2 + bX + c = 0, where X = 1 - RHK. The constants

a, b and c can be extracted from equation 3.24. The two roots of the quadratic equation

area

acbbX2

42

1−+−

= , a

acbbX2

42

2−−−

= . Since the recovery of the heavy key

component, RHK must be positive and less than unity, the variable X must be positive.

Therefore, only the positive root of the solution is acceptable.

Thus, the solution to equation 3.24 is:

( )( ) ( )

( )( )12

11

114

1 1FenskKirk

Kirk

1/2

FenskKirk

2

FenskKirk

Kirk

−+

+

−+

−

+−=

φφφ

φφφφφ

HKR (3.25)

This equation calculates the recovery of the heavy key component, given the values of

the terms φKirk and φFensk. Then, the recovery of the light key component can be

estimated from equations 3.19 and 3.25, as follows:

( )( ) ( )

( )( )12

1114

1 1Fensk

Kirk

1/2

Fensk

Kirk

2

Fensk

Kirk

−+

+

−+

−+

−=φφ

φφ

φφ

LKR (3.26)

After calculating the key component recoveries, the product key compositions (mole

fractions) can be calculated from equations 3.2 and 3.4, and hence the key component

flow rates. On the other hand, the recoveries of the non-key components are calculated

as follows:

1. For the components lighter than the light key, they are assumed to appear

completely in the distillate and at zero mole fractions in the bottom product.

Similarly, the components heavier than the heavy key appear completely in the

bottom product and at zero mole fractions in the distillate (King, 1980; p. 325).


37

2. The Underwood equation is used to determine the distribution of non-key

components with volatilities between those of the key components in the top and

bottom sections at minimum reflux.

3. After calculating the minimum number of stages Nmin, the Fenske equation is

used in another form to calculate the distribution of intermediate-boiling non-

key components at total reflux as follows (King, 1980; p. 426):

( ) ( )minN

HK

iHKi DRDR

=

αα

(3.27)

Where (DR)i is defined as the distribution ratio of component i and is equal to the ratio

of the recovery of the component i between the top and bottom products. The

distribution ratio of the heavy key component can be obtained, providing the recovery to

the top and bottom products obtained previously.

4. Then, the distribution of the non-key components for the given operating

condition can be obtained by linear interpolation between their distributions at

minimum and total reflux as suggested by Treybal (1979) and King (1980; p.

434) (Suphanit, 1999). The recovery of non-key components can then be

calculated from the distributions.

5. The mole fractions of the non-key components in the top and bottom products

can then be calculated from similar relationships to those in equations 3.2 and

3.4, for the calculated recoveries from the previous steps. Hence, the non-key

components flow rates are then calculated.

The temperatures of the products are calculated by carrying out bubble and dew point

calculations. Then, an enthalpy balance around the various column sections calculates

the condenser and reboiler duties.

The retrofit shortcut model for simple reboiled distillation columns is represented

through the equations 3.6 to 3.10, 3.13, 3.14, 3.17, and 3.25 to 3.27.

In the application of the shortcut model for retrofit design of simple reboiled distillation

columns, the model equations mentioned above are solved simultaneously. Note that

equation 3.17 requires initial values for the key component recoveries to initialise the

calculations of the top and bottom product flow rates required in the calculation of the


38

term φKirk. The product flow rates are obtained for the initial recovery values by

applying the grassroots shortcut model of Suphanit (1999). The input data to the retrofit

model are the feed specifications (i.e. flow rate, temperature, pressure, composition),

and the number of existing stages in each section, as well as the operating conditions

(i.e. reflux ratio, column temperature, column pressure). The model output includes the

product compositions, temperatures and flow rates, and the heat duties of the condensers

and reboilers. The procedure of applying the retrofit shortcut model in simulations of

existing simple distillation columns with reboilers can be outlined as follows:

1. The feed composition, flow rate, temperature and pressure are specified. An

initial value is provided for the key component recoveries in the products.

2. The number of existing stages in the top and bottom sections is fixed.

3. The retrofit model calculates the product compositions (key and non-key

component mole fractions) and flow rates, for given operating conditions

including reflux ratio, and column pressure and temperature.

4. Bubble and dew point calculations determine the product temperatures.

5. Enthalpy balances calculate the condensing and reboiling duties.

The retrofit shortcut model is also applicable for design of sequences of simple

distillation columns with reboilers, including those shown in Figure 3.2. These

sequences may also include the combination of direct and indirect simple column

configurations. These column configurations are used to separate one feed mixture into

more than two products. As seen in these configurations, the simple columns are

connected in direct or indirect sequences. The connection between columns in the

indirect configurations is the top product streams of the upstream columns. In the direct

configurations, this connection is the bottom product streams of the upstream columns.

In these configurations, the downstream columns have no direct or indirect influence on

the upstream columns. Therefore, the retrofit shortcut model can be applied directly and

sequentially to each simple column in the sequences, starting with the first columns.

Thus, the sequence configurations and the number of stages in each section of each

column are fixed. The model calculates the product compositions, temperatures and

flow rates and the various duties of the condensers and reboilers, for given operating

conditions.


39

(a) Indirect sequence (b) Direct sequence

Figure 3.2: Sequences of two simple reboiled distillation columns

3.2.1.1. Degrees of freedom for simple reboiled distillation columns

To design a reboiled distillation column, four independent variables must be set, after

the feed and column pressure have been specified (King, 1980; p. 793). Therefore, to

describe a distillation problem, four degrees of freedom are available. The variables,

which might be used, are those listed in Table 3.1. However, any or all of the four listed

variables could be replaced with other independent variables in which we are more

interested. When using the retrofit shortcut models in design, the independent variables

that need to be set reduce to three. This results from the fact that the ratio between the

key component recoveries is specified by the Kirkbride correlation and is given as

equation 3.13. Thus, this relationship indirectly sets the fourth independent variable.

Table 3.1: Typical degrees of freedom for design of reboiled distillation columns

Rigorous model Retrofit shortcut model 1 Number of stages in top section Number of stages in top section 2 Number of stages in bottom section Number of stages in bottom section 3 Reflux ratio Reflux ratio 4 Distillate flow rate RLK/RHK = f (NR, NS) (equation 3.16)

3.2.1.2. Illustrative example – a simple reboiled distillation column

An existing simple reboiled distillation column separates a mixture of aromatic

hydrocarbons into two products. The feed mixture data and column specifications are

given in Table 3.2. The task of the distillation column is to separate the benzene from


40

toluene with 99% recovery of both components. The physical and thermodynamic

properties of feed and the product streams are calculated by the Peng Robinson model.

HYSYS simulation program (HYSYS, 1999) is used to provide results from a rigorous

model for comparison to validate the retrofit shortcut model results. A reflux ratio of

2.082 is calculated in HYSYS simulation to separate the given feed mixture in the

existing distillation column into the top product with the required specifications, i.e.

99% recovery of benzene to top product and 99% recovery of toluene to bottom

product. A hundred stages are used to calculate the minimum reflux ratio in the rigorous

simulation.

Table 3.2: Feed data and column specifications

Feed mixture Flow rate (kmol/h) Benzene 200 Toluene 100 Ethyl benzene 100 m-Xylene 200 o-Xylene 100 Total 700 Feed conditions Pressure (bar) 2.0 Temperature (oC) 135.9 Column specifications Top stages 15 Bottom stages 15 Column pressure (bar) 2.0

The existing distillation column is simulated using the retrofit shortcut model. The

existing numbers of stages in the top and bottom sections are specified, as are the

operating conditions, i.e. the reflux ratio of 2.082 and the operating pressure of 2.0 bars.

The key component recoveries, the product flow rates and temperatures and the duties

of the reboiler and condenser are calculated. The results are summarised in Table 3.3,

and compared with the rigorous simulation (HYSYS) results.

It is clear from Table 3.3 that the retrofit shortcut model predicts results with a very

good agreement with those of the rigorous simulations. This agreement is expected for a

simple distillation column separating a relatively ideal mixture; the maximum deviation

of the results of the retrofit model is less than 2% compared to rigorous models. This

good agreement between the results validates the retrofit shortcut model.


41

Although the rigorous simulation provides a value for the reflux ratio to the retrofit

shortcut calculations, the same calculations of the reflux can be done within the retrofit

shortcut model. In this case, the retrofit model applies to calculate the reflux ratio for

specified key component recoveries. Thus, the retrofit shortcut model can be applied

independently of the rigorous simulations, and it can initialise the rigorous simulations.

Table 3.3: Retrofit shortcut and rigorous model results (HYSYS)

Parameter Rigorous model Retrofit shortcut model

Top product flow (kmol/h) 199.0 198.9 Top product temp. (oC) 104.4 104.4 Condenser duty (kW) 5003 5002 Bottom product flow (kmol/h) 501.0 501.1 Bottom product temp. (oC) 158.7 158.8 Reboiler duty (kW) 5431 5427 Recovery of benzene to distillate (%) 99.00* 98.92 Recovery of toluene to bottom product (%)

99.00* 98.87

Reflux ratio, R 2.082 2.082* Minimum reflux ratio, Rmin 1.931 1.967

*: specified

3.2.2. Retrofit shortcut model for complex configurations of reboiled distillation columns

In this section, the retrofit shortcut model developed for simple reboiled distillation

columns is extended to different types of complex configurations of reboiled distillation

columns. In the complex column configurations considered, a single feed is separated

into more than two products, using main columns with side-strippers and side-rectifiers,

and prefractionators.

3.2.2.1. Complex distillation columns with side-strippers or side-rectifiers

Columns with a side-stripper or a side-rectifier are complex columns, which can

separate one feed mixture into three products. Retrofit modelling for such complex

configurations is more difficult than for simple sequences. In modelling such columns,

the complex configurations are decomposed into thermodynamically equivalent

sequences of simple columns (Carlberg and Westerberg, 1989a). Using this well-known

decomposition technique, the design of such configurations becomes more easy and

systematic. Each simple column in these equivalent sequences can be designed


42

individually, using the retrofit shortcut model for simple reboiled distillation columns.

Then, a retrofit shortcut model based on that for simple reboiled distillation columns is

proposed for the complex configurations.

Figure 3.3 shows complex columns with side-strippers and side-rectifiers and their

equivalent sequences of simple columns. The figure also shows the distribution of the

existing stages of the complex columns into the equivalent sequences. An indirect

sequence of two thermally coupled simple columns is equivalent to a complex column

with a side-stripper. For the complex column with a side-rectifier, the equivalent

sequence is a direct sequence of two thermally coupled simple columns.

Figure 3.3: Decomposition of reboiled complex distillation columns (with full thermal coupling), showing distribution of existing number of stages

The connections between the simple columns in the equivalent sequences are one

vapour stream and one liquid stream. This connection is known as a thermal coupling,

because of the direct heat transfer between column sections. In the thermally coupled

NR1

NS1

NS2

NR2 NR1

NS1

NS2

NR2

NR2

NR1

NS1 NS2

NR1

NS1

NR2

NS2

Column with side-stripper Indirect sequence

Equivalent simple sequences Complex columns

Column with side-rectifier Direct sequence


43

direct sequences, the downstream column is fed by a liquid stream from the upstream

column and returns a vapour stream to the upstream column. In the thermally coupled

indirect sequences, the downstream column returns a liquid stream to the upstream

column and receives a vapour phase feed from the upstream column.

When changing from a simple uncoupled sequence to a thermally coupled sequence,

thermal coupling replaces the condenser or reboiler of the first column (Figure 3.4).

Thus in the indirect sequence, the total heat duty of the condenser of the first column is

shifted into the condenser of the second column. Similarly for the direct sequence, the

total cooling duty of the condenser of the first column is shifted into the condenser of

the second column. At this condition, the thermal coupling connection is known as a full

thermal coupling.

Figure 3.4: Simple uncoupled sequences versus thermally coupled sequences

Based on the retrofit shortcut model for simple reboiled distillation columns and the

models of Suphanit (1999) for grassroots design, a retrofit shortcut model for complex

columns with side-strippers and side-rectifiers is proposed. This retrofit model can be

summarised as follows (Figure 3.5):

1. The complex column is decomposed into the thermodynamically equivalent

sequence of simple columns with thermal coupling.

2. The existing stages of the main column are distributed into the first column and

the top section of the second column. The existing stages of the side-stripper or

side-rectifier are distributed into the bottom section of the second column.

Simple uncoupled sequences Thermally coupled sequences


44

3. The retrofit shortcut model is applied to each simple column in the sequence,

starting with the first column. For each simple column, the retrofit model fixes

the existing stages in each column section, and, for the given operating

conditions, it calculates the product flow rates, temperatures and compositions,

and the duties of the reboiler and condenser.

Figure 3.5: Retrofit algorithm for complex column configurations

4. Due to the thermal coupling connections, an iterative procedure is carried out.

The iteration starts with the solutions obtained in step 3. New recoveries for key

components are updated in each iteration step through a linear relation with the

previous recoveries. Iterations terminate once the calculated number of stages

corresponds to the number of existing stages in each thermally coupled column.

5. For the thermally coupled sequences, the iterative procedure results in the

product compositions, flow rates and temperatures, the flow rates of the liquid

No

Update new recoveries(R LK , R HK )

Initial guess recoveries (RLK, RHK)

Retrofit simulationresults

Modified FUG (N'R, N'S)

Equivalent sequence ofsimple columns

Retrofit shortcut model

Complex column (NR, NS)

Thermal coupling

║NR – N´R║≤ ε ║NS – N´S║≤ ε

[

Yes Final retrofit results


45

and vapour streams of the thermal coupling connection, and the duties of the

reboilers and condensers.

For a complex column with a side-stripper or side-rectifier (equivalent to two fully

thermally coupled columns), there are eight degrees of freedom; four for each column.

Table 3.4 shows these degrees of freedom when using the retrofit shortcut models,

compared with those when using the rigorous models. As shown, when applying the

retrofit shortcut model, only six independent design variables need to be specified, since

the ratios of the key component recoveries for the two columns are described using the

Kirkbride correlation (see equation 3.13). However, eight independent variables must be

specified in the case of using the rigorous models.

Table 3.4: Typical degrees of freedom for design of fully thermally coupled sequences of reboiled distillation columns

Rigorous model Retrofit shortcut model 1 Number of stages in top section

of first column Number of stages in top section of first column

2 Number of stages in bottom section of first column

Number of stages in bottom section of first column

3 Liquid flow rate between columns Liquid flow rate between columns 4 Bottom product flow rate of

second column RLK/RHK = f (NR, NS) for first column (equation 3.16)

5 Number of stages in top section of second column

Number of stages in top section of second column

6 Number of stages in top section of second column

Number of stages in bottom section of second column

7 Reflux ratio of second column Reflux ratio of second column 8 Bottom product flow rate of

second column RLK/RHK = f (NR, NS) for second column (equation 3.16)

3.2.2.2. Illustrative example – a reboiled distillation column with a side-stripper

An existing complex distillation column with a side-stripper separates the feed mixture

given in Table 3.2 into three products: benzene, toluene and ethyl benzene. The

complex column configuration is equivalent to two fully thermally coupled reboiled

columns. The existing stages and column operating conditions, including the reflux ratio

are listed in Table 3.5. The task of the first column in the sequence is to separate

between toluene and ethyl benzene with 99% recoveries of each component to the top

and bottom of the column. The second column then recovers 99% of the benzene in the

column feed to the top product and 99% of the toluene in the column feed to the bottom


46

product. The physical and thermodynamic properties of the feed and product streams

are calculated by the Peng Robinson model.

The existing distillation unit is first simulated using a rigorous simulation (HYSYS).

Then, the retrofit shortcut model simulates the existing distillation unit. The existing

stages are specified, as are the operating conditions including the reflux ratio and the

reboiler duty of the first column obtained from the rigorous calculations. The simulation

results, summarised in Table 3.6, include the product flow rates and temperatures, the

key component recoveries, the condenser and reboiler duties, and the vapour and liquid

stream flow rates in the thermal coupling connection. These results are compared with

the rigorous simulation (HYSYS) results.

Table 3.5: Column specifications for thermally coupled indirect sequence

Column 1 Column 2 Top stages 18 18 Bottom stages 25 12 Pressure (bar) 2.0 2.0 Reflux ratio 4.50

Table 3.6: Retrofit shortcut and rigorous model results

Parameter Rigorous model Retrofit shortcut model

Column 1 Bottom product flow (kmol/h) 399.5 399.8 Bottom product temperature (oC) 166.7 166.8 Reboiler duty (kW) 7738 7738* Bottom recovery of ethyl benzene (%) 99.0* 99.5 Thermal coupling Vapour flow (kmol/h) 831.4 836.6 Liquid flow (kmol/h) 528.6 536.4 Column 2 Reboiler duty (kW) 1649 1673 Condenser duty (kW) 8874 8904 Top product flow (kmol/h) 198.0 198.6 Top product temperature (oC) 104.3 104.4 Bottom product flow (kmol/h) 102.5 101.6 Bottom product temperature (oC) 135.7 135.3 Top recovery of benzene (%) 99.0* 98.8 Bottom recovery of toluene (%) 99.0* 98.9 Reflux ratio 4.50* 4.50*

*: specified

1

2

Benzene

Toluene

Ethyl benzene

Feed

Vapour

Liquid


47

It can be seen very clearly that the results of the retrofit shortcut model are in very good

agreement with those of the rigorous simulation. The deviation of the results for most

design variables is less than 1%, except that the reboiler duty of the second column

shows a deviation of 1.4%. The temperature difference of the various streams is less

than 0.5 oC.

3.2.2.3. Complex distillation columns with side-exchangers

In industry, some complex distillation columns use side-exchangers in order to reject

heat at a high temperature and hence increase the heat recovery. The best-known side-

exchangers used in industrial applications are pump-arounds and side-heaters. Pump-

arounds are usually installed at the upper sections of the complex column; their function

is to withdraw a liquid stream from a certain stage, cool it down and return it back to the

column. Side-heaters may be installed in the lower section of the column; they withdraw

a liquid stream at a certain location, vaporise it and return it to the column.

The use of side-exchangers in complex columns with side-strippers or side-rectifiers

represents the condition between the uncoupled sequences and the fully thermally

coupled sequences, shown in Figure 3.6. This condition is called partial thermal

coupling. The partially thermally coupled indirect sequence is equivalent to a complex

column with a side-stripper and a pump-around.

(a) Partial thermal coupling (b) Full thermal coupling

Figure 3.6: Thermal coupled complex column configurations (direct and indirect sequences)


48

In the partially thermally coupled sequences, only some of the heat load of the

condenser or the reboiler in the first column is shifted to the subsequent column. A side-

cooler is installed at the top of the first column instead of the condenser, in partially

coupled indirect sequences. In partially coupled direct sequence, a side-heater is

installed at the bottom of the first column instead of the reboiler of the first column. In

the partially coupled sequences, degree of thermal coupling is defined for indirect

sequences as the ratio of the liquid flow rate at the top of the column when using a side-

cooler to that when no side-cooler is installed (Suphanit, 1999). Similarly, the degree of

thermal coupling for direct sequences is the ratio of the vapour flow rate at the bottom

of the column when using a side-heater to that when no side-heater is installed. In

uncoupled sequences, the degree of thermal coupling is equal to zero, which means

there is no liquid or vapour from the subsequent column. In fully coupled sequences,

this value is unity; the duty of the side-cooler and side-heater is zero.

Modelling these partially thermally coupled sequences in retrofit design is carried out in

a similar way to that for fully thermally coupled sequences. A retrofit model is built by

combining the retrofit shortcut model for simple reboiled distillation columns and the

model of Suphanit (1999), for the calculation of the side-cooler and side-heater duties.

The retrofit shortcut model is iterative due to the presence of the thermal coupling and

the side-cooler or side-heater. The iterative procedure is similar to that shown in Figure

3.5, for fully thermally coupled sequences. For existing partially thermally coupled

sequences, and for the given operating conditions, the retrofit shortcut model calculates:

1. Product flow rates, temperatures and compositions.

2. Flow rates of liquid and vapour streams in thermal coupling connection.

3. Duties of side-coolers and side-heaters.

4. Flow rates of liquid through side-coolers and side-heaters.

5. Duties of condensers and reboilers.

For the partially thermally coupled sequences shown in Figure 3.6, the degrees of

freedom increase to ten. Two degrees are added for the side-coolers or side-heaters to

those eight for the fully thermally coupled sequences. The new two degrees are the heat

duty of the side-exchanger and either the flow rate or the temperature drop of the liquid

recycled through that side-exchanger. As seen before, when applying the retrofit


49

shortcut model, only eight independent design variables are specified since that the

Kirkbride correlation specifies the other two degrees as the ratio of the key component

recoveries for each column.

3.2.2.4. Illustrative example – a reboiled distillation column with a side-stripper and a pump-around

In this example, a pump-around is added to the top of the first column in the existing

distillation unit of example 3.2.2.1. The column specifications are the same as those

given in Table 3.5, except that the reflux ratio is reduced to 3.20. The pump-around duty

is 2250 kW, and it has a temperature drop of 20 oC.

The existing distillation unit is simulated using the retrofit shortcut model. The

simulation results are summarised in Table 3.7, and compared with the rigorous model

results.


Parameter Rigorous model


Column 1 Bottom product flow (kmol/h) 399.4 399.7 Bottom product temperature (oC) 166.7 166.8 Reboiler duty (kW) 7763 7763* Bottom recovery of ethyl benzene (%) 99.0* 99.5 Temperature drop across pump-around (oC) 20.0* 20.0* Pump-around duty (kW) 2250* 2250* Liquid flow through pump-around (kmol/h) 2381 2392 Thermal coupling Vapour flow (kmol/h) 580.1 582.7 Liquid flow (kmol/h) 279.5 282.4 Column 2 Reboiler duty (kW) 1780 1798 Condenser duty (kW) 6780 6804 Top product flow (kmol/h) 198.1 198.6 Top product temperature (oC) 104.3 104.4 Bottom product flow (kmol/h) 102.5 101.7 Bottom product temperature (oC) 135.7 135.2 Top recovery of benzene (%) 99.0* 98.7 Bottom recovery of toluene (%) 99.0* 98.8 Reflux ratio 3.20* 3.20*

*: specified

Table 3.7 shows that the results of both models are in very good agreement. It can be

seen that most design variables of the retrofit shortcut model have approximately the


50

same values as those of the rigorous model. The reboiler of the second column shows a

maximum deviation of 1.1%. The temperatures of the various product streams are

nearly identical.

3.2.2.5. Complex distillation columns with prefractionators

Another complex column design alternative to those presented previously, for

separating one feed mixture into three products is the complex column with a

prefractionator (Figure 3.7). In Figure 3.7(a), the prefractionator column is used to

separate the feed mixture into two (intermediate) streams, which are then separated in

the main column into the top, bottom and the middle products. This column

configuration is more energy efficient than the conventional sequence of simple

columns. This is because the remixing effect that occurs at the top or bottom of the

simple sequence is reduced in a complex column with a prefractionator (Triantaflyllou

and Smith, 1992).

(a) Column with prefractionator (b) Petlyuk column

Figure 3.7: Different configurations of complex columns with prefractionators

Similar to complex columns with side-strippers or side-rectifiers, thermal coupling can

be used in a complex column with a prefractionator instead of the condenser and

reboiler of the prefractionator, as shown in Figure 3.7(b). This column configuration is

called a fully thermally coupled column or a Petlyuk column (Petlyuk et al,, 1965).


51

Retrofit modelling of these column configurations is carried out in the same manner as

is their grassroots design done. The three-column model (Carlberg and Westerberg,

1989b; Triantafyllou and Smith, 1992) is used. The main column is split into two simple

columns as shown in Figure 3.8(b). The section above the middle product draw

becomes column 2 and the section below becomes column 3. The bottom product of

column 2, combined with the top product of column 3, gives the middle product.

Figure 3.8: Modelling of column with a prefractionator and Petlyuk column

Top

Middle

Bottom

Top

Middle

Bottom

NS1

NS2

NR3

NR2

NS3

NS2

NR3

NR2

NS3

NR1

NS1

NR1

Top

Middle

Bottom

Top

Middle

Bottom

NS2

NR3

NR2

NS3

NS2

NR3

NR2

NS3

NS1

NR1

NS1

NR1

(a) Column with prefractionator (b) Equivalent three-column sequence

(a) Petlyuk column (b) Equivalent three-column sequence

2

3

1

2

3

1


52

The retrofit models for these column configurations are built based on the retrofit

shortcut model presented previously and the grassroots design model (Suphanit, 1999).

The retrofit models can be summarised for columns with prefractionators as follows:

1. The column is decomposed, by using the three-column model approach, into the

equivalent three-column configurations. The equivalent three-column

configuration can be seen as a simple column connected to other two columns in

direct and indirect sequence at the same time.

2. The existing stages are distributed into the corresponding sections in the

equivalent three-column configuration (Figure 3.8).

3. The retrofit shortcut model for simple reboiled distillation columns is directly

applied to column 1. The top and bottom product flow rates, compositions and

flow rates are calculated, as well as the condensing and reboiling duties. The top

and bottom product streams feed columns 2 and 3 respectively.

4. Then, the same retrofit model is applied to columns 2 and 3 simultaneously. A

middle key component needs to be specified for the product specifications at the

bottom of column 2 and at the top of column 3, in addition to the normal key

components for the top and bottom products.

5. Since the bottom section of column 2 is connected to the top section of column 3

in the real main column, a vapour flow constraint is therefore necessary in the

design using the three-column model. In this case, the vapour flow rate in the

bottom section of column 2 and that in the top section of column 3 are set to be

equal (Suphanit, 1999).

6. Finally, the retrofit model results in the top, bottom and middle product flow

rates, temperatures and compositions, and the various heat duties, for a fixed

number of stages and given operating conditions.

The retrofit model for Petlyuk columns is similar to that for columns with

prefractionators. In this case, the retrofit shortcut model for thermally coupled

sequences is applied simultaneously to the three columns of the equivalent three-column

configuration. The same vapour flow constraint is applied. Eleven degrees of freedom

exist for design of Petlyuk columns; six for the existing stages; one for the reflux ratio;

three for the ratio RLK/RHK = f (NR, NS) in each column.


53

It is also possible that side-exchangers can be installed at the top and bottom of the

prefractionator column. The resulting column configuration represents the condition

between columns with prefractionators and Petlyuk columns. In this case, the retrofit

model includes those retrofit models for partially thermally coupled sequences. The

degrees of freedom in this case increase to fifteen; by adding four degrees of freedom (2

variables × 2 side-exchangers) to the eleven for Petlyuk columns.

3.2.2.6. Illustrative example – a reboiled distillation column with a prefractionator

An existing complex distillation column using a prefractionator (see Figure 3.7(a)) is

used to separate the feed mixture given in Table 3.2, into three products. The existing

stages for the prefractionator and main column, the key component recoveries and the

operating conditions are given in Table 3.8. The Peng Robinson model calculates the

physical and thermodynamic properties of the feed and all product streams.

Table 3.8: Data for column with a prefractionator

Parameter Prefractionator Main column Stages in top section 11 12 Stages in bottom section 11 24 Stages in top of middle section 8 Stages in bottom of middle section 18 Top recovery of benzene (%) 99.5 99.0 Bottom recovery of ethyl benzene (%) 99.5 99.0 Middle recovery of toluene (%) 99.0 Column pressure (bar) 2.0 2.0 Reflux ratio 1.243 3.904

The existing distillation unit is simulated using both the retrofit shortcut model and

rigorous model simulation (HYSYS). The simulation results of both models are

summarised in Table 3.9.

One can observe that the results of the retrofit shortcut model compare very well with

those obtained from the rigorous simulation.

3.2.3. Summary

Retrofit models have been developed for designing reboiled distillation columns. The

models apply to various column configurations; many column configurations are

considered. The column configurations include simple columns, sequences of simple

columns, columns with side-strippers and side-rectifiers, columns with side-exchangers,


54

columns with prefractionators and Petlyuk columns. These retrofit models account for

the change in molar overflow and relative volatilities through the distillation columns.

In all applications, the retrofit model calculates the product stream flow rates,

temperature and compositions, and the various heating and cooling duties, for a fixed

column configuration and stages, and given operating conditions. Very good agreement

is observed between the retrofit shortcut models and the rigorous simulation results for

well-behaved mixtures of hydrocarbons. The applicability of the shortcut models is

restricted to well-behaved systems.

Table 3.9: Results for column with prefractionator



Prefractionator column Top recovery of benzene (%) 99.5* 99.8 Bottom recovery of ethyl benzene (%) 99.5* 99.8 Condenser duty (kW) 2527 2466 Reboiler duty (kW) 4945 4855 Reflux ratio 1.243 1.243* Main column Top recovery of benzene (%) 99.0* 99.7 Middle recovery of toluene (%) 99.0* 99.6 Bottom recovery of ethyl benzene (%) 99.0* 99.5 Reflux ratio 3.904 3.904* Condenser duty (kW) 7915 7974 Reboiler duty (kW) 6009 6106 Top product flow (kmol/h) 198.1 198.6 Top product temperature (oC) 104.3 104.3 Middle product flow (kmol/h) 102.6 100.7 Middle product temperature (oC) 135.7 136.1 Bottom product flow (kmol/h) 399.4 399.8 Bottom product temperature (oC) 166.7 166.8

*: specified

3.3. Retrofit models for design of steam-stripped distillation columns

Some distillation applications, including crude oil distillation, use stripping steam as a

vaporisation mechanism. Live steam is injected directly into the bottom of the column

as a stripping agent. Steam is condensed in the condenser and can be separated from

liquid hydrocarbons at the top of the crude oil column because it is immiscible with

most hydrocarbons.


55

As discussed in Chapter 2 (Section 2.3), the separation characteristics of steam-stripped

columns, such as temperature profile and vaporisation mechanism, are different from

those of reboiled column. Therefore, the retrofit shortcut models for reboiled distillation

columns cannot be applied directly to steam-stripped distillation columns.

In this section, shortcut models are developed for retrofit design of steam-stripped

distillation columns, based on the grassroots models of Suphanit (1999). The new

retrofit models overcome the main drawbacks of the standard FUG method; constant

molar overflows and relative volatilities. The relative volatility of each component is the

geometric mean of the values at the top section, bottom section and the feed stage.

Enthalpy balances around column sections are performed to correct the constant vapour

flow rates at both minimum and actual reflux conditions. Firstly, a retrofit shortcut

model for simple distillation columns is built. Then, retrofit models are proposed for

different complex configurations of steam-stripped distillation columns. Illustrative

examples are also presented to show the application of the retrofit models, and to

validate these models for the design of steam-stripped distillation columns.

3.3.1. Retrofit shortcut model for simple steam-stripped distillation columns

In simple steam-stripped distillation columns, the column is divided into two sections,

the rectifying and stripping sections, as shown in Figure 3.9. The retrofit modelling for

such columns is carried out separately for each column section. For the rectifying

section with a given number of stages, solving new forms of the basic equations of the

grassroots model, simultaneously with material balance equations, builds the retrofit

shortcut model.

A material balance is carried out around the column for the light key component,

resulting in:

DxFRx dLKLKfLK = (3.28)

DFRx

x LKfLKdLK = (3.29)


56

Figure 3.9: Simple distillation column using stripping steam

The Fenske equation is rewritten for an existing distillation column to give the

composition of the key components in the distillate as a function of the minimum

number of stages, as follows:

SteamdHK

dLK

xx

ϕ= (3.30)

where

minN

HK

LK

HKFS

LKFSSteam x

x

=

αα

ϕ (3.31)

To account for the change in the volatility, the volatility of the key components is

calculated as the geometric mean of the values at the top and bottom of the column and

the feed stage.

The minimum number of stages in the rectifying section can be calculated from the

Gilliland correlation represented by the equation of Molokanov et al. (1972), given the

existing number of stages and operating conditions:

( ) GillGillRNN ψψ −−= 1min (3.32)

where

−

++

−= 5.0

12.11711

4.541exp1ξξ

ξξψ Gill (3.33)

Top product

Bottom product

NR

NS Steam

Water

Feed


57

1min

+−

=R

RRξ (3.34)

The number of theoretical stages in the rectifying section NR is obtained from the actual

stages in that section by a similar relationship to that in equation 3.13.

As mentioned previously, the vapour flow rate is not constant throughout the column,

especially for multicomponent systems. Thus, to account for this aspect, the procedure

of Suphanit (1999) is applied. The vapour flow rates at the top and bottom pinch zones

are calculated by the Underwood equation for multicomponent systems as suggested by

King (1980). Then, the vapour flow rate at the top of the column is calculated by

enthalpy balance around the top section. Thus, the minimum reflux ratio required in

equation 3.31 can be calculated.

By solving equations 3.29 and 3.30, the recovery of the light key component can be

calculated as a function of the existing stages in the rectifying section and the bottom

composition:

FxDx

RfLK

SteamdHKLK

ϕ= (3.35)

The retrofit shortcut model for the rectifying sections is represented through equations

3.31 to 3.35. The model calculates the recovery of the light key component for a given

number of existing stages in the rectifying section.

For the stripping section, the retrofit model is based on the consecutive flash

calculations presented in Chapter 2 (Section 2.3). The retrofit model is shown in Figure

3.10; it determines the bottom product composition for a given number of stages in the

stripping section and steam flow rate. The model calculation starts by assuming a

bottom product composition, in terms of a heavy key component recovery. Then, in an

iterative procedure, consecutive flash calculations are carried out from the bottom stage

towards the feed stage. In each step, the number of stages is counted from the bottom

stage to the feed stage. Then, the bottom product composition, which is related to the

heavy key component recovery, is updated through a linear relation with the previous

bottom composition. The new recovery value of the heavy key component is equal to

the old value of the heavy key component recovery minus a recovery difference. This

recovery difference is directly proportional to the ratio of the difference of the existing


58

number of stages and the current number of stages to the difference of the current

number of stages and the number of stages from the previous iteration. At the first

iteration, the recovery difference is assumed to be small value. The iterations terminate

when the calculated number of stages and existing number of stages are identical.

Hence, the correct bottom product composition is obtained.

Figure 3.10: Retrofit algorithm for stripping sections

The retrofit shortcut models for the rectifying and stripping sections comprise the model

for simple steam-stripped distillation columns. The model for the rectifying section

determines the light key component recovery, while the retrofit algorithm for the

stripping section calculates the recovery of the heavy key component. Thus, the

distribution of the key components (flow rates) in the distillate and bottom products can

be determined. On the other hand, the distribution of the non-key components is

calculated as follows:

1. For the components lighter than the light key, they are assumed to appear

completely in the distillate and at zero mole fractions in the bottom product.

Similarly, the components heavier than the heavy key are to appear completely

in the bottom product and at zero mole fractions in the distillate (King, 1980; p.

325).

Assume bottom composition

Carry out consecutive flash calculations from bottom stage towards feed stage

Number of stages is counted until stage vapour flow ≥ vapour below feed stage

Existing stages (NS) εNN SS ≤−′

Correct bottom composition

Yes

No

Update new bottom composition


59

2. The Underwood equation is applied to calculate the distribution of intermediate-

boiling non-key components in the top and bottom sections at minimum reflux.

3. After calculating the minimum number of stages Nmin in equations 3.32 to 3.34,

the Fenske equation is used to calculate the distribution of intermediate-boiling

non-key components at total reflux (King, 1980; p. 426).

4. Then, the distribution of the non-key components for the given operating

condition can be obtained by linear interpolation between their distributions at

minimum and total reflux as suggested by Treybal (1979) and King (1980; p.

434) (Suphanit, 1999). Thus, the recovery of the non-key components can then

be calculated from their distributions.

5. The mole fractions of the non-key components in the top and bottom products

can then be calculated from similar relationships to those in equations 3.2 and

3.4, for the calculated recoveries from the previous steps. Hence, the non-key

components flow rates are then calculated.

The temperature of the distillate is calculated by carrying out bubble and dew point

calculations. The temperature of the bottom product is calculated by performing

enthalpy balance. Then, an enthalpy balance around the condenser calculates the

condenser duty.

In the application of the retrofit model, the shortcut model for rectifying section is

solved simultaneously with that for stripping sections. Overall, the model fixes the

number of stages in each section and, for the given operating conditions and steam flow

rate, it calculates the product stream flow rates, temperatures and compositions, and the

condenser duty.

(a) Indirect sequence (b) Direct sequence

Figure 3.11: Sequences of direct and indirect simple steam-stripped distillation columns


60

The same retrofit model is applicable for sequences of simple steam-stripped columns

(e.g. Figure 3.11). As shown, the link between each pair of columns in the indirect

sequence is the top product stream from the upstream column to the downstream

column. In the direct sequence, the bottom product from the upstream column feeds the

downstream column. These configurations consist of simple columns connected with no

thermal coupling. Thus, the retrofit model for simple steam-stripped columns is applied

directly and sequentially to each simple column in these configurations, starting with

the first column.

3.3.1.1. Degrees of freedom for simple steam-stripped distillation columns

In the design of steam-stripped distillation columns, there are four degrees of freedom,

as for reboiled distillation columns. Thus in the design of such columns, four

independent design variables need to be specified. Table 3.10 lists examples of these

design variables for both the rigorous and shortcut models. The listed variables can be

replaced by any other design variables of interest.

Table 3.10: Typical degrees of freedom for steam-stripped distillation columns

Rigorous model Retrofit shortcut model 1 Number of stages in top section Number of stages in top section 2 Number of stages in bottom section Number of stages in bottom section 3 Reflux ratio Reflux ratio 4 Steam flow rate Steam flow rate

3.3.1.2. Illustrative example – a simple steam-stripped distillation column

An equimolar C8-C23 mixture of normal paraffins is separated into two products by a

simple distillation column using stripping steam. The feed data and column

specifications are given in Table 3.11. Superheated steam at 160 oC and 3 bar is used as

a stripping agent. The task of the distillation column is to recover 95% of n-C14 to the

top product and 95% of n-C19 to the bottom product.

The existing distillation column is simulated using both the retrofit shortcut model and

rigorous simulation (HYSYS). Both models use the Peng Robinson model for the

physical and thermodynamic property calculations of the feed and product streams. In

the rigorous simulation, a steam flow rate of 1540 kmol/h and a reflux ratio of 0.411 are

predicted to separate the given feed in the existing column into the required

specifications. When using the retrofit shortcut model, the number of stages in each


61

section and the column operating conditions are fixed. The steam flow rate and reflux

ratio, which are obtained from the rigorous simulation, are specified. Then, the model

calculates the product flow rates and temperatures, the key component recoveries (or

flow rates) and the duty of condenser. The results are summarised in Table 3.12, and

compared with the rigorous simulation results.

As seen in the table, there is a very good agreement between the results of the retrofit

shortcut model and the rigorous simulation. The deviation in product flow rates does not

exceed 1%; however, the key component (n-C14) flow rate in the top product shows a

deviation of 3.5%. The condenser duty shows approximately the same results for both

models. The maximum temperature difference of the various product streams is less

than 1.5 oC

Table 3.11: Feed mixture data and column specifications

Feed specifications C8-C23 mixture Flow rate (kmol/h) 1000 Pressure (bar) 3.0 Temperature (oC) 300 Column specifications Rectifying stages 6 Stripping stages 8 Column pressure (bar) 3.0 Flow rate of n-C14 in top product (kmol/h) (95% recovery)

59.4

Flow rate of n-C19 in bottom product (kmol/h) (95% recovery)

59.4

Table 3.12: Retrofit shortcut and rigorous simulation (HYSYS) results



n-C14 in top product (kmol/h) 59.4* 61.5 n-C14 in top product (kmol/h) 59.4* 59.9 Top product flow (kmol/h) 564.6+ 564.0 Top product temp. (oC) 130.4 131.4 Condenser duty (MW) 40.0 40.4 Bottom product flow (kmol/h) 435.4+ 435.9 Bottom product temp. (oC) 224.6 225.8 Steam flow (kmol/h) 1540 1540* Reflux ratio 0.411 0.411*

*: specified +: water-free basis


62

3.3.2. Retrofit shortcut model for complex configurations of steam-stripped distillation columns

Complex distillation configurations may use steam stripping for vaporisation rather than

using reboilers. Using steam stripping is more economical than using reboilers due to

the lower cost required for steam and its availability. The shortcut models for simple

steam-stripped distillation columns cannot be applied directly to retrofit these column

configurations.

In this section, retrofit modelling of complex configurations of steam-stripped

distillation columns is presented. Different complex configurations are considered, such

as columns with side-strippers, side-rectifiers, side-exchangers and thermal coupling.

The configurations considered are alternatives to simple columns for separating a single

feed mixture into more than two products.

3.3.2.1. Complex distillation columns with side-strippers or side-rectifiers

Retrofit modelling of these complex configurations is carried out by using the column

decomposition approach. The complex configurations are decomposed into

thermodynamically equivalent sequences of simple columns with thermal coupling.

This step makes the retrofit design easier. Then, a retrofit model, based on that for

simple steam-stripped distillation columns, is proposed.

The equivalent sequences to complex columns with side-strippers and side-rectifiers are

shown in Figure 3.12. These sequences are fully thermally coupled simple columns in

direct or indirect link. As seen in the indirect coupled sequence, the thermal coupling

connection is the vapour feed to the downstream column from the upstream column and

the liquid stream from the downstream column to the upstream column. The condenser

of the second column provides reflux for both columns. In the direct coupled sequence,

the downstream column receives its feed from the bottom liquid of the upstream column

and returns a vapour stream to the upstream column. The reboiler of the second column

provides vapour for both columns.


63

Figure 3.12: Decompositions of steam-stripped complex columns, showing

distribution of existing number of stages (full thermal coupling)

The retrofit shortcut for modelling complex steam-stripped columns with side-strippers

and side-rectifiers is summarised as follows:

1. The complex columns are decomposed into the thermodynamically equivalent

sequences of simple columns with thermal coupling connections.

2. The existing stages of the main column are distributed into the first column and

the top section of the second column, while the existing stages of the side-

stripper or side-rectifier are distributed into the bottom section of the second

column (Figure 3.12).

3. The retrofit shortcut model for simple columns is applied to each column in the

sequences, starting with the first column. For each simple column, the retrofit

NR1

NS1

NS2

NR2 NR1

NS1

NS2

NR2

NR2

NR1

NS1

NS2

NR1

NS1

NR2

NS2

Column with side stripper Indirect sequence

Equivalent simple sequences Complex columns

Column with side-rectifier Direct sequence


64

model calculates the product stream flow rates, temperatures and compositions,

and the duties of the reboilers and condensers.

4. Due to the thermal coupling connections, an iterative procedure is carried out.

The iterations start with the simulation results obtained in step 3. New recoveries

for key components are updated in each iteration step through a linear relation

with the previous recoveries. The iterations terminate when the calculated

number of stages and the existing stages are identical.

5. For the thermally coupled sequences, the iterative procedure results in the

product compositions, flow rates and temperatures, the flow rates of the liquid

and vapour streams of the thermal coupling connection, and the duties of the

reboilers and condensers.

In design of columns with side-strippers or side-rectifiers (equivalent to two thermally

coupled columns), there are eight degrees of freedom, four for each column. These

degrees of freedom are four for the existing stages in the rectifying and stripping

sections for the two columns; two for the steam flow rates to each column; one for the

reflux ratio of the second column; and one for the liquid flow rate between the two

columns.

3.3.2.2. Illustrative example – a steam-stripped distillation column with side-stripper

In this example, the same hydrocarbon feed mixture, specifications for which are given

in Table 3.11, is separated into three products using a steam-stripped distillation column

with a side-stripper (equivalent to the thermally coupled indirect sequence of two steam-

stripped columns). The specifications for each column are given in Table 3.13,

including the flow rates of stripping steam and the key components. Stripping steam

used for both columns is at 160 oC and 3 bar.

The existing distillation column is first rigorously simulated using HYSYS. A reflux

ratio of 2.59 is calculated to give the required specifications, for the given number of

stages and steam flow rates. When using the retrofit model, the column specifications

and the operating conditions, including the reflux ratio and steam flow rates, are

specified. Then, the model calculates the product stream flow rates and temperatures,

the key component flow rates and the condenser cooling duty. The results are


65

summarised in Table 3.14, and compared with those results obtained from the rigorous

simulation. Both models use the Peng Robinson model for the calculation of the

physical and thermodynamic properties of the feed and product streams.

The retrofit shortcut and rigorous simulation results show good agreement. Some

deviation is observed between the results of the retrofit shortcut model and rigorous

simulation. This deviation is due to the complexity in the column configuration, the

presence of the stripping steam and the large number of components. Temperature

differences of up to 7.6 oC and flow rate differences of products of up to 3.7 % are

observed. However, a very good agreement can be observed between the results of the

key component flow rates, and the condenser duty.

Table 3.13: Column and product specifications

Column specifications Column 1 Column 2 Rectifying stages 6 8 Stripping stages 8 4 Column pressure (bar) 3.0 3.0 Steam flow (kmol/h) 1100 100 n-C10 flow in top product (kmol/h)

59.4

n-C19 flow in bottom product (kmol/h)

59.4




Column 1 Bottom product flow (kmol/h) 454.5+ 448.1 Bottom product temperature (oC) 236.5 237.3 n-C19 flow in bottom product (kmol/h) 59.4* 59.48* Steam flow (kmol/h) 1100* 1100* Thermal coupling Vapour flow (kmol/h) 1940 1875 Liquid flow (kmol/h) 261 224 Column 2 Top product flow (kmol/h) 277.5+ 273.4 Top product temperature (oC) 127.8 128.7 n-C10 flow in top product (kmol/h) 59.4* 59.3 Bottom product flow (kmol/h) 268.5+ 278.5 Bottom product temperature (oC) 228.1 235.7 Condenser duty (MW) 30.7 30.8 Steam flow (kmol/h) 100* 100* n-C13 flow in botoom product (kmol/h) 57.8 61.2 Reflux ratio 2.59 2.59*


1

2 Top 2

Bottom 2

Bottom 1

Feed

Vapour

Liquid

Steam

Steam


66

3.3.2.3. Complex distillation columns with side-exchangers

Similar to reboiled distillation columns, side-exchangers may be added to steam-

stripped complex distillation columns. A side-cooler is added at the upper section of a

complex column with a side-stripper, while a side-heater is added at the lower section of

a complex column with a side-rectifier. This condition, which is called partial thermal

coupling, represents a condition between the uncoupled sequences and the fully

thermally coupled sequences. Figure 3.13 shows complex columns with side-

exchangers (partially thermally coupled), versus fully thermally coupled sequences. In

the partially thermally coupled sequences, some of the duty of the condenser or the

reboiler of the second column is shifted to the side-exchanger. In fully thermally

coupled sequences, the side-exchanger duty is zero, i.e. no side-exchanger exists.

(a) Partial thermal coupling (b) Full thermal coupling

Figure 3.13: Thermal coupled complex columns with steam (direct and indirect sequences)

A similar retrofit model to that for fully thermally coupled sequences is proposed for

complex columns with side-exchangers, i.e. partially thermally coupled sequences. This

model is based on the shortcut model for simple steam-stripped distillation columns and

the model of Suphanit (1999), for the calculation of side-exchanger duties. The retrofit

model uses the column decomposition approach, and has an iterative procedure. For

fixed existing stages and given operating conditions, the retrofit model calculates the


67

various product stream flow rates, temperatures and compositions, the duties of the

condensers and side-exchangers, and the flow rate of the liquid through the side-

exchangers.

There are ten degrees of freedom in design of these column configurations; two are

added, for the side-exchangers, to those eight degrees for fully thermally coupled

sequences.

3.3.2.4. Illustrative example – a steam stripped distillation column with a side-stripper and a pump-around

An existing steam-stripped distillation column with a side-stripper and a pump-around

is used to separate the same hydrocarbon feed mixture given in Table 3.11, into three

products. The column specifications, including the existing stages and column pressure,

are the same as in example 3.3.2.2. The pump-around has a cooling duty of 3056 kW

and a temperature drop of 20 oC.

The retrofit shortcut model simulates the existing distillation unit. The results are

summarised in Table 3.15, and compared with results from rigorous simulation. The

physical and thermodynamic properties of the various streams are predicted using the

Peng Robinson model.

One can note that the shortcut model predicts results in good agreement with those of

the rigorous simulation. The maximum error in the product flow rates and the condenser

duty is less than 4%. The temperatures of the products, predicted by the shortcut model,

deviate from those of the rigorous simulation by up to 9.5 oC.

3.3.3. Summary

New shortcut models have been developed for retrofit design of steam-stripped

distillation columns. The retrofit models overcome the main drawbacks of the

Underwood-based models: constant molar overflow and constant relative volatilities.

The models account for different column configurations such as simple columns,

sequences of simple columns, and columns with side-strippers, side-rectifiers and side-

exchangers. Good agreement is found between the results of the shortcut and the

rigorous models.


68


Parameter Retrofit shortcut model

Rigorous model

Column 1 Bottom product flow (kmol/h) 453.6 465.1+ Bottom product temperature (oC) 233.2 242.8 n-C19 flow in bottom product (kmol/h) 59.4* 59.4* Steam flow (kmol/h) 900* 900* Temperature drop across pump-around (oC) 20.0* 21.0 Pump-around duty (kW) 3056* 3056* Liquid flow through pump-around (kmol/h) 842.4 842.4* Thermal coupling Vapour flow (kmol/h) 1566 1544 Liquid flow (kmol/h) 120 118 Column 2 Top product flow (kmol/h) 271.5 262.4+ Top product temperature (oC) 128.7 127.6 n-C10 flow in top product (kmol/h) 59.4* 58.6 Bottom product flow (kmol/h) 274.9 270.5+ Bottom product temperature (oC) 222.9 214.4 Condenser duty (MW) 26.99 25.77 Steam flow (kmol/h) 150* 150* n-C13 flow in botoom product (kmol/h) 61.0 59.0 Reflux ratio 2.179 2.179*


3.4. Retrofit modelling for design of refinery distillation columns

Crude oil distillation is a process of great importance in the refining industry. The

process is both energy and capital intensive. Retrofit of these units is a common design

activity aiming to increase profit by using the existing equipment more efficiently.

Established crude oil distillation units use a combination of steam-stripped and reboiled

columns. For retrofit design of such configurations, the new shortcut models developed

for reboiled columns and steam-stripped columns can be applied sequentially.

Conventionally, the design of crude oil distillation units is carried out by the

specification of the cut point and temperature gap, or product flow rates (Watkins,

1979). Rigorous model-based simulations apply this conventional method for design of

crude oil distillation systems; i.e. the simulators specify the cut point and temperature

gap for separation products. Conventional shortcut models (e.g. FUG method) require

the specification of key components for the required separation. In crude oil distillation

columns, pseudo-components are specified for each pair of successive products when


69

using the shortcut models in design. Real components can be specified as the key

components for the separation of the light ends.

Various column configurations exist for distillation of crude oil in refineries. A tower

with three side-strippers and pump-arounds is a configuration that is typical of

atmospheric distillation towers used worldwide. This column configuration mostly uses

steam for stripping in the main tower and in the first side-stripper; it is equivalent to

four thermally coupled columns with three pump-arounds. In the design of such

configurations, there are twenty-two degrees of freedom. Summary of these degrees of

freedom, when using the retrofit shortcut model is given in Table 3.16.

Table 3.16: Typical degrees of freedom for atmospheric crude oil tower

Number of degrees of freedom


8 2 (stages in top and bottom sections) × 4 (columns) 3 Product flow rate for first three columns 2 Steam flow rate for first two columns 6 2 (∆T, duty of pump-around) × 3 (pump-arounds) 1 Reflux ratio 2 RLK/RHK = f (NR, NS) for last two columns

Total 22

In retrofit design of crude oil distillation units, the column configurations, number of

stages in each section, locations of pump-arounds and side-strippers are fixed. Then for

given operating conditions, including the steam flow rates, the retrofit models for

reboiled columns and those for stripped columns are applied sequentially to each

corresponding column. The models calculate the various process stream flow rates,

temperatures and compositions, the different heating and cooling duties of reboilers,

condensers and pump-arounds.

The significance of these retrofit models is that they are applicable for any column

configurations of crude oil distillation units, and they provide reliable results.

3.4.1. Illustrative example – an atmospheric crude oil distillation column

An atmospheric crude oil distillation unit processes 100,000 barrels/day (2610 kmol/h)

of crude oil at 25 oC and 3 bar into five products: light naphtha (LN), heavy naphtha

(HN), light distillate (LD), heavy distillate (HD), and residue (RES). Superheated steam

at 260 oC and 4.5 bar is used as a stripping agent.


70

The true boiling point data (crude assay) of the crude oil are shown in Table 3.17; these

data are based on a textbook example (Watkins, 1979). The crude assay is represented

using 25 pseudo-components, using the oil characterisation technique embedded within

a rigorous simulation package (HYSYS, 1999). The physical and thermodynamic

properties of each pseudo-component (e.g. molecular weight, vapour pressure, boiling

temperature, critical properties, etc.) are calculated using the Peng Robinson model, and

are then extracted from HYSYS. The key components for the separation of each pair of

products are shown in Table 3.18. The composition of the feed in terms of the pseudo-

components and their boiling temperatures and flow rates are given in Table 3.19.

The distillation column configuration is shown in Figure 3.14, which also shows the

equivalent sequence of four-thermally coupled columns. Table 3.20 gives the existing

stages in each section of the column, the operating conditions, the steam flow rates, and

the pump-around temperature differences and the cooling duties

The existing atmospheric unit is simulated using the retrofit shortcut model. In the

calculations, the existing stages in the rectifying and stripping sections are fixed, as are

the operating conditions, including the steam flow rates. The reflux ratio, the

temperature drops along the pump-arounds, and the pump-around duties are then

specified. The retrofit model calculates the product flow rates and temperatures, the

flow rates of the liquid recycled through the pump-arounds, the duties of the condenser

and reboilers, and the key component flow rates. The results are summarised in Table

3.21, and are then used to initialise the calculations of the rigorous simulation

(HYSYS), for the same column and feed specifications, and the operating conditions.

The rigorous simulation results are then compared with those from the retrofit shortcut

model in Table 3.21.

The table shows that the results predicted by the retrofit shortcut model are in good

agreement with those obtained from the rigorous simulations; no significant deviations

are observed. The maximum temperature difference is 10 oC, and the deviations in all

flow rates are within 7%. However, the other variables have very close results.

As seen, the distillation column configuration is very complex with many side-strippers

and pump-arounds; moreover it has a large number of components (25 pseudo-

components + water). So this good agreement is an important result for such column

complexity and large component numbers. The good agreement illustrates the adequacy


71

of the retrofit shortcut model and supports the application of the new model for retrofit

studies.

In refinery distillation products, the product composition and the true boiling curves are

of great importance since these features determine the product specifications and hence

the product qualities to meet the market requirements. Figures 3.15 to 3.19 show the

product compositions obtained from the retrofit shortcut models and compare them with

results from rigorous simulation. The figures are plotted as the pseudo-component mole

fraction in the product against the pseudo-component number. As shown, the retrofit

models predict product compositions in good agreement with rigorous simulation.

Furthermore, in Figures 3.20 to 3.24, the true boiling curves (TBP curves) of the various

products for the retrofit model and the rigorous simulation are shown. The true boiling

curves are obtained from HYSYS by inputting the product stream data (temperature,

pressure, component mole fractions) that are predicted by the shortcut models. It is clear

that there is good agreement between the results.

A conclusion can be drawn from this example that the retrofit shortcut models

developed for design of crude oil distillation columns are reliable for applications with

good accuracy compared with rigorous models. The models predict product separations

with comparable specifications and qualities with those obtained by using rigorous

simulations. The predicted results include flow stream rates, compositions, duties and

true boiling curves. In addition, the models can initialise rigorous calculations.

Table 3.17: Crude oil assay data

% Distilled (vol) TBP (oC) 0 -3.0 5 63.5 10 101.7 30 221.8 50 336.9 70 462.9 90 680.4 95 787.2 100 894.0

density = 865.4 kg/m3


72

Table 3.18: Key components for the separation of each pair of products

Successive products Key component LN and HN HN and LD LD and HD HD and RES Light key 4 7 11 13 Heavy key 6 9 14 16

Table 3.19: Feed composition of crude oil mixture (derived from assay data)

Component number NBP (oC) Flow rate (kmol/h) 1 9 110.9 2 36 106.9 3 61 139.3 4 87 175.8 5 111 175.8 6 136 169.7 7 162 169.4 8 187 166.2 9 212 156.6 10 237 140.1 11 263 127.9 12 288 115.6 13 313 106.2 14 339 101.3 15 364 94.5 16 389 84.6 17 414 73.9 18 447 95.2 19 493 61.8 20 538 49.2 21 584 54.5 22 625 39.3 23 684 40.2 24 772 28.2 25 855 26.6

Total 2610.6


73

(a) Complex configuration (b) Equivalent sequence

Figure 3.14: Atmospheric crude oil distillation column, showing the equivalent sequence of simple columns

Table 3.20: Specifications of atmospheric crude oil distillation column

Column specifications Column 1 Column 2 Column 3 Column 4 Rectifying stages 9 10 8 9 Stripping stages 5 5 7 6 Column pressure (bar) 2.5 2.5 2.5 2.5 Feed preheating temp. (oC)

365

Vaporisation mechanism

Steam Steam Reboiler Reboiler

Steam flow (kmol/h) 1200 250 Pump-around ∆T (oC) 30 50 20 Pump-around duty (MW)

12.87 18.03 11.25

Top product flow (kmol/h)

680.7

Bottom product flow (kmol/h)

633.9 149.8 652.8 493.0

Reflux ratio 4.77

1

3

4

2

Light naphtha (LN)

Heavy naphtha (HN)

Light distillate (LD)

Heavy distillate (HD)

Residue (RES)

Crude oil

LN

HN

LD

HD

RES

Water

Steam

Steam Steam

Water


74

Table 3.21: Results of atmospheric crude oil distillation column

Parameter Shortcut model

Rigorous model

Column 1 Bottom product (RES) flow rate (kmol/h) 633.9* 624+.0 Bottom product temperature (oC) 334.7 333.4 Key component flow rate in bottom product (kmol/h)

83.5 83.5*

Pump-around duty (MW) 12.87* 12.87* Pump-around flow rate (kmol/h) 2187 2187* Pump-around temperature drop (oC) 30.0* 27.1 Steam flow rate (kmol/h) 1200* 1200* Column 2 Bottom product (HD) flow rate (kmol/h) 149.8* 140.5+ Bottom product temperature (oC) 256.1 265.9 Key component flow rate in bottom product (kmol/h)

69.9 69.9*

Pump-around duty (MW) 18.03* 18.03* Pump-around flow rate (kmol/h) 2305 2305* Pump-around temperature drop (oC) 50.0* 47.0 Steam flow rate (kmol/h) 250* 250* Column 3 Bottom product (LD) flow rate (kmol/h) 652.8* 673.3+ Bottom product temperature (oC) 282.7 286.2 Key component flow rate in bottom product (kmol/h)

149.7 149.7*

Pump-around duty (MW) 11.25* 11.25* Pump-around flow rate (kmol/h) 5790 5790* Pump-around temperature drop (oC) 20.0* 21.4 Reboiler duty (MW) 9.38 9.38* Column 4 Top product (LN) flow rate (kmol/h) 680.7 691.4+ Top product temperature (oC) 76.9 77.1 Key component flow rate in top product (kmol/h) 174.6 175.5 Bottom product (HN) flow rate (kmol/h) 493.0 491.5+ Bottom product temperature (oC) 189.6 191.2 Key component flow rate in bottom product (kmol/h)

168.2 168.2*

Condenser duty (MW) 52.20 52.10 Reboiler duty (MW) 6.72 6.72* Reflux ratio 4.77* 4.79



75

Figure 3.15: Light naphtha composition for shortcut and rigorous models

Figure 3.16: Heavy naphtha composition for shortcut and rigorous models

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0 5 10 15 20 25 30

Pseudo-comp. no.

Com

p m

ole

frac

tion

Shortcut

Rigorous

0

0.05

0.1

0.15

0.2

0.25

0.3

0 5 10 15 20 25 30

Pseudo-comp. no.

Com

p m

ole

frac

tion

Shortcut

Rigorous


76

Figure 3.17: Light distillate composition for shortcut and rigorous models

0

0.1

0.2

0.3

0.4

0.5

0.6

0 5 10 15 20 25 30

Pseudo-comp. no.

Com

p m

ole

fract

ion

Shortcut

Rigorous

Figure 3.18: Heavy distillate composition for shortcut and rigorous models

0

0.05

0.1

0.15

0.2

0.25

0 5 10 15 20 25 30

Pseudo-comp. no.

Com

p m

ole

frac

tion

Shortcut

Rigorous


77

Figure 3.19: Residue composition for shortcut and rigorous models

-20

0

20

40

60

80

100

120

140

0 20 40 60 80 100 120

% Volume distilled

Boi

ling

tem

pera

ture

(o C)

RigorousShortcut

Figure 3.20: True boiling curves of light naphtha for shortcut and rigorous

models

00.020.040.060.080.1

0.120.140.160.18

0 5 10 15 20 25 30

Pseudo-comp. no.

Com

p m

ole

frac

tion

Shortcut

Rigorous


78

0

50

100

150

200

250

0 20 40 60 80 100 120

% Volume distilled

Boi

ling

tem

pera

ture

(o C)

RigorousShortcut

Figure 3.21: True boiling curves of heavy naphtha for shortcut and rigorous

models

0

50

100

150

200

250

300

350

400

0 20 40 60 80 100 120

% Volume distilled

Boi

ling

tem

pera

ture

(o C)

RigorousShortcut

Figure 3.22: True boiling curves of light distillate for shortcut and rigorous

models


79

0

50

100

150

200

250

300

350

400

0 20 40 60 80 100 120

% Volume distilled

Boi

ling

tem

pera

ture

(o C)

RigorousShortcut

Figure 3.23: True boiling curves of heavy distillate for shortcut and rigorous

models

0

100

200

300

400

500

600

700

800

900

1000

0 20 40 60 80 100 120

% Volume distilled

Boi

ling

tem

pera

ture

(o C)

RigorousShortcut

Figure 3.24: True boiling curves of residue for shortcut and rigorous models

3.5. Summary and conclusions

New shortcut models for retrofit design of distillation columns have been presented in

this chapter. Reboiled and steam-stripped distillation columns are considered. The

retrofit models are valid for various column configurations. Simple distillation columns,

sequences of simple columns, columns with side-strippers or side-rectifiers, columns

with side-exchanger, and columns with prefractionators are covered by the retrofit

models. In the applications, the models calculate the flow rates, compositions and


80

temperatures of the various process streams including the end products, and the

different duties required for the distillation process, for fixed column configurations and

given operating conditions. A number of examples have been presented for different

distillation applications. The retrofit models showed a good prediction of the results

compared with the existing rigorous simulations.

The main contribution of the new shortcut models is that they are particularly developed

for retrofit designs, and they include algorithm for applying to complex column

configurations. The models cover a wide range of column configurations of distillation

applications. Moreover, the models take account of the changes in both the molar

overflow and the relative volatilities throughout the columns, which were considered

previously as the main limitations of the standard FUG method. The retrofit models are

reliable for very complex structure of distillation columns with wide applications and

large number of well-behaved components, such as crude oil distillation processes.

The retrofit models provide a basis for optimising and improving the operating

conditions of existing distillation columns for energy-related, economic and

environmental benefits. The models can also be applied to calculate the additional

heating and cooling requirements for increased throughput to an existing distillation

process. In additions, the models can be utilised to assess retrofit modifications, such as

adding side-coolers or replacing stripping steam with a reboiler. Furthermore, these

models can be combined with hydraulic models, for the calculation of column

diameters, to assess the effect of increasing throughput on the hydraulic capacity of the

distillation columns.

The retrofit models presented in this chapter will be used in the subsequent chapters to

represent an existing distillation column in developing a retrofit approach for design of

heat-integrated crude oil distillation systems.

Chapter 4 Modelling for retrofit of heat-integrated crude oil distillation systems

81

Chapter 4: Modelling for retrofit of heat-integrated crude oil distillation systems

In this chapter, models are proposed for retrofit of heat-integrated crude oil distillation

systems. Existing heat-integrated crude oil distillation systems consist of distillation

columns with direct connection to the associated heat exchanger networks. Models are

proposed to fix an existing design of crude oil distillation column. Other models are

proposed to account for the details of the existing heat exchanger network and pressure

drop constraints, and to model the application of heat transfer enhancement in retrofit

design. Different issues of existing heat-integrated crude oil distillation systems are

modelled. These issues include the installation of a preflash unit or a prefractionator

column to an existing distillation column for throughput enhancement, de-bottlenecking

and reducing energy consumption. Taking account of the environmental impact of

existing crude distillation processes and the possible modifications for friendly-

performance are also considered. Furthermore, replacing trays with packing for

throughput enhancement and de-bottlenecking is modelled.

The chapter presents the modelling of these issues. The models will be applied in

Chapter 5 for retrofit design of heat-integrated crude oil distillation systems.

4.1. Modelling existing crude oil distillation columns

Existing crude oil distillation units have many interlinked columns in various

configurations, and include different vaporisation mechanisms. The standard column

configuration that is extensively built worldwide in most refineries is the atmospheric

crude oil tower. This column uses a number of side-strippers and pump-arounds for

separating the crude oil into the various products. Both reboilers and steam are used for

vapour generation. While the atmospheric tower is the most common configuration, an

energy-efficient progressive distillation has been installed at the Mider Refinery at

Leuna in Germany in 1997. Progressive distillation uses a succession of prefractionation

steps in three distillation columns.

In retrofit design of heat-integrated crude oil distillation systems, the complexities of the

existing distillation columns need to be taken into account. While retrofit design of


82

these systems is carried out, existing equipment constraints must be met. Shortcut

models would be of great importance to consider the details of existing crude oil

distillation columns and their constraints in retrofit design.

The retrofit shortcut models developed in Chapter 3 is valuable tool to specify an

existing crude oil distillation column and take its complexity into account. This

complexity includes the column configuration, existing number of stages in each

section, types of vaporisation mechanism involved, and the locations of side-columns

and side-exchangers. The shortcut models fix the existing distillation design and

calculate the product and internal stream flow rates, compositions and temperatures, and

the different heat duties for given operating conditions and steam flow rates.

4.1.1. Hydraulic analysis for existing crude oil distillation columns

The capacity of an existing crude oil distillation column is limited by the hydraulic

capacity of the stages. Throughput can be enhanced when the stages have an extra area

that can be utilised in the separation. The distillation column is hydraulically

bottlenecked on stages where flooding limits are exceeded.

To consider the hydraulic capacity of an existing distillation column, the diameter of the

stages needs to be calculated. The flooding limits data for sieve trays can be correlated

into the following form Fair’s correlation (Cited in Kister, 1992; Chapter 6).

LV

o

FC

ooSB eBAC−

−= (4.1)

The constant parameters A, B, and C are based on the tray spacing, and they can be

found in Suphanit (1999). The flow parameter FLV is defined as:

L

VLV V

LFρρ

= (4.2)

CSB is Souders and Brown (Kister, 1992) flooding constant, and is known as the

capacity factor. The flooding velocity maxU is a function of the capacity factor, the

liquid and vapour densities, and the liquid surface tension as follows:

V

VLSBCU

ρρρσ −

=

2.0

max 20 (4.3)


83

In design, the velocity must be lower than the flooding velocity in order to prevent the

flooding in the column. Thus, the design vapour velocity desU is calculated as a fraction

of the flooding velocity. Then, the diameter of stage can be calculated by:

)1(4

DCUV

Ddes

VT −=

π (4.4)

where

maxUUU ratiodes = (4.5)

For an existing crude oil distillation column, the diameter is calculated for the stages, on

which there is a significant change in the vapour and liquid flow rates. These stages

include the top, feed and bottom stages in the main column, the pump-around stages, the

stages above and below the pump-arounds, the stages of the stripping section, and the

stages of the side-strippers. Stage diameter is calculated using the above equations and

from the vapour and liquid flow rates. The calculation of the diameters for different

stages gives an analysis for the hydraulic performance of the existing distillation

columns. This hydraulic analysis is useful to specify the hydraulic limitations of an

existing distillation column. It is also valuable tool to determine the potential of existing

distillation units for increasing throughput. Furthermore, evaluating the hydraulic

performance of an existing distillation unit and its maximum hydraulic capacity (actual

diameters) can identify column bottlenecks that limit the throughput enhancement.

Column bottleneck is a hydraulic capacity violation and occurs on stages, which require

larger diameters than the actual column diameters. Process operation changes and

column structural modifications can remove column bottlenecks. The process changes

may include changing the pump-around temperature drop, flow rates of steam and

recycled liquid in pump-around, and reflux ratio or adjusting the feed preheating

temperature. The installation of preflash drum or prefractionator column, and the

replacement of the bottlenecked trays with packing can also remove column

bottlenecks.

The hydraulic model presented in this section is combined with those shortcut models

for retrofit distillation design to represent an existing distillation column with its

hydraulic capacity limitations in a retrofit design approach in the next chapter.


84

4.2. Modelling existing heat exchanger networks

Most previous retrofit approaches for heat-integrated crude oil distillation systems have

not taken the details of the existing heat exchanger networks into account. Those

approaches considered the area and energy targets of grassroots design instead of the

existing networks. Accounting for the details of existing heat exchanger network (HEN)

requires a model that represents these details. A shortcut model, based on network pinch

analysis (Asante, 1996), is proposed to describe an existing heat exchanger network and

its details.

In heat-integrated crude oil distillation systems, the heat exchanger network known as

the preheat train recovers heat from the distillation process in order to preheat the

incoming crude oil feed. This heat is recovered in the condenser and the pump-arounds,

and by cooling the various product streams. The details of the existing heat exchanger

networks affect the opportunities of recovering this heat. Existing exchanger network

has a complex structure (topology) of various exchangers that exchange heat between

cold and hot process streams. Hot utility exchangers, including furnaces (fired heaters)

heat process cold streams to their target temperatures. Similarly, cold utility exchangers

(coolers) cool process hot streams to their target temperatures. The more heat recovered

from the distillation process the lower utilities consumed in the utility exchangers.

Figure 4.1 illustrates a typical preheat train of crude oil distillation column. Cold steams

run from right to left, while hot streams run from left to right. The first cold stream at

the top represents the crude oil feed, which is the main cold stream in the process.

An area retrofit model is proposed to represent an existing HEN and take into account

its details, including the exchanger matches, the exchanger areas and duties, and the

utility consumption. The model relates the additional exchanger area, Areq, required for

retrofit to the energy requirement, E, and is described by:

)(EfAreq = (4.6)


85

Figure 4.1: Existing heat exchanger network of a crude oil distillation unit

The model mathematically describes a retrofit curve of an existing heat exchanger

network. Retrofit curve is a graphical representation of the capital-energy trade-offs in

retrofit, and it consists of a plot of retrofit area versus energy demand. The retrofit curve

is obtained from an extensive retrofit study on the network using network pinch. The

mathematical formula of the model may take various forms; however a power law form,

such as that given in equation 4.7, and a 3rd order polynomial function, such that in

equation 4.8, are proposed. The procedure of obtaining the retrofit curve of an existing

HEN and the retrofit model will be discussed in the next section.

creq EmA )(= (4.7)

012

23

3 )()()( bEbEbEbAreq +++= (4.8)

4.2.1. Retrofit curve for an existing heat exchanger network

In retrofit of heat exchanger networks, the heat recovery achieved can be increased by

the addition of area to exchanger units. The scope of the heat recovery that can be

achieved was found to be limited by the presence of a network pinch (Asante, 1996).

This network pinch is caused by some exchanger matches, which limit the driving force


86

as the HEN heat recovery increases. Network pinch analysis suggests that topology

changes are necessary to overcome the limits caused by the network pinch. The

concepts and procedure of network pinch analysis are exploited and applied to obtain a

retrofit curve for an existing HEN, and to derive a simple model that describes the

exchanger network.

For a given existing exchanger network, the topology and existing area are fixed. For no

topology changes, the existing network is optimised (SPRINT, 2002) in order to reduce

the energy consumption and increase the heat recovery; this can be achieved by adding

area to some existing exchanger units. This step provides a retrofit curve for zero

topology modifications for reducing energy demands, as shown by the solid line in

Figure 4.2. This retrofit curve results in the minimum exchanger area required to

achieve a feasible degree of heat recovery without changing the existing structure. Then,

a single change to the HEN topology is allowed to reduce the utility consumption. This

topology modification is selected from network pinch analysis. First, the pinching

matches that cause the network pinch are determined, providing the minimum

temperature approach of the existing network. Then, one topology modification is

proposed to overcome the network pinch. Different types of topology modifications are

recommended by network pinch analysis. These modifications include the introduction

of stream splits, addition of new exchangers and the relocation of existing matches. The

modifications result in a network with a higher potential for heat recovery. Each

modification results in a different trade-off between the heat recovery and area

requirement, and can be represented by a specific retrofit curve as shown in Figure 4.2.

The one topology modification retrofit curve can be plotted together with the

modification retrofit curves, and by definition it forms the lower bound to all

modification retrofit curves. The figure shows that at low heat recovery levels, the

topology modification A requires the least additional area, whereas the topology

modification B requires the least additional area at medium heat recovery levels. At

high heat recovery, the topology modification C requires the least additional area. The

topology modification, which shows the largest increase in heat recovery potential and

cheapest cost implications over the whole range of heat recovery level, is selected.

Economic criteria may also be applied in the selection between different topology

modifications. It is clear that the topology change C is the closest curve to that of the


87

single topological modification and shows the smallest deviation. It has also the largest

heat recovery potential. Therefore, the topology modification C is selected to

approximate the single topology modification retrofit curve. Any point on this retrofit

curve such as HEN1 represents the existing network that achieves an amount of heat

recovery corresponding to EHEN1 and requires additional area of AHEN1. The maximum

heat that can be recovered in the existing HEN is the value corresponding to that at the

end of the retrofit curve and the additional exchanger area required can be determined

correspondingly on the area axis.

When the first topology modification is implemented to the existing HEN, the driving

forces (temperature differences) in the exchangers change and this implies that a new

set of pinching matches and a new network pinch exist. Thus, network pinch analysis

needs to be applied again to the modified network. In a similar way, the retrofit curve

for two topological modifications is obtained. In this case, network pinch analysis is

applied to the modified HEN with one modification to propose a set of topology

modifications. The retrofit curves for the different types of topology modifications (e.g.

topologies A, B, C, etc.) are plotted together over a range of heat recovery levels,

starting from the end point of the single modification retrofit curve. The retrofit curve

for two topological modifications can then be plotted in a similar way to that in Figure

4.2. The topology modification that shows the largest heat recovery potential and the

smallest deviation from the two modifications retrofit curve is selected. Eventually, a

retrofit curve similar to that in Figure 4.2 is obtained for the two topological

modifications.

The previous procedure can be applied for any number of topology modifications

allowed for increasing heat recovery. Figure 4.3 shows retrofit curves of an existing

exchanger network for three topology modifications to reduce energy consumption.

After carrying out each modification, the modified network is optimised to reduce the

additional exchanger area for a fixed amount of heat recovery achieved. An overall

retrofit curve is plotted connecting the end points obtained for each modification. This

curve represents the existing network for a given number of topology modifications.

The retrofit curve determines the exchanger additional area required for an existing

network to reduce the energy consumption. The retrofit curve over the whole range of

energy consumption consists of discrete points; each represents an intermediate HEN


88

with a certain amount of heat recovery achieved for a selected topology modification.

When the retrofit study is carried out for reasonably small ranges of energy

consumption, a continuous curve can provide a reasonable approximation of the set of

discrete points. Therefore, the whole range of energy consumption can be divided into

small ranges; for each range, a continuous retrofit curve is then determined.

ExistingHEN

Topology B(e.g. new exchanger)

Topology C(e.g. relocate exchanger)

Topology A(e.g. stream split)

HEN Heat Demand

HEN Area

Zero modificationsretrofit curve

One modificationretrofit curve

EexistEzeroEAEBEC

Aexist

EHEN1

AHEN1

HEN1

Figure 4.2: Retrofit curve of zero and one modification to the HEN topology

The retrofit curve in Figure 4.3 is a function of both the details of the existing network

and the selected topology modifications. The data points on the retrofit curve are

regressed to obtain the proposed model parameters. However, each range of energy

consumption can be specified using its own retrofit model. In this case, different

parameters are determined for each range. For an existing exchanger network with a

total existing area of Aexist, the model that relates the retrofit area Aret to the energy

consumption is given by:

existreqret AAA −= (4.9)

Areq represents the additional area required to the existing heat exchangers in order to

reduce the energy demand, E, of the existing network. This additional area can be


89

calculated from equation 4.7 or 4.8. As mentioned above, equation 4.9 can be applied

for different ranges of energy consumption. It is also possible that each range could

have a different form for the retrofit model.

HEN Heat Demand

Aexist

Eexist

ExistingHEN

0 modification

1st modification2nd modification

HEN retrofit curve

Retrofit area

Areq

Eret

New design

3rd modification

HEN Area

Figure 4.3: Retrofit curve of an existing HEN

While obtaining the retrofit curve for an existing HEN, practical constraints may be

imposed. Practical constraints that could be of great importance in retrofit such as

forbidden exchanger matches and limiting number of topology modifications or

sequence of applying them can be considered in the course of obtaining the retrofit

curve. Therefore, a retrofit curve for an existing network can be obtained for a given

number of modifications, and fulfilling existing practical constraints. The procedure of

obtaining the retrofit model is summarised in Figure 4.4.

As shown in Figure 4.4, the existing HEN is first optimised; the energy consumption

and required exchanger areas are determined. Then, network pinch analysis is applied to

determine pinching matches and evaluate topology modifications. For each number of

modifications, select the modification, which provides the largest heat recovery, as

mentioned earlier. Any practical constraints are examined. Then, the energy

consumption and additional areas are determined. This procedure is repeated for the

given number of topology modifications. Data obtained are then regressed to determine

the retrofit model parameters.


90

No

Network pinch analysis(pinching matches, topology changes)

Yes

Select n = 1 modificationwith largest heat recovery

Existing HEN

HEN optimisation

Eexist

Aexist

Ezero

Azero

Fulfil given practical constraints?

Modifications > givennumber

Eone

Aone

Data regression (retrofit parameters)

Retrofit model

No

Yes

Modification n = n+1

En

An

Figure 4.4: Procedure of obtaining retrofit model of an existing HEN

Although the proposed retrofit model is simple, it is of great significance in retrofit

design since it includes all details of existing networks, such as exchanger matches and

areas, duties, energy consumption and possible retrofit modifications. It can also be

subject to existing practical constraints. This retrofit model will be used in the next

chapter to represent the details of an existing HEN in retrofit of heat-integrated crude oil

distillation systems. In this situation, as will be seen later, economic considerations are

added to the model to account for the exchanger area costs. These costs include the

additional area, new exchanger units and modification costs.


91

4.3. Modelling the installation of preflash units to existing crude oil distillation units

As mentioned in the literature, preflash units are often installed to crude oil distillation

columns in retrofit in order to increase throughput, reduce energy consumption and

remove column bottlenecks. Modelling the installation of a preflash unit to an existing

crude oil distillation column for retrofit is presented.

Preflash units are empty vessels installed upstream of the distillation column to separate

the vapour associated with the crude oil stream. Light hydrocarbons vaporise in the

crude oil due to the continuous increase in the temperature in the preheat train. As a

result of separating the vapours, the heat load on the process furnace reduces, and also

the vapour distribution inside the column changes. Figure 4.5 illustrates a preflash unit

added to an existing crude oil distillation column. The figure also shows different

options for feeding the preflash vapours to the distillation column. Each configuration

has a different implication on the potential for saving energy and increasing throughput;

these aspects will be discussed in the next chapter.

Crude oilfeed

Light HCs

Furnace

Residue

Middle product

Light productPreflash

Main column

Steam

Steam

Figure 4.5: A crude oil distillation column with a preflash unit

Modelling preflash units is carried out using flash calculations (isothermal) and material

balances. For a given crude oil feed with flow rate, temperature, pressure and

component mole fractions (Figure 4.6), flash calculations are carried out to calculate the


92

enthalpy of that feed stream, HFI. Then for a given temperature for the preflash column,

TF, flash calculations estimate the composition of the liquid and vapour, and the

enthalpy, HF, of the crude oil stream. Dew point calculations for the vapour stream and

bubble point calculations for the liquid stream are used to calculate the molar enthalpy

of both streams. The heat duty required for flashing the crude oil is then calculated by:

FIFPreflash HHQ −= (4.10)

Feed, FZTFIPFIHFI

Vapour, VYTPHV

Liquid, LXTPHL

FZTFPFHF

QPreflash

QPreheat

To distillationcolumn

FeedColumn = LZColumn = XTColumnPColumnHColumn

Figure 4.6: Sketch for preflash calculation variables

Material balances around the preflash column are performed to determine the vapour

and liquid product flow rates; the following equations are obtained:

−

−

=1

1

XYXZF

V (4.11)

VFL −= (4.12)

The liquid stream from the preflash column is fed into the distillation column for

fractionation, after being heated up to the required processing temperature TColumn.

Similarly, the enthalpy of the feed to the distillation column is calculated from flash

calculations at the temperature TColumn. The heat duty of the preheater is calculated from:

LColumnPreheat HHQ −= (4.13)


93

Note that a pressure drop needs to be specified across both the preflash exchanger and

the preheater.

The vapours from the preflash column can be fed into the distillation column at different

locations as seen in Figure 4.7. Modelling this issue is explained for an atmospheric

crude oil tower with a preflash vessel in Figure 4.7. Side-strippers and pump-arounds

are not shown in the figure due to space limitations. As shown, the atmospheric crude

column is decomposed into four simple columns with thermal coupling. The location of

entering the preflash vapours to the distillation column is defined as XX. In the case of

an atmospheric crude oil tower with a preflash unit, there are only four different

locations based on the retrofit shortcut models.

ð

ñ

ò

XX=1

=2

=3

=4 XX=4

=3

=2

=1ï

Figure 4.7: Preflash vapour mixing scheme

When the location XX is equal to 1, this means the preflash vapour feeds the bottom of

the column. In this case, the vapour is mixed with the main crude oil feed to the

distillation column. For a location of XX=2, the vapour from the preflash is mixed with

the vapour stream from column 1, and then the vapour mixture is fed into column 2.

Similarly for the other two locations XX=3 or 4, the preflash vapour is mixed with the

vapour streams from column 2 or 3. Then the vapour mixtures are fed into column 3 or

4 respectively. Note that, only one location for feeding the preflash vapour is possible.

However, splitting the preflash vapours into different locations can be easily modelled.

For a given preflash vapour with known properties to be mixed with a vapour stream

from a certain column, material and enthalpy balances are performed to determine the

properties of the vapour mixture. Assume that the vapour stream from column, n, has

the following properties: flow rate Vn, composition Yn, enthalpy Hn and pressure Pn. The


94

corresponding properties for the preflash vapour are V, Y, HV and P. The properties of

the vapour mixture obtained from material and energy balances are:

nmix VVV += (4.14)

nmix

n

mixmix Y

VV

YVVY += (4.15)

nmix

nV

mixmix H

VV

HVVH += (4.16)

nmix

n

mixmix P

VV

PVVP += (4.17)

Then, a flash calculation is carried out for the vapour mixture with known flow rate,

composition, pressure and enthalpy to calculate the temperature of the mixture at these

conditions.

When the preflash vapour is mixed with the main feed to the distillation column

(XX=1), similar relationships are obtained to calculate the properties of the mixture,

except that the pressure of the mixture is the minimum of both stream pressures since

liquid and vapour streams exist.

After the flow rates of the liquid and vapour inside the preflash column have been

calculated, column sizing and costing can be performed. Sizing is carried out based on

the quantity of the flashed crude oil (drum liquid) and the flash drum vapour flow rate.

The preflash drum should be sized for a reasonable residence time (e.g. 5 – 20 minutes)

for the flashed crude oil (Golden, 1997). Assume that the preflash drum has a height

HFlash and a diameter DFlash; the ratio of the height to the diameter is RFlash. If the

residence time for the flashed crude oil is θR, and the volumetric flow rate of flashed

liquid is VL. The dimensions of the preflash drum can simply be calculated from the

following equation:

3/14

=

Flash

RLFlash R

VD θπ

(4.18)

FlashFlashFlash DRH = (4.19)


95

The capital cost of the preflash column is calculated from the cost of the column shell.

The cost correlations from Douglas (1988) are used to estimate the installed cost of

preflash columns, as follows:

( ) ( ) )18.2(280

9.101 802.0066.1PmFlashFlash

IndexFInst FFHD

MSCost +

= (4.20)

The values of the cost scaling index, MSindex can be found in (Peters and Timmerhaus,

1980; Chemical Engineering Journals). The MSindex value for the 3rd quarter of the year

2001 is 1094.3 (Chemical Engineering, 2001); this value is used for equipment cost

estimations in the case studies of Chapter 6. Correction factors for shell materials Fm

and pressures FP are assumed to be 1.0 for carbon steel and less than 50 psi.

4.3.1. Illustrative example: an existing crude unit with a preflash drum

A preflash drum is installed upstream to an atmospheric crude oil distillation tower. The

crude unit processes 100,000 barrels per day (2610.7 kmol/h) of the crude oil given in

example 3.4.1.1. The crude oil is heated from 25 oC to 207 oC before entering the

preflash column, and is then fed into the distillation column at 365 oC.

The product stream flow rates from the preflash column and the different heat duties are

calculated using the proposed model, and are shown in Table 4.1. The table also shows

the sizing and costing results, compared with rigorous simulation (HYSYS, 1999). A

good agreement is observed between the results of the proposed model and the rigorous

simulations.

Table 4.1: Preflash column results

Parameter Proposed shortcut model

Rigorous model (HYSYS)

Liquid stream flow rate (kmol/h) 2120.7 2120.5 Vapour stream flow rate (kmol/h) 489.9 490.2 Preflash heat duty (MW) 68.9 72.2 Preheat duty (MW) 78.7 82.4 Molar fraction of component 4 in vapour stream

0.1668 0.1672

Molar fraction of component 8 in liquid stream

0.0689 0.0690

Preflash drum diameter (m) 2.60 2.74 Preflash drum height (m) 14.4 15.1 Capital cost ($) 117,850 109,100*

*: calculated within HYSYS; θR = 5 min, RFlash= 5.0


96

The shortcut model proposed in this section is combined with those developed for

retrofit, and is applied in retrofit design of crude oil distillation units with preflash

drums. The models are then useful in installing preflash drums to existing crude oil

distillation columns for increasing throughput and saving energy. The models can also

be applied to investigate the effect of changing the vapour feed location on the

performance of an existing unit and on opportunities for throughput enhancement. The

models will be used in the next chapter to optimise the temperature before the preflash

drum. This optimum temperature determines the optimum location of the preflash drum

in the preheat train.

4.4. Modelling the installation of prefractionator columns to existing crude oil distillation units

In retrofit of crude oil distillation columns, prefractionator columns are installed when

throughput enhancement and energy saving are objectives. Prefractionator columns use

reboilers or stripping steam, and are installed on the upstream feed to fractionate the

crude oil and take the light hydrocarbons as products. Prefractionators can be installed

as a single column or a succession of two columns to the main distillation column.

These columns reduce the vapour loads inside the main distillation tower significantly

compared with preflash drums since they produce the first light products of the main

distillation tower. Figure 4.8 shows an atmospheric crude oil distillation tower with a

single prefractionator. When adding a prefractionator to an existing unit, the column

configuration becomes two sub-units, and it is decomposed into two sub-problems; an

existing unit and a new column design. Modelling such configurations includes the

shortcut models developed for retrofit in Chapter 3 and those of Suphanit (1999) for

grassroots. The new model applies sequentially to the given problem; so the grassroots

model is applied to design the new column and calculate the number of stages and

column diameter required for the separation of the first light product. Then, the retrofit

shortcut models are used to simulate the existing unit. In overall, the model fixes the

existing distillation column and designs the prefractionator.

Cost of the prefractionators can then be calculated as the cost of the column shell and

the stages (Douglas, 1988). Assume that the calculated actual number of stages for the

column is NActual, with tray spacing of NSpacing, the column diameter is DColumn, and the

height from the top stage to the bottom stage is HPrColumn. The installed cost of the


97

prefractionator, assuming carbon steel material of construction and sieve trays, can be

then calculated as the sum of the column shell and stages costs as follows:

( ) ( ) 802.0ColumnPrColumnPr

066.1ColumnPrPrShell 157.1 HHDMSCost Index ∆+= (4.21)

( ) ( )ColumnPrColumnPrIndexPrStages HDMSCost 55.10168.0= (4.22)

)1(Pr −= ActualSpacingColumn NNH (4.23)

An extra height ∆HPrColumn is added to the column at the top and bottom of the

prefractionators. In the case studies that will be presented in the next chapter, this height

is set to 2 meters.

Light naphtha

Heavy naphtha

Light distillate

Residue

Heavy distillate

Steam

Steam

Steam

Figure 4.8: An atmospheric crude oil tower with a prefractionator column

As mentioned, more than one prefractionator column can be installed in revamping a

crude oil unit. When two columns are installed, the configuration is known as

progressive distillation. This configuration saves more energy compared with

conventional atmospheric tower (Rhode, 1997). However, this column configuration is

not yet applied widely. An existing atmospheric crude oil unit with one prefractionator

column is rather common.

The temperature of the feed to the prefractionator is of great importance, since it affects

the potential of increasing throughput and saving energy and it reduces the heat load on

the furnace. It also affects the design of the column. There is a trade-off between the

capital cost of the column and the cost of the energy consumption; this trade-off will be


98

considered in more details in the next chapter. Therefore, the optimum temperature and

hence the location of the prefractionator are significant in retrofit design.

The new model can be applied for design of an existing crude oil unit with installed

prefractionators. The model calculates the capital cost of the new columns and the

heating and cooling requirements for the process. It can also evaluate the potential for

increasing capacity and saving energy, and economically can assess the feasibility of the

retrofit objectives. In addition, the model can explore the effect of the temperature of the

crude oil stream entering the prefractionator on the performance of the crude unit. In

conclusion, the model will be applied in the next chapter within a retrofit approach for


4.4.1. Illustrative example: an existing crude unit with a prefractionator

An existing crude oil distillation unit processes 100,000 barrels per day of crude oil in a

conventional column. The column configuration is a main distillation tower using 3

side-strippers and 3 pump-arounds. Five products are produced: Light naphtha, heavy

naphtha, light distillate, heavy distillate and residue. The minimum hot utility

consumption of the unit is 70.5 MW, determined by pinch analysis for a minimum

approach temperature in the existing preheat train of 30 oC. A prefractionator with a

reboiler is installed upstream to the existing distillation unit so that the energy

consumption can be reduced.

The existing number of stages in the main distillation tower needs to be reallocated after

installing the prefractionator to the existing unit. The prefractionator will produce the

light naphtha as a top product. Many options can be considered in order to reallocate the

existing number of stages. For instance, the top side-stripper can be blocked, and hence

the number of stages below the draw-line to that stripper will be added to the stages in

the upper section. Another option is to block the middle side-stripper; therefore, the

existing stages between the draw-line of this stripper and the lower one are added to the

stages in the section below the top stripper draw-line.

The new model is then applied to the revamped unit with a prefractionator. The column

is sized in order to separate the light naphtha product with the required specifications. A

temperature of 215 oC is specified for the crude feed to the prefractionator. The model

calculates 11 and 14 stages in the top and bottom sections respectively and a diameter of


99

4.4 meters. The installed cost of the prefractionator column is 360,000 £. The hot utility

consumption of the revamped unit is 65.9 MW, with a reduction in energy demand of

6.5%. The product flow rates of the unit are summarised in Table 4.2 and compared

with those of the original unit. In addition to the energy saving, an increase in the

production capacity of the unit is expected due to the decrease of the vapour load on the

main column; however this aspect is not considered in this example. The potential for

increasing production capacity, the details of the existing preheat train and the different

consideration, including the trade-off between capital and the energy will be considered

in the case studies in Chapter 6.

Table 4.2: Product flow rates of an existing crude oil unit

Flow rate (kmol/h) Original unit Unit with prefractionator

Light naphtha 680 688 Heavy naphtha 493 485 Light distillate 652 647 Heavy distillate 149 148 Residue 633 641

4.5. Modelling carbon dioxide emissions from an existing refinery distillation for reducing environmental impact

4.5.1. Introduction

According to the records of global climate, the Earth's surface temperature has increased

by about 1 oF in the past century, with accelerated warming during the past two decades

(EPA, 2002). There is worldwide strong evidence that most of the warming over the last

50 years is attributed to human activities. Human activities have changed the chemical

composition of the atmosphere through the accumulation of some gases, mainly carbon

dioxide, water vapour, methane and nitrous oxide. Gases such as carbon dioxide and

water vapour trap heat in the atmosphere by absorbing long wave radiation. These gases

are called greenhouse gases since they act similar to greenhouses. The greenhouse gases

reemit the heat in all directions, causing the Earth’s surface and lower atmosphere

temperatures increase. This effect is called greenhouse effect or global warming.

The rising temperature of the Earth’s surface has serious implications for the global

climate. These implications include rising sea levels, extreme weather conditions and


100

severe changes to the agricultural activities, wildlife and water resources. Other

dangerous life alterations may be observed, such as the recent melting of natural ice

formations in the North Pole. A significant conclusion may be drawn is that global

warming has become the biggest threat to our life.

Carbon dioxide as a greenhouse gas plays a vital role in global warming; studies show

that it is responsible for about two thirds of the enhanced greenhouse effect (Houghton,

2002). A significant contribution to the carbon dioxide that emitted to the atmosphere is

attributed to principally fossil fuel combustion, which accounts for almost 98% of total

CO2 emissions in the US for the year 1999 (EPA, 2002) and 95% of total CO2 emissions

in the UK for the year 2000 (DTI, 2002).

Environmental damage that arises from global warming and greenhouse effect has

become of major concern. As a response to that, the United Nations has adopted a

strong legislation to restrict the emissions of greenhouse gases. Many national and

international treaties have been signed to reduce the greenhouse gases emissions. The

tighter regulations were imposed by the Kyoto Protocol to the United Nations

Framework Convention on Climate Change held in Japan, December 1997. The overall

targets set for greenhouse emissions are at least 6% cut from 1990 levels by 2008-2012

(EIA, 2002).

To meet these tight environmental regulations, refining industries are challenged to

reduce the greenhouse gases emissions, in particular the CO2 emissions. The reason is

that refineries produce substantial amounts of CO2 gases from the combustion of fossil

fuel to provide process heat and power. The task of reducing CO2 emissions is a

significant expense, because refineries are required to implement new ideas and

technologies with increased investment, especially when increasing production capacity

is planned.

In this work, a model is proposed based on previous work (Delaby, 1993; Manninen,

1999), for accounting for carbon dioxide emissions from refinery distillation processes.

The model calculates the emissions from different sources in existing crude oil

distillation units, for various types of fuels.


101

4.5.2. Model for CO2 emissions

Carbon dioxide is generated in crude oil distillation units mainly from furnaces, gas

turbines and boilers. The utility devices burn fuel to produce heat, steam and power.

The fuel is combusted when mixed with air, producing CO2 according to following

stoichiometric equation:

OHyxCOOyxHC yx 222 24+→

++ (4.24)

Figure 4.9 shows an existing crude oil distillation unit with furnace integrated with gas

turbine. The figure shows the possible sources of CO2 emissions. Typical fuels used in

the heating devices are light and heavy fuel oils, natural gas and coal.

Air is assumed to be in excess to ensure complete combustion, so that no carbon

monoxide is formed. Then, CO2 emissions (kg/h) are related to the amount of fuel burnt

in a heating device as follows:

α

=

100%

2C

NHVQ

Emiss CO Fuel (4.25)

This equation calculates the CO2 emissions from a heating device that burns QFuel.

Equation 4.25 shows that both the type of fuel used and the heating device affect the

CO2 emissions. The heating device affects through the amount of fuel burnt. The fuel

effect can be seen in the terms C%, NHV and α. The effect can be lumped in a so-called

fuel factor (FuelFact), as:

=

100%C

NHV uelF Fact

α (4.26)

The amount of fuel burnt will be calculated in the next sections for different heating

devices.


102

Air

Fuel

Power

FeedHEN

Crude

CO2

CO2

Fuel

BFW

Fuel

Distillation

Unit

Figure 4.9: Sources of CO2 emissions from a crude oil distillation unit

4.5.2.1. CO2 emissions from furnaces

The combustion process of fuel with air produces hot flue gases that are used in heating

crude oil. Theoretical flame temperatures of the flue gases are usually in the region of

1800 oC (Linnhoff and DeLeur, 1988). The heat provided by flue gases is the heat

released when they are cooled from the flame temperature (TFTF) to the stack

temperature (TStack). Stack temperature should not be lower than the corrosion limit; a

typical stack temperature of 160 oC is adopted (Smith and Delaby, 1991).

The amount of fuel burnt in a furnace can be related to the heat duty required by the

process QProc, and the efficiency of the furnace, as follows:

Furn

ProcFuel

Q Qη

= (4.27)

The furnace efficiency is defined as the ratio of the useful heat delivered to the process

to the amount of fuel burnt, as:

oFTF

StackFTFFurn TT

TT−

−= η (4.28)

The carbon dioxide emissions from the furnace can then be calculated from equations

4.25, 4.27 and 4.28 for the required heat duty by the process.


103

4.5.2.2. CO2 emissions from steam boilers

Boilers produce steam from the combustion of fuel. Steam is delivered to the process at

the temperature required by the process or at a higher temperature and then throttled.

Steam is used for either heating or stripping purposes. The flame temperature is lower in

a boiler than in a furnace because the heat of combustion is removed quickly to the

steam. However, the same flame temperature of 1800 oC may be still used. The stack

temperature of 160 oC is also used in the calculations. The amount of fuel burnt can be

calculated from (Delaby, 1993):

( )StackFTB

oFTBProc

Proc

ProcFuel TT

TTh

Q Q

−−

−= 419λ

(4.29)

The above equation is obtained from a simple steam balance around the boiler to relate

the fuel necessary in the boiler to provide a heat duty of Qproc; the boiler feed water is

assumed at 100 oC (Enthalpy of boiler feed water = 419 kJ/kg) (Delaby, 1993).

Equations 4.25 and 4.29 calculate the CO2 emissions from steam boilers. Note that the

duty QProc in equation 4.29 includes the heat duty required by the process and that

provided by the stripping steam.

4.5.2.3. CO2 emissions from gas turbines

A gas turbine is used in refineries either as a stand-alone unit or in an integrated context

with a process furnace. In both cases, the gas turbine provides heat to the process and

delivers power. Only natural gas and light fuel oil can fuel gas turbines. Integration of a

gas turbine with a process enables refineries to produce electricity for the same amount

of heat requirement. The generated power can be either consumed in the refinery site or

exported to other consumers. The integration of gas turbines leads to a reduction in the

operating costs due to fuel saving, and it provides flexibility in importing and exporting

power. Two different models will be used in this work in modelling CO2 emissions

from gas turbines. The model of Delaby (1993) is used when a gas turbine is used

separately to provide the process heat duty. On the other hand, when a gas turbine is

integrated with a process furnace, a more detailed model is used (Manninen, 1999).

When a gas turbine is used to supply the process heat duty QProc, the amount of fuel

burnt can be calculated from (Delaby, 1993):


104

CGT

ProcFuel

Q Q

ηη −=

11 (4.30)

The Carnot Factor ηC for a gas turbine is defined as (Delaby, 1993):

273+−

=inlet

outletinletC T

TT η (4.31)

The temperature at the inlet (Tinlet) of the gas turbine (combustion temperature) and the

temperature at the outlet (Toutlet) of the gas turbine (flue gas temperature) vary according

to the turbine design. However, a value of 1027 oC for the inlet and 720 oC for the outlet

temperatures may be used (Smith and Delaby, 1991). Any correlation that calculates the

outlet temperature can be used, as will be seen for integrated gas turbines. The gas

turbine heat recovery efficiency (ηGT) is defined as the ratio of the useful heat delivered

by the gas turbine to the total heat available in the exhaust (Delaby, 1993):

ooutlet

StackoutletGT TT

TT−−

= η (4.32)

The power delivered by a gas turbine is obtained (Delaby, 1993) from the Carnot Factor

and the amount of fuel burnt in the gas turbine, as follows:

FuelCGT Q W η90.0= (4.33)

The carbon dioxide emissions from a gas turbine can then be calculated from equations

4.25, 4.30 and 4.31.

Gas turbines are integrated with process furnaces to allow power production by refinery

distillation units. Furnaces have a high combustion temperature, low heat losses, but do

not produce power. On the other hand, gas turbines have a large power output, but the

exhaust temperatures are too low to satisfy most process heat requirements. Thus,

integrating a gas turbine with a process furnace combines the advantages of both units.

The exhaust gas from the gas turbine is partially fired in the furnace together with extra

fuel. In this case, the process heat duty is partially provided by the gas turbine by

burning a certain amount of fuel and producing CO2 emissions. The rest of the heat duty

to the process is provided in the furnace. To model the integration of a gas turbine with

a process furnace, the exhaust gas is assumed to provide the heat duty from the

theoretical flame temperature after firing in the furnace to the stack temperature


105

(Linnhoff, 1987). The flow rate of the flue gases required can then be calculated from:

( )StackFTFP

ProcFG TTC

Q M−

= (4.34)

The power generated in the gas turbine is correlated (Manninen and Zhu, 1999) with the

amount of flue gases, as follows:

9.21000

FGGT

MW = (4.35)

The outlet temperature of the exhaust gases from the gas turbine is calculated from

(Manninen and Zhu, 1999):

42.493)10(4.0 3 += −GTExhaust WT (4.36)

The heat duty provided by the gas turbine is calculated from:

FGStackExhaustPGT MTTCQ )( −= (4.37)

Then, the heat duty required from the furnace is calculated from an enthalpy balance:

GTProcFurn QQ Q −= (4.38)

The amount of fuel consumed in both the gas turbine and furnace is calculated as

follows (Manninen and Zhu, 1999):

33.7)10(84.2 3 += −GT

GTFuel W Q (4.39)

Furn

FurnFurnFuel

Q Qη

= (4.40)

Then, the total fuel consumption in the gas turbine and furnace can be calculated as:

(assuming that there is not heat loss)

FurnFuel

GTFuelFuel QQQ += (4.41)

The CO2 emissions from the gas turbine integrated with the process furnace can be

calculated from equations 4.25 and 4.34 to 4.41.

In the calculation of CO2 emissions, we considered only the process plant including the


106

furnace and the gas turbine. The CO2 emissions generated in this case is called local

emissions. The power generated from the gas turbine is either consumed at the site itself

or exported to other consumers. In both cases, the central power station, which is

situated outside the site, has the possibility of reducing electricity production by the

amount as much as that generated by the gas turbine. In this case, some amount of fuel

corresponds to that electricity can be saved at the central power station. This leads to a

saving in the CO2 emissions at the central power plant. Therefore, integration of a gas

turbine with a process enables the central power station to reduce CO2 emissions. If we

consider the central power station together with the process plant as a one unit, the net

CO2 emissions are then called global emissions.

The reduction in fuel consumption at the central power station is related to the power

generated in the gas turbine and the power station efficiency, as follows (Manninen and

Zhu, 1999):

PS

GTPSFuel

W Q

η= (4.42)

The efficiency of the central power station is assumed to be 28% (Delaby, 1993). The

reduction in CO2 emissions at the power station can then be calculated from equation

4.25 and 4.42, for a given type of fuel and a power. Coal is commonly used in power

stations as a fuel. The global CO2 emissions from the process plant and the central

power stations are defined as:

Global emissions = Emissions from process plant – Emissions saved at central power

station (4.43)

Although the integration of gas turbines with process furnaces reduces the operating

costs due to reduced fuel consumption, it incurs a substantial capital investment. There

is a trade-off between the capital cost of the gas turbines and the benefits obtained. The

capital cost of gas turbines can be calculated from (Manninen and Zhu, 1999):

2.2529)10(1.195 3 += −GTGT WCost (4.44)

On the other hand, the gas turbine enables the process plant to increase profit by

producing electricity. The cost of electricity is given by:


107

GTCostGT

Power WPUnitCost = (4.45)

So, when a gas turbine is integrated with a process, the CO2 emissions can be calculated

locally or globally. At the same time, the capital investment and the income of the

power generated are evaluated.

For an existing crude oil distillation unit, the CO2 emissions are calculated individually

for each heating device, i.e. process furnace, steam boilers and gas turbine. The total

emissions are then determined for the process plant and for the process together with the

central power station. The economic features of the unit are also evaluated.

The model proposed for the calculation of CO2 emissions will be used in the next

chapter within a retrofit approach for reducing the flue gas emissions from existing


4.6. Modelling heat transfer enhancement in existing heat exchanger networks

As stated in Chapter 2, heat transfer enhancement (HTE) is an important application in

retrofit of heat exchanger networks. It is an alternative technique to implement the

additional area to existing exchanger units in retrofit situations. The models of Zhu et al.

(2000), Nie (1998) and Polley et al. (1990) are extended in this work to propose a

retrofit model for an existing HEN with heat transfer enhancement and pressure drop

constraints

4.6.1. Retrofit model for enhanced heat exchanger networks

The procedure presented in this chapter (Section 4.2) for obtaining a network retrofit

model is applied to an existing HEN with heat transfer enhancement. Only tube-side

heat transfer enhancement is considered in this work. The reason for this is that

techniques for tube-side enhancement are well developed compared with shell-side

enhancement, in terms of both theoretical development and industrial application and

experience. The procedure for developing the model for an existing HEN with heat

transfer enhancement includes two different methods; the first one is outlined as

follows, for given an existing network with exchanger matches and existing areas and

duties:


108

1. The controlling side for heat transfer of each exchanger unit is determined by

applying the procedure of Zhu et al. (2000)

2. For each exchanger with tube controlling side, the maximum area that can be

achieved by using heat transfer enhancement is calculated (see equation 2.10,

Chapter 2).

3. The total maximum area that can be achieved for exchangers with tube-side

enhancement can then be calculated as:

( )∑=

−=∆

n

iiexist

itube

shellmaxtotal A

hh

A1

121 (4.46)

4. Then, the same procedure as in Section 4.2 is applied to the existing HEN to

derive the retrofit model.

5. The retrofit model for the exchanger network with heat transfer enhancement is

then defined as:

maxtotal

creq AEmA ∆−= )( (4.47)

The model relates the additional area required for retrofit to the energy consumption,

considering the area that can be achieved for exchangers using heat transfer

enhancement. The latter area is considered to be available in the existing exchangers,

and therefore the net area that needs to be added to the exchanger units for retrofit

reduces.

The main drawback of this method is that it assumes that the maximum area that can be

achieved by enhancing existing exchangers is implemented, no matter whether this area

is needed or not. However this procedure can still give an evaluation of the additional

area required in retrofit after considering heat transfer enhancement. Thus, the model

can be used to represent an existing enhanced network in retrofit approach as will be

presented in Chapter 5.

The second method, which considers a detailed retrofit analysis of the existing network

with enhancement, is summarised as follows:

1. The controlling side for heat transfer of each exchanger unit is determined by


109

applying the procedure of Zhu et al. (2000)

2. The maximum area that can be achieved with enhancement is calculated as

before (see equation 2.10, Chapter 2).

3. The procedure of Section 4.2 for obtaining the HEN retrofit model is applied.

4. In each retrofit step, the additional area is determined from the network pinch

analysis for each exchanger unit.

5. Then, this area is eliminated or reduced based on the amount that is required,

compared to the maximum area available by enhancement, according to the

following:

a. If the additional area required is larger than the maximum area achieved

by enhancement, additional area needs to be added to the existing

exchanger unit, after applying the enhancement for the maximum area.

b. If the additional area required is less than or equal to that available by

enhancement, no additional area is required and only enhancement is

applied for the required area.

c. In a step to consider heat transfer enhancement in more efficient way, the

additional area required can also be enhanced. Therefore, more reduction

in the total additional area is achieved.

6. The retrofit model for the enhanced existing HEN can then be obtained with the

following form:

creq EmA ′′= )( (4.48)

The model parameters m′ and c′ include the effect of the heat transfer enhancement in

retrofit, and they are different from those of equation 4.47.

As seen in the above method, the procedure has the advantage of analysing the necessity

of applying heat transfer enhancement to each exchanger for retrofit. Thus, the retrofit

model obtained accounts for each enhanced exchanger. Exchangers are only enhanced

when it is necessary and for the required amount.

Figure 4.10 shows the retrofit curve of the enhanced network compared with that of the


110

network without enhancement. The figure indicates that at any heat recovery level, the

additional area required for the enhanced network is less than that for the plain tubes.

The energy consumption of the existing network is reduced by enhancing some existing

exchangers; this explains why the retrofit curve for the enhanced network starts from

lower energy consumption. Each point on the retrofit curve represents an existing

network with enhancement for achieving a certain energy recovery level.

The retrofit model for the enhanced exchanger networks will be used in Chapter 5 to

consider enhancement in retrofit. In this case, enhancement costs, which are

proportional to the enhanced area, are evaluated and incorporated in the retrofit design.

Figure 4.10: Retrofit curve for enhanced heat exchanger networks

4.6.2. Pressure drop considerations in enhanced exchanger networks

In retrofit of preheat trains, additional exchanger area installed will increase the pressure

for the modified exchanger units. Furthermore, heat transfer enhancement techniques

have a significant impact on pressure drop. This is due to the increase of the friction

factor of the fluid inside the enhanced exchanger tubes. Therefore, as the heat recovery

increases, the pressure drop for crude oil stream increases. The reason is when the heat

recovery increases, the temperature of the crude oil stream before the furnace increases.

As the temperature of the crude oil increases, the saturation pressure increases; hence

the pressure drop increases (Nie, 1998). If the pressure drop for the crude oil stream

exceeds the capacity of the existing pump, new pump needs to be installed, which can

Area

Retrofit curve (plain tubes)

Retrofit curve (enhanced tubes)

Energy Consumption


111

be very expensive. Therefore, pressure drop of crude oil streams in preheat trains is of

major importance in retrofit. Thus, pressure drop constraints must be met; otherwise a

large capital investment in new pump is required. In this section, a model is proposed to

consider the pressure drop of a crude oil feed in an existing preheat train.

Assume that the existing exchanger unit has an area of Aexist, and tube-side and shell-

side heat transfer coefficients are htube, and hshell respectively. The pressure drops for the

tube-side and shell-side (∆Ptube, ∆Pshell) can be calculated from (Polley et al., 1990):

5.31 tubeexisttube hAKP =∆ (4.49)

1.52 shellexistshell hAKP =∆ (4.50)

The parameters K1 and K2 are dependent on the physical properties of the fluid (e.g.

density, thermal conductivity, specific heat, etc.), the geometry parameters of the

exchanger (tube and shell diameters), and the volumetric flow rate of the fluid. The

parameters K1, K2 can be calculated from equations 4.49, 4.50, for an existing exchanger

with existing area, and given heat transfer coefficients and pressure drops. Then, they

can be kept constant throughout the retrofit calculations. However, if the physical

properties change significantly with temperatures, the parameters can be calculated

from the definitions found in Polley et al. (1990) (see Appendix A.1).

If enhancement is applied to this exchanger, the tube heat transfer coefficient will

increase to htubeen. The pressure drop for the tube-side can be calculated from:

tube

a

tube

entube

en Ph

haP ∆

=∆

2

1 (4.51)

The pressure drop for the shell-side is assumed to be unchanged. Equation 4.51 can

calculate the pressure drop for the enhanced tubes for two retrofit cases. Firstly, when

the additional area required for retrofit (∆A) is smaller than the maximum achieved by

enhancement (∆Amax), the pressure drop can be calculated from (see Appendix A.2, for

derivation):

( )tube

a

tubeshellexist

existshellen P

AhhAAAh

aP ∆

∆−∆+

=∆2

1 (4.52)


112

However, if this additional area is equal to the maximum area, the pressure drop is

calculated by (Appendix A.2):

tube

a

exist

maxen P

AA

aP ∆

+

∆=∆

2

12

1 (4.53)

The maximum area can be calculated for an existing exchanger, given the shell-side and

tube-side heat transfer coefficients.

Secondly, if the additional area required for retrofit is larger than the available area, a

combination of enhancement and adding new area can be adopted. The new additional

area can be added in different ways, such as new shells in series or parallel and inserting

new tubes into an existing unit. Assume first that the new additional area is added by

inserting tubes. The pressure drop will be calculated as the sum of the pressure drop

from equation 4.53 and that for the new area as given by equation 4.49, as follows:

( ) 5.311

2

12

tubemaxtube

a

exist

maxen hAAKP

AA

aP ∆−∆+∆

+

∆=∆ (4.54)

When the additional area is added as new shells in series to the existing shell, the

pressure drop is the summation of the individual pressure drop for the existing and the

new shells, as follows:

newshell

existshell

totalshell PPP ∆+∆=∆ (4.55)

The pressure drop and the heat transfer coefficients for the existing shell are the same as

those before adding the new shell.

For parallel shell arrangements, the pressure drop is calculated by:

( )newshell

existshell

totalshell PPP ∆∆=∆ ,max (4.56)

In this case, the pressure drop and the heat transfer coefficients for the existing shell

need to be recalculated as a function of the split ratio, as follows (Lord et al., 1979;

Coulson et al., 1983):

( ) shellexist

shell PrP ∆=∆ 2 (4.57)


113

( ) shellexist

shell hrh 8.0= (4.58)

( ) tubeexist

tube hrh 6.0= (4.59)

Now, the pressure drop can be calculated for different arrangements of the additional

area to the existing exchanger units. The pressure drop model for the crude oil stream

preheated in an existing HEN is obtained as follows:

1. The procedure of obtaining the area retrofit model for the enhanced HEN is

applied (see Section 4.6.1)

2. In each step, the additional area required for retrofit is determined.

3. Then, the necessity of the addition of the new area combined with the heat

transfer enhancement is evaluated. It follows that some existing exchangers

require only heat transfer enhancement either for the maximum area or part of it,

whilst other exchangers require an extra area to be added after applying the

enhancement for the maximum area.

4. The pressure drops for the exchangers on the crude oil stream are then calculated

from equations 4.49 to 4.59.

a. If the crude oil stream is split into more than one branch, the pressure

drop for each branch is obtained by summing up the individual pressure

drop for each exchanger on that branch.

b. Then, the pressure drop for the crude oil stream is taken as the maximum

of that for each branch.

5. Retrofit data are obtained as pressure drops for the crude oil stream for different

levels of energy consumption.

6. These data are plotted as pressure drop versus energy consumption.

7. The data are then regressed to obtain a pressure drop correlation.

8. The correlation obtained relates the pressure drop for the crude oil stream to the

energy consumption in the preheat train. It may take a power law form such as

∆PC.O. = p E z, or polynomial functions; ∆PC.O. = f(E).


114

The pressure drop correlation can calculate the pressure drop for the crude oil stream in

a preheat train for a given energy consumption.

While the retrofit model proposed for the enhanced network can represent the area-

energy relationship of the existing HEN with heat transfer enhancement, the pressure

drop correlation can consider the constraint of pressure drop for the crude oil stream.

Thus, the retrofit model together with the pressure drop correlation will be used in

Chapter 5 for retrofit of heat-integrated crude oil distillation systems.

Although the pressure drop correlation in this section is derived for an existing network

with heat transfer enhancement, it can also be obtained for networks where no

enhancement is applied. Therefore, the pressure drop correlation can also be combined

with the retrofit model from Section 4.2, and used in retrofit design.

4.7. Modelling packed sections in existing crude oil distillation columns

As mentioned earlier, replacing bottlenecked sections in crude oil distillation columns

with packing is an attractive retrofit option. Packing has a wide range of benefits such as

increasing vapour-liquid contact area and hence increasing the separation efficiency,

and enhancing capacity through minimising resistance to vapour flow. Although it is an

expensive option, it can remove the column bottlenecks and it allows greater throughput

or separation in existing columns (Coker, 1991). Inexpensive modifications to the

existing design are always preferable before considering this retrofit option. In this

section, modelling packed sections is presented for retrofit of crude oil distillation

columns.

When a bottlenecked section is identified (see Section 4.1.1), overloaded trays, which

require larger diameters than the existing ones, can be determined. If these trays are to

be replaced with packing, the packed bed is designed so that it provides the required

separation performed by trays. Selection of the packing materials and sizing the packed

bed are the design tasks that need to be addressed.

Selection of packing materials in retrofit design is a more complex task than in

grassroots design. In this case, packing materials must provide a higher capacity and

efficiency than those of trays, and also meet existing constraints such as the physical


115

space in the distillation columns. King (1980, p 604), Kister et al. (1994) and Humphrey

and Keller (1997; p. 67-72) presented a comparison of different types of packing

materials with trays for different aspects including capacity, efficiency, pressure drop,

cost, etc.

If the bottlenecked stages that need to be replaced with packing are NOVLDTray. The

required height of the packing material can be calculated based on the equation:

( )( )HETPNTPhpack = (4.60)

where:

OVLDTrayOP NNTP η= (4.61)

The overall plate efficiency ηOP is the efficiency of trayed section to be replaced by

packing. This efficiency can be calculated if the Murphree tray efficiency is known

(Kister, 1992; Chapter 7). An empirical equation for calculating the overall plate

efficiency in refinery columns is presented by Drickamer and Bradford (Peters and

Timmerhaus, 1980; p. 727), and is given by:

avgFoc ,log1.6117 µη −= (4.62)

The height equivalent to a theoretical plate, HETP, is determined once a packing

material is selected. The mass transfer efficiency of a packed bed is incorporated in the

HETP. Values of HETP mainly depend on packing type and size, liquid viscosity, and

surface tension. Kister (1992) discussed the estimation of HETP for different types of

packing materials, and also provided values as rules of thumb.

In equation 4.60, the calculated packing height is limited by that of the overloaded

stages, i.e. hpack has to be smaller than the product NOVLDTray × tray spacing. Therefore,

the selected HETP needs to be checked, and thus proper packing is selected.

The diameter of the packed section is determined so that safely avoids flooding, as

follows:

1. The flow parameter FLV is calculated as defined in equation 4.2 (Section 4.1.1).

2. Seader and Henley (1998; Chapter 6) provide a graph that relates the flooding

velocity in packed columns to the flow parameters, as a function of pressure


116

drop. The abscissa on this graph is the flow parameter FLV, but the ordinate is a

function of flooding velocity f{uo}, and is given by:

{ } { } { }LLOH

Vpackoo ff

gFu

uf µρρρ

2

2

= (4.63)

The packing factor Fpack is determined for the selected packing material; it can be found

in Seader and Henley (1998; p. 328-329) and Kister (1992; p. 638-650), for wide ranges

of packing materials and sizes. The functions f{ρL} and f{µL} are corrections for liquid

properties, and are graphically given in Seader and Henley (1998; p. 331). Data for

flooding velocity and correction functions are extracted from the figures and then

regressed to provide correlations that can be easily used. Figure 4.11 is a plot of the

logarithm of flooding data from Seader and Henley (1998). The regressed correlation

relates the flooding velocity function to the flow parameter, and is given in equation

4.64. The regression parameters (h0, h1, h2, h3, h4) are found on the plot. The maximum

deviation of data predicted by the correlation is 2.6% compared to the experimental data

of Seader and Henley (1998).

{ } ( ) ( ) ( ) ( ) 012

23

34

4 logloglogloglog hFhFhFhFhuf LVLVLVLVo ++++= (4.64)

y = 0.0052x4 - 0.0028x3 - 0.2885x2 - 1.0481x - 1.6407R2 = 0.9997

-3.5

-3

-2.5

-2

-1.5

-1

-0.5

0-2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5

Flow parameter

f(uo

)

logf(uo)Poly. (logf(uo))

Figure 4.11: Regression of flooding data from Seader and Henley (1998)


117

Similarly, a linear correlation (equation 4.65) is proposed, as shown in Figure 4.12 to

regress the correction data for the liquid density f{ρL}. Data in Figure 4.12 are plotted as

correction factor versus density ratio of water to liquid.

{ } 4750.05437.1 2 −

=

L

OHLf

ρρ

ρ (4.65)

The linear model predicts a correction factor of 1.578 for methanol-ethanol mixture

compared with an experimental reading of 1.533 (Seader and Henley, 1998; p. 331). For

typical crude oil mixture with density of 865 kg/m3, the predicted correction factor is

1.308.

y = 1.5437x - 0.4750R2 = 0.9899

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

0.5 0.7 0.9 1.1 1.3 1.5

Density ratio of water to liquid

f( ρL)

f(pL)Linear (f(pL))

Figure 4.12: Regression of density data from Seader and Henley (1998)

The correction factor data for liquid viscosity from Seader and Henley (1998) are

plotted in Figure 4.13, as logarithms of correction factor versus liquid viscosity. The

regressed model for the data is given by the following equation. The regression

parameters (g0, g1, g2, g3, g4) can be obtained from the plot. The maximum average error

in the predicted data is less than 0.1% compared to the experimental data from Seader

and Henley (1998).

{ } ( ) ( ) ( ) ( ) 012

23

34

4 logloglogloglog gggggf LLLLL ++++= µµµµµ (4.66)


118

y = -0.0095x4 + 0.0206x3 - 0.044x2 + 0.2083x + 0.0048R2 = 1

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

0.2

0.25

-1 -0.5 0 0.5 1 1.5

Liquid viscosity (cP)

f( µL) log(uL)

Poly. (log(uL))

Figure 4.13: Regression of viscosity data from Seader and Henley (1998)

3. For given packing material, and fluid flow rates and properties, the flooding

velocity can then be calculated from equations 4.63, 4.64. The correction factors

for the liquid properties are obtained from equations 4.65 and 4.66.

4. Then, a fraction of flooding f is selected (usually from 0.5 to 0.7) (Seader and

Henley, 1998). The diameter of the packed section is then calculated from:

5.04

=

Vopack fu

VDπρ

(4.67)

5. Check that the calculated diameter is at least 8 times the packing size. If not,

select a smaller packing and repeat the procedure till reasonable diameter is

obtained (Robbins, 1991).

6. The pressure drop at flooding through packed beds is strongly dependent on the

packing factor Fpack, and can be calculated by (Kister, 1992):

701150 .packflood F.P =∆ (4.68)

Seader and Henley (1998) suggested that the pressure drop at flooding for all packing

ranges from 2.5 to 3 in. of water head per foot packing (2.044 to 2.453 kPa/m of


119

packing).

7. The pressure drop ∆Ppack can then be linearly interpolated at the values of the

flow parameter, f{uo) and f{f×uo). Where f is a fraction of the flooding velocity

(0.5 – 0.7) (Seader and Henley, 1998). Alternatively, it can be calculated from

Robbins’ correlation (1991):

421

1.02

1 )10(000,20

4.010 22 FF LqF

FLqFpack VqLVqP

+=∆ (4.69)

where:

( ) 81 104.7 −=q (4.70)

( ) 52 107.2 −=q (4.71)

atm 1.0P for F

VV pd

VfluxF ≤

=

5.05.0

20075.0ρ

(4.72)

atm 1.0 P for F

VV Vpd

VfluxF >

= ρ

ρ3.0

5.05.0

1020

075.0 (4.73)

15 F for F

LL pdLpd

LfluxF ≥

= 1.0

5.0

204.62 µ

ρ (4.74)

15 F for F

LL pdLpdL

fluxF <

= 1.0

5.0204.62 µ

ρ (4.75)

Fpd is dry-bed packing factor and can be obtained from Kister (1992; p. 500), for

various packing materials. Coker (1991) developed a more simple correlation for

pressure drop calculation (see Appendix A.3). He also suggested that the design

pressure drop for fractionators is in the range 0.40 - 0.80 in. H2O/ft height (0.327 -

0.654 kPa/m of packing).

The cost of the packing materials is evaluated after determining the diameter and height.

Costs of packing materials per unit volume PCKVcost can be found in Douglas (1988; p.

576), Perry (1974; p. 18.54) and Peters and Timmerhaus (1980; p. 770), for different

types and sizes. The installed cost of a packed section can then be calculated,


120

providing the diameter and height, as follows: an installation cost of 75% of the packing

purchase cost is assumed (Peters and Timmerhaus, 1980; p. 169):

( ) ( )cost2

475.1 PCKVDCost packpack

π= (4.76)

The model for designing packed sections is combined with those in Section 4.1 for

retrofit, to account for the use of packing materials in retrofit. The model can evaluate

the potential for removing hydraulic bottlenecks and increasing throughput of a crude

oil distillation column using packing.


Modelling of heat-integrated crude oil distillation systems for retrofit design has been

presented. Column retrofit models can specify an existing design of crude oil distillation

unit, by fixing the existing stages in each section and the column configuration. The

hydraulic capacity limitations of distillation columns can be accounted for through the

calculation of the stage diameters required for separation. HEN retrofit model considers

the details of the existing preheat train and relates the exchanger area required to the

energy consumption, for all possible topology modifications. Practical constraints such

as pressure drop and limited number of modifications are taken into account.

The installation of preflash and prefractionator columns for throughput enhancement

and energy saving are modelled. The models determine the required number of trays,

diameters and capital investment for a given separation in prefractionators. For preflash

units, the dimensions and the capital costs are determined. Various options of

configuring the preflash with the distillation column are addressed.

Replacing bottlenecked trays with packing for increasing throughput and

debottlenecking is also modelled, including selection, sizing and costing. In addition,

the use of heat transfer enhancement in preheat trains, for retrofit, is presented. Models

incorporate the effect of enhancing existing exchangers in retrofit design and

accommodate pressure drop implications.

Furthermore, the environmental impact of existing refinery distillation processes on

atmospheric pollution, in particular the CO2 emissions, is modelled. CO2 emissions are

evaluated from various sources and for different types of fuels. Integration of gas


121

turbines with refinery distillation processes is also addressed.

The proposed models form the basis for retrofit of heat-integrated crude oil distillation

processes in order to achieve various objectives, while fulfilling existing constraints and

limitations. The retrofit objectives may include increasing throughput, reducing energy

demands and operating costs, minimising flue gas emissions, increasing profit and

changing product specifications. Thus, the models will be used in Chapter 5 to fix

existing designs of crude oil distillation unit and preheat train and accommodate the

various aspects mentioned above for retrofit design.

Chapter 5 Retrofit design of heat-integrated crude oil distillation systems

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Chapter 5: Retrofit design of heat-integrated crude oil distillation systems

This chapter presents a new approach for retrofit of heat-integrated crude oil distillation

systems. This approach is optimisation based, and uses the shortcut models developed

in Chapter 3 for distillation columns and those of Chapter 4 for different aspects of

retrofit design. The approach considers both the distillation column and the exchanger

network at the same stage. It is applicable for different retrofit design objectives

including throughput increase, energy saving, emissions reduction and new feed/product

specifications. The retrofit approach concentrates on the efficient reuse of existing

equipment without major modifications; however, it also accounts for changing the

structure of the distillation column and the exchanger network.

The chapter starts with an introduction to the retrofit design problem of heat-integrated

crude oil distillation systems. The retrofit philosophy followed in this work is presented.

The effect of the distillation column design parameters on the performance of the

overall system is also discussed. Then, a retrofit approach for heat-integrated crude oil

distillation systems is presented. A procedure for different applications of the retrofit

approach is also outlined.

5.1. Features of heat-integrated crude oil distillation systems

In the refining industry, crude oil distillation units consist of many interlinked columns,

use side-coolers and side-heaters, and contain different types of energy inputs such as

stripping steam and reboilers. In addition to the complex structure, the existing columns

are directly connected to an existing heat recovery system (preheat train or exchanger

network). The crude oil feed is heated to an intermediate temperature in the exchanger

network in which the distillation heat sources reject heat to heat sinks. Then, the feed is

heated further to the processing temperature in furnace or fired heater. The heat sources

in crude oil distillation columns include the top vapour product, the hot recycled liquids

in pump-arounds, and the hot distillation product streams. On the other hand, the heat

sinks are the crude oil feed, the stripping steam, the cold recycled liquids in side-heaters

and the reboilers. The product streams are cooled to their target (or storage)

temperatures by heating the crude oil feed or generating steam. Similarly, the hot


123

recycled liquids through pump-arounds are cooled against the crude oil feed. The top

vapour is condensed by providing heat to the crude oil feed and other cold streams in

the process.

Figure 5.1 illustrates a typical heat-integrated crude oil distillation unit. In this unit, the

crude oil is heated in a set of exchanger units and a furnace before entering the

atmospheric distillation tower in which it is separated into various products. The top

vapour from the atmospheric tower is sent to the naphtha stabiliser to be then separated

into naphtha and gasoline in subsequent processes. The residue of the atmospheric

tower is heated in a furnace and then is processed in the vacuum tower. The side

products obtained in the side-strippers are stored for marketing or further processing.

The features of the heat-integrated crude oil distillation systems can be summarised as:

(a) systems have complex structure and consist of various units; each has its capacity

limitations and structure constraints, (b) strong interactions exist between distillation

processes and heat recovery systems, (c) wide range of energy-related products are

produced, (d) systems consume large amounts of energy; typically, energy consumption

in atmospheric towers is about 2% of crude oil distilled (Rhode, 1997), (e) existing

systems are expensive to modify, and economically and environmentally challenged. As

a result, retrofit of such systems is of major importance to refining industry.

The amount of energy exchanged between the heat sources and heat sinks is a measure

of the energy efficiency of the distillation process and the exchanger network. Systems

with high energy efficiency consume less utility and have low operating costs, while

systems with low energy efficiency have high operating costs.


124

Atmospheric tower

Vacuum tower

Stabiliser

Flash

Furnace

Furnace

Flash

DesalterCrude oil

Figure 5.1: Heat-integrated crude oil distillation system including

atmospheric and vacuum towers and naphtha splitter

The crude oil distillation column and the heat recovery system have strong interactions

between them; these interactions are represented by the operating conditions of the

distillation columns. The operating conditions include the feed preheating temperature,

the reflux ratio, the steam flow rates, the degree of thermal coupling between the

different column sections, and the flow rates and temperature changes of the recycled

liquids in the pump-arounds and side-heaters. The operating parameters play a key role

in the retrofit design of the overall system, i.e. the distillation column and the heat

recovery system. Any changes in these operating parameters individually or

simultaneously may influence the quality and quantity of the heat sources and heat

sinks, and therefore affect the energy efficiency of the overall systems. In addition,

these changes alter the vapour-liquid flow rates inside the column. Consequently, they

may affect opportunities for enhancing the production capacity.


125

5.2. Retrofit design philosophy in refinery distillation systems

Retrofit design of heat-integrated crude oil distillation systems aims to reuse the existing

equipment more efficiently in order to achieve various objectives. Objectives for retrofit

design include increasing production capacity, reducing utility consumption, minimising

CO2 emissions, and processing new feedstocks or changing product specifications.

These objectives are achieved most preferably by minor modifications to the existing

equipment. While retrofit objectives are achieved, existing equipment constraints must

be met. As pointed out in Chapter 2, the modifications suggested by previous

approaches change the structure of existing equipment; therefore, a large capital needs

to be invested. In this work, we follow another retrofit philosophy in which the existing

equipment is efficiently reused before considering any major modifications. The details

of existing equipment together with its physical constraints are taken into account.

Furthermore, the benefits of modifying the structure of the existing equipment, e.g.

changing column internals, adding preflash drums, etc., are also considered. When

structural modifications to existing equipment are made, a large capital investment is

needed. Therefore, a trade-off between capital investment and operating cost saving

exists. The retrofit approach established in this work concentrates on those column

modifications which require little or no capital investment.

As reviewed earlier in Chapter 2, the conventional retrofit approaches for refinery

distillation systems were carried out sequentially, in which the distillation column is

initially retrofitted. Then, the additional area and modification requirements for the

exchanger network are determined. In these approaches, any benefits of the interactions

between the distillation columns and the exchanger networks have been lost.

Furthermore, the approaches result in column modifications without efficient reuse of

the existing equipment. An example of such an approach is that proposed by Liebmann

(1996). This approach has two steps and requires manual iterations; it does not

simultaneously account for system interactions. In addition, the approach considers

pinch analysis insights for heat integration and not the existing heat recovery system,

nor does it account for hydraulic capacity limitations. As summarised previously, very

few researchers have looked simultaneously at the distillation columns and the heat

recovery systems. As yet, none has considered the details of the heat recovery system or

taken the distillation hydraulic constraints into account. Alternatively, the heat recovery


126

opportunities, determined by pinch analysis, were maximised. In these approaches, such

that of Bagajewicz (1998), the details of the existing heat recovery system are not

accounted for. Besides, the hydraulic capacity limitations of the distillation columns are

not taken into consideration. Consequently, the retrofit design may result in bottlenecks

in both the distillation column and the heat recovery system.

In the retrofit approach presented in this chapter, the details of the heat recovery

systems and the distillation columns are considered at the same stage. The hydraulic

capacity constraints of the distillation columns are incorporated.

5.3. Interactions between distillation operating conditions, heat recovery potential and column hydraulics

As discussed above, the interactions (operating conditions) affect the performance of the

overall refinery distillation system, i.e. crude distillation column and heat recovery

system. Therefore, retrofit design of such systems is highly dependent on these

interactions. For existing designs of crude distillation columns and exchanger networks,

the effect of the operating conditions on the performance of the overall system will be

investigated in terms of both the heat recovery and the hydraulic performance of the

distillation columns:

1. When the feed preheating temperature in a reboiled column increases, the hot utility

required in the reboiler reduces. This will create a heat sink at a lower temperature

than that in the reboiler, and hence this will benefit the heat recovery. In addition,

the vapour-liquid traffic below the feed stage will decrease. Thus, the required

diameter in this section is reduced. However, this effect will incur heat load

penalties due to higher total cooling and heating requirements. In this case, the

diameter required in the upper sections is increased.

In the case of steam-stripped columns, increasing the temperature of the feed

preheating will require less stripping steam for the same separation, and hence the

bottom product temperature increases. This creates more opportunities for

recovering heat at a higher temperature and saves hot utility requirements. At the

same time, the diameter required in the bottom section is reduced, while that in the

upper sections is increased.


127

2. Generally, as the reflux ratio in the distillation column increases, larger energy

consumption is expected because the condenser and reboiler duties increase. As a

result, the vapour and liquid loads inside the column sections increase, and hence

this will require larger diameters. On the other hand, the stripping steam flow rate

needs to be increased as the condenser duty and the reflux ratio increase. This will

lead to a reduced bottom product temperature, and therefore poorer opportunities for

heat recovery exist.

3. Increasing the flow rate of the stripping steam leads to a decrease in the bottom

product temperature and an increase in the vapour and liquid traffic in the column

section. This means a larger diameter is needed and poorer heat recovery

opportunities are expected. On the other hand, with a smaller steam flow rate, heat

recovery opportunities improve because of the higher temperature of the bottom

product, and also the required column section diameter is reduced. In order to keep

the separation performance fixed, other operating variables need to be changed. This

leads to trade-offs between the effects of the different operating variables, and hence

the problem becomes more complex.

4. As explained in Chapter 3, the retrofit modelling of complex distillation columns is

carried out with the equivalent sequence of simple columns connected with thermal

coupling. So, by increasing the degree of thermal coupling of two simple reboiled

columns from partial to full thermal coupling, the reboiler duty of the downstream

column will reduce, while the condenser duty of the downstream column will

increase. As a net result, the total heating and cooling duties of the structure are

reduced, but the quality of the heat source (pump-around) will be degraded since the

pump-around duty is shifted completely to the condenser of the downstream column

which operates at a lower temperature level.

In the case of using stripping steam in both columns, less steam is needed by the

downstream column as a result of increasing the degree of thermal coupling. The

condenser duty of the downstream column will become higher because of shifting

the pump-around duty. Therefore, a higher quality heat source is created at the

bottom of the downstream column, while the degradation of the pump-around

stream leads to poorer heat recovery opportunities.


128

5. The pump-arounds reject heat at temperatures higher than that of the condenser, and

hence the opportunities for heat recovery are improved. They can also reduce the

vapour and liquid traffic in the column section. Therefore, this can enhance the

throughput of the distillation columns. When the temperature difference across the

pump-around decreases, the flow rate of the recycled liquid will be larger for a fixed

cooling duty. This increases the liquid traffic inside the column, and hence a larger

diameter is required around the pump-around. However, the pump-around will

operate at a higher temperature level, which improves heat recovery opportunities.

On the other hand, if the pump-around duty increases for a fixed liquid flow rate, the

temperature difference across the pump-around will increase. Therefore, the pump-

around outlet stream will operate at a lower temperature level reducing the vapour

traffic around the pump-around due to more condensation. This leads to a reduced

required diameter on the trays near the pump-around; however, the quality of the

heat source for heat recovery is now lower.

In the case of using side-heaters in the distillation columns, if the temperature

difference across the side-heater decreases, the flow rate of the recycled liquid will

increase for a fixed heating duty. This requires a larger diameter around the side-

heater; however, a heat sink at a lower temperature level is created which benefits

the heat recovery potential. Similarly, increasing the temperature difference across

the side-heater for a fixed heat duty will result in a reduced diameter requirement,

while a heat sink at a higher temperature level is created which consumes more hot

utility.

From the above discussion, a conclusion can be drawn that the interactions between the

distillation column and exchanger network affect significantly the heat recovery

opportunities of the overall structure and that the operating conditions affect the

hydraulic performance of distillation column. Some changes benefit the heat recovery in

the exchanger network, while others can enhance the processing capacity of the crude

oil distillation column. These interactions can be, however, exploited to remove column

bottlenecks where exist. Therefore, the interactions represented by the operating

conditions of the distillation processes need to be considered simultaneously in an

optimisation approach for retrofit design. In the following sections, the retrofit approach

developed for heat-integrated crude oil distillation systems will be explained in detail.


129

5.4. New approach for retrofit of heat-integrated crude oil distillation systems

The retrofit design of heat-integrated crude oil distillation systems is carried out by

incorporating the distillation column retrofit and the heat recovery system retrofit

models into an optimisation framework simultaneously. The optimisation strategy for

retrofit design of the distillation column and heat recovery system needs then to be

selected.

There exist two different modelling strategies for optimisation of chemical processes,

sequential modular and equation-oriented strategies (Biegler et al., 1997). In the

sequential modular strategy, each unit is modelled individually as a subsystem.

Therefore, it is easier to construct each unit model. The design models and

thermodynamic (physical properties) model are then solved simultaneously from

upstream to downstream units of the process. This strategy is robust for non-linear

process calculations, since only one unit is handled at a time. It is also easier to initialise

the problem variables. However, this strategy consumes a substantial calculation effort.

On the other hand, the equation-oriented strategy combines the unit models and the

thermodynamic model into a large set of equations, and solves them simultaneously.

Therefore, it converges faster than the sequential modular approach, and requires less

computational time. It is, however, difficult to construct and initialise the problem. This

strategy is generally selected for the systems with simple design models (Suphanit,

1999).

For the retrofit design of heat-integrated crude oil distillation systems, the retrofit

shortcut models developed for distillation columns in Chapter 3 and the design models

for exchanger networks and modelling the various aspects of refinery distillation

systems are highly non-linear. Moreover, the Peng Robinson thermodynamic model for

the physical properties calculation (HYSYS, 1999) is highly non-linear. Therefore, it

will be difficult to solve these models simultaneously using the equation-oriented

strategy. Hence, the sequential modular approach is more appropriate for solving the

retrofit design models.

In the next sections, the retrofit approach established in this work will be explained, as

well as the formulation of the optimisation framework and procedure, and the possible

applications.


130

5.4.1. Retrofit design strategies for heat-integrated crude oil distillation systems

As reviewed in Chapter 2, previous researchers approached the retrofit problem of heat-

integrated crude oil distillation systems by considering the heat integration constraints

of the heat recovery system. They did not include the details of the heat recovery system

in their works, and only involved the minimum utility consumption provided from pinch

analysis.

In this work, however, the details of the existing heat recovery system are considered at

the same stage as the distillation column in a simultaneous optimisation strategy. The

retrofit design strategy can be carried out in two different ways. The first strategy is to

consider the superstructure of the existing heat recovery system in the optimisation

framework of the entire system. In this strategy, the superstructure will be included in

addition to the structure of the distillation column. The retrofit problem is then

formulated as a mixed-integer non-linear programming (MINLP). The advantage of this

strategy is that it accounts for the capital cost details of the structural modifications and

the structure of the heat recovery system in the optimisation. In addition, this strategy

accounts for the change in the process conditions of the distillation column during the

optimisation. However, the problem is a rather complex and becomes too difficult to

formulate (e.g. with structural decisions such as preflash, prefractionator, etc.) and solve

for large processes, particularly when the system is highly non-linear such as crude oil

distillation.

Alternatively, the details of the existing heat recovery system can be approximated by

using the retrofit model proposed in Chapter 4. In this strategy, the existing heat

recovery system is studied in isolation for retrofit design and then the retrofit model

(equations 4.7 or 4.8 and 4.9; Chapter 4) is obtained. This model accounts for the

structure of the network, the exchanger areas and duties and the energy consumption.

The overall retrofit problem is then formulated as a non-linear programming (NLP)

problem. The advantages of this strategy over the previous one are that the complexity

of the problem reduces and it is quicker to solve. This strategy is selected as the retrofit

strategy proposed in this work.

The design strategy for retrofit of heat-integrated distillation systems is proposed in

Figure 5.2. In this retrofit strategy, the existing designs of the crude oil distillation


131

column and the associated heat recovery system are accounted for simultaneously. The

shortcut models developed in Chapter 3 are appropriate for distillation column retrofit,

and they are included in the retrofit approach to fix the existing design. The existing

design details include the column configuration, the number of real stages in each

column section, and the locations of reboilers, condensers, side-exchangers and side-

strippers. On the other hand, the structure and the details of the heat recovery systems

are incorporated into the approach and represented by the model proposed in Chapter 4.

Hence, the exchanger matches, existing areas and duties, and utility heat loads are

considered. Moreover, the models of Chapter 4 that account for different issues in

refinery distillation systems are included within the retrofit approach. These issues

include CO2 emissions, installation of preflash and prefractionator units, heat transfer

enhancement and pressure drop consideration and replacement of trays with packing.

The overall retrofit model is formulated by combining the various models mentioned

above, and hence the retrofit problem is a non-linear programming problem since the

individual models are non-linear.

The successive quadratic programming (SQP) algorithm is selected as the optimisation

solver for the retrofit of heat-integrated distillation systems. In particular, the

optimisation subroutine E04UCF of the NAG FORTRAN library (NAG, 1990) is used

to implement the Quasi-Newton method (Dennis and Schnabel, 1983) in the solution.

The SQP method is a popular procedure for process optimisation. It requires fewer

iterations than the reduced gradient method and is suitable for black-box models that

involve few variables. The details of the SQP algorithm are explained in Biegler et al.

(1997).

The retrofit strategy shown in Figure 5.2 involves no structural design decisions; in

other words, the flowsheet structure is fixed throughout the optimisation and no

structural changes may be made. However, structural modifications can be applied to

the retrofit strategy as retrofit options prior to the optimisation. The structural

modifications considered in this work focus on the installation of preflash drums or

prefractionator columns, the replacement of trays with packing, and the integration of

gas turbines with process furnaces.


132

Figure 5.2: Simultaneous retrofit strategy for heat-integrated distillation systems

5.4.2. Objective functions for optimisation of heat-integrated crude oil distillation systems

The purpose of the optimisation is to minimise or maximise a specified objective

function. The objective function depends on the retrofit objectives. In this work, the

objective functions take the form of the total annualised cost of the expenses incurred

less the profit. The process expenses include the operating costs of stripping steam, hot

utilities and cold utilities, and the price of crude oil feed and the capital costs of process

modifications. In this work, the costs for minor modifications that do not incur capital

expenditure (e.g. downtime, labour, consumables, etc.) are not accounted for. However,

if these cost components are available, they can be incorporated into the model The

costs for new equipment are taken into account. The process modifications may contain

preflash drums, prefractionator columns, gas turbines and exchanger network

modifications. On the other hand, the process profit includes the value of products and

the value of power generated. Having defined the objective function, the appropriate

issues of the refinery distillation systems are involved through using the relevant

models. Then, the necessary cost components for the particular retrofit objective can be

Existing distillation column design

Existing heat recovery system design

NLP Optimisation

Optimum existing heat-integrated distillation

process design


133

evaluated. For instance if the aim of the retrofit is to minimise the total annualised cost

with a preflash installed, the model that considers preflash drum in an existing system

will be included. Hence, the capital cost of the preflash is evaluated and added to the

objective function. The calculation of cost components required for the various

objective functions will be explained in the following sections.

5.4.2.1. Objective function in retrofit for energy reduction

When the retrofit is applied to reduce the energy consumption of the crude oil

distillation unit, the objective function will be the operating costs of the utility

consumption and the stripping steam, and the capital costs of the additional area

required by exchangers of new units and of network modifications. As seen in Section

4.2, the HEN retrofit model relates the area required by existing units, new exchangers

and network modifications with the energy demand of the existing network. Therefore,

the amount of additional area required is determined from the retrofit model based on

the level of energy consumption. Then, the capital cost associated with each required

area can be evaluated. The capital cost of exchanger area is calculated from cost

correlations, as function of the area required:

exCretexexNex ABAHXCost += (5.1)

Where, the parameters Aex, Bex and Cex can be found in different sources, such as

Douglas (1988) and Nasr and Polley (2000). In this work, different cost data are

collected from Douglas (1988), Peters and Timmerhaus (1980) and SPRINT (2002).

The data are plotted and then regressed to determine the average cost parameters for the

cost model, as: Aex ($) = 0, Bex ($/m2) = 1530, Cex = 0.63 (see Appendix B.1, for details).

The parameter, Aex, is given a reasonable value for the installation costs of the new

exchanger units; for example this value may be $13,000 (Nie, 1998).

On the other hand, the utility costs are calculated knowing the cost per energy unit and

the heat load of each utility. Similarly, the cost of stripping steam is calculated

providing the unit cost and the flow rate. Therefore, the total annual costs of the energy

and capital investment can be evaluated during the course of the optimisation.

If heat transfer enhancement is applied to the existing exchanger network, the cost for

the enhancement material (as mentioned earlier, labour cost is not accounted for) is


134

therefore added. This cost can be obtained by knowing the amount of the enhanced area

as mentioned in Chapter 4, as follows:

enex AccEnCost 21 += (5.2)

The cost parameters c1, c2 are dependent on the type of the enhancement material and

can be obtained from enhancement providers. For instance, these parameters for the

type of coiled wire 5.5 are 0 ($) and 60 ($/m2) respectively (Nie, 1998).

If preflash drum or prefractionator column is installed in an existing system to reduce

the energy consumption, the capital cost, calculated from the relevant models of Chapter

4, will be added to the objective function.

5.4.2.2. Objective function in retrofit for throughput enhancement

In case of increasing the production capacity of the existing unit, the objective function

will include the value of the products and cost of the crude oil feed in addition to the

costs of the energy and stripping steam and the HEN capital investment. The value of

the products and cost of crude oil feed can be determined knowing the unit prices and

the production flow rates.

If installing a preflash drum or a prefractionator column is to be selected for increasing

capacity, the capital costs of the modifications will be added to the objective function.

Similarly, when column trays are to be replaced, the cost of the packing is added. In

these cases, the models proposed in Chapter 4 for calculating the costs of the

modifications are used.

5.4.2.3. Objective function in retrofit for profit increase

For a fixed flow rate of crude oil feed, process profit can be maximised. This can be

done by allowing product flow rates to change; hence, the value of products will

increase. In this case, the objective function includes the value of products and cost of

crude oil feed, in addition to the operating costs and the network modification costs.

Moreover, profit can be maximised for a given crude oil distillation unit with structural

modification, such as preflash or prefractionator. The modification cost will be then

added to the objective function where applicable.


135

5.4.2.4. Objective function in retrofit for CO2 emissions reduction

If the goal of the retrofit project is to minimise the emissions from the existing refinery

distillation system, the objective function must account for the emissions tax or

investment in emissions-abatement infrastructure. Therefore, the cost penalties of

meeting the environmental regulations are taken into account. Two approaches are used

to consider emissions; one is to consider the carbon tax imposed by the government.

The other is to add the capital invested for reducing the emissions. In the first scenario,

the carbon tax is added to the operating costs and the capital cost of the HEN

modifications. The flow rate of the CO2 is evaluated in the existing system; hence, the

total tax can be determined, knowing the penalty value (tax) per unit mass of CO2.

In the second scenario, the capital investment for emissions reduction is determined

prior to optimisation. This task can be done by carrying out a separate study on the

existing crude distillation unit in order to reduce the emissions. Various retrofit

modifications may be performed, such as modifying the distillation column or the

exchanger network, and using different fuels in combustion. The possible modifications

to the distillation column include firstly changing the operating conditions; these

changes have cost implications on the existing network. Then, adding a preflash drum

or prefractionator column may be made. In addition, the existing network may be

modified and a gas turbine can be integrated with the process. Therefore, the capital

investment required for each reduction in the emissions can be evaluated. Then, the data

of the investment can be plotted against the flow rate of the CO2 (Figure 5.3). As

shown, the plot can be divided into intervals of energy consumption, for which the slope

of the curve can be estimated as shown by the dotted lines. The inverse of these slopes

indicates the investment per unit mass of CO2, and it can represent the costs required for

reducing the emissions for a given range. The objective function is then the summation

of the emissions reduction investment and the operating costs. Alternatively, the curve

can be regressed to obtain a model that correlates the capital investment to the flow rate

of the CO2 emissions. Then, this model can be incorporated into the overall retrofit

model to estimate the investment for a given emission level.

When a gas turbine is integrated with the process furnace, the capital and power costs of

the gas turbines need to be added to the objective function. Therefore, the models of

Chapter 4 that calculate the capital and power costs are employed.


136

CO2 flow(kg/h)

Investment ($)

Existing processEmissionsreduction curve

Slope =kg/h$

Decreasingenergy demand

Figure 5.3: CO2 emissions reduction strategy for refinery distillation systems

As seen above, although the retrofit objectives are presented for individual purposes,

multiple objectives can also be accommodated, such as reducing total cost together with

improving profit, or increasing capacity and reducing emissions. In this case, the

objective function will be the summation of the total annualised cost of all objectives

involved. This may allow the retrofit design and modifications options to be explored

for several objectives at the same time, e.g. gas turbine and prefractionator can be added

for reducing emissions and increasing production capacity.

5.4.3. Optimisation procedure for existing heat-integrated crude oil distillation systems

Figure 5.4 shows the optimisation framework for retrofit design of heat-integrated

distillation systems. In this framework, column retrofit models, HEN model, cost

models and other models accounting for structural modifications are solved

simultaneously within a black-box. Column retrofit models account for the details of an

existing column design including column configuration, number of actual stages and

existing column diameter. HEN retrofit model takes the details of the existing heat

exchanger network into account. The optimisation variables are the operating conditions

of the distillation process, including feed preheat temperature, preflash prefractionator

temperatures, steam flow rates, reflux and reboiling ratios, and side-exchanger

temperature drops and flow rates. The lower and upper boundaries of the optimisation

variables need to be set. These boundaries may depend on practical experience or they


137

can be imposed as constraints. For instance, the feed preheating temperature of crude oil

should not exceed a certain limit depending on the crude oil origin to avoid unwanted

thermal decomposition (Watkins, 1979). Similarly, the pump-around temperature

differences may have constrained values. Any practical constraints in the existing

system can be applied in the optimisation framework. SQP is used as the NLP

optimisation solver.

The optimisation is applied to any existing crude oil distillation column with its

associated exchanger network. Throughout the optimisation, the given column structure

is fixed, as are the number of actual stages in each section and the locations of the

condensers, reboilers and side-exchangers. In addition, the stage diameter is calculated

as discussed in Chapter 4, and hence the existing diameter of the distillation column can

act as a constraint throughout the optimisation. On the other hand, the details of the

existing heat recovery systems are taken into account using the area retrofit model. The

operating conditions of the distillation processes are changed simultaneously for the

fixed system design to minimise or maximise an objective function.

Figure 5.4: Simultaneous retrofit strategy for heat-integrated distillation systems

The optimisation starts with the calculation of the energy consumption and total

exchanger area of the existing distillation unit. Then, in every iteration, the heating

Column retrofit model HEN retrofit model

Cost model Other models

NLP optimiser (SQP)

Existing distillation column

Existing heat recovery system

Objective: Minimise or maximise

objective function

Optimum existing heat-integrated distillation process

Parameter boundaries

Practical constraints


138

demand at each temperature level, associated with available utilities, is calculated for

the new process stream data using the process grand composite curve (Smith, 1995).

This is done by forming a linear programming (LP) problem based on the problem table

algorithm of Linnhoff et al. (1982) to determine the optimum use of available utilities

given their unit costs (Parker, 1989). The HEN model provides the exchanger additional

area required for retrofit at each heating demand level.

The drawback of the approach is that the model for the heat exchanger network retrofit

is obtained for fixed process conditions. However, during the optimisation, these

process conditions change. As a result, the capital-energy trade-off given by this retrofit

model does not account for the changes in process conditions and the retrofit

requirements to the existing heat exchanger network. This shortcoming can be

overcome by repeatedly applying the optimisation approach to the existing system.

After each optimisation run, a new retrofit model is obtained for the optimum process

conditions. Then, this model is used for the next optimisation step. The optimisation

runs end when there is no scope for a better solution to be obtained. Overall, a retrofit

model is obtained which relates the capital-energy trade-off in the existing heat

exchanger network and the process changes.

As mentioned, various objective functions can be handled. Thus, according to the

objective function employed, the appropriate aspects of the refinery distillation systems

are involved through applying the relevant models. This means that, for instance, if the

retrofit objective is to minimise the CO2 emissions, the emissions will be calculated and

the corresponding objective function will be evaluated.

During the course of the optimisation, practical constraints can be applied. The practical

constraints considered in this work include: (1) hydraulic capacity limitations of the

distillation column, (2) allowable pressure drop for crude oil feed in heat recovery

system, (3) maximum heat duty of pump-arounds or side-heaters and duties of the

condenser and reboilers, (4) product component recoveries and (5) product flow rates. If

a constraint is violated, a penalty value will be added to the value of the objective

function. A square penalty function is used in the optimisation framework, as follows:

( )2lim,ii xxKPenalty −= if xi ≤ xi,lim (5.3)


139

The values of the dependent variables, xi, are obtained from the appropriate retrofit

models presented in Chapters 3 and 4, while the limiting values of these variables, xi,lim,

are specified. For example, if the pump-around duty is to be constrained, the duty will

be calculated from the column retrofit models during the course of optimisation. Then,

the value will be compared with the maximum duty and therefore a penalty value will

be imposed where the duty is violated.

The results of the optimisation will be the optimum design of the existing crude oil

distillation column and the associated heat recovery system. The optimum operating

conditions of the distillation process are obtained. On the other hand, the total additional

exchanger area, the type of network modifications and the the energy consumption of

the optimum distillation unit can also be obtained. However, the detailed network

requirements, including the additional area to the individual exchanger units, the

modification implications for the existing design and the utility consumption, are

determined by applying the optimum process conditions to the existing exchanger

network.

5.5. Implementation of retrofit models and applications of retrofit approach

The column retrofit models, the exchanger network model, and the other models

accounting for the various aspects in refinery distillations are coded in FORTRAN

(FTN77) and then embedded within COLOM software (2002). The physical property

model of Peng Robinson with zero interaction parameters (Suphanit, 1999) is also

included. The overall procedure for applying the retrofit approach to an existing heat-

integrated crude oil distillation system is illustrated in Figure 5.5, and can be

summarised as follows:

1. The crude oil mixture is specified in HYSYS software (HYSYS, 1999) using the

crude assay data. Then, the crude oil is cut into a number of pseudo-components,

each with known physical properties such as molecular weight, vapour pressure,

boiling temperature, critical properties, etc. HYSYS has a built-in facility to break

down a given crude oil mixture into a specified number of pseudo-components. In

addition to the pseudo-components, chemical components, such as light ends, can

also be specified.


140

2. The physical property parameters of all existing components in the oil mixture are

extracted from HYSYS. A macro interface namely Expcmpn was written by

Suphanit (1999) for data extraction to transfer the physical property parameters from

HYSYS to COLOM software.

3. The structure of the existing distillation column is specified in the form of the

decomposed sequence of columns, as are the number of actual stages and the actual

diameter in each column section, and the product specifications. Moreover, the

current operating conditions including feed temperature, steam flow rates, reflux

ratio, pump-around flow rates, etc. need to be specified. The cost of crude oil feed

per unit barrel and the unit value of products need to be specified.

4. The existing exchanger network is represented using the retrofit model, and thus the

model parameters need to be specified. On the other hand, the utility consumption

and the total exchanger area are specified. The unit cost of each utility and the

exchanger model cost parameters are also specified.

5. The optimisation variables are then selected. The lower and upper boundaries of the

optimisation variables are set.

6. Existing practical constraints such as hydraulic limitations, pressure drop, maximum

duties, etc. are specified.

7. The optimisation can then be performed for a user-defined retrofit objective.

8. The results of the optimisation will be the optimum operating conditions for the

specified retrofit objective, and the optimum energy consumption and the network

retrofit requirements.

The optimum process changes to the distillation column do not require any capital

investment because they are only changes to the operating conditions. The HEN

retrofit requirements include the total additional exchanger area and the type of

network modifications, which can be determined from the optimum energy

consumption and the HEN retrofit curve.

9. The detailed retrofit implications including the additional area to each exchanger

unit can be determined by reconsidering the existing HEN and the optimum process

conditions.


141

As known, for non-linear optimisation problems, global solutions cannot be guaranteed

(Edgar and Himmelblau, 1998). Hence, the optimisation with different initial values of

the optimisation variables may reveal the direction of the global solution and improve

the optimum solutions obtained. This procedure can be carried out repeatedly till a

satisfactory solution is obtained.

Figure 5.5: Overall procedure for heat-integrated crude oil distillation systems

The overall retrofit procedure can be applied to any crude oil distillation column and the

associated heat recovery system for various objectives, as will be explained in the next

sections.

5.5.1. Retrofit for energy reduction

Applying the retrofit approach can reduce energy consumption of existing crude oil

distillation units. The objective is therefore to minimise the total annualised cost of

utility consumption, stripping steam, and exchanger network modifications. The

optimisation results in the optimum process changes of the distillation column and the

minimum total of utility consumption and modification costs of the existing exchanger

network. The energy consumption can be further reduced by installing preflash or

prefractionator units, and using heat transfer enhancement. In these cases, the selected

modification is applied to the existing unit, and then the retrofit approach is applied.

Therefore, the objective is to minimise the total annualised cost including the cost of the

installed units and the operating costs and the network modification costs. The results of

Retrofit shortcutmodels

PA3

LD

HN

LNWater

PA2

HD

Steam1RES

PA1

Feed

Steam2

HEN

Column decomposition

Optimiser(SQP)

Existing HEN retrofit model

Optimum existing system

Water

LN

HN

LD

Steam2

HD

Steam1

RES

PA3

PA2

Feed

PA1

Existing system


142

the approach will include the optimum location and the sizing of the installed unit. The

location of the installed unit in the existing preheat train is determined by the optimum

temperature of the feed entering these units.

5.5.2. Retrofit for throughput enhancement

Increasing the throughput of existing crude distillation column is limited by the

hydraulic capacity limitations and the maximum heat load of the process furnace. Each

column has an actual diameter which cannot be exceeded. When the diameter required

for separation is smaller than the actual one, the column can allow the throughput to be

increased. The maximum throughput increase can be determined from the information

of the required diameter for separation and the actual diameter. This can be done by

using the FUA technique (Liu, 2000). Alternatively, it can be determined from the

hydraulic analysis evaluation, as discussed in Chapter 4. The retrofit approach is applied

for increasing the throughput of an existing crude oil distillation unit. In this case, the

feed flow rate is, therefore, an optimisation variable. The HEN retrofit model needs to

be modified to include the effect of throughput increase. The procedure of Section 4.2 is

thus applied for different feed flow rates. Therefore, the retrofit model may have the

form of equation 5.4, taking into account the effect of feed flow rate. The form of

equation 5.4 was found suitable for the data obtained. The equation has power form, and

relates the retrofit exchanger area with both the feed flow rate and energy demand. The

objective in these retrofit situations is to maximise the profit by changing the crude oil

feed flow rate. The maximum throughput increase, as well as the optimum operating

conditions can be determined from the optimisation results. The network retrofit

requirements are also obtained.

( )( ) Fnnreq EFmmA 10

10++= (5.4)

On the other hand, by modifying the structure of the existing distillation flowsheet,

throughput can be increased further. The modifications are the installation of a preflash

drum or a prefractionator column to the existing distillation column, and the

replacement of column trays with packing. Similarly, the retrofit approach is then

applied to the existing unit with the modifications for increasing the throughput.


143

5.5.3. Retrofit for profit increase

In the case of reducing energy consumption, the product flow rates are kept constant.

However, product flow rates can be allowed to change in order to maximise profit for a

given feed flow rate. In this case, the retrofit approach is applied to increase the net

value of the products through changing the flow rates, and reduce the process expenses;

hence the profit is maximised. The results in this situation are the maximum profit

together with the optimum product flow rate distribution. The optimum operating

conditions and the heat exchanger network modifications are also resulted.

5.5.4. Retrofit for CO2 emissions reduction

The retrofit approach can be applied to reduce the environmental impact of an existing

crude oil distillation unit. The objective is to minimise the total annualised cost of the

carbon tax, and the operating costs and the network modification costs. The

optimisation results will be the optimum operating conditions and the network

modifications. On the other hand, a gas turbine can be integrated to reduce the CO2

emissions further. The results in this case include the optimum operating conditions, the

optimum size of the gas turbine and its load, and the heat exchanger network

modifications.

5.5.5. Retrofit for product/feed specification changes

Due to the change in the market demands and the specifications of the crude oil,

refineries may process new feed specifications or change the product specifications.

Changes in product specifications include the increase of a specific product at the

expense of another one. The retrofit approach can be applied for these situations, in

which the new product specifications (key component recoveries) need to be specified.

The retrofit approach results in the optimum operating conditions required for the new

changes to the feed or the product specifications. The required exchanger network

modifications are also obtained.


In this chapter, an optimisation-based approach for retrofit design of heat-integrated

crude oil distillation systems has been developed. The distillation columns and the


144

associated heat recovery systems are considered simultaneously. The column retrofit

models consider the existing designs of distillation columns, while the HEN model

accounts for the details of the exchanger networks. The hydraulic limitations of the

distillation columns are also taken into account. The effects of the interactions between

the distillation column and exchanger network on the performance of the overall system

have been discussed. These interactions are considered simultaneously in the retrofit

approach.

An optimisation framework has been established for the retrofit of heat-integrated crude

oil distillation systems. The overall retrofit problem is formulated as a non-linear model

by combing the individual retrofit models. The column retrofit models, the HEN model

and the other models are included in the optimisation framework. Successive quadratic

programming (SQP) method is employed for the non-linear optimisation problem. The

objective functions of the optimisation are flexible and account for various retrofit

objectives such as energy saving, throughput enhancement, emissions reduction, etc.

The procedure for applying the retrofit approach has been discussed.

Although the application of the retrofit approach has been presented individually for

certain objectives, the approach can apply for combined objectives or modifications.

This means that, for instance, preflash or prefractionator units can be installed in the

retrofit for the emissions reduction or the change in feed/product specifications.

Similarly, the gas turbine can be integrated with the process for the energy reduction

objectives.

The retrofit approach presented in this work has advantages over previous work. These

advantages are the simultaneous consideration of the distillation and exchanger network

systems, the accounting for the hydraulic limitations of the distillation columns.

Furthermore, the new approach handles various objectives; besides, it considers

structural modifications.

In the next chapter, case studies will be presented to illustrate the applications of the

retrofit approach to crude oil distillation units.

Chapter 6 Case studies

145

Chapter 6: Case studies

In this chapter, the retrofit approach, presented in Chapter 5, is applied to different case

studies for the purposes of illustration. Various retrofit goals are considered. Case

studies are based on industrial and textbook data. For each case study, the problem data

and specifications are presented, the objective is set, and the key results of the approach

are discussed. Supplementary data are given in appendices for each study. The case

studies are categorised according to the retrofit objectives.

Unit costs of utility and stripping steam, capital costs per unit area of exchanger units

and utility data will be given for the first case study. Then, the same data are used

throughout all case studies; otherwise, it is stated that the data are different and new

values are provided. This is valid for any problem data which are not given for a

particular case study.

6.1. Reducing energy consumption of an existing atmospheric crude oil distillation tower

6.1.1. Base case problem data

The base case for this case study is an atmospheric tower for crude oil distillation. The

existing column configuration, as shown in Figure 6.1, uses 3 side-strippers and 3

pump-arounds. Figure 6.1 also shows the equivalent sequence of four simple columns.

Steam at 4.5 bar and 260 oC is used for stripping at the bottom of the main column and

in the bottom side-stripper, while reboiling is employed in the top and middle side-

strippers. The distillation aspects of the case study are based on a textbook example of

an atmospheric crude oil distillation tower; the crude oil mixture is the assay of Tia

Juana Light (Venezuela) (Watkins, 1979). The column design was presented by

Suphanit (1999) as an illustrative case study for applying a new grassroots model for

design of crude oil distillation columns. The design was carried out by following the

design insights for crude oil distillation columns presented by Watkins (1979). These

design insights include the column configuration, limited number of stages in each

section for a specific separation, calculations of steam flow rates, crude oil feed

preheating temperature, etc. The existing heat exchanger network is based on industrial


146

data; the network structure is shown in Figure 6.2. The true boiling data of the crude oil

are given in Table 6.1. This assay is cut into 25 pseudo-components by using HYSYS

(1999) process simulator; the flow rates and normal boiling temperatures of these

components can be seen in Table C.1.1 (Appendix C).

The existing distillation tower processes 100,000 barrels per day (2610.7 kmol/h) of

crude oil to produce five products: light naphtha (LN), heavy naphtha (HN), light

distillate (LD), heavy distillate (HD) and residue (RES). The key components for the

separation of each pair of products are shown in Table 6.2. The crude oil, shown in

Figure 6.2 as the first cold stream, is heated from 25 oC to 226.5 oC by exchanging heat

with process hot streams in preheat train, and is then further heated in the furnace to 365 oC before entering the distillation tower. The existing numbers of stages in each section

of the atmospheric distillation tower are given in Table 6.3, for the various sections and

side-strippers. The actual column diameters are also provided in Table 6.3. The current

hot and cold utility requirements of the crude oil distillation tower are 99.0 MW and

83.3 MW respectively, with total operating cost of 17.018 MM$/yr, including stripping

steam cost. The unit operating costs of the individual utilities and stripping steam can be

seen in Table C.1.2 (Appendix C). The existing process requirements, including energy

requirement and operating costs, are summarised in Table C.1.3 (Appendix C). The

existing heat exchanger network contains 27 exchanger units, including the process

furnace, with a total area of 4015.8 m2. The detailed exchanger data of existing area,

heat loads, U×A values and temperature differences are summarised in Table C.1.4

(Appendix C). Data for process hot and cold streams and utility streams, which are

shown and numbered in Figure 6.2, are given in Table C.1.5 (Appendix C). Data

include supply and target temperatures, and the enthalpy change of each stream.

The current operating conditions of the distillation tower base case are summarised in

Table 6.4, for the equivalent simple sequence. These operating conditions are the feed

preheating temperature, flow rate and temperature difference of the liquid through

pump-arounds, flow rate of stripping steam and the reflux ratio. Table 6.4 also provides

the product flow rates and recoveries of light and heavy key components in each

column.


147

(a) Atmospheric distillation tower (b) Equivalent simple sequence

Figure 6.1: Atmospheric crude oil distillation column, showing the equivalent sequence of simple columns (numbers in (a) refer to section numbers, while in (b) refer to column numbers)

Figure 6.2: Structure of existing heat exchanger network (streams data are given in Table C.1.5, Appendix C) (light shaded exchangers indicate heaters; dark shaded exchangers indicate coolers)

Middle PA

Bottom PA

Crude oil

Heavy naphtha (HN)

1

3

4

2

LN

HN

LD

HD

RES

Steam

Water

Light naphtha (LN)

Light distillate (LD)

Heavy distillate (HD)

Residue (RES)

Water

Steam

Steam

Top PA HN SS

LD SS

HD SS Steam

1

2

3

4

5

3

9

7

6

1

2

2 5

1 82 2

1 5 7 9

1 3 2 6 8 1 0

11

12

14

8

4

5

13

4 1 2

10

1 41 13

2 1

1 9

1 6

1 7

1 5

2 6

2 7

2 4

2 3

2 8

4


148

Table 6.1: Crude oil assay data

% Distilled (by volume TBP (oC) 0 -3.0 5 63.5 10 101.7 30 221.8 50 336.9 70 462.9 90 680.4 95 787.2 100 894.0

density = 865.4 kg/m3

Table 6.2: Key components for separation of crude oil products (recoveries are presented in Table 6.4)

Separation Key component LN and HN HN and LD LD and HD HD and ResidueLight key 4 7 11 13 Heavy key 6 9 14 16

Table 6.3: Number of actual stages and existing diameters of distillation tower sections

Column section* Parameter 1 2 3 4 5

Top SS Middle SS

Bottom SS

Number of actual stages

5 9 10 8 9 6 7 5

Existing diameter (m)

5.5 8.0 8.0 7.5 7.0 3.0 3.5 3.0

*: see Figure 6.1; SS: side-stripper

Table 6.4: Operating conditions of base case for crude oil distillation tower

Parameter Column 1

Column 2

Column 3

Column 4

Feed preheating temperature (oC) 365 Column pressure (bar) 2.5 2.5 2.5 2.5 Steam flow rate (kmol/h) 1200 250 Pump-around ∆T (oC) 30 50 20 Pump-around flow rate (kmol/h) 2186.56 2305.66 5790.85 Pump-around duty (MW) 12.870 18.030 11.250 Reflux ratio 4.78 Condenser duty (MW) 52.160 Reboiler duty (MW) 9.376 6.715 Top product flow rate (kmol/h) 680.69 Bottom product flow rate (kmol/h) 633.89 149.82 652.84 493.45 Recovery* of LK component (%) 99.25 97.23 97.87 99.81 Recovery* of HK component (%) 99.13 68.95 95.56 98.66

*: component recoveries are calculated based on the fresh feed to the distillation tower


149

6.1.2. Retrofit objective and approach results

The purpose of the case study is to reduce the energy consumption and operating costs

of the existing unit. Therefore, the objective of the optimisation is to minimise the total

annualised costs of utility consumption and stripping steam and the exchanger network

modification costs. Before applying the retrofit approach to the case study, the retrofit

curve for the existing network is obtained, by following the procedure of Section 4.2

(Chapter 4). Details of the retrofit study are summarised in Appendix C.2. The

parameters of the retrofit model (see equation 4.7, Chapter 4), obtained from data

regression analysis (Figure C.2.1, Appendix C), are 9.731276·106 and -1.6959

respectively. Where the units of area are m2 and those of energy demand are MW.

To reduce the energy consumption and operating costs, the retrofit approach is applied

to the existing unit. The established retrofit approach procedure, presented in Chapter 5,

is applied. The existing column distillation design is fixed, including number of real

stages, column configuration and existing diameter. The structure of the existing heat

exchanger network is fixed, as are the exchanger area and heat loads. The operating

conditions of the distillation column, indicated in Table 6.5, are optimised

simultaneously to minimise the total annualised cost of utility and steam consumption,

and exchanger network modifications.

The lower and upper limits for the feed preheating temperature are 100 and 370 oC

respectively, while for the pump-around temperature drops, these limits are 10 and 80 oC respectively. The flow rate of stripping steam in optimisation is replaced by the

partial pressure at the feed stage; then, these limits are taken as that the lower limit is

1% of the column pressure and the upper limit is 99% of the column pressure (Suphanit,

1999). The reflux ratio boundaries are between the values 1.05 and 2.0 of the minimum

reflux ratio. The flow rates of the liquid between different columns are related to the

reflux ratio of hypothetical condenser at the top of the downstream column; therefore,

the limits of the liquid flow rates are corresponding to the reflux ratio limits. Similarly,

the flow rates of the liquid across the pump-arounds are calculated as function of the

degree of thermal coupling (Suphanit, 1999). Therefore, when the degree of thermal

coupling is equal to zero, the flow rate of the pump-around liquid is minimal (i.e. no

pump-around exists); on the other hand, at full thermal coupling (i.e. degree of thermal

coupling is 1.0), the liquid flow rate is maximal. These limits of the optimisation


150

variables will be used throughout all studies in this chapter.

Table 6.5: Optimisation variables for crude oil distillation tower

Column Optimisation variables 1 1. Feed preheating temperature

2. Flow rate of pump-around liquid 3. Temperature drop across pump-around 4. Flow rate of stripping steam

2 5. Flow rate of pump-around liquid 6. Temperature drop across pump-around 7. Flow rate of stripping steam

3 8. Flow rate of pump-around liquid 9. Temperature drop across pump-around 10. Flow rate of liquid between columns 3 and 4

4 11. Reflux ratio

During the optimisation, the existing designs of distillation column and heat exchanger

network are fixed, while the feed preheating temperature, flow rate of steam and pump-

around liquid, pump-around temperature drop, flow rate of liquid between column

sections and the reflux ratio are changed at the same time in order to minimise the

operating cost of the process and the network modification capital costs. The existing

column diameter is imposed as a constraint to the optimisation; therefore, the results

will ensure that the optimum column operation is feasible. The flow rates of the various

products are also fixed.

The optimum operating conditions that give minimum total annualised cost are

summarised in Table 6.6, and are compared with the values of the base case. The

product flow rates of the optimum unit are given in Table C.3.1 (Appendix C), and

compared with those of the existing unit. Table C.3.2 (Appendix C) compares the key

component recoveries of the products for the optimum unit well with those for the

existing unit. The stream data of the optimum crude oil distillation unit are summarised

in Table C.3.3 (Appendix C).

Table 6.6 indicates that the existing crude oil distillation unit performs relatively far

from the optimum conditions. As seen, the crude oil feed temperature needs to be

increased by 5 oC over the current value. This affects the opportunities for recovering

more heat from the distillation hot products and internal streams. Also, the optimum

distribution of the pump-around duties changes from the existing unit. For example, the

optimum pump-around of column 1 will operate at higher temperature of 301.7 oC,

compared to 299.1 oC for the existing unit; this creates more opportunities for heat


151

recovery. In addition, the pump-around of column 2 will operate optimally at 258.0 oC,

versus 252.1 oC in the existing unit. Therefore, the quality of heat sources is improved.

Furthermore, the optimum liquid flow rate between columns 3 and 4 and the reflux ratio

are reduced. This results in reduced heat loads in the reboilers of these columns from

9.4 and 6.7 MW for the existing unit to 5.8 and 3.1 MW for the optimum unit

respectively.

Table 6.6: Optimum values of operating conditions

Column Optimisation variable Base case value

Optimum value

Feed preheating temperature oC 365 370 Flow rate of pump-around liquid kmol/h 2186.56 1698.56 Temperature drop across pump-around oC 30.00 40.10

1

Flow rate of stripping steam kmol/h 1200.00 1078.04 Flow rate of pump-around liquid kmol/h 2305.66 2762.49 Temperature drop across pump-around oC 50.00 37.27

2


3

Flow rate of liquid between columns 3 and 4

kmol/h 1067.48 866.33

4 Reflux ratio 4.78 3.66

It is apparent that these process changes do not require expensive modifications to the

existing column equipment. This is because that while these conditions are obtained, the

hydraulic constraints of the existing distillation equipment are satisfied.

Table 6.7 summarises the energy consumption of the optimum crude oil distillation unit,

operating cost savings, and the capital cost of the modifications. Modifications are only

required to the existing heat exchanger network (Figure 6.3). One topological

modification to the existing exchanger network is determined from the optimisation

results, i.e. the optimum energy consumption of 86 MW corresponds to one structural

modification in the retrofit curve data. The HEN modifications include the addition of

exchanger area to some exchanger units, as indicated in Figure 6.3, and the relocation of

exchanger number 4 from the existing location to the location that is shown in the same

figure. This modification is the most beneficial one recommended by network pinch

analysis for the optimum process conditions. When the topology modification is

implemented in the exchanger network with the optimum process conditions, the energy

consumption is reduced significantly to 76 MW. The details of the additional area and

capital cost are given in Table C.3.4 (Appendix C). The capital cost, given in Table 6.7,


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is that required for the additional exchanger area. The cost of the resequence

modification is neglected; the costs of the piping work, as illustrated in Figure 6.4, are

expected to be minor. However, if the piping work and material costs, in other

situations, are significant, the cost implications can be included in the economic

analysis.

Table 6.7: Energy consumption and operating costs for optimum unit

Parameter Existing unit

Optimum unit

% Reduction

Hot utility demand MW 99.0 76.4 22.8 Cold utility demand MW 83.3 60.9 26.9 Furnace heat load MW 73.5 62.4 15.1 Crude oil temperature before furnace

oC 226.5 233.1 -

Utility operating cost MM$/yr 15.284 11.772 22.9 Stripping steam operating cost MM$/yr 1.734 1.551 10.6 Additional exchanger area m2 - 1407.3 - HEN capital investment MM$ - 0.393 - Total operating cost MM$/yr 17.018 13.323 22.5 ∆Tmin oC 30.0 25.0 - Payback yr - 0.11 -

utility and steam unit and area costs can be seen in Table C.1.2 and Table C.3.2 (Appendix C)

8

3

9

7

6

1

2

2 5

1 82 2

1 5 7 9

1 3 2 6 8 1 0

11

12

14

4

5

13

1 2

10

1 41 13

2 1

1 9

1 6

1 7

1 5

2 6

2 7

2 4

2 3

2 8

Additional area

44

Relocation

4

Figure 6.3: Modifications to existing heat exchanger network


153

4

2 2

2 7

2 1 1 8 1 5

10

1

3

621 31 4

Existing location

New location

Figure 6.4: Relocating exchanger number 4 in the existing exchanger

network

As seen in Table 6.7, the hot utility demand of the optimum unit reduces significantly to

76.4 MW (including furnace duty) with saving of 22.6 MW over the existing unit

demand. The total operating cost savings are approximately 3.7 MM$/yr, while the

capital investment required is relatively small. The savings in energy consumption and

operation costs are achieved without requiring any expensive modifications to the

existing column, because the results of the optimum operating conditions take into

account the existing diameter. The payback required is very low (0.1 year), as the

modifications required to the existing exchanger network are minor and relatively

inexpensive.

The case study considered in this section shows that the new retrofit design approach is

applicable to an existing crude oil distillation column and the associated heat recovery

system for reducing energy consumption and operating costs. Significant reductions in

energy consumption and operating cost are reached with low payback time. The

application of the retrofit approach reveals that existing designs of heat-integrated crude

oil distillation systems can perform more efficient by changing the operating conditions

of the distillation process. These changes are easy and do not demand expensive

modifications to the existing column structure; only minor changes to the exchanger

network are necessary.

The results of the above case study were obtained for the given crude oil specifications

(i.e. crude assay, density, etc.) and for fixed production capacity. If the specification of


154

crude oil changes due to processing new feedstock or mixing two different crude oils,

the results of the optimum operating conditions and network modification requirements

might not be optimum for the new case. Therefore, the retrofit approach needs to be

reapplied to the new feed specification case, in which the new data of the crude oil are

specified. A new set of optimum operating conditions can be obtained for the new feed

specifications; the modifications required by the existing exchanger network are also

evaluated. However, the retrofit approach can be repeatedly applied for various crude

feedstocks, each of which has different feed specifications; thus, satisfactory set of

operating conditions and exchanger network modifications can be selected which satisfy

the different feed specifications. The heat exchanger network modifications which can

handle the process heating and cooling requirements for all the range of feed

specifications are selected; therefore, the capital investment obtained will account for

the requirements of the different feed specifications. The operation of the distillation

column can then be manipulated using the control system with different set points, each

set point is for one set of optimum operating conditions required for a particular feed

specifications. Therefore, the distillation column operator can select the set point that

corresponds to the operating conditions which suits the processed feed specifications.

On the other hand, for increasing the crude oil capacity of the distillation unit, the

approach can be applied to the unit with the new conditions; this case will be considered

in Section 6.3.

6.1.3. Comparison of new retrofit approach with previous work

The new retrofit approach is compared with previous research work to emphasise the

advantages of the approach over conventional approaches. As mentioned in earlier

chapters, the major limitations of previous approaches are: none has considered the heat

exchanger network in retrofit at the same stage as the distillation column, they have

considered heat integration targets and not the details of the heat exchanger network and

they cannot guarantee feasible distillation operation. For the comparison of the new

retrofit approach with the previous research work, two previous approaches are selected

to be applied to the same existing heat-integrated crude oil distillation unit. The first

approach is network pinch analysis (Asante, 1996), whereas the second is the energy

target-based approach (Bagajewicz, 1998). These two approaches are selected for

comparison because they represent two different advantageous retrofit philosophies, i.e.


155

the first is for retrofit of heat exchanger networks, while the second is for retrofit of

heat-integrated crude oil distillation systems (see Chapter 2).

The network pinch analysis is applied to the same existing heat exchanger network to

reduce the energy consumption and the utility costs. Note that the process streams data

are the same as those for the base case of the existing unit. The additional area required

to the existing exchanger units and the associated capital costs are summarised in Table

C.4.1 (Appendix C). An existing exchanger unit needs to be relocated to a new location,

as shown in Figure C.4.1 (Appendix C). The network pinch approach does not consider

changes to the distillation process operating conditions; it only considers modifying the

existing exchanger network for reducing energy consumption.

On the other hand, the approach based on the optimisation of the existing crude

distillation unit with respect to the energy target is applied. The operating conditions of

the existing distillation column are optimised to minimise the energy cost calculated

from energy target calculated in turn by pinch analysis. The details of the existing heat

exchanger network are not accounted for. Although for the purpose of comparison, the

retrofit of the existing heat exchanger network is considered to provide the additional

exchanger area and capital cost values. The results of the optimum operating conditions

from this approach are detailed in Table C.4.2 (Appendix C) and compared with those

obtained from the new retrofit approach. The additional area to the existing exchangers

and capital costs are given in Tables C.4.3 (Appendix C). The energy target-based

approach does not consider the distillation process simultaneously with the existing heat

exchanger network; it only optimises the operating conditions of the distillation process

to minimise energy cost. It also does not account for both the retrofit exchanger area and

the capital costs required during the optimisation.

The results of these approaches are given in Table 6.8, compared with the new retrofit

approach. Table 6.9 compares the different results of the additional area to the

individual exchanger units.


156

Table 6.8: Comparison of new approach results with previous work

Parameter New approach

Energy based

Network pinch

Hot utility demand* MW 76.4 92.7 83.1 Cold utility demand MW 60.9 77.1 67.49 Furnace heat load MW 62.4 72.1 60.00 Crude oil temperature before furnace

oC 233.1 206.9 233.9

Utility operating cost MM$/yr 11.772 14.304 13.252 Stripping steam cost# MM$/yr 1.551 1.681 1.734 Additional exchanger area m2 1407.3 136.1 1668.0 ∆Tmin oC 25.0 25.0 25.0 HEN capital investment MM$ 0.393 0.111 0.414 Total operating cost+ MM$/yr 13.323 15.985 14.986 Total operating cost savings MM$/yr 3.695 1.033 2.032 Payback yr 0.11 0.11 0.21

utility and steam unit and area costs can be seen in Table C.1.2 and Table C.3.2 (Appendix C) *: base case energy consumption = 99.0 MW; +: base case total cost = 17.018 MM$/yr; #: base case steam cost = 1.734 MM$/yr

Table 6.9: Comparison of additional area to existing exchanger

Additional exchanger area (m2) Exchanger No.

Existing area (m2) New

approach Energy based

approach Network pinch

approach 1 333.6 - - 132.1 2 316.9 249.5 - 111.3 4 1.7 340.8 4.5 345.6 5 147.9 287.7 - 407.8 7 313.5 94.7 - 186.4 9 15.8 14.5 7.2 35.0 10 37.7 30.3 15.2 69.4 11 19.0 - 0.4 - 13 254.6 147.9 - 162.4

16hu 25.2 6.4 16.8 - 17hu 14.2 - 7.4 - 18 28.8 167.5 19.8 151.2

19hu 4.6 5.7 6.3 - 21hu 6.8 - 6.5 - 22 159.9 49.2 43.9 42.0

26cu 117.6 13.2 7.1 24.7 27cu 233.3 - 1.1 -

Total (m2) 1407.3 136.1 1668.0 hu: hot utility exchanger; cu: cold utility exchanger

The comparison in Table 6.8 shows that the new approach achieves better results

compared with previous approaches. The optimum unit of the new approach has energy

consumption 76.4 MW, with saving of 22.6 MW of hot utility, versus 6.3 MW for


157

energy-based approach and 15.9 MW for network pinch. There is large saving of 3.7

MM$/yr in the operating costs of the new approach. However, the best of the previous

approaches (i.e. network pinch) saves only 2.0 MM$/yr of operating costs. Besides, the

total additional areas required by the new approach are smaller than those obtained from

network pinch.

In conclusion, the new retrofit approach demonstrates significant benefits over previous

approaches. Larger energy and higher operating cost savings are achieved with

inexpensive modifications. Relatively, low payback time is required.

6.2. Increasing profit of an existing atmospheric crude oil distillation tower

In this case study, the profit of an existing crude oil distillation unit is to be increased.

The existing unit of this problem is that crude oil atmospheric distillation tower for

which details, including crude oil assay data, distillation column configuration and

operating conditions, number of existing stages, column diameters, and existing

exchanger network, are given in Section 6.1. The existing distillation column is that

shown previously in Figure 6.1, while the associated heat exchanger network can be

seen in Figure 6.2. Table 6.10 summarises the income from the products, cost of the

crude oil feed and the operating costs of the existing crude oil distillation unit. The

current net profit of the existing unit is 365.1 MM$/yr.

The purpose of this case study is then to maximise the process profit, i.e. the expenses

of the operating and modification costs and the price of the crude oil feed less the value

of the products. The unit cost of the distillation tower products and the crude oil feed are

given in Table D.1 (Appendix D).

The retrofit approach is applied to the existing system. The existing design of the

distillation tower and details of the existing heat exchanger network are fixed. The

operating conditions of the distillation process are changed to maximise the process

profit. Due to the optimisation, the product flow rates will change, for the same crude

oil feed flow rate, to increase the products value, while the process expenses decrease.

Therefore, the net profit, i.e. products value less expenses, will increase.


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Table 6.10: Products income and operating costs of existing unit

Stream Production capacity (bbl/d)

Products value/feed cost (MM$/yr)

Crude oil feed 102,021 604.737 Light naphtha (LN) 12,102 220.778 Heavy naphtha (HN) 11,801 202.614 Light distillate (LD) 22,133 212.464 Heavy distillate (HD) 6,841 64.676 Residue (RES) 49,143 286.279 Cost and profit Operating* costs (MM$/yr) 17.018 Net+ profit (MM$/yr) 365.056

*: utility consumption and stripping steam; +: income from products - operating costs - feed cost

The optimisation results are summarised in Table 6.11, including the optimum product

flow rates, operating costs, capital investment and the net profit. Table 6.11 shows that

the net profit of the optimum unit increases by about 6.7 MM$/yr above the profit of the

existing unit. The capital investment required by the modifications to the HEN is 0.4

MM$/yr. The exchanger network modifications are additional area to existing units and

relocating an existing exchanger, as shown in Figure D.1 (Appendix D); the detailed

additional area to exchanger units and the associated capital costs can be seen in Table

D.2 (Appendix D). The modification costs are for the capital costs of the additional area

to the existing HEN, since the cost of the relocation modification is not expected to be

significant (see Figure 6.4). The new product flow rates distribution shows that the

residue flow rate increases by 4.36%; and the light and heavy distillate flow rates

decrease by 0.90% and 1.30% respectively, based on the crude oil feed volumetric flow

rate. It is clear also from Table 6.11 that the flow rates of both the light and heavy

naphtha products do not change significantly with respect to the existing unit flow rates.

At the optimum operating conditions, the product flow rates allow the products value to

increase relative to that of the existing unit. The optimum operating conditions for

maximum profit are summarised in Table 6.12. Table D.3 (Appendix D) provides the

stream data of the optimum unit. As the product flow rates distribution changes with

respect to the existing unit, the product key component recoveries change. Table D.4

(Appendix D) provides the new recoveries of the product key components compared

with those of the existing unit.


159

Table 6.11: Retrofit results of optimum unit with maximum profit

Stream Production capacity (bbl/d)

Products value (MM$/yr)

Light naphtha (LN) 12,100 220.736 Heavy naphtha (HN) 11,775 202.179 Light distillate (LD) 21,217 203.674 Heavy distillate (HD) 5,512 52.105 Residue (RES) 53,588 312.171 Energy and cost savings Operating* costs (MM$/yr) 14.386 Energy consumption (MW) 83.7 HEN additional area (m2) 1120.6 Capital+ investment (MM$) 0.384 Net++ profit (MM$/yr) 371.741 *: utility consumption and stripping steam costs; +: HEN additional area costs; ++: products value - operating costs - feed cost

Table 6.12: Optimum values of operating conditions for maximum profit

Column Optimisation variable Optimum value

Base case value

Feed preheating temperature oC 343.90 365.00 Flow rate of pump-around liquid kmol/h 1285.21 2186.56 Temperature drop across pump-around oC 33.99 30.00

1


2


3


kmol/h 1037.57 1067.48


The results obtained were based on given prices of the product and crude oil feed (Table

D.1, Appendix D). Therefore, these results may not be optimum for any fluctuation

occurred in the prices, which are related to market needs. The study can then be

performed using different prices in order to find the best distribution of product flow

rates and optimum process changes that lead to maximum profit covering wide range of

unit prices. The study will result in different optimum operating conditions and

exchanger network modifications for the different product and crude oil prices

employed. The exchanger network modifications that can provide the process heating

requirements for all the different prices can be chosen. Therefore, the associated capital

cost will account for all capital costs required by the different product and crude oil

prices. On the other hand, the control system of the distillation column operation can


160

use different set points for the different optimum operating conditions obtained. Then,

the distillation column operator can select the operation set point according to the

refinery policy and any changes in the market demands and prices.

As seen in this case study, the net profit of existing crude oil distillation unit can be

increased by changing the product flow rates distribution through new process operating

conditions, for the same crude oil feed capacity. Note that in the previous case study, the

operating cost and energy consumption were reduced for the same crude oil feed

capacity and the same product flow rates distribution. The two cases are compared in

Table 6.13. The comparison shows that when the product flow rates of the existing

process are allowed to change, the net profit increases significantly compared with the

case in which the product flow rates are fixed. The net profit in this case increases by

6.7 MM$/yr, compared with 3.7 MM$/yr for the case of fixed product flow rates. As

would be expected, the energy savings are greater when the objective is energy

reduction (22.6 MW) than when the objective is profit improvement (15.7 MW).

Therefore, according to the strategy of the refinery operator and the market demands,

the most appropriate retrofit objective can be chosen.

Table 6.13: Comparison of energy reduction and increasing profit optimum cases


Energy reduction

Increasing profit

Income from products * MM$/yr 382.074 382.024 386.127 Total+ operating costs MM$/yr 17.018 13.323 14.386 Energy consumption MW 99.0 76.4 83.7 Additional exchanger area m2 - 1361.4 1120.6 HEN capital investment MM$ - 0.404 0.384 ∆Tmin oC 30.0 25.0 25.0 Net++ profit MM$/yr 365.056 368.701 371.741 Increase in net profit MM$/yr Base 3.645 6.685

*: products value - feed cost; +: utility and steam costs; ++: income from products - operating costs

The case study of this section illustrates that the new retrofit approach can be applied to

increase the profit of existing crude oil distillation process. The objective is achieved

with minor modifications and low payback time. This will allow the refinery to increase

the profit significantly from the crude oil distillation unit with inexpensive

modifications; this profit improvement takes into account the price changes of the oil

products. In addition, the refinery will have more operational options to be selected, i.e.

reduce energy demand or increase profit.


161

6.3. Increasing throughput of an existing atmospheric crude oil distillation tower

This section includes two parts for throughput enhancement of existing crude oil

distillation unit; the first is for increasing the current throughput by a given value, and

the second is for increasing the current capacity to the maximum. The maximum

capacity increase is the capacity that can be processed in the existing column without

requiring any structural modifications to the existing internals. A 20% increase in the

production capacity is set as an objective for the first part. In the second part, more

attention is paid to the hydraulic constraints of the existing distillation column to

evaluate the potential for increasing its capacity. Any enhancement retrofit project lies

within these two parts; however, in some situations, refineries aim to increase the

capacity above the maximum capacity. In this case, column modifications, such as

adding preflash unit or prefractionator column, or replacing column internals with

packing, are essential for debottlenecking purposes.

6.3.1. Increasing current throughput by 20% over base case capacity

The crude oil atmospheric distillation unit, presented in Section 6.1, is reconsidered in

this section as the base case. The data of the crude oil feed and the details of the existing

structures of distillation column and heat exchanger network are the same as those given

in Section 6.1 (Figures 6.1 and 6.2). The current process capacity is 100,000 barrels per

day of crude oil. Five products are obtained: light and heavy naphtha, light and heavy

distillate and residue. The operating conditions of the base case and the product flow

rates are summarised in Table E.1.1 (Appendix E). The details of the exchanger areas

and heat duties in the existing exchanger network are given in Table E.1.2 (Appendix

E); the process streams and utility data, including temperatures and enthalpy changes

are summarised in Table E.1.3 (Appendix E). Table 6.14 gives the energy consumption

of the existing unit, the operating costs and the current net profit. The current energy

consumption is 99.5 MW of hot utility; the total operating cost, including stripping

steam, is 17.093 MM$/yr. The existing exchanger network has total area of 3552.8 m2.

Table 6.15 provides the diameter required for separation in the base case, compared

with the actual column diameter.

The objective of the retrofit in this case study is to increase the current throughput of the


162

existing crude oil distillation unit by 20% (i.e. the new capacity is 120,000 bbl/d) and

reduce the associated operating cost per barrel of feed and minimise capital investment.

Table 6.14: Energy consumption, operating costs and profit for base case

Parameter Base case Hot utility consumption MW 99.5 Cold utility consumption MW 83.8 Furnace heat load MW 73.9 Crude oil temperature before furnace oC 196.8 Utility operating cost* MM$/yr 15.359 Stripping steam operating cost* MM$/yr 1.734 Total operating cost MM$/yr 17.093 Existing exchanger network total area m2 3552.8 ∆Tmin oC 30.0 Crude oil cost MM$/yr 604.737 Income from products MM$/yr 986.487 Net+ profit MM$/yr 364.657

+: products value - operating costs - feed cost *: utility and steam unit costs can be seen in Table C.1.2 and Table C.3.2 (Appendix C)

Table 6.15: Column diameter required for base case and actual diameter

Column diameter+ (m) Column section*

Base case Actual diameter

Main column section 1 3.63 5.50 Main column section 2 7.00 8.00 Main column section 3 6.78 8.00 Main column section 4 6.78 7.50 Main column section 5 6.12 7.00 Top side-stripper 2.37 3.00 Middle side-stripper 2.90 3.50 Bottom side-stripper 1.35 3.00

*: see Figure 6.1; +: model used for diameter calculations (Section 4.1.1, Chapter 4), tray spacing = 0.6 m, flooding factor = 0.7, down-comer area ratio = 0.12

The initial calculations of the 20% throughput increase result in the required diameter

given in Table 6.16. The calculation assumptions are given with Table 6.15. These

diameters are obtained by increasing the throughput of the base case by 20% using the

retrofit models presented in Chapter 3. The diameter results show that the operation of

the column with 20% increased capacity is feasible, i.e. the capacity increase is

achievable without any column bottlenecks. The flow rate of the stripping steam to the

main column and stripper, pump-around duties and liquid recycled at the top of the

column are also increased by 20% to yield the required product flow rates with the same

product specifications as those for the base case. The operating conditions of the unit


163

with 20% capacity increase, including product flow rates, pump-around duties and flow

rates, etc., are summarised in Table E.2.1 (Appendix E). The resultant requirements of

the existing exchangers, to meet the new heating and cooling demands, are summarised

in Table 6.17.

Table 6.16: Column diameter for 20% increase in throughput on base case

Column diameter (m) Column section*

20% Increase Actual diameter


*: see Figure 6.1

Table 6.17: Process requirements for 20% increase in throughput


20% Increase

Hot utility consumption MW 99.5 117.7 Cold utility consumption MW 83.8 98.9 Furnace heat load MW 73.9 86.6 Utility operating cost MM$/yr 15.359 18.180 Stripping steam* flow rate kmol/h 1450.0 1740.0 Stripping steam operating cost MM$/yr 1.734 2.081 Additional exchanger area m2 - 776.2 HEN capital investment MM$ - 0.274 ∆Tmin oC 30.0 30.0 Total operating cost MM$/yr 17.093 20.261 Crude oil cost MM$/yr 604.737 725.684 Income from products MM$/yr 986.487 1,183.467 Net+ profit MM$/yr 364.657 437.522

*: including steam to main column and stripper (see Figure 6.1) +: products value - operating costs - feed cost

As stated above, the required diameter for 20% increase in the throughput of the crude

oil distillation tower does not exceed the existing column diameter. However, if there is

violation of the existing diameter, the column will be bottlenecked. In this case, the

operating conditions of the distillation column need to be modified, as discussed in

Section 5.3 (Chapter 5), to remove the hydraulic bottlenecks. This may include

changing the pump-around temperature drop, flow rates of steam or recycled liquid in a


164

pump-around, or reflux ratio or the feed preheating temperature.

Table 6.17 shows that the distillation unit with 20% increase in capacity consumes

energy of 117.7 MW, with an increase of about 20% over the current energy capacity.

The heat load in the furnace also increases from 73.9 MW for the base case to 86.6

MW. This however may lead to energy bottleneck in the fired heater, if it currently

operates at the maximum heat load. Then, the study in this case becomes energy

debottlenecking and throughput enhancement. In this study, it will be assumed that the

maximum furnace duty is 73.9 MW. The exchanger units that need to be modified are

shown in Figure E.2.1 (Appendix E); the associated additional area and capital costs can

be seen in Table E.2.2 (Appendix E). The temperatures and enthalpy changes of the

exchanger network process streams are given in Table E.2.3 (Appendix E).

The retrofit approach can now be applied to the new case of 20% increased capacity.

The case then becomes similar to the case study considered in Section 6.1; some key

differences will be discussed later.

As the throughput of the distillation column increases, the product and internal stream

flow rates change significantly compared to the base case flow rates. The retrofit model

obtained in Section 6.1, for the existing heat exchanger network cannot be used for the

optimisation of the unit with 20% increased capacity. The reason is that this model,

however, accounts for the details of the existing heat exchanger network, it is obtained

for the stream data (i.e. temperatures, flow rates and enthalpy changes) of the existing

unit and not the enhanced unit. The model therefore does not account accurately for the

area and energy requirements for the unit with 20% increase in throughput; thus, a new

retrofit model needs to be obtained.

The retrofit model of the HEN unit with 20% enhanced throughput is obtained by

applying the procedure of Section 4.2 (Chapter 4). Details of the retrofit study

performed are summarised in Appendix E.3. The retrofit model parameters m and c (see

equation 4.7, Chapter 4), are calculated from data regression (Figure E.3.1, Appendix

E), and their values are 1.002176·108 and -2.1016 respectively, where the units of area

are m2 and of energy consumption are MW. Figure E.3.6 (Appendix E) compares the

retrofit model of the enhanced throughput with that of the existing unit. As seen in

Figure E.3.6 (Appendix E), the retrofit model of the existing unit underestimates the

retrofit area required for the unit with 20% increase in capacity.


165

Now, the existing designs of the distillation column and the associated heat exchanger

network are fixed, the operating conditions of the distillation column, with 20%

increase in throughput, are optimised simultaneously to minimise the total annualised

cost of the utility consumption, stripping steam and the exchanger network

modifications. Throughout the optimisation, the existing column diameter is fixed. The

results of energy consumption, operating costs, profit, etc., are summarised in Table

6.18. The optimum values of the optimisation variables are given in Table 6.19.

Operating conditions of the optimum unit are detailed in Table E.4.1 (Appendix E). The

optimum modified exchanger network is shown in Figure E.4.1 (Appendix E); network

modifications are additional area and relocating an existing exchanger unit. The

additional area and the associated capital costs are detailed in Table E.4.2 (Appendix E).

Table E.4.3 (Appendix E) summarises the supply and target temperatures, and enthalpy

changes of the process streams.

Table 6.18: Optimisation results for optimum unit with 20% increase in capacity

Parameter Base case

20% Increase

20% Increase optimum

Hot utility consumption MW 99.5 117.7 87.8 Cold utility consumption MW 83.8 98.9 26.9 Furnace heat load MW 73.9 86.6 72.9 Utility operating cost MM$/yr 15.359 18.180 13.540 Stripping steam cost MM$/yr 1.734 2.081 1.699 Stripping steam flow rate kmol/h 1450.0 1740.0 1419.4 Additional exchanger area m2 - 776.2 1756.9 HEN capital investment MM$ - 0.274 0.468 Total operating cost MM$/yr 17.093 20.261 15.239 ∆Tmin oC 30.0 30.0 30.0 Crude oil cost MM$/yr 604.737 725.684 725.684 Income form products MM$/yr 986.487 1,183.467 1,184,569 Net+ profit MM$/yr 364.657 437.522 443.667 Increase in net profit MM$/yr Base 73.065 79.010

u1tility and steam unit and area costs can be seen in Table C.1.2 and Table C.3.2 (Appendix C) +: products value - operating costs - feed cost

The column diameter calculated for the separation after optimisation is given in Table

6.20, and compared with the actual column diameter. It is clear that the operation of the

optimum unit with 20% increase in capacity is feasible, because the required diameter

for separation from optimisation is less than the actual column diameter.

The values of the optimisation variables in Table 6.19 indicate that the optimum feed

temperature is about 4 oC hotter than the base case temperature. This creates more


166

opportunities to recover heat from process hot streams. Also, the hottest pump-around

will operate optimally at 302.7 oC, compared with 300.2 oC for the base case (100,000

bbl/d). This improves slightly the quality of the pump-around heat source. Similarly, the

reduced liquid flow rate between columns 3 and 4 reduces the heat loads in the reboilers

of these columns. As overall result, the duties of the mentioned reboilers are 6.6 and 3.5

MW respectively for the optimum unit, compared to 11.3 and 8.1 MW for the base case

(100,000 bbl/d). These process changes lead to increase in the heat recovery in the

existing HEN.

Table 6.19: Optimum values of operating conditions for 20% capacity increase

Column Optimisation variable Optimum value

Base case value


1


2


3


kmol/h 500.14 1082.16


Table 6.20: Column diameter for optimum unit with 20% capacity increase

Column section* Optimum unit with 20% capacity increase (m)

Actual unit diameter (m)


*: see Figure 6.1

As seen in Table 6.18, after optimising the process with 20% increased throughput, the

energy consumption reduces from 117.7 MW to 87.8 MW with significant savings of

energy consumption and operating costs. The results show that the furnace of the

optimum enhanced unit still operates below the maximum load; therefore, there is no

energy bottleneck for the 20% capacity increase as was the case before optimisation.


167

The operating cost savings of the optimum enhanced unit based on the base case (i.e.

before throughput enhancement) are 1.9 MM$/yr, with capital investment of 0.5 MM$.

Very short time is needed for paying the network modifications capitals back. The net

profit of the optimum unit with enhanced capacity is estimated to be 79.0 MM$/yr over

the base case profit. In addition to the cost savings and profit achieved using the

optimisation, the modifications to the existing exchanger network for the optimum unit

are minor compared to the case where the capacity of the base case is increased by 20%

without optimisation. The modifications in the case of the optimum unit are additional

area to 17 exchanger units and relocating one existing unit (Figure E.2.1, Appendix E).

However, without optimisation, 23 exchanger units need to be modified (Figure E.2.1,

Appendix E), in addition to modifying the furnace.

The above study illustrates that the new retrofit approach enables the existing unit to

increase its capacity and still save energy and reduce operating costs.

6.3.2. Increasing current throughput to maximum capacity

In this case study, the maximum throughput of an industrial existing crude oil

distillation unit is determined. The original objective of the project was to increase the

capacity by 16% over the current operation. For reasons of commercial sensitivity,

details of the case study cannot be reported.

The crude oil distillation unit is based on industrial data; Figure 6.5 shows the

atmospheric configuration of the crude oil distillation column. The column uses three

side-strippers and two pump-arounds, and separates the crude oil into naphtha,

kerosene, light gas oil (LGO), heavy gas oil (HGO) and residue. Stripping steam and

reboiler are used for heating in the bottom section; all strippers use steam. The existing

unit processes 130,000 barrels per day (2933 kmol/h) of crude oil. The crude oil feed is

mixture of two light and heavy oil feedstocks, and gas stream; the molar composition of

the mixture is 82.5, 15.8 and 1.7% respectively. The number of real stages and column

diameter are given in Table 6.21. The crude oil at 43 oC is heated in preheat train to a

temperature of 246 oC, and then in the fired heater to 330 oC. A sketch of the preheat

train is shown in Figure 6.6. The total hot utility consumption is 69.235 MW; the total

exchanger area is 14,302 m2.


168

HGO

Naphtha

Kerosene

LGO

Steam

Residue

TPA

MPA

FeedSteam

Steam

SteamTSS

MSS

BSS

2

1

3

4

5

Water

Figure 6.5: Existing crude oil distillation column (numbers refer to section

numbers)

Table 6.21: Number of stages and existing diameters of distillation tower sections

Column section* Parameter 1 2 3 4 5

Top SS Middle SS

Bottom SS

Number of real stages

9 13 4 4 3 4 4 4

Existing diameter (m)

5.33 5.64 5.64 5.64 4.42 1.83 1.83 1.83

*: see Figure 6.5, SS: side-stripper

The existing distillation column is simulated using rigorous simulation (HYSYS, 1999).

The simulated column diameter is shown in Figure 6.7, and compared with the actual

column diameter. The figure shows that there is potential for increasing the capacity of

the existing unit over the current operation. The ratio of the active area that is required

for separation in the current operation to the maximum area, which is available (actual

diameter), is then calculated. This ratio, which is known as FUA (Liu, 2000), is plotted

against the stage diameter (Figure 6.8). The maximum throughput that can be achieved

for the existing distillation unit is determined from the value of the maximum FUA

along the column. An FUA value of 1.0 indicates that the column diameter required for

separation reaches the actual diameter. By analogy, the maximum value of FUA of the

current column operation is inversely proportional to the maximum capacity that can be


169

increased in the existing unit. From Figure 6.7, the maximum FUA value is 0.734.

Then, the maximum throughput of the existing unit is estimated to be 36% (≈ 1/0.734)

over the current operation. This means that the current capacity of the existing

distillation column can be increased by up to 36% without requiring any

debottlenecking modifications. However, if the purpose is to increase the capacity over

the maximum value, the distillation column will be bottlenecked; therefore, column

modifications will be of importance.

Figure 6.6: Preheat train structure of existing crude oil distillation unit

Using the rigorous simulation, the current capacity of the existing distillation unit is

increased by 36%; the column simulated diameter is then plotted with the actual

diameter in Figure 6.9. The figure shows that the diameter required for separation of the

36% enhanced capacity is equal to the actual column diameter on some stages. This

indicates that the enhanced unit operates on the actual diameter. It is also shown in

Figure 6.9 that the simulated column diameter for 16% (original objective) increase in

the current capacity is smaller than the actual diameter; therefore, the operation of 16%

increased capacity is viable.


170

2.02.53.03.54.04.55.05.56.06.57.0

0 5 10 15 20 25 30 35

Stage number

Stag

e di

amet

er (m

)

current operation

Actual diameter

Figure 6.7: Simulated stage diameter for current operation (stages from top

of column to bottom) (tray sizing using HYSYS)

0.100.200.300.400.500.600.700.800.901.00

0 5 10 15 20 25 30 35

Stage number

FUA

Figure 6.8: FUA plot for existing distillation column (column stages: top-

bottom)


171

2.02.53.03.54.04.55.05.56.06.57.0

0 5 10 15 20 25 30 35

Stage number

Stag

e di

amet

er (m

)current operation

Actual diameter 36%

16%

Figure 6.9: Simulated stage diameter for maximum capacity increase (stages

from column top-down) (tray sizing using HYSYS)

After determining the maximum capacity, the retrofit approach can be applied to the

enhanced unit for either reducing energy consumption and costs or increasing process

profit. The approach can also be applied to the existing unit with 16% capacity increase.

In these cases, the retrofit model will be obtained for the existing heat exchanger

network. Then, the operating conditions are optimised simultaneously for the fixed

designs of distillation column and preheat train to achieve the specified retrofit

objective. As mentioned above, the throughput of the existing unit can be increased

above the maximum value. In this case, column modifications are required to remove

column bottlenecks. Then, the retrofit approach is applied to the modified unit.

The case studies presented in this section show that the retrofit approach can be applied

for increasing the throughput of an existing unit. Large savings in operating costs and

reduction in energy consumption are obtained for inexpensive modifications and with

low payback time.

6.4. Reducing CO2 emissions from an existing crude oil distillation unit

In the previous case studies, attention was paid to energy and cost savings, profit

increase and capacity enhancement. However in some situations, environmental issues


172

are of great concern. The purpose in this case study is to decrease carbon dioxide

emissions (atmospheric pollution) from an existing crude oil distillation unit. The base

case is the same atmospheric crude oil tower for which data are presented in Section

6.1.1. Two hot utilities, flue gases and HP steam, are used for the heating purposes in

the heat exchanger network. Data of the hot utilities, unit costs and heat loads are given

in Table F.1.1 (Appendix F).

The current flow rates of CO2 emissions from the base case are calculated from equation

4.25 (Chapter 4), and are summarised in Table 6.22. The heating fuel used in the heat

exchanger network is heavy fuel oil; the net heating value and carbon content are given

in Table F.1.2 (Appendix F). The total CO2 emissions are calculated from the utility

steam boiler and the furnace.

Table 6.22: CO2 emissions from base case of crude oil distillation tower

Parameter Existing unit Total hot utility consumption MW 99.0 Utility steam heat load MW 9.375 Flue gas heat load MW 80.281 Stripping steam heat load MW 9.344 CO2 emissions from steam boiler kg/h 4,155 CO2 emissions from flue gas kg/h 25,632 CO2 emissions from stripping steam kg/h 2,983 Total CO2 emissions kg/h 32,766 Crude oil temperature before furnace oC 226.5 Stripping steam flow rate kmol/h 1450.0 Total operating cost* MM$/yr 16.263 ∆Tmin in existing HEN oC 30.0

*: including utilities and steam; unit costs can be seen in Table F.1.1 (Appendix F)

Note that part of the heat load required by the stripping steam is provided by the flue

gas in the furnace; the other part is provided by the process streams. The reason is that

the stripping steam is produced at the refinery site from the heat recovered from the

distillation process and heat supplied by flue gas. Therefore, the heat load on the flue

gas includes that load required by the stripping steam. So, the emissions from steam

heat loads are calculated in similar way to those from flue gas.

Table 6.22 indicates that the existing unit produces total emissions of 32,766 kg/h from

the furnace and steam boiler. The retrofit approach is now applied to optimise the

operating conditions of the existing crude oil distillation tower to reduce the carbon

dioxide emissions. Integration of a gas turbine with the process furnace is considered as


173

a design option in this context. The objective is to minimise the total annualised cost of

utility consumption, stripping steam, carbon tax and the capital costs of the gas turbine

and network modifications, less the value of the power generated by the gas turbine. A

carbon tax of 15 $/ton of CO2 is assumed (Chew, 2001; p. 101). Heavy fuel oil is

utilised in the steam boiler and central power station, while natural gas is burned in the

central power station. A summary of the retrofit approach results is presented in Table

6.23. Details of the results and the economic parameters can be seen in Tables F.2.1 and

F.2.2 (Appendix F).

Table 6.23: CO2 emissions from optimised unit with integrated gas turbine

Parameter Optimum unit Total energy consumption MW 79.780 Utility steam heat load MW 6.087 Flue gas total heat load# MW 73.693 Heat load on gas turbine MW 15.588 Heat load on furnace MW 58.105 CO2 emissions from steam boiler kg/h 2,698 CO2 emissions from gas turbine kg/h 9,092 CO2 emissions from furnace kg/h 18,552 Total local CO2 emissions kg/h 30,342 CO2 emissions saved at power station kg/h -13,411 Total global CO2 emissions kg/h 16,931 Power generated in gas turbine MW 14.0 Capital cost of gas turbine MM$ 5.261 Value of power generated MM$/yr 3.920 Stripping steam flow rate kmol/h 1255.0 Total operating costs* MM$/yr 13.232 HEN capital investment MM$ 0.429 Crude oil temperature before furnace oC 230.7 Total operating cost saving+ MM$/yr 6.951 Total capital investment MM$ 5.690 Payback yr 0.82 ∆Tmin in existing HEN oC 25.0

#: including stripping steam load; *: including utilities and steam +: including value of power generated

Note that when the gas turbine is integrated, the initial total heat load of 73.693 MW in

the furnace, which includes stripping steam load, is distributed into two parts. The gas

turbine provides the first part of 5.588 MW; then, the furnace supplies the remaining

part of 58.105 MW.

As seen in Table 6.23, the total carbon dioxide reduces in the refinery site from 32,766

kg/h for the base case to 30,342 kg/h for the optimum unit with gas turbine, with


174

emissions reduction of 7.4%. This reduction, however, seems little, but the emissions of

the central power station will be reduced by 13,411 kg/h of CO2 because of the decrease

in the power demand of the site. This leads to considerable reduction of 48.3% of the

emissions globally, when the power station is considered at the same time. In addition to

the emissions reduction, the refinery can increase profit by producing electricity of 14.0

MW. This power can be consumed at the refinery site or exported to other consumers.

The total operating cost savings, including the value of generated power, are about 7.0

MM$/yr. Less than a year is required to pay back the capital investment required for the

gas turbine and exchanger network modifications. The exchanger network modifications

required are additional area and relocation of one existing unit.

The optimum values of the operating conditions, obtained from the retrofit approach,

are given in Table F.2.3 (Appendix F). Details of the additional exchanger area,

modified network and the process streams of the optimum unit are summarised in Table

F.2.4, Figure F.2.1, and Table F.2.5 (Appendix F) respectively.

The results of the optimisation variables (Table F.2.3, Appendix F) indicate that by

increasing the feed temperature by about 5 oC, reducing the steam flow rates and

recycling more hot liquid through the pump-arounds, the existing crude oil distillation

system is able to reduce the emissions locally by 7% and by 48% overall.

As studied above, CO2 emissions from existing crude oil distillation unit are reduced by

applying the retrofit approach. The emissions reduction is greatest when a gas turbine is

integrated with the process furnace. If the gas turbine is not to be integrated, the retrofit

approach can also be applied. The objective in this case will be to minimise the total

annualised cost of utility consumption, stripping steam, carbon tax and the exchanger

network modifications. The approach results are summarised in Table 6.24. The details

of the modifications required to the existing exchanger network, additional area and

capital cost, optimum operating conditions and the process stream data can be seen in

Appendix F.3.

The results in Table 6.24 illustrate that the emissions from the existing unit can be

reduced by 22% without considering gas turbine, versus 7% (locally) when gas turbine

is integrated. Savings in operating costs and energy consumption are also attained for

little capital investment and with low payback time. However, the gas turbine leads to

significant global reduction of 48% in the emissions, with big income from generating


175

electricity at the refinery site. Note that the two scenarios for emissions reduction save

approximately the same amount of energy; this is because that the energy costs are

dominant in this problem.

Table 6.24: CO2 emissions from optimum unit without integrated gas turbine


Optimum unit

Optimum unit with GT

Energy consumption MW 99.0 78.020 79.780 Utility steam heat load MW 9.375 6.157 6.087 Flue gas heat load MW 80.281 71.873 73.693 Total CO2 emissions kg/h 32,766 25,677 30,342 Total global CO2 emissions kg/h 32,766 25,677 16,931 Total operating costs MM$/yr 16.263 12.976 9.312* HEN additional area m2 - 1239.2 1459.4 Total capital investment MM$ - 0.379 5.690+ Payback yr - 0.12 0.82 ∆Tmin in existing HEN oC 30.0 25.0 25.0

*: including value of power generated; +: including cost of gas turbine

The optimum results recommend that a gas turbine is to be integrated when the

emissions in both the refinery and central power station are of interest. On the other

hand, the existing configuration can also be optimised to reduce emissions, without

changing the infrastructure of the existing heating system (i.e. the furnace).

The cases studied in this section illustrate that the carbon dioxide emissions of an

existing crude oil distillation unit can be reduced significantly by changing the operating

conditions of the distillation process. Integrating a gas turbine with the existing process

is explored for further reduction in the emissions and site profit flexibility.

6.5. Changing product yields of an existing crude oil distillation unit

This case study accounts for the changes in product yields of an existing crude oil

distillation unit; the study is based on data presented in Suphanit (1999; p. 128). The

base case of the study is the atmospheric crude oil tower and the associated heat

exchanger network presented in Section 6.1. The existing exchanger network has a

structural constraint that only additional area is allowed and any topology modifications

are forbidden. The retrofit goal is to increase the product yield of heavy distillate and

reduce the yield of light distillate in order to meet the changes in the market demand.

The yields of the light and heavy naphtha and residue products are kept fixed. The


176

objective is preferably achieved with low energy consumption and operating costs and

little capital investment. The product yields of the base case are given in Table 6.25.

Key components of the separation of the products for the base case can be seen in Table

6.2 (Section 6.1).

The study aims specifically at increasing the heavy distillate yield of the existing unit by

70%, i.e. the new flow rate of the heavy distillate is 1.70 times that of the base case

(heavy distillate) flow rate. This can be done by changing the key components of the

separation between light distillate and heavy distillate, as shown in Table 6.26. The

operating conditions of the distillation column also need to be changed to obtain the

required product yields. This step is performed using the shortcut models of Chapter 3,

in which the existing design is fixed and the key components are specified. Then, the

operating conditions are changed from the base case values to achieve the required

yields. Results of the operating conditions of the modified unit are summarised in Table

G.1 (Appendix G), compared with the base case values. The product yields obtained

from the new unit are given in Table 6.27.

Table 6.25: Product yields of base case

Product Base case yield (kmol/h)

Light naphtha (LN) 680.7 Heavy naphtha (HN) 493.5 Light distillate (LD) 652.8 Heavy distillate (HD) 149.8 Residue (RES) 633.9

Table 6.26: Key components of separation for product yield changes to increase HD

LK/HK Separation between products Base case New product yield

LN/HN 4/6 4/6 HN/LD 7/9 7/9 LD/HD 11/14 11/13 HD/RES 13/16 13/16

LK: light key component; HK: heavy key; total number of components = 25

The existing unit with new product yields has different heating and cooling demands

from those of the base case, because the process streams have different temperatures,

enthalpy changes and flow rates. Therefore, the existing exchanger network needs to be

modified to provide the new heating and cooling requirements. The energy


177

consumption, and area and capital cost requirements are listed in Table 6.28. As shown,

the energy consumption of the new unit with product yield is nearly the same as that of

the existing unit. The operating costs of the modified unit are slightly higher than the

operating cost of the existing unit. Additional exchanger area is required. Details of the

additional area and capital costs are given in Table G.2 (Appendix G); data of new

process streams are in Table G.3 (Appendix G).

Table 6.27: Product yields of new unit with yield changes

Product Base case yields (kmol/h)

New product yields (kmol/h)

Change (%)*

Light naphtha (LN) 680.7 679.55 ≈ 0 Heavy naphtha (HN) 493.5 498.17 + 0.9 Light distillate (LD) 652.8 545.06 - 6.5 Heavy distillate (HD) 149.8 254.05 + 69.6 Residue (RES) 633.9 633.87 ≈ 0

*: relative to product yield in base case

Table 6.28: Energy consumption and operating costs for new product yield units


New product

yield

Optimum unit

Energy consumption MW 99.0 98.8 90.8 Utility operating cost MM$/yr 15.284 15.266 14.016 Stripping steam flow rate kmol/h 1450 1800 1657 Stripping steam operating cost MM$/yr 1.734 2.153 1.981 Additional exchanger area m2 - 264.8 235.9 HEN capital investment MM$ - 0.140 0.115 Total operating cost MM$/yr 17.018 17.559 16.112 ∆Tmin oC 30.0 30.0 30.0 Payback yr Base - 0.13 utility and steam unit and area costs can be seen in Table C.1.2 and Table C.3.2 (Appendix C)

The existing unit with new product yields requires some additional area, although the

utility consumption is nearly the same as that of the base case. It is preferred if the

objective is achieved with minimum capital investment and with energy cost savings if

possible.

The retrofit optimisation approach can then be applied to the unit with new product

yield to reduce the energy consumption and operating costs. The existing design of the

distillation column and exchanger network are fixed; all operating conditions are

optimised to minimise the operating costs and capital investment. Ideally, the retrofit

model of the exchanger network needs to be obtained for the new process conditions;

however, the retrofit model obtained for the existing unit (see Section 6.1) can


178

approximately be used since it accounts for the same structure of the existing exchanger

network. This will allow the retrofit goal to be studied without the need of analysing the

unit with new product yields and obtaining a new HEN retrofit model. The network

structural constraints can be applied during the optimisation. The structural constraints

will be related to the energy consumption level of the retrofitted unit; therefore, they can

be systematically imposed as constraints in terms of the energy consumption levels

during the optimisation. Alternatively, they can be taken into account in the detailed

retrofit analysis of the optimum exchanger network, which is the case used in this case

study.

The results of the retrofit approach are obtained for reducing the energy consumption

and the operating costs of the unit with new product yields. The results are shown in

Table 6.28, and are compared with those of the unit with new product yield (before

optimisation). Table 6.28 includes three different cases, existing unit or base case,

modified unit with new product yields but not optimised and optimised unit with new

product yields. The optimum operating conditions are given in Table G.4 (Appendix G),

and compared with those of the base case and the unit with new product yields (i.e. not

optimum). Results of exchanger area modifications and costs, and new process stream

data are summarised in Tables G.5 and G.6 (Appendix G).

The results of the optimisation (Table 6.28) indicate that the energy demand of the

optimum unit with new product yields reduces to 90.8 MW, with a saving of 8.0 MW

with respect to the unoptimised design with new product yields. Consequently, the

operating costs are reduced by 1.5 MM$/yr. The additional exchanger area is also

reduced. Very low payback time is therefore needed.

As illustrated in this case study, the retrofit approach can be applied to reduce the

energy demand and the operating costs for changing product yields of an existing crude

oil distillation unit. The approach achieves the retrofit objective with lower energy

consumption and less operating and capital costs than if the base case is retrofitted

without optimisation. In a similar manner, the approach can be applied to the existing

unit for any other product yield changes, for instance, if the yield of light distillate is to

be increased. In addition, the approach can be applied for other retrofit objectives such

as reducing emissions or increasing capacity.

Although the approach in this study is applied to a unit with new product yields, it can


179

be similarly applied to an existing unit with any new process conditions. These process

conditions may include changes to the pump-around flow rates and duties for

debottlenecking, or changes to the feed preheating conditions, or for processing new

crude oil feedstock. For these cases, the existing unit is first simulated using the models

of Chapter 3, for the new process conditions. Then, the retrofit model is obtained for the

modified exchanger network with the new process conditions. The retrofit approach can

thus be applied for reducing utility costs or for any other retrofit objectives.

6.6. Installing preflash drum or prefractionator column in existing crude oil distillation unit

In the studies presented so far, retrofit was carried out by exploiting the existing column

equipment without considering the benefits of changing the existing structure. In this

section, the structure of the existing distillation process is modified for large and diverse

benefits.

The section accounts for modifying the structure of the existing crude oil distillation

process for reducing energy consumption and increasing tower capacity and process

profit, or producing different product flow rate distribution (e.g. increase light naphtha

yield). This includes the installation of a preflash drum or prefractionator column

upstream to an existing crude oil distillation unit.

6.6.1. Installing a preflash drum for reducing energy consumption and increasing capacity

In this case study, a preflash drum is added to the existing crude oil distillation process

presented in Section 6.1. Different retrofit goals will be considered in this situation. The

base case is the atmospheric crude oil distillation column and the associated heat

exchanger network, the existing design details and specifications of which are those

given in Section 6.1 and Appendix C.

First, we consider the addition of a preflash drum to reduce energy consumption and

operating costs of the existing crude oil unit. The preflash drum is placed somewhere in

the preheat train, before the furnace. The location of the preflash is determined

according to the temperature of the preflash. As the base case, the preflash temperature

is assumed to be 190 oC. As discussed in Chapter 4, there are four locations in which the


180

preflash vapours may enter the main distillation column; we consider that, in this case,

the vapour enters at the bottom of the column. This means that the vapour is mixed with

the hot crude oil from the furnace, before entering the column.

The shortcut models presented in Chapters 3 and 4 are employed to simulate the

existing unit with a preflash drum (base case with preflash). The existing column design

is fixed; the preflash variables are calculated (see Section 4.3, Chapter 4). Then, the

main tower product flow rates, temperatures and compositions, and heat duties are

calculated. Also, the energy requirements for the new process conditions are evaluated

for the associated heat exchanger network. Results of product flow rates, energy

requirements and network modifications are summarised in Table 6.29. Note that the

base case unit with preflash drum is not optimised; this means that the operating

conditions of the base case are fixed.

Table 6.29 shows that the installed preflash drum reduces the energy consumption by

3.0 MW compared with the base case. Little time is required as a payback the capital

cost of the additional exchanger areas. In addition, no significant changes are observed

in the product flow rates obtained from the unit with preflash drum. Data of the process

streams of the new conditions and details of exchanger additional area and costs are

given in Tables H.1 and H.2 (Appendix H).

Table 6.29: Retrofit results for base case with preflash

Parameter Existing unit Base case with preflash

Light naphtha flow rate kmol/h 680.69 681.05 Heavy naphtha flow rate kmol/h 493.45 493.28 Light distillate flow rate kmol/h 652.84 641.00 Heavy distillate flow rate kmol/h 149.82 137.06 Residue flow rate kmol/h 633.89 658.31 Crude oil preheating duty (from 25 to 365 oC)

MW 154.50 150.30

Energy consumption MW 99.0 96.0 Utility operating cost MM$/yr 15.284 14.514 Stripping steam operating cost MM$/yr 1.734 1.734 Additional exchanger area m2 - 114.8* HEN capital investment MM$ - 0.077 Preflash capital cost# MM$ - 0.114+ Total operating cost MM$/yr 17.018 16.248 ∆Tmin oC 30.0 30.0 Payback yr Base 0.25

utility and steam unit and area costs can be seen in Table C.1.2 and Table C.3.2 (Appendix C) *: additional area only, #: installation cost = 45% purchase cost (Peters and Timmerhaus, 1980)


181

+: diameter = 2.7 m; height = 15.1 m

The retrofit optimisation approach can now be applied to the existing unit with installed

preflash to reduce further the energy consumption and operating costs. The temperature

of the preflash can be optimised, together with all operating variables of the distillation

column. The optimum location of the preflash drum in the preheat train is determined

from the optimum preflash temperature. The existing exchanger network needs to be

reanalysed after installing the preflash drum to obtain the retrofit model; however, the

model obtained in Section 6.1 can still be used.

The retrofit approach optimises the preflash temperature, together with the operating

conditions of the distillation tower, to minimise the total annualised costs of the utility

consumption and stripping steam, and the capital costs of the preflash and network

modifications. The results are shown in Table 6.30.

The energy consumption of the optimum unit with preflash reduces to 89.8 MW, with

saving of operating costs of 1.5 MM$/yr, compared with the existing unit. The total

capital cost of exchanger area and preflash required is little, for which the payback time

required is 0.20 year. The optimum operating conditions, including the preflash

temperature, can be seen in Table H.3 (Appendix H). Note that not all the operating

conditions of the distillation tower are optimised; the reason is because this case is to

show the optimisation of the preflash temperature. Also, no topology changes are

considered. Therefore, the energy consumption could be reduced further by applying

structural changes. Details of process streams data, and additional area and costs, for the

optimum unit with preflash, are summarised in Tables H.4 and H.5 (Appendix H).

Table 6.30: Energy consumption and operating costs for optimum unit with preflash

Parameter Existing unit Optimum unit with preflash

Energy consumption MW 99.0 89.8 Utility operating cost MM$/yr 15.284 13.849 Stripping steam operating cost MM$/yr 1.734 1.698 Additional exchanger area m2 - 443.4* HEN capital investment MM$ - 0.155 Preflash capital cost MM$ - 0.114+ Total operating cost# MM$/yr 17.018 15.547 ∆Tmin oC 30.0 25.0 Payback yr - 0.20

utility and steam unit and area costs can be seen in Table C.1.2 and Table C.3.2 (Appendix C) *: additional area only, #: installation cost = 45% purchase cost (Peters and Timmerhaus, 1980) +: diameter = 2.8 m; height = 15.1 m


182

Figure 6.10 shows the temperature profile of the optimum unit with preflash. As shown,

the optimum temperature of preflash is 179 oC. The base case value before optimisation

was 190 oC; this indicates that for the given vapour location, the optimum conditions

favour the lower temperature of preflash vapour. This is due to the mixing of the

preflash vapour with the incoming crude oil from the furnace, which leads to lower

crude oil temperature before entering the column.

25o

226.6o

178.9o

365o

To column

Figure 6.10: Temperature profile for existing unit with preflash

As a result of installing preflash drum into the existing unit, the crude oil stream routing

needs to be altered; this means that repiping works are necessary. Figure 6.11 shows

that when preflash is added to the existing exchanger network, the crude oil feed stream

enters the set of exchanger units prior to the preflash drum. Then, the bottom liquid of

the preflash is preheated further to the required temperature. This is accompanied by

repiping some specific exchangers; repiping time and costs are incurred. Also,

additional area to some exchangers is necessary. Repipe works and time are related to

the temperature value of the preflash drum, and they change accordingly. Throughout

the studies of this section, the cost implications of repiping are not included due to the

lack of data. However, the data can easily be incorporated where available.

As mentioned in the above study, preflash vapour enters the distillation column at the

bottom section, i.e. vapour is mixed with crude oil feed. Changing the location of

feeding the vapour inside the column has different implications on the opportunities for

energy saving and increasing capacity, or changing the product flow rates. To find the

most beneficial location of preflash vapour for minimum energy costs, screening study


183

is necessary, in which all possible locations are tested. Then for each location, the

existing unit with installed preflash is optimised for minimising the total annualised cost

of operating and capital expenses. The location with the minimum total costs is then

selected.

1

1

1 5 7 9

1 3 2 6 8 1 04 1 24

1 13

1 5 7 9

1 3 2 6 8 1 04 1 24

1 13

Base case - crude oil stream routing

Unit with preflash - crude oil stream routing Figure 6.11: Crude oil stream routing in existing HEN with preflash

For increasing throughput, the hydraulic performance of the existing unit with preflash

and for given vapour location needs to be analysed, as seen in Section 6.3.2. This

determines the maximum capacity that can be processed in the existing unit with

preflash for given vapour location. Hence, the location, which shows maximum capacity

increase, can be chosen. Overall, the location that exhibits lower energy consumption

and more capacity can be explored.

Now, we consider another location of feeding the preflash vapour to the distillation

column, different from the location of the above case, in order to explore the effect on

energy consumption and product flow rate changes. The preflash vapour, in this case,

enters the main column at the middle section, as shown in Figure 6.12.

The base case temperature of the preflash is set to 195 oC. For this location, the existing

unit with preflash is first simulated using the shortcut models of Chapters 3 and 4. Since

the preflash vapour enters the distillation column at a higher section of the column, the

flow rates of the light products (i.e. light and heavy naphtha) are expected to increase.

Then, the preflash temperature, together with all operating conditions of the distillation

column, are optimised to minimise the total annualised costs of utility consumption,

stripping steam, exchanger network modifications and preflash capital cost. The


184

optimum unit energy consumption and operating costs are reported in Table 6.31, and

compared to the base case conditions. Results of the optimisation variables, product

flow rates, and the details of the additional exchanger area, capital costs and the process

streams of the optimum unit can be seen throughout Tables H.6 to H.9 (Appendix H).

1

2

3

4

Figure 6.12: Location of preflash vapour to enter main column

Table 6.31: Energy consumption and operating costs for optimum unit with preflash

Parameter Existing unit Optimum unit with preflash

Energy consumption MW 99.0 76.4 Utility operating cost MM$/yr 15.284 11.869 Stripping steam operating cost MM$/yr 1.734 1.584 Additional exchanger area m2 - 1916.5 HEN capital investment MM$ - 0.479 Preflash capital cost# MM$ - 0.112* Total operating cost MM$/yr 17.018 13.453 ∆Tmin oC 30.0 25.0 Payback yr - 0.17

utility and steam unit and area costs can be seen in Table C.1.2 and Table C.3.2 (Appendix C) *: diameter = 2.7 m; height = 14.9 m #: installation cost = 45% purchase cost (Peters and Timmerhaus, 1980)

The results given in Table 6.31 indicate that by optimising the preflash temperature and

the operating conditions of the distillation tower, the energy consumption is reduced

significantly by 22.6 MW of hot utility from the base case demand. The corresponding

savings of the base case operating costs, including steam, are 3.6 MM$/yr. The

optimum preflash temperature is 203.8 oC (see Figure 6.13). This high temperature

allows the separation of the light components from the crude oil (453.4 kmol/h of light

components), which are then fed to the upper sections of the distillation column. The


185

optimum modifications required by the existing exchanger networks are additional area

to some particular exchangers and relocating exchanger unit 8 (see Figure H.1,

Appendix H). Note that if the relocation modification is applied to exchanger unit

number 4, which is the beneficial modification of the case study of Section 6.1, the

energy consumption is 77.2 MW. The modifications have low cost expenses; the

payback time needed is 0.17 year. Table H.7 (Appendix H) indicates that the flow rate

of light naphtha product increases by 9.6% (by mole); similarly, the residue product

flow rate increases by 1.5%. The flow rates of the other products decrease (see Table

H.7, Appendix H). The changes in product flow rates have implications on the income

from products of the optimum unit. These implications need to be accounted for when

the objective is related to profit.

242.9o

203.8o

368.2o

To column

To column

25o

453.4kmol/h

2157.3kmol/h

2610.7kmol/h

Figure 6.13: Temperature profile for optimum unit with preflash

As seen in the above case, the location of preflash vapour affects the opportunities for

saving energy and yield of light products. Table 6.32 compares the results of the two

cases considered previously; case 1 indicates that the preflash vapour is mixed with the

crude oil before the distillation tower, and case 2 is for the unit where the preflash

vapour is entering the column at upper section.

Table 6.32: Comparison of results of unit with different locations of preflash vapours

Parameter Base case

Case 1 Case 2

Energy consumption MW 99.0 89.8 76.4 Reduction in energy consumption MW 9.2 22.6 Total operating cost# MM$/yr 17.018 15.547 13.453 Additional exchanger area m2 - 443.4* 1916.5+ HEN capital investment MM$ - 0.155 0.479 Preflash capital cost MM$ - 0.114 0.112 Saving in total operating cost MM$/yr 1.471 3.565 ∆Tmin oC 30.0 25.0 25.0 Payback yr - 0.20 0.17


186

#: includes utility and steam; *: additional area only; +: includes area for resequencing

The comparison (Table 6.32) shows that case 2 achieves better savings in total operating

costs, and more reduction in energy demands, compared to case 1. Note that in case 1,

topology changes to the existing exchanger network were not considered. As mentioned

previously, all possible locations of preflash vapours may be examined along with the

optimisation; then, the most beneficial configuration for energy and operating cost

savings can be selected.

Now, if case study 2 is compared against the case study presented in Section 6.1 since

both cases have the same objective, you may notice that the two cases achieve almost

the same energy saving for one topology modification. However, it was expected that

preflash drum will show more energy saving. The unexpected results arise from the fact

that the preflash temperature and the given network structure affect the energy saving

opportunities. When preflash is installed, there is one constraint added to the

opportunities for heat recovery. This constraint is the preflash temperature, which must

be fixed by the column process conditions and cannot be changed. Fixing the preflash

temperature may lead to poor heat recovery opportunities. On the other hand, when

there is no preflash installed, the existing HEN has more flexibility, i.e. no temperature

constraints; hence, the intermediate temperatures between the exchanger units can be

changed in order to maximise the heat recovery. We may conclude that the energy

saving chances for installed preflash drums are much dependent on the value of preflash

temperature and the existing network structure.

The cases considered in the present section, so far, reveal that the installation of preflash

drum in an existing crude oil distillation tower creates opportunities for reducing energy

consumption and operating costs. In addition, as mentioned previously, preflash drum

allows more capacity to be processed in the existing distillation tower. Now, we

consider this feature in more detail. Figure 6.14 shows the column diameters for the

base case and the existing unit with preflash drum, compared with the actual column

diameters. The preflash vapour in this case enters the upper section of the main

distillation column, as shown earlier in Figure 6.12. The column diameter for the base

case is obtained by simulating the existing unit using rigorous (HYSYS) simulator.

Similarly, for the unit with preflash and the vapour location mentioned above, the


187

modified unit is rigorously simulated; the preflash temperature is set to 200 oC. The

simulated stage diameters are then plotted versus the stage number, and compared with

the existing diameter.

2

3

4

5

6

7

8

9

0 10 20 30 40

Stage number

Stag

e di

amet

er (m

)

Base case

Unit with preflash

Existing diameter

Figure 6.14: Column diameter for base case and unit with preflash (stages from top to bottom of the column) (tray sizing using HYSYS)

As shown in Figure 6.14, the column diameter required for separation when a preflash is

installed is smaller than that required by the base case. The preflash, as mentioned

previously, reduces the vapour and liquid flow rates inside the column; therefore, the

required diameter is reduced. Figure 6.14 indicates that the unit with preflash drum can

process more throughput than the base case. The maximum capacity that can be

processed can be estimated by calculating the FUA throughout the column (see Section

6.3.2). The FUA values along the column are then plotted in Figure H.2 (Appendix H);

the maximum FUA value indicates that the capacity can be increased maximally by

38% over the base case capacity. On the other hand, the maximum diameter of the base

case (7.01 m on stage 31; versus 8.0 m actual diameter) shows that the capacity can be

increased by up to 30% over the base case. This means that an extra capacity increase of

8% can be processed by installing preflash to the existing column structure.

The crude oil distillation tower with preflash is then rigorously simulated with 38%

increase in capacity; the column required diameter is shown in Figure 6.15, compared


188

with the existing diameter. It is obvious that the required diameter for 38% capacity

increase is equal to the existing diameter on particular stages. This indicates that the

capacity cannot easily be increased above that value, i.e. 38% capacity increase

represents the maximum level that can be achieved. However, if the production capacity

is to be increased above that maximum value, column modifications in this case are

necessary.

2

3

4

5

6

7

8

9

0 5 10 15 20 25 30 35 40 45

Stage number

Stag

e di

amet

er (m

)

Existing diameterWith preflash_baseWith preflash_38%

Figure 6.15: Column diameter for 38% capacity increase of unit with preflash,

compared with existing unit and base case (stages: top-bottom)

The optimisation approach can then be applied to the unit with preflash and 38%

throughput increase. The operating conditions can be optimised in order to minimise the

total annualised costs. Other vapour locations may be explored for more capacity

increase.

In summary, the studies presented in this section demonstrate that preflash drums enable

existing crude oil distillation units to reduce energy consumption, save operating costs

and process more throughputs; hence, more profit is obtained. Although the section

concentrates on reducing energy consumption and increasing capacity, other objectives

such as increasing profit or reducing emissions can also be addressed.


189

6.6.2. Installing prefractionator column for reducing energy consumption and increasing capacity

In this case study, a prefractionator column is added to an existing crude oil distillation

unit to reduce utility consumption and operating costs, and enhance throughput. The

base case is the atmospheric crude oil tower and the associated heat recovery system

presented in Section 6.1.

When a new distillation column (prefractionator) is added upstream of the existing main

distillation tower, the first light product will be produced in this new column. Thus, in

this case, light naphtha is drawn from the prefractionator column. The existing

distillation column needs to be reconfigured due to that change. It is easier to block one

of the side-stripper draw lines than to keep all strippers operational because the latter

option requires a higher reflux ratio. Moreover, major changes to the existing heat

exchanger network, in this situation, are necessary because the number of process

streams increase by one stream over the existing unit. However, this option can still be

exploited in some situations to increase the production flow rate of the light naphtha

fraction, which will be produced in the prefractionator and the main column at the same

time. The location of the installed prefractionator column in the preheat train is

determined by the temperature of the feed to this column. Thus, the optimum location

will correspond to the optimum feed temperature.

Figure 6.16 shows one of the reconfiguration options that can be made when

prefractionator is added. This option indicates that the draw line of the uppermost

stripper is blocked; hence, the existing number of stages of section 2 will be added to

the stages of section 1. In addition, the duty of the top pump-around will be shifted to

the condenser of the main distillation column; i.e. the pump-around will replace the

condenser. The condenser of the main column will be used as the condenser of the

prefractionator column. If the pump-around is to be used in its location, an additional

exchanger unit will be required as a condenser for the main column. The redistributed

numbers of stages into the sections of the equivalent decomposed sequence of main

column are shown in Figure I.1 and Table I.1 (Appendix I). The prefractionator column

will produce the light naphtha as top product, while the main column will produce the

heavy naphtha, light distillate, heavy distillate and residue. The same product

specifications of the light naphtha obtained from the main column in the base case are


190

produced in the prefractionator column. Another possible reconfiguration option is to

shut down the middle stripper; the corresponding pump-around will operate as the main

condenser in this case. Consequently, the section between the upper and lower strippers

will include the stages of sections 2 and 3. Generally, repiping of the existing exchanger

network is necessary to accommodate the changes in the locations in the preheat train of

the product and side streams of the new process.

Water

Steam

Steam

Crude oil Light naphtha

Heavy naphtha

Light distillate

Residue

Heavy distillate

X1

3

4

5

Prefractionator

Main column

TPA

MPA

BPA

2

To prefractionator

To condenserlocation

Figure 6.16: Reconfiguration of existing crude oil distillation tower with

installed prefractionator

The reconfiguration option of the existing crude oil distillation tower, shown in Figure

6.16, will be considered as the base case for the unit with a prefractionator for studies in

this section. Therefore, the base case unit with prefractionator uses a prefractionator

column and main column with 2 side-strippers and 2 pump-arounds. The prefractionator

feed temperature is set to 230 oC. The design data and product specifications of the

prefractionator column are given in Table I.2 (Appendix I). The modified crude oil

distillation tower, i.e. existing distillation unit with prefractionator, is simulated using

the models presented in Chapters 3 and 4, in which the design specifications of the

prefractionator column and the new allocation of existing stages in the main column are

specified. Some operating variables, such as reflux ratio, need to be changed to achieve

the same products specification and flow rate as those of the base case. Operating


191

conditions of the existing unit with prefractionator are listed in Table I.3 (Appendix I).

Product flow rates and key recoveries obtained by simulation can be found in Tables I.4

and I.5 (Appendix I), where they are compared with the base case flow rates. As seen in

these tables, there are no big differences between the results of the product flow rates

and key component recoveries of the modified unit and those of the base case. Although

some streams show deviations in results, these deviations can be reduced by adjusting

the operating conditions of the distillation tower and the prefractionator column.

Repiping the existing exchangers in the heat exchanger network is essential as the

connections between the process streams change from the base case due to the addition

of the prefractionator. No additional exchanger units are needed since the number of

process streams and exchanger units are still the same as those in the existing network.

Table I.6 (Appendix I) shows the match of the process streams of the modified unit

HEN (with prefractionator) with those of the base case exchanger network. The major

changes that exist between the two cases: (a) the top pump-around of the base case

column is used as the condenser of the main column in the modified unit, (b) the

condenser of the base case column is reused as the condenser of the prefractionator

column and (c) the reboiler of the HN side-stripper is reused as the reboiler of the

prefractionator. These changes need repiping of the relevant exchanger units. In

addition, the exchangers on the crude oil stream need to be repiped, as seen previously

in the case of adding preflash drums. Also, some exchanger units require additional

area. The modified heat exchanger network is shown in Figure I.2 (Appendix I). The

location of the prefractionator column in the preheat train is shown in Figure 6.17.

Supply and target temperatures and enthalpy changes of the process streams can be seen

in Table I.7 (Appendix I).

The energy requirements of the modified unit and the design specifications of the

prefractionator column are summarised in Table 6.33.

1

1 5 7 91 3 2 6 8 1 04 1 24

1 13

Figure 6.17: Location of installed prefractionator in existing preheat train


192

Table 6.33: Retrofit results for existing unit with prefractionator

Parameter Existing unit Unit with prefractionator

Energy consumption MW 99.0 83.8 Prefractionator feed temperature oC - 230.0 Utility operating cost MM$/yr 15.284 12.932 Stripping steam operating cost MM$/yr 1.734 1.674 Additional exchanger area m2 - 2907.7 HEN capital investment MM$ - 0.644 Prefractionator capital cost MM$ - 0.796+ ∆Tmin oC 30.0 25.0 Payback yr - 0.60 Income from products (products value – feed price)

MM$/yr 382.074 385.625

Net profit (income from products – operating costs)

MM$/yr 365.056 371.019

Increase in net profit MM$/yr Base 5.963 Prefractionator design specifications Feed preheat duty MW - 80.63 Main column feed preheat duty MW 154.50 59.99 Theoretical stages in rectifying section

- 4.7

Theoretical stages in stripping section

- 12.1

Prefractionator column diameter M 3.95 Prefractionator stage efficiency % 59* Reflux ratio - 2.72 Condenser duty MW 20.85 Reboiler duty MW 18.37

utility and steam unit and area costs can be seen in Table C.1.2 and Table C.3.2 (Appendix C) *: calculated from Equation 4.62 (Chapter 4); average viscosity = 0.203 cP +: installation cost = 45% of purchase cost (Peters and Timmerhaus, 1980)

Table 6.33 indicates that the energy demand of the existing unit with prefractionator

becomes significantly lower than the base case demand, with a reduction of

approximately 15 MW. This reduces operating costs by about 2.4 MM$/yr, compared

with the base case. Modifications to the existing exchanger network include large

amount of additional area; the capital cost required is 0.6 MM$. Details of the additional

area and capital cost can be seen in Table I.8 (Appendix I).

It is clear from the results in Table 6.33 that the income from products on adding the

prefractionator increases significantly, compared to the existing unit. The increase in net

profit of the modified unit, accounting also for operating costs, is approximately six

million dollars per year.


193

The prefractionator column requires 4.7 and 12.1 theoretical stages in the rectifying and

stripping sections, respectively; the average column diameter is found to be

approximately 4 meters. The installed cost of the prefractionator column is about 0.8

MM$. The payback time for all modification expenditure, including exchanger network

and prefractionator capital costs, is less than eight months.

The above study recommends the addition of a prefractionator column to an existing

crude oil distillation unit to reduce energy consumption and operating costs. However,

the energy and cost savings can be further increased by optimising the operating

conditions of the prefractionator and the main distillation column. The retrofit approach

is then applied to the modified unit to minimise the total annualised costs of the utility

consumption and stripping steam, and the capital costs of prefractionator and exchanger

network modifications. Ideally, the HEN retrofit model needs to be obtained for the

modified exchanger network by following the procedure presented in Section 4.2

(Chapter 4). However, the model, obtained in Section 6.1.2, can still be used in the

optimisation, since it describes the same existing exchanger network.

During the optimisation, the existing designs of the main distillation column and

exchanger network are fixed; however, the design of the prefractionator column is

allowed to change to accommodate energy-capital trade-offs. The prefractionator

temperature is optimised to minimise the total annualised cost of the modified

distillation process; optimisation results are summarised in Table 6.34. Detailed results

of the optimum unit with prefractionator column, including operating conditions,

product flow rates and key component recoveries, process stream data, and additional

area and cost, can be seen in Tables I.9 to I.13 (Appendix I).

Table 6.34 indicates that the optimisation of the prefractionator temperature reduces

further operating cost and energy demand. Approximately 3 MW of hot utility

consumption and 0.5 MM$/yr of operating cost can be saved after optimising the

prefractionator temperature. Furthermore, the additional exchanger area required is

reduced by 606 m2 for the optimum unit, compared with base unit with prefractionator.

Note that only additional area is considered as modification to the existing network. The

capital cost of the prefractionator for the optimum unit is relatively expensive compared

with the base unit with prefractionator, although the payback time is still less than one

year. The costs of modifications to the existing exchanger network, which include only


194

additional area, are cheaper than those for the base unit with a prefractionator. The net

profit of the optimum unit, relative to the existing unit, increases to around 7.5 MM$/yr,

versus net profit of 6.0 MM$/yr for the base unit with a prefractionator (unoptimised).

Thus, optimising the prefractionator feed temperature increases net profit by

approximately 1.5 MM$/yr.

Table 6.34: Optimum results of unit with prefractionator

Unit with prefractionator Parameter Existing unit

Base Optimum

Prefractionator feed temperature oC 230.0 207.5 Energy consumption MW 99.0 83.8 80.6 Utility operating cost MM$/yr 15.284 12.932 12.435 Stripping steam operating cost MM$/yr 1.734 1.674 1.674 Additional exchanger area m2 - 2907.7 2301.7 HEN capital investment MM$ - 0.644 0.558 Prefractionator capital cost MM$ - 0.796+ 1.776+ ∆Tmin oC 30.0 25.0 25.0 Payback yr - 0.60 0.80 Income from products (products value – feed price)

MM$/yr 382.074 385.625 386.636


MM$/yr 365.056 371.019 372.527

Increase in net profit MM$/yr Base 5.963 7.471 Prefractionator design specifications Feed preheat duty MW - 80.63 69.10 Main column feed preheat duty MW 154.50 59.99 61.00 Theoretical stages in rectifying section

- 4.7 12.9


- 12.1 35.3

Prefractionator column diameter m 3.95 4.03 Prefractionator stage efficiency % 59* 59* Reflux ratio - 2.72 1.25# Condenser duty MW 20.85 12.93 Reboiler duty MW 18.37 20.69

utility and steam unit and area costs can be seen in Table C.1.2 and Table C.3.2 (Appendix C) *: calculated from Equation 4.62 (Chapter 4); average viscosity = 0.203 cP +: installation cost = 45% of purchase cost (Peters and Timmerhaus, 1980); #: R/Rmin = 1.01

The optimum design of the prefractionator column requires a large number of

theoretical stages in both the rectifying and stripping sections. The required number of

stages can be decreased by increasing the reflux ratio. For instance, when the reflux

ratio is increased by 38% to 1.73, the number of theoretical stages required in the

rectifying and stripping sections is decreased to 5.4 and 14.2 respectively. Accordingly,


195

the capital cost of the prefractionator column is reduced to 0.632 MM$. In this case, the

impact on heat recovery and operating costs needs to be considered.

In the above study, if both the prefractionator temperature and the operating variables of

the main distillation column are optimised for the same objective, the energy

consumption is reduced further, i.e. increasing the net profit. The results for this case are

summarised in Table 6.35. Tables I.14 to I.18 (Appendix I) give the details of the

optimum conditions, product flow rates, component recoveries, process streams and the

additional area for the optimum unit with prefractionator. Figure I.3 (Appendix I) shows

the topology modification that is applied to the existing exchanger network.

Table 6.35: Optimum results of unit with prefractionator (all operating conditions)


Base Optimum

Prefractionator feed temperature oC 230.0 215.7 Energy consumption MW 99.0 83.8 63.8 Utility operating cost MM$/yr 15.284 12.932 9.897 Stripping steam operating cost MM$/yr 1.734 1.674 1.556 Additional exchanger area m2 - 2907.7 2979.8 HEN capital investment MM$ - 0.644 0.612 Prefractionator capital cost MM$ - 0.796+ 1.076+ ∆Tmin oC 30.0 25.0 25.0 Payback yr - 0.60 0.31 Income from products (products value – feed price)

MM$/yr 382.074 385.625 386.698

Net profit (income from product– operating costs)

MM$/yr 365.056 371.019 375.245

Increase in net profit MM$/yr Base 5.963 10.189 Prefractionator design specifications Feed preheat duty MW - 80.63 73.29 Main column feed preheat duty MW 154.50 59.99 60.59 Theoretical stages in rectifying section

- 4.7 7.1


- 12.1 18.9

Column diameter m 3.95 3.94 Column efficiency % 59* 59* Reflux ratio - 2.72 1.52# Condenser duty MW 20.85 14.06 Reboiler duty MW 18.37 18.51

utility and steam unit and area costs can be seen in Table C.1.2 and Table C.3.2 (Appendix C) *: calculated from Equation 4.62 (Chapter 4); average viscosity = 0.203 cP +: installation cost = 45% of purchase cost (Peters and Timmerhaus, 1980); #: R/Rmin = 1.01


196

Table 6.35 illustrates that when all operating conditions of the modified distillation unit

with prefractionator are optimised, large reductions in the energy consumption and

operating costs are achieved. The results show that the optimum unit consumes energy

of 63.8 MW, compared with 80.6 MW for the unit with prefractionator where the

prefractionator temperature is only optimised and 99.0 MW for the existing unit. The

profit of the optimum unit with prefractionator is improved by 10 MM$/yr over the

existing unit.

The studies considered above concentrate only, for the added prefractionator, on the

energy savings and profit increase for fixed crude oil feed capacity. In addition to these

benefits, a prefractionator may enable the existing crude oil distillation unit to increase

the production capacity considerably. The maximum capacity that can be achieved in

the existing distillation unit with the installed prefractionator can be determined, in a

similar way to that discussed in previous sections. The key difference for this design

option is that the new column can be designed as a grassroots in order to accommodate

increased flow rates.

The hydraulic performance of the base unit with a prefractionator unit is analysed using

rigorous simulation. The configuration of this base unit is that shown previously in

Figure 6.16; design specifications and operating conditions are the same as those given

in Tables I.1 to I.5 (Appendix I). The modified unit is simulated; the stage diameters

required for separation are obtained. Stage diameters are plotted in Figure I.4 (Appendix

I) versus stage numbers, and plotted against the existing column diameter The FUA

curve is obtained from the information of the stage diameters, together with the existing

column diameters (Figure 6.18). The maximum FUA value is found 0.628; it indicates

that the maximum capacity is 59% (≈1/0.628) over the existing unit capacity. This

means that the throughput of the unit with a prefractionator can be increased from the

current level by up to 59% without requiring any column modifications. Note that the

existing unit without a prefractionator allows the capacity to be increased by a

maximum of 30%; also the existing unit with a preflash drum shows potential of

increasing the capacity by up to 38% over the existing capacity (see Section 6.6.1). It is

clear that prefractionator shows huge capacity enhancement potential over the preflash

and the existing unit capabilities.


197

0.0

0.2

0.4

0.6

0.8

1.0

0 10 20 30 40

Stage number

FUA

Figure 6.18: FUA curve for unit with prefractionator (stages from column top-

down)

If the capacity of the crude distillation unit is to be increased by 59% over the current

capacity, the energy consumption and operating costs increase as well by 59% over the

existing values assuming that heat recovery is also increased proportionally. However,

the process profit will increase substantially. The capacity of the unit with a

prefractionator is increased to the maximum value, i.e. 59% over the current level.

Operating conditions, including steam flow rates and pump-around duties and flow

rates, are scaled by the factor 1.59. The simulated column diameter for the 59%

increased capacity is shown in Figure 6.19, compared with the actual column diameter.

It is clear that the column diameter required for separation on some specific stages

equals the existing column diameter. This means that the capacity of the unit reaches its

maximum.

Table 6.36 summarises the energy requirements, operating costs and profit analysis for

the unit with maximum capacity increase. As expected, there is huge increase in the

process profit when the capacity increases to the maximum level. However, the energy

consumption and the additional area required are also large, with considerable capital

investment and operating costs. In order to reduce the operating costs, energy

consumption and the additional area, the retrofit approach is applied to change all

operating conditions of the entire system to minimise the total annualised cost.

Optimisation results are summarised in Table 6.37, while detailed results, including

optimisation variables, optimum prefractionator design, additional exchanger area and


198

costs and process stream data, can be seen in Tables I.19 to I.24 (Appendix I).

2

3

4

5

6

7

8

9

0 10 20 30 40

Stage number

Stag

e di

amet

er (m

)

Existing diameter

Prefract with 59%

Figure 6.19: Simulated column diameter for 59% increased throughput to unit

with prefractionator (number of stages is top-down) (tray sizing using HYSYS)

Table 6.36: Energy and cost requirement and profit for maximum capacity increase


Base 59% Increase

Prefractionator feed temperature oC 230.0 230.0 Energy consumption MW 99.0 83.8 133.4 Utility operating cost MM$/yr 15.284 12.932 20.595 Stripping steam operating cost MM$/yr 1.734 1.674 2.662 Additional exchanger area m2 - 2907.7 6049.3 HEN capital investment MM$ - 0.644 1.065 Prefractionator capital cost MM$ - 0.796+ 1.031+ ∆Tmin oC 30.0 25.0 25.0 Income from products (products value – feed price)

MM$/yr 382.074 385.625 613.144


MM$/yr 365.056 371.019 589.887

Increase in net profit MM$/yr Base 5.963 224.831 utility and steam unit and area costs can be seen in Table C.1.2 and Table C.3.2 (Appendix C) +: installation cost = 45% of purchase cost (Peters and Timmerhaus, 1980)

Table 6.37 shows that there is an improvement in the energy reduction and the cost

savings for the optimum unit, compared with the base unit without optimisation. About


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10 MW of energy demand of the base unit with prefractionator and maximum capacity

increase is reduced by optimisation. In addition, the total exchanger area required is

reduced by 694 m2. Further energy reduction is expected when the topology changes to

the network structure are considered. The net profit increases after optimisation by

approximately one million dollars per year, compared to the unit with 59% increased

throughput (unoptimised).

Table 6.37: Optimisation results of optimum unit with maximum capacity increase

59% Increase on unit with prefractionator


Optimum Base

Prefractionator feed temperature oC 218.5 230.0 Energy consumption MW 99.0 123.6 133.4 Utility operating cost MM$/yr 15.284 19.062 20.595 Stripping steam operating cost MM$/yr 1.734 2.662* 2.662 Additional exchanger area m2 - 5355.8 6049.3 HEN capital investment MM$ - 0.967 1.065 Prefractionator capital cost MM$ - 1.274+ 1.031+ ∆Tmin oC 30.0 25.0 25.0 Income from products (products value – feed price)

MM$/yr 382.074 612.591 613.144


MM$/yr 365.056 590.867 589.887

Increase in net profit MM$/yr Base 225.811 224.831 utility and steam unit and area costs can be seen in Table C.1.2 and Table C.3.2 (Appendix C) *: steam is not optimised; +: installation cost = 45% of purchase cost (Peters and Timmerhaus, 1980)

The latter case study discussed in this section shows that an added prefractionator has

potential to reduce energy consumption and operation costs and enhance production

capacity more than that can be achieved with the existing hardware. It also shows that

the optimisation of the unit with a prefractionator reduces energy consumption,

operating costs and additional exchanger area and increases process profits

significantly.

Although the section focuses on energy demand and operating costs, preflash and

prefractionator units can also be considered for debottlenecking purposes, where the

existing column cannot accommodate the desired increase in throughput. In this case,

preflash or prefractionator units can be added to increase the capacity and remove

column bottlenecks.

Furthermore, the retrofit strategy followed in this section can be applied to situations


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where an existing distillation column is bottlenecked and the bottlenecked section is to

be replaced with packing materials. In this case, the retrofit approach is applied in

parallel with the relevant models of Section 4.7 (Chapter 4).

6.7. Heat transfer enhancement in preheat train for retrofit of crude oil distillation tower

In this study, heat transfer enhancement is applied to retrofit preheat train for reducing

energy costs of existing crude oil distillation tower. The base case for this study is the

existing atmospheric crude oil tower and the associated preheat train, given in Section

6.1. The purpose in this case study is to reduce the energy consumption and operating

costs of the existing unit by using heat transfer enhancement in the preheat train. In this

study, we consider the case that heat transfer enhancement is applied to the exchangers

heating the crude oil feed stream, shown in Figure J.1 (Appendix J). Data for these

exchangers are given in Table J.1 (Appendix J). We assume that, in this study, the heat

transfer is controlled by tube side.

The retrofit model for the preheat train with heat transfer enhancement needs to be

obtained. The procedure, presented in Section 4.6.1 (Chapter 4, method 2), is applied to

the enhanced heat exchanger network. First, the maximum area, which can be achieved

by using heat transfer enhancement in exchanger unit, is calculated and shown in Table

J.2 (Appendix J). Then, for each energy consumption level, the additional exchanger

area required is eliminated or reduced based on the heat transfer area required and the

maximum area achievable by enhancement. Additional exchanger area combined with

enhancement is necessary when the retrofit area required is larger than the maximum

area for enhancement. However, for area requirements less than this maximum area,

enhancement alone is sufficient. Tables J.3 and J.4 (Appendix J) summarise the

calculation results. A detailed explanation of the calculations is provided with these

tables in Appendix J. Data of retrofit area with heat transfer enhancement are plotted

and regressed in Figure J.2 (Appendix J) to obtain the model parameters. Note that a

pressure drop correlation can also be obtained in this case by following the procedure

presented in Section 4.6.2 (Chapter 4). The correlation can then be used in parallel with

the retrofit model in the optimisation.

The retrofit approach can now be applied to the existing crude oil distillation tower and


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the associated preheat train with heat transfer enhancement to minimise the total

annualised costs of utility consumption and stripping steam, and the capital costs of

additional area and heat transfer materials. The optimum energy consumption is

expected to be reduced to less than that if heat transfer enhancement is not applied, as is

the case in Section 6.1.

The retrofit approach results are summarised in Table 6.38, and compared with the

results obtained in Section 6.1, where no heat transfer enhancement is used. The

optimum values of the operating conditions are given in Table J.5 (Appendix J). Details

of the product flow rates and recoveries, additional and enhanced area and the

associated capital costs, and the process streams for the optimum unit can be found in

Tables J.6 to J.9 (Appendix J). The modifications required by the existing preheat train

include additional area, enhanced exchangers and relocating an existing exchanger unit

(Figure J.3, Appendix J).

Table 6.38: Retrofit approach results for optimum unit with heat transfer enhancement (HTE)

Optimum Parameter Existing unit with HTE No HTE

Energy consumption MW 99.0 73.2 76.4 Furnace heat load MW 73.5 58.7 62.4 Crude oil temperature before furnace

oC 226.5 240.4 233.1

Utility operating cost MM$/yr 15.284 11.282 11.772 Stripping steam operating cost MM$/yr 1.734 1.631 1.551 Additional exchanger area m2 - 1292.1 1407.3 Heat transfer enhancement area m2 - 615.1 - Enhancement material capital costs MM$ - 0.037* 0.- Additional area capital costs MM$ - 0.329 0.393 Total operating cost MM$/yr 17.018 12.913 13.323 ∆Tmin oC 30.0 25.0 25.0 Payback yr - 0.09 0.11

utility and steam unit and area costs can be seen in Table C.1.2 and Table C.3.2 (Appendix C) *: 60 $/m2 for coiled wire 5.5 material (Nie, 1998)

The results of the retrofit approach show that by using heat transfer enhancement, the

energy consumption of the optimum unit is reduced by 25.8 MW from the base case,

with an increase of 3.2 MW over that achieved for the optimum case without heat

transfer enhancement. Although the energy consumption is lower, the total additional

area required by the exchanger units is also smaller than that required by the optimum

existing unit. The reduction in the total operating costs is approximately 4 MM$/yr,


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compared to the existing unit. The capital investment required is relatively small and

results in a very short payback time.

The above case study makes it clear that when heat transfer enhancement is applied to

an existing preheat train, further reductions in the operating costs and energy

consumption are achieved. Furthermore, the additional area required by the existing

exchanger units is also reduced. The pressure drop constraints of the existing exchanger

network can also be considered while achieving the retrofit objective with using heat

transfer enhancement. In this case, the retrofit model that accounts for the pressure drop

for the crude oil feed stream is obtained. Then, the maximum allowable pressure drop

for crude oil feed stream is imposed as a constraint to the optimisation. The results for

this case are expected to show less reduction in the energy consumption and operating

costs, compared with the case where the pressure drop constraints are not accounted for;

however, the results obtained will guarantee that no pumping capital costs are required.

6.8. Case studies comparison

The retrofit studies considered in this chapter demonstrate diverse objectives with

various design options, a comparison of options achieving similar goals can be made.

Table 6.39 summarises the various retrofit objectives for different design options.

Values in the table are given as percentage of the base case values; only profit values

are given as profit increase in MM$/yr over the base case.

Table 6.39: Case studies comparison

Optimum case Energy demand

Capacity* Profit Emissions

Base case value 99.0 MW

100,000 bbl/d

365.1 MM$/yr

32,766 kg/h

Existing unit -22.8% +30% +3.7 -21.6% Modified unit with heat transfer enhancement

-26.1% +4.1

Existing unit with added preflash drum

-22.8% +38% +2.1

Existing unit with installed prefractionator column

-35.6% +59% +10.2

Existing unit integrated with gas turbine

-19.4% +7.8+ -48.3%

*: maximum level; +: includes value of power generated

The results in this table may be used as guidance for retrofit projects in crude oil

distillation systems. Prefractionator columns show the maximum capacity increase,


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energy savings and profit improvement; an integrated gas turbine displays the largest

potential for emissions reduction.


This chapter presented different case studies to illustrate the application of the

established retrofit approach. Different retrofit objectives for crude oil distillation

systems were considered. The chapter explained how the retrofit approach is applied to

an existing crude oil distillation system to achieve a particular goal. For every case

study, the problem data were presented and the objective was defined. The retrofit

approach was applied to achieve the objectives. Results obtained were discussed and

key issues were concluded. Similar objectives with various design options were

compared. The base case column design was reasonably obtained by applying both the

grassroots design models for crude oil distillation columns (Suphanit, 1999) and the

design insights proposed by Watkins (1979). On the other hand, the heat exchanger

network was based on industrial data.

Several goals were covered in the studies, including reducing energy consumption and

operating costs, increasing plant capacity and process profit, changing production yields

and reducing environmental emissions. Case studies showed that the retrofit approach is

applicable to heat-integrated crude oil distillation systems to achieve retrofit aims with

minimal capital investment and great savings in the energy consumption and operating

costs. The existing crude oil distillation process could reduce its energy consumption

and operating costs by up to 23% of the base case values. No expensive modifications

are required to the existing distillation column; instead, beneficial changes to the

process conditions were identified. Modifications are only needed to the existing

exchanger network. The capital costs of modifications are minor, compared with the

savings achieved. These savings increase to up to 26% more by using heat transfer

enhancement in some particular exchanger units.

Process profit can be maximised by changing the production flow rates, for the same

feed capacity, using optimisation. Current profit can almost be doubled by applying the

approach to increase the products value, and obtain the optimum product flow rates

distribution.

It has been shown that the production capacity of an existing crude oil distillation unit


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can be increased to achieve a target value, in parallel with the reduction in the operating

costs and energy consumption. In addition, the hydraulic performance of an existing

distillation column is analysed to determine the maximum capacity increase. The retrofit

approach can reduce significantly the process expenses, which are anticipated when the

capacity of existing unit is increased to the maximum amount.

Atmospheric emissions can be reduced considerably by applying the retrofit approach.

A gas turbine can be integrated for further emissions reduction and increasing profit.

Furthermore, a gas turbine enables the existing refinery distillation unit to produce

power and gain more flexibility. Typical results demonstrated that the emissions from

the existing unit are decreased by up to 22%; emissions reduction can be increased by

up to 48% when a gas turbine is integrated.

Product yields can be changed and while simultaneously operating costs are reduced.

Benefits of 8 % of reductions in energy costs are achieved together with the retrofit

objective.

A new column can be installed upstream of the existing crude oil distillation unit to

reduce operating costs and utility consumption and enhance throughput. This study

showed that a preflash drum could reduce operating costs and energy consumption by

up to 22% and enable the existing unit to increase its capacity by up to 8% over the

maximum increase that could be achieved by the existing equipment. Locations of the

preflash vapour feed to the main distillation column and the implications for energy

saving and throughput increase opportunities have been addressed by the retrofit

approach. Similarly, a prefractionator column led to savings in energy costs of 35% and

increased the capacity by up to 29% over the maximum increase achievable by the

existing hardware. In addition, the optimum location of the preflash or prefractionator in

the preheat train is determined by the optimisation approach. The preflash and

prefractionator feed temperature is a key degree of freedom that can be manipulated for

greater energy savings and capacity enhancement.

The chapter has conclusively showed that the new approach is applicable for retrofit of

heat-integrated crude oil distillation systems. It is capable of achieving various retrofit

objectives with relatively cheap solutions, and large savings in energy demand and

operating costs. The application of the new approach account for changes in the

specifications of the crude oil feed stream or to the product prices. The approach


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presents diverse and flexible options for increasing process profit. The retrofit

optimisation solution takes approximately 3000 CPU seconds on a 1 GHz, 256 MB

RAM Pentium III PC.

It has been shown that, the results obtained in the studies presented in this chapter could

not have been achieved easily using simulations and sequential approaches or

engineering insights. Although the global optimisation solution is not guaranteed, the

results are still outstanding.

Although the approach was mainly applied to crude oil distillation systems, the retrofit

aspects discussed and the procedure are still valid and can be applied to other distillation

systems, such as naphtha fractionation and separation of catalytic cracking and

hydrocracking reactor products.

Chapter 7 Conclusions and future work

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Chapter 7: Conclusions and future work

7.1. Conclusions

In this thesis, a retrofit design methodology, modelling and optimisation framework for

heat-integrated crude oil distillation systems, i.e. crude oil distillation column and

associated heat exchanger network, have been presented. The approach is optimisation

based, and considers the existing design of distillation unit and the associated exchanger

network simultaneously. Shortcut models have been developed for particular application

to retrofit design of distillation columns. Also, a model that characterises the details of

exchanger area, matches and heat duties of an existing heat exchanger network has been

proposed. In addition, models for retrofit design aspects of the entire system, such as

installing a preflash drum and prefractionator column upstream to the existing

distillation unit, replacing column internals with packing, integrating a gas turbine and

enhancing heat transfer in exchanger tubes, have been developed and included in the

retrofit design approach.

The main contributions of the work presented in this thesis are summarised below:

7.1.1. Shortcut models for retrofit design of distillation columns

Shortcut models for grassroots design of distillation columns have been widely

developed and extensively applied to many situations. However, shortcut models for

retrofit design do not exist.

In this thesis, new shortcut models have been developed specifically for retrofit design

of distillation columns. These models are based on the Fenske-Underwood-Gilliland

method. The models include the corrections of the fixed molar overflow and constant

relative volatility assumptions made in the Underwood model (Suphanit, 1999).

The new shortcut models have been developed for various types of distillation columns,

such as simple columns and complex column configurations; they are also valid for both

reboiled and steam-stripped distillation columns. Design of complex columns is carried

out by applying shortcut models to sequences of simple columns, where the sequence is

equivalent to the complex column in terms of mass and energy balances.


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Many chemical industries use reboilers to provide vapour to the bottom of distillation

columns. For distillation columns using reboilers, new retrofit shortcut models have

been developed. The models are a reformulation of grassroots design models developed

by Suphanit (1999), where the Underwood method is modified to overcome the

underlying assumptions. The models are for design of simple columns and various

complex configurations including columns using side-strippers or rectifiers, side-

exchanger, prefractionators and Petlyuk columns. These models may be applied to

calculate product and intermediate stream flow rates, compositions and temperatures,

and the heat duties required for existing physical design and configuration of a

distillation column, feed data and operating parameters (e.g. reflux ratio, column

pressure, etc.). The new models have been applied to many illustrative examples. The

results compare very well with those of existing rigorous models. Agreement was

especially good for well-behaved mixtures with relatively few components and

conventional and simple column designs.

Some distillation applications, such as solvent recovery units and crude oil distillation,

utilise stripping steam to facilitate vaporisation in the column (at the bottom), new

shortcut models have been developed for retrofit of such distillation columns. The

models are based on the modified Underwood equation and consecutive flash

calculations for the rectifying and stripping sections, respectively. As for reboiled

columns, both simple and complex configurations using stripping steam have been

addressed. Many possible complex column arrangements are accommodated. These

configurations include sequences with side-columns and side-exchangers. The model

calculations are carried out for a given physical design of a distillation column and

given operating conditions (e.g. steam flow rate) and feed specifications to obtain the

various stream flow rates, temperatures and compositions and the process heating and

cooling requirements. The new models for steams-stripped distillation columns showed

good agreement when applied to various examples mostly of which were

multicomponent mixtures being separated in complex distillation columns.

Crude oil distillation columns are of major importance in refineries. These particular

columns may use reboilers, but primarily use stripping steam to provide vapour in the

main column and side-strippers; they process a large number of crude oil components

and have a very complex structure. A shortcut model has been developed for retrofit

design of such columns. The model includes models for reboiled and steam-stripped


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distillation columns. The retrofit model is valid for different configurations for crude oil

distillation, including the conventional design of the atmospheric tower and progressive

column configuration (Rhode, 1997). The model has been successfully applied to

simulate an existing atmospheric crude oil distillation unit with a realistic number of

pseudo-components. Simulation results are the flow rates, temperatures and

compositions of product, key internal and pump-around streams, and the duties of the

condensers, reboilers and the pump-arounds.

One limitation of the model for crude oil distillation towers is the inflexibility regarding

the location of pump-arounds. They are forced to be on the same stage as liquid is

drawn to and vapour is returned from the side-strippers. Pump-arounds on intermediate

stages cannot be modelled currently. This limitation can be overcome by decomposing

the section that includes the pump-around stage. This section includes the stages that lie

between the two draw lines of the side products around the pump-around location. In

this case, two sections with thermal coupling connection will be obtained; lower section

includes the pump-around stage on top and the stages that are between the pump-around

stage and the draw-stage of the lower side product, and upper section includes those

sages above the pump-around stage and till the draw-stage of the upper side product.

The shortcut model can then be applied to the two sections simultaneously. The feed to

the lower section is the top vapour from the upstream section, while the top vapour of

this section enters the upper section. There is a liquid stream recycled (thermal coupling

connection) from the upper section to the lower section; also, the bottom liquid product

of the lower section is recycled to the upstream section.

7.1.2. Retrofit modelling for heat-integrated crude oil distillation systems

Modelling of existing heat-integrated crude oil distillation column is of major

importance to retrofit applications. The shortcut models developed for distillation

retrofit provide a foundation for considering other important issues, such as flue gas

emissions, and design options to achieve retrofit goals. A number of different features

and design options required for retrofit have been modelled in this thesis, including the

existing heat exchanger network, preflash and prefractionator units, hydraulic

performance and CO2 emissions. These issues are outlined below:


209

A model has been proposed for an existing heat exchanger network; this model is based

on network pinch analysis (Asante, 1996) for retrofit of heat exchanger networks. The

proposed model is expressed by a simple parametric correlation; it accounts for the

structure of an existing exchange network and incorporates detailed informations about

the exchanger areas and matches, heat duties and proposed retrofit modifications. The

model parameters are obtained by regression from the results of an extensive retrofit

study on the existing exchanger network.

A similar model, that accounts for pressure drop for the crude oil feed stream, has been

proposed. This model uses pressure drop correlations (Polley et al., 1990; Nie, 1998),

and is obtained alongside the retrofit model for the exchanger network. In addition, the

application of heat transfer enhancement in exchanger tubes has been modelled.

Installing a preflash drum upstream of an existing crude oil unit can have several

benefits. A retrofit model has been developed to address the addition of preflash drum

to existing distillation unit. The model is based on flash calculations and material and

energy balances, and is combined with the retrofit models for distillation design. The

consequences of different choices of vapour feed location to the distillation column to

which the preflash vapours may be sent have been modelled. This is done by

decomposing the complex column configuration into its equivalent simple sequence and

then allowing the preflash vapour to be mixed with the feed to each simple column. The

model has been validated by comparison with existing rigorous models; good agreement

has been found. Although the model is for retrofit design, it can be used for new design

as well.

Similarly, a prefractionator column may be installed upstream of an existing column

configuration to bring more benefits. A retrofit model has been proposed for such a

feature; the model involves the retrofit model developed for distillation and the

grassroots distillation column design model of Suphanit (1999). The overall model

divides the new problem (i.e. the existing distillation column with the added

prefractionator) into two sub-problems, grassroots design of the new column and retrofit

design of the existing unit. Reconfiguration options of the modified unit and the

associated heat recovery system have been presented.

In some situations, bottlenecked trays in an existing column may be replaced by

packing. To evaluate this option, a model based on packed bed design equation (e.g.


210

Kister, 1992) and hydraulic and pressure drop correlations has been suggested. This

model applies in parallel with the retrofit model for distillation design. The model

evaluates the potential for increasing capacity in the modified section. The proposed

model can also be used in grassroots design situations

Due to increased concern about emissions to the atmosphere and tighter legislative

requirements for pollution control, a model has been proposed for evaluating CO2

emissions from an existing crude oil distillation unit. This model is based on models and

correlations developed by Delaby and Smith (1995) and Manninen and Zhu (1999). The

model considers various sources for emissions and is valid for different types of fuels

used in boilers and fired heaters. Integration of a gas turbine with the existing heating

system has been addressed as a design option. Although the new model was proposed

for emissions from an existing unit, it can similarly be applied for new design purposes.

7.1.3. Retrofit design methodology and its application

In this thesis, a simultaneous approach has been proposed for retrofit design of heat-

integrated crude oil distillation systems. The distillation process and the associated heat

recovery system have been considered at the same time. All design parameters of the

distillation process have also been considered simultaneously, including crude oil feed

preheating temperature, stripping steam flow rate, reflux ratio, pump-around duties, etc.

The approach is optimisation based; it incorporates the models for distillation retrofit,

existing exchanger network and the other relevant models, depending on the retrofit

design objectives and the design options being considered. The optimisation problem

was formulated as a non-linear programming (NLP) problem, because all retrofit

models are non-linear; successive quadratic programming (SQP) was utilised as the

optimisation algorithm.

A new optimisation framework for retrofit design has been developed. This framework

involves retrofit models and cost models, and uses a flexible objective function. For

retrofit applications, the existing design of process equipment is fixed; all operating

conditions (e.g. crude oil feed temperature, flow rate of stripping steam and pump-

around liquid, reflux, etc. ) of the distillation process are optimised to minimise or

maximise a specific objective function. Practical and physical equipment constraints,

such as hydraulic limitations of distillation columns, allowable pressure drop and


211

maximum heat loads of particular exchangers are taken into account during

optimisation. Several objectives have been considered for retrofit applications,

including energy reduction, throughput enhancement, profit improvement, product yield

changes and emissions reduction. Structural modifications to the distillation process and

the exchanger network have also been considered for retrofit.

The optimisation framework does not guarantee a global solution, since the problem is

non-linear optimisation. Hence, optimisation with different initial values of the

optimisation variables allow improved solutions to be obtained. This optimisation can

be carried out repeatedly until a confidence in the optimality of the best solution is

gained.

The main limitation of the new retrofit approach is that the retrofit model for the heat

exchanger network is obtained for fixed process conditions. However, during the

distillation process optimisation, these process conditions change. Therefore, the

capital-energy trade-off given by this retrofit model does not accurately represent the

simultaneous changes in the process conditions and the retrofit requirements to the

existing heat exchanger network. However, this shortcoming can be overcome, ideally,

by modelling the superstructure of the existing heat exchanger network and

incorporating the detailed existing design into the optimisation framework. In this case,

the given structure and details of the heat exchanger network will be considered

simultaneously with the change in the distillation process conditions. Thus, the

optimisation problem would be formulated as a rather complex mixed integer non-linear

programming (MINLP) problem. Alternatively, the problem can be partially overcome

by repeatedly applying the optimisation approach to the existing system. After each

optimisation run, a new retrofit model is obtained for the optimum process conditions.

Then, this model is used for the next optimisation step. The optimisation runs end when

there is no scope for a better solution to be obtained. Overall, a retrofit model is

obtained which relates the capital-energy trade-off in the existing heat exchanger

network and the process changes.

Another limitation involved in the new approach is that the structural modifications are

not considered during the optimisation; they need to be selected as retrofit decisions

prior to the optimisation. This can similarly be overcome by including the structural

decisions as part of the optimisation; the problem then becomes a mixed integer non-


212

linear programming problem. The main advantage of this is that the structural options

can be screened. However, the approach developed in this work leads to similar results

by considering each design option at a time.

The limitations of the shortcut models for retrofit design of distillation columns result

from their underlying assumptions. These underlying assumptions, for instance, are that

the Fenske equation assumes that the relative volatility is constant throughout the

column, and the minimum number of theoretical stages depends only on the degree of

separation of the two key components and their relative volatilities and not on the feed

condition (Seader and Henley, 1998). In addition, in the Gilliland correlation, the

number of theoretical stages is calculated based on the minimum reflux ratio, minimum

theoretical number of stages and the actual reflux ratio; no parameter that involves the

feed condition is included. Robinson and Gilliland (1950) stated that a more accurate

correlation should include the effect of the feed condition. The number of theoretical

stages decreases with increasing feed vaporisation and the Gilliland correlation appears

to be conservative for feeds with low values of the ratio of liquid to feed molar flow

rates (Seader and Henley, 1998). Furthermore, the feed stage location is influenced by

the feed condition and it is not only determined by the product flow rates and the key

component compositions in the feed and products as given in the Kirkbride correlation.

The new approach has been applied to a number of industrially-relevant cases of crude

oil distillation for different retrofit objectives. The retrofit results showed substantial

improvements to the energy and cost savings, profit, capacity increase and the emissions

reduction. Large benefits have been achieved by optimising simultaneously the design

parameters of the distillation process. The application of the retrofit approach examined

diverse design options for the same objective and revealed that prefractionator column

is the most attractive decision for profit improvement and energy saving, while a gas

turbine is favoured for emissions control.

7.2. Future work

Future research in this area could address some of the limitations of the work mentioned

above. Some of the other issues relevant to retrofit design of heat-integrated crude oil

distillation columns that still need to be addressed include:


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1. The new retrofit models and approach presented in this thesis can be extended to

include other relevant issues for other distillation applications such as crude oil

vacuum distillation and other refining distillation processes, as well as

petrochemicals and chemicals distillation.

2. The new retrofit models and approach can be modified to consider reconfiguration

of an existing crude oil distillation system.

3. Retrofit of fired heaters (Jegla et al., 2000) can be addressed, modelled and

incorporated to the retrofit approach.

4. The new retrofit models can provide a foundation for overcoming the network pinch

in an existing heat exchanger network through automated identification of beneficial

changes to the existing distillation process. Also, the algorithm can be modified to

search for process changes to debottleneck an existing exchanger unit or furnace.

5. The shortcut models for distillation retrofit can be extended to systematically

identify debottlenecking process changes (e.g. pump-around liquid flow rates) for

capacity enhancement.

7.3. Closing remarks

The new retrofit models and approach presented in this thesis constitute a major step

forward in retrofit design of heat-integrated crude oil distillation systems. The

significance of the work is that it has moved the research and industrial benefits in this

area from the ‘state-of-the-art’ as it was previously forward to the simultaneous retrofit

design philosophy with a systematic and automated procedure for various retrofit

decisions and design alternatives. Furthermore, the new shortcut models presented in

this thesis for retrofit designs of distillation columns are a big step forward in the design

modelling of distillation processes; they now can fill the gap left by the previous

researchers. The application of these new shortcut models can go beyond the area of

retrofit of crude oil distillation processes; they can find importance in other research

areas, such as design of reaction-separation systems, where distillation process is

adopted.

214

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Associated publications and conference presentations

Gadalla, M., M. Jobson, and R. Smith, Optimization of existing heat-integrated refinery distillation systems, accepted to be published in Trans IChemE in January, 81(A) (2003)

Gadalla M., Jobson M., Smith R., A Systematic Approach to Increasing Capacity and Decreasing Energy Demand of Existing Refinery Distillation Systems, accepted to be published in Chemical Engineering Progress (2003)

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220

Gadalla, M., M. Jobson, and R. Smith, Shortcut models for retrofit design of distillation columns, submitted to Chem. Eng. Res. Des. in July (2002)

Gadalla M., Jobson M., Smith R., Retrofit of refinery distillation systems, The International Conference on Distillation & absorption, Baden-Baden, Germany, September (2002)

Gadalla M., Jobson M., Smith R., Retrofit shortcut models for design of existing distillation systems, AIChE Spring Meeting, New Orleans, March (2002)

Gadalla M., Jobson M., Smith R., A systematic approach to increasing capacity and decreasing energy demand of existing refinery distillation systems, AIChE Spring Meeting, New Orleans, March (2002)

Appendix A: Pressure drop correlations

221


A.1. Parameters for pressure drop correlations of Polley et al. (1990)

5.31

321

kkk

K = (A.1)

3/18.0

1 Pr023.0

=

µλ dd

k (A.2)

2.0

2 092.0−

=

µρλ d

dk (A.3)

TVdk

43 = (A.4)

( ) 1.51

322

kkk

K′

′′= (A.5)

3/11 Pr36.0

dk λ

=′ (A.6)

18.02 2

vd

ke

ρΦ=′ (A.7)

( )S

tt

dVdPP

kπ

−=′ 4

3 (A.8)

A.2. Pressure drop for enhanced HEN

The pressure drop for the enhanced tubes is given by (Nie, 1998):

2

1

a

tube

entube

tube

en

hh

aPP

=

∆∆

(A.9)

The pressure drop for the plain tubes is defined as (Polley et al., 1990):

5.31 tubeexisttube hAKP =∆ (A.10)


222

tube

a

tube

entube Ph

haP ∆

=∆

2

1en (A.11)

The heat transfer coefficient of the enhanced tube is related to the plain tube heat

transfer coefficient as follows (Nie, 1998):

existtube

shell

existtube

shell

tube

entube

AA

hh

AA

hh

hh

∆−

∆+

=1

(A.12)

Substitute from equations A.10 and A.12 into equation A.9:

tube

a

existtube

tubeshellexist

exist

exist

tube

shell

en P

AhAhhA

AAA

hh

aP ∆

∆−

∆+

=∆

2

1 (A.13)

Simplifying equation A.13 results in the pressure drop for the enhanced exchanger, to

which the additional area is smaller than the maximum area achieved by enhancement:

( )tube

a

tubeshellexist

existshellen P

AhhAAAh

aP ∆

∆−

∆+=∆

2

1 (A.14)

For the case where the additional area is equal to the maximum, substituting ∆A =

∆Amax in equation A.14:

( )tube

a

tubeshellexist

existshellen P

hAhAAAh

aP ∆

∆−

∆+=∆

2

max

max1 (A.15)

tube

a

tubeshellexist

shellshellexisten P

hAhAhAhA

aP ∆

∆−∆+

=∆2

max

max1 (A.16)

The maximum area can be calculated as:

existtube

shellmax A

hh

A

−=∆ 1

21 (A.17)


223

12

+∆

=exist

max

tube

shell

AA

hh

(A.18)

Rewrite equation A.16 to the form:

tube

a

shell

tube

exist

existen P

hh

AA

AA

aP ∆

∆−

∆+

=∆

2

max

max

1

1

1 (A.19)

Solve equation A.18 and A.19 simultaneously:

tube

a

exist

exist

exist

existen P

AAA

AA

AA

aP ∆

+∆

∆−

∆+

=∆

2

max

max

max

1

21

1 (A.20)

tube

a

exist

existen P

AAA

AA

aP ∆

+∆∆

−

∆+

=∆

2

max

max

max

1

21

1 (A.21)

Rewrite equation A.21 in the following form:

tube

a

exist

exist

exist

exist

en P

AAAAA

AAA

aP ∆

+∆∆−+∆

∆+

=∆

2

max

maxmax

max

1

22

(A.22)

tube

a

exist

existen P

AAA

aP ∆

+∆=∆

2

max1

2 (A.23)

tube

a

existen P

AA

aP ∆

+

∆=∆

2

12 max

1 (A.24)

The last equation gives the pressure drop for the enhanced tubes with the maximum

area.


224

A.3. Pressure drop correlation for packed beds

The pressure drop correlation proposed by Coker (1991) is given by:

=

V

fluxL

pack

VP L

OHflux

ρκ∆ ρ

ρυ 22

10 (A.25)

The constant κ and υ can be found in Coker (1991), for different packing types and

sizes.

Appendix B: Heat exchanger costs

225


Figure B.1 summarises exchanger cost data obtained from various sources. These data

are collected, as shown, from Douglas (1988), Peters and Timmerhaus (1980) and

SPRINT (2002). The range of the exchanger areas used in this cost models is from 20 to

400 m2. It is clear that Sprint (2002) model underestimates capital cost of exchanger

units. The model that gives reasonable costs comparable with the various cost models is

shown in the figure. The cost parameters of the new model are obtained from data

regression as shown in Figure B.2. Table B.1 shows the exchanger area and cost data

used for model parameter regression.

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

0 50 100 150 200 250 300 350 400 450

Exchanger Area (m2)

Exch

ange

r Cos

t ($)

Douglas

Sprint

Peters(45%)

Peters(35%)

Model

Figure B.1: Exchanger cost data from various sources (45 and 35% represent

the ratios of the installation cost of exchangers to their purchase costs as recommended by Peters and Timmerhaus, 1980)


226

y = 1529.3x0.63

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

0 100 200 300 400 500

Exchanger area (m2)

Exch

ange

r cos

t ($)

Figure B.2: Exchanger cost curve, showing model regression parameters

(data used for regressions are in Table B.1)

Table B.1: Exchanger area and cost data

Exchanger area (m2)

Exchanger cost ($)

20 10,096 50 17,983 100 27,829 200 43,068 300 55,602 400 66,650

Appendix C: Data for case study 6.1

227


C.1. Problem data

Table C.1.1: Feed composition of crude oil mixture

Pseudo-component

NBP (oC) Flow rate (kmol/hr)

1 9 110.9 2 36 106.9 3 61 139.3 4 87 175.8 5 111 175.8 6 136 169.7 7 162 169.4 8 187 166.2 9 212 156.6 10 237 140.1 11 263 127.9 12 288 115.6 13 313 106.2 14 339 101.3 15 364 94.5 16 389 84.6 17 414 73.9 18 447 95.2 19 493 61.8 20 538 49.2 21 584 54.5 22 625 39.3 23 684 40.2 24 772 28.2 25 855 26.6

Total flow rate (kmol/h) 2610.7

Table C.1.2: Utility, stripping steam and exchanger unit costs

Parameter Unit cost Flue gas (1500 – 800 oC) 150.0 $/kW.yr Cold water (10 – 40 oC) 5.25 $/kW.yr Stripping steam* (100 psig) 3.77 $/1000 lb Exchanger additional area 1530 × (additional area) 0.63 $ New exchanger unit 13000 + 1530 × (exchanger area) 0.63 $

*: 260 oC and 4.5 bar


228

Table C.1.3: Energy consumption and operating costs for existing unit Parameter Existing unit

Heat energy consumption MW 99.0 Cold energy consumption MW 83.3 Furnace heat load MW 73.5 Crude oil temperature before furnace oC 226.5 Utility operating cost MM$/yr 15.284 Stripping steam operating cost MM$/yr 1.734 Heat exchanger network total area m2 4015.8 Total operating cost MM$/yr 17.018 ∆Tmin oC 30.0

Table C.1.4: Heat exchanger data

Exchanger No.

Area (m2) Heat duty (MW)

UA (kW/oC) ∆T approach (oC)

1 333.6 13.246 166.821 49.16 2 316.9 13.188 158.481 54.50 3 20.7 0.861 10.377 69.27 4 1.68 0.106 0.844 125.42 5 147.9 7.473 73.979 99.37 6 152.5 7.326 76.261 96.07 7 313.5 11.416 156.780 52.89 8 307.1 11.521 153.577 56.28 9 15.8 0.889 7.902 84.89 10 37.7 2.053 18.873 80.75 11 19.0 2.370 9.535 237.04 12 14.3 1.736 7.155 240.04 13 254.6 8.760 127.328 65.12

14hu Furnace 73.544 78.484 755.98 15 39.6 3.232 26.864 74.41

16hu 25.2 9.375 16.856 528.76 17hu 14.2 6.714 9.524 684.51 18 28.8 1.051 19.572 43.71

19hu 4.6 3.757 4.7662 682.50 21hu 6.8 5.586 6.988 722.46 22 159.9 8.165 108.374 36.87

23cu 45.4 1.329 32.490 30.00 24cu 1146.2 52.161 818.724 60.57 25cu 57.4 3.290 41.043 40.01 26cu 117.6 11.252 84.064 125.62 27cu 233.3 12.070 166.680 30.00 28cu 82.4 3.235 58.879 30.00

∆Tmin = 30.00 oC; hu: hot utility exchanger; cu: cold utility exchanger


229

Table C.1.5: Process and utility stream data for existing unit

No. Stream name

Supply temperature (oC)

Target temperature (oC)

Enthalpy change (MW)

1 Crude oil feed 25.00 365.00 154.490 2 Bottom PA 299.09 269.09 -12.868 3 Bottom steam 20.00 260.00 18.035 4 Residue 334.67 100.00* -49.373 5 Middle PA 252.10 202.10 -18.033 6 HD steam 20.00 260.00 3.757 7 Heavy distillate 256.12 50.00* -5.203 8 Top PA 172.45 152.45 -11.253 9 LD reboiler 271.24 282.71 9.375 10 Light distillate 282.71 40.00* -20.342 11 Condenser 101.64 76.96 -52.162 12 Light naphtha 76.96 40.00* -1.329 13 HN reboiler 182.74 189.58 6.715 14 Heavy naphtha 189.58 40.00* -6.179 h Flue gas 1500.00 800.00 -98.978 c Cooling water 10.00 40.00 83.340

-: refers to enthalpy of hot stream; *: product cooling target temperature; h: hot utility stream; c: cold utility stream

C.2. Retrofit curve data for existing HEN

Data, obtained from retrofit study performed on the existing heat exchanger network, are given in the following table:

Table C.2.1: Retrofit data for

Point on

curve

Energy consumption

(MW)

Additional area required

(m2)

Retrofit area (m2)

Type of modification to existing HEN

∆Tmin (oC)

0 99.0 4015.8* 4015.8 Existing unit 30.0 1 96.6 85.1 4285.1 Optimisation 30.0 2 83.8 1625.6 5825.6 Resequence hx 4

(outlet of hx 22) 29.0

2′ 83.8 1625.6 5825.6 Optimisation+ 29.0 3 82.5 2270.1 6470.1 Resequence hx 9


3′ 82.5 1864.3 6064.3 Optimisation+ 29.0 4 75.5 2379.1 6579.1 Repipe hx 10


4′ 75.5 2379.1 6579.1 Optimisation+ 29.0 5 68.7 3422.6 7438.4 New hx (outlet of

hx 8, inlet of 26) 25.0

5′ 68.7 3279.3 7295.1 Optimisation+ 25.0 *: existing heat exchanger network; hx: exchanger unit; +: for constant energy consumption (for details of exchanger locations in existing HEN, see Figure 6.2, Chapter 6)


230

The new locations of the exchanger units considered in the retrofit study can be seen in

Figures C.2.2 to C.2.5.

Data of retrofit area and energy consumption are plotted (Figure C.2.1), and then

regressed to obtain the retrofit model parameters m and c (see equation 4.7, Chapter 4).

Note that only the data points representing the optimum modified networks are plotted,

i.e. points 0, 1, 2′, 3′, 4′, 5′ and 6′ are only used to plot the retrofit curve. The existing

exchanger network on the retrofit curve corresponds to the first data point, while the

second point represents the data of the optimised existing HEN, etc. The model

parameters are obtained from regression, and are then adjusted to minimise the

deviations between the retrofit area predicted by the model and those obtained from the

retrofit study. The adjusted values of the model parameters m and c, as shown in Figure

C.2.1, are 9.731276·106 (m2/MW) and -1.6959 respectively. The percentage average

deviation of the predicted values from those obtained by the retrofit study is reduced

from 3.3 to 2.9%.

y = 9731276x-1.6959

R2 = 1.0000

30003500400045005000550060006500700075008000

60 65 70 75 80 85 90 95 100

Energy consumption (MW)

Ret

rofit

are

a (m

2 )

Aret

Power (Amodel)

Figure C.2.1: Retrofit curve and data regression for existing exchanger network

(Aret: area obtained from retrofit study, Amodel: area obtained from regressed model)


231

3

1

1 82 2

1 5 91 3 2 6 8 1 04 1 2

10

41 41 13

2 1 1 5

2 7

44

4 42 2

Figure C.2.2: Relocation of exchanger 4 in existing HEN (points 2, 2′)

1

1 5 7 91 3 2 6 8 1 0

14

4 1 241 41 13

2 8

9

9

91 0


1

1 5 7 91 3 2 6 8 1 0

11

14

4 1 241 41 13

2 4

2 89

1 0

1 0

1 0


1

1 5 7 91 3 2 6 8 1 0

8

4 1 241 41 13

2 6

Figure C.2.5: Introduction of new exchanger in existing HEN (points 5, 5′)


232

C.3. Data of retrofit results for optimum unit

Table C.3.1: Product flow rates of optimum unit

Product Existing unit (kmol/h)

Optimum unit (kmol/h)

Light naphtha (LN) 680.7 682.2 Heavy naphtha (HN) 493.5 495.4 Light distillate (LD) 652.8 651.2 Heavy distillate (HD) 149.8 149.2 Residue (RES) 633.9 632.7

Table C.3.2: Key component recoveries of products for optimum unit

Recovery* of key component of product

Existing unit Optimum unit


*: recoveries are calculated based on the fresh feed to the distillation tower

Table C.3.3: Process stream data for optimum unit

No. Stream name





-: refers to enthalpy of hot stream; *: product cooling target temperature h: hot utility stream; c: cold utility stream


233

Table C.3.4: Additional area and cost for case study 6.1

Exchanger No.

Existing area (m2)

Additional area (m2)

Capital cost ($)

2 316.9 249.4* 59557.2 4 1.68 340.7* 69689.4 5 147.9 287.7* 63948.6 7 313.5 94.6+ 25340.7 9 15.8 14.5* 20680.9 10 37.7 30.2* 25260.1 13 254.6 147.9+ 33571.7

16hu 25.2 6.3+ 4633.8 18 28.8 167.4* 49197.3

19hu 4.6 5.6* 17205.9 22 159.9 49.1+ 16777.5

26cu 117.6 13.1+ 7316.5 Total ($) 393,179.6

*: Capital cost ($) = 13000 + 1530 × (additional area) 0.63

+: Capital cost ($) = 1530 × (additional area) 0.63

Costing of additional areas more than 70% (as area costing constraint) of existing area is

performed on the basis that these areas are considered as new exchanger units.

C.4. Comparison of new retrofit approach with previous work

The section compares the results obtained by the network pinch (Asante, 1996) and the

energy-based (Bagajewicz, 1998) approaches with the new approach results. Network

pinch approach only modifies the existing exchanger network for the reducing energy

consumption. The distillation process conditions are not considered. Energy-based

approach optimises the operating conditions of the distillation process to minimise the

energy costs. The details of the existing exchanger network are not accounted for

simultaneously with the distillation process. The details of additional area and capital

cost required by the optimum process conditions are obtained in the present work to

provide basis for comparison; although in this previous work (Bagajewicz, 1998), this is

not done.


234

Table C.4.1: Additional area and cost for network pinch approach (no modifications to column operating conditions)

Exchanger No.

Existing area (m2)


Capital cost ($)

1 333.6 132.1+ 31252.4 2 316.9 111.3+ 28064.9 4 1.7 345.6* 70199.3 5 147.9 407.8* 76492.8 7 313.5 186.4+ 38830.4 9 15.8 35.0* 26433.0 10 37.7 69.4* 33748.1 13 254.6 162.4+ 35609.4 18 28.8 151.2* 46940.1 22 159.9 42.0+ 15181.7

26cu 117.6 24.7+ 10872.5 Total ($) 413,624.3



3

1

1 82 2

1 5 91 3 2 6 8 1 04 1 2

10

41 41 13

2 1 1 5

2 7

44

4 42 2

Figure C.4.1: Relocation of exchanger 4 for network pinch approach results

Table 0C.4.2: Comparison of optimisation results with minimum energy approach of Bagajewicz (1998)

# Optimisation variable Base case value

New+ approach

Energy* approach

Feed preheating temperature oC 365.00 370.00 365.55 Flow rate of pump-around liquid kmol/h 2186.56 1698.56 2153.83 ∆T across pump-around oC 30.00 40.10 30.67

1

Flow rate of stripping steam kmol/h 1200.00 1078.04 1175.60 Flow rate of pump-around liquid kmol/h 2305.66 2762.49 2883.03 ∆T across pump-around oC 50.00 37.27 40.22

2

Flow rate of stripping steam kmol/h 250.00 218.63 230.04 Flow rate of pump-around liquid kmol/h 5790.85 8348.25 6510.05 ∆T across pump-around oC 20.00 15.94 19.28

3


kmol/h 1067.48 866.33 886.38

4 Reflux ratio 4.78 3.66 4.06 #: number of column; *: previous approach of Bagajewicz (1998); +: new retrofit approach


235

Table C.4.3: Additional area and cost for energy-based approach#

Exchanger No.

Existing area (m2)


Capital cost ($)

4 1.7 4.5* 16612.5 9 15.8 7.2+ 4984.6 10 37.7 15.2+ 7990.8 11 19.0 0.4+ 808.1

16hu 25.2 16.8+ 8534.0 17hu 14.2 7.4+ 5105.0 18 28.8 19.8+ 9454.2

19hu 4.6 6.3* 17491.2 21hu 6.8 6.5* 17581.2 22 159.9 43.9+ 15620.7

26cu 117.6 7.1+ 4957.0 27cu 233.3 1.1+ 1502.5

Total ($) 110,641.8 #: previous approach of Bagajewicz (1998); *: Capital cost ($) = 13000 + 1530 × (additional area) 0.63


Appendix D: Data for case study 6.2

236


Table D.1: Product value and crude oil price*

Feed/product stream

Unit cost ($/bbl)

Crude oil feed 24.36 Light naphtha (LN) 74.97 Heavy naphtha (HN) 70.56 Light distillate (LD) 39.45 Heavy distillate (HD) 38.85 Residue (RES) 23.94

*: based on web prices (Oil Prices, 2002)

3

9

7

6

1

2

2 5

1 82 2

1 5 7 9

1 3 2 6 8 1 0

11

12

14

8

4

5

13

1 2

10

1 41 13

2 1

1 9

1 6

1 7

1 5

2 6

2 7

2 4

2 3

2 8

Additional area

44

Relocation

4

Figure D.1: Modifications to existing heat exchanger network


237

Table D.2: Additional area and capital cost

Exchanger No.

Existing area (m2)


Capital cost ($)

2 316.9 290* 64271.1 4 1.68 292* 64471.2 5 147.9 183* 51371.3 7 313.5 99+ 26112.2 9 15.8 18* 21922.4 10 37.7 53* 30640.7 13 254.6 76+ 22105.7

16hu 25.2 30* 25278.6 17hu 28.8 17* 21754.4 18 28.8 32+ 12834.4

19hu 4.6 4.8* 16764.9 21hu 6.8 4.8* 16752.5 23cu 117.6 0.56+ 1002.39 26cu 117.6 15.8+ 8207.9

Total ($) 383489.3 *: Capital cost ($) = 13000 + 1530 × (additional area) 0.63


Table D.3: Process stream data for optimum unit of maximum profit

No. Stream Name




1 Crude oil feed 25.00 343.90 142.569 2 Bottom PA 292.023 258.03 -8.327 3 Bottom steam 20.00 260.00 150.896 4 Residue 322.231 100.00* -48.306 5 Middle PA 252.57 205.76 -13.304 6 HD steam 20.00 260.00 212.506 7 Heavy distillate 245.56 50.00* -3.936 8 Top PA 173.63 148.84 -13.041 9 LD reboiler 269.50 281.25 10.667 10 Light distillate 281.25 40.00* -19.475 11 Condenser 103.00 76.87 -50.166 12 Light naphtha 76.87 40.00* -1.321 13 HN reboiler 181.90 189.20 8.045 14 Heavy naphtha 189.20 40.00* -6.160

-: refers to enthalpy of hot stream; *: product cooling target temperature


238

Table D.4: Key component recoveries* of products for maximum profit

Recovery of key component of product

Existing unit Maximum profit unit



Appendix E: Data for case study 6.3

239


E.1. Problem data

Table E.1.1: Operating conditions of base case

Parameter Column 1

Column 2

Column 3

Column 4

Feed preheating temperature (oC) 360 Column pressure (bar) 2.5 2.5 2.5 2.5 Steam+ flow rate (kmol/h) 1200 250 Pump-around ∆T (oC) 40 50 20 Pump-around flow rate (kmol/h) 1228.06 2396.3 5867.78 Pump-around duty (MW) 9.586 18.800 11.410 Reflux ratio 4.819 Condenser duty (MW) 52.430 Reboiler duty (MW) 9.457 6.754 Top product flow rate (kmol/h) 680.82 Bottom product flow rate (kmol/h)

642.20 136.81 657.40 493.48

Recovery* of LK component (%) 98.10 98.30 98.31 99.26 Recovery* of HK component (%) 98.67 65.27 96.48 99.14

*: component recoveries are calculated based on the fresh feed to the distillation tower +: stripping steam is at 260 oC and 4.5 bar

Table E.1.2: Heat exchanger data

Exchanger No.

Area (m2) Heat duty (MW)

UA (kW/oC) ∆T approach (oC)

1 231.6 12.793 115.818 80.56 2 237.5 13.189 118.735 81.91 3 7.3 0.437 3.650 111.51 4 3.9 0.301 1.939 154.24 5 132.9 8.239 66.450 121.14 6 118.6 7.327 59.306 119.45 7 266.1 11.417 133.061 59.55 8 273.1 11.522 136.568 59.29 9 15.3 0.890 7.642 88.15 10 36.6 2.056 18.294 83.07 11 20.1 2.370 10.056 208.86 12 14.5 1.736 7.229 239.77 13 114.4 5.481 57.186 88.65

14hu Furnace 73.910 77.287 785.67 15 39.5 3.233 26.727 74.86

16hu 25.5 9.458 17.009 528.52 17hu 14.4 6.754 9.577 684.75 18 23.0 1.051 15.595 55.49


240

19hu 4.7 3.757 4.765 682.62 21hu 6.8 5.587 6.987 722.42 22 163.6 8.165 110.792 35.67

23cu 45.5 1.330 32.480 30.00 24cu 1151.4 52.431 822.427 60.64 25cu 54.7 3.290 39.075 40.00 26cu 119.3 11.408 85.199 125.50 27cu 234.4 12.071 167.437 30.00 28cu 82.4 3.236 58.851 30.00

∆Tmin = 30.00 oC; hu: hot utility exchanger; cu: cold utility exchanger

Table E.1.3: Process and utility stream data for base case

No. Stream name



Enthalpy change(MW)

1 Crude oil feed 25.00 360.00 151.670 2 Bottom PA 300.16 260.16 -9.587 3 Bottom steam 20.00 260.00 18.035 4 Residue 331.35 100.00* -48.923 5 Middle PA 252.91 202.91 -18.798 6 HD steam 20.00 260.00 3.757 7 Heavy distillate 256.44 50.00* -4.778 8 Top PA 172.66 152.66 -11.408 9 LD reboiler 271.48 283.00 9.458 10 Light distillate 283.00 40.00 -20.537 11 Condenser 101.72 76.97 -52.431 12 Light naphtha 76.97 40.00* -1.330 13 HN reboiler 182.77 189.61 6.754 14 Heavy naphtha 189.61 40.00* -6.181 H Flue gas 1500.00 800.00 -99.465 C Cooling water 10.00 40.00 83.765



241

E.2. Data of retrofit results for 20% capacity increase on base case

Table E.2.1: Operating conditions of 20% capacity increase on base case

Parameter Column 1

Column 2

Column 3

Column 4

Feed preheating temperature (oC) 360 Column pressure (bar) 2.5 2.5 2.5 2.5 Steam+ flow rate (kmol/h) 1440 300 Pump-around ∆T (oC) 40 50 20 Pump-around flow rate (kmol/h) 1473.05 2874.26 7038.97 Pump-around duty (MW) 11.50 22.54 13.08 Reflux ratio 4.819 Condenser duty (MW) 62.880 Reboiler duty (MW) 11.340 8.102 Top product flow rate (kmol/h) 816.77 Bottom product flow rate (kmol/h)

770.40 164.06 788.66 592.03



1

13

3

8

7

9

6

2

2 5

1 82 2

1 5 7 9

1 3 2 6 8 1 0

11

12

14

4

5

1 2

10

1 41 13

2 1

1 9

1 6

1 7

1 5

2 6

2 7

2 4

2 3

2 8

Additional area

4

Figure E.2.1: Modifications to existing heat exchanger network for optimum unit with 20% capacity increase


242

Table E.2.2: Additional area and cost for 20% increase on base case

Exchanger No.

Existing area (m2)


Capital cost ($)

1 231.6 129.1+ 30806.6 2 237.5 102.6+ 26656.1 3 7.3 18.4* 21924.2 5 132.9 23.2+ 10453.7 6 118.6 19.8+ 9467.4 7 266.1 16.9+ 8567.5 8 273.1 7.2+ 5013.2 9 15.3 14.2* 20575.8 10 36.6 12.7+ 7156.3 12 14.5 0.1+ 382.5 13 114.4 48.3+ 16577.7

16hu 25.5 5.1+ 4012.4 17hu 14.4 2.8+ 2782.9 18 23.0 5.5+ 4232.7

19hu 4.7 0.9+ 1367.4 21hu 6.8 1.9+ 2170.9 22 163.6 46.6+ 16218.9

23cu 45.5 9.1+ 5791.2 24cu 1151.4 230.3+ 44364.3 25cu 54.7 4.8+ 3885.5 26cu 119.3 24.8+ 10907.5 27cu 234.4 45.6+ 15988.4 28cu 82.4 6.3+ 4618.0

Total ($) 273,920.7 *: Capital cost ($) = 13000 + 1530 × (additional area) 0.63



243

Table E.2.3: Process and utility stream data for 20% increase of base case capacity

No. Stream name



Enthalpy change(MW)



E.3. Retrofit curve data for existing unit with 20% capacity increase

Data, obtained from retrofit study performed on the existing heat exchanger network

with 20% increase, are summarised in Table E.3.1.

Table E.3.1: Retrofit data

Point on

curve

Energy consumption

(MW)

Additional area required

(m2)

Retrofit area (m2)

Type of modification to existing HEN

∆Tmin (oC)

0 117.1 520.2 4535.2 Existing unit 30.0 1 115.9 582.7 4597.7 Optimisation 29.0 2 100.7 3131.7

7146.7 Resequence hx 1 (outlet of hx 2)

29.0

2′ 100.7 2783.8 6798.8 Optimisation+ 29.0 3 90.1 5173.3

9188.3 Repipe hx 9 (inlet of 24)

29.0

3′ 90.1 5173.3 9188.3 Optimisation+ 29.0 4 80.6 5875.6

9890.6 New hx (outlet of hx 10, inlet of 10)

29.0

4′ 80.6 5838.6 9853.6 Optimisation+ 29.0 5 79.2 6182.4

10197.4 Resequence hx 3 (outlet of hx 10)

29.0

5′ 79.2 5756.3 9771.3 Optimisation+ 29.0 hx: exchanger unit; +: for constant energy consumption (for details of exchanger locations in existing HEN, see Figure 6.2)


244

The new locations of the exchanger units considered in the retrofit study can be seen in

Figures E.3.2 to E.3.5.

Data of retrofit area and energy consumption are plotted (Figure E.3.1), and then are

regressed to obtain the retrofit model parameters m and c (see equation 4.7, Chapter 4).

Note that only the data points representing the optimum modified networks are plotted,

i.e. points 0, 1, 2′, 3′, 4′, 5′ and 6′ are only used to plot the retrofit curve. The existing

exchanger network on the retrofit curve corresponds to the first data point, while the

second point represents the data of the optimised existing HEN, etc. The model

parameters are obtained from data regression, and are then adjusted to minimise the

deviations between the retrofit area predicted by the model and those obtained from the

retrofit study. The best-fit values of the model parameters m and c, as shown in Figure

E.3.1, are 1.002176·108 (m2/MW) and -2.1016 respectively. The percentage average

deviation of the predicted values from those obtained from the retrofit study is reduced

from 5.9 to 5.3%.

y = 100217605x-2.1016

R2 = 1.0000

0

2000

4000

6000

8000

10000

12000

0 15 30 45 60 75 90 105 120 135Energy consumption (MW)

Ret

rofit

are

a (m

2 )

AretPower (Amodel)

Figure E.3.1: Retrofit curve and data regression (Aret: area obtained from retrofit study, Amodel: area obtained from regressed model)


245

1

1 5 7 91 3 2 6 8 1 0

4

4 1 241 41 131

71

1

82

Figure E.3.2: Relocation of exchanger 1 in existing HEN (points 2, 2′)

1

1 5 7 91 3 2 6 8 1 0

11

14

4 1 241 41 13

2 4

2 8

9

9

1 0

9


1

1 5 7 91 3 2 6 8 1 0

8

4 1 241 41 13

2 6

Figure E.3.4: Introduction of new exchanger in existing HEN (points 4, 4′)

1

7 2 5

1 5 7 91 3 2 6 8 1 04 1 241 4

1 133

1 833



246

0.0

2000.0

4000.0

6000.0

8000.0

10000.0

12000.0

14000.0

16000.0

18000.0

20000.0

0 20 40 60 80 100 120


Ret

rofit

are

a (m

2 ) 20% Increase

Existing throughput

Figure E.3.6: Comparison of retrofit model of unit with 20% increased throughput with existing throughput (vertical dotted line represents existing energy demand)

E.4. Data of retrofit results for optimum unit with 20% capacity increase

Table E.4.1: Operating conditions of optimum unit with 20% capacity increase

Parameter Column 1

Column 2

Column 3

Column 4

Feed preheating temperature (oC) 364.30 Column pressure (bar) 2.5 2.5 2.5 2.5 Steam+ flow rate (kmol/h) 1172.50 246.87 Pump-around ∆T (oC) 16.14 31.95 20.36 Pump-around flow rate (kmol/h) 3693.65 5806.56 8018.72 Pump-around duty (MW) 11.84 28.31 15.0 Reflux ratio 2.551 Condenser duty (MW) 42.05 Reboiler duty (MW) 6.624 3.450 Top product flow rate (kmol/h) 808.50 Bottom product flow rate (kmol/h)

776.91 161.90 786.61 598.08




247

8

7

3

9

6

1

2

2 5

1 82 2

1 5 7 9

1 3 2 6 8 1 0

11

12

14

4

5

13

1 2

10

1 41 13

2 1

1 9

1 6

1 7

1 5

2 6

2 7

2 4

2 3

2 8

Additional area

44

Relocation

4

Figure E.4.1: Modifications to existing heat exchanger network for optimum

unit with20% capacity increase

Table E.4.2: Additional area and cost for optimum unit with 20% capacity increase

Exchanger No.

Existing area (m2)


Capital cost ($)

1 231.6 65.9 20172.6 2 237.5 366.0 72302.1 4 3.9 329.8 68531.7 5 132.9 382.5 73974 7 266.1 108.4 27597.8 9 15.3 24.9 23837.4 10 36.6 80.7 35816.6 13 114.4 200.6 53570.4 15 39.5 17.7 8823.1

16hu 25.5 10.5 6346.5 17hu 14.4 0.7 1118.4 18 23 10.4 6319.5

19hu 4.7 7.4 17965.7 22 163.6 58.1 18634.8

23cu 45.5 12.9 7202.1 24cu 1151.4 13.4 7380.2 25cu 54.7 56.4 18283.1




248

Table E.4.3: Process stream data for optimum unit with 20% capacity increase

No. Stream name



Enthalpy change(MW)



Appendix F: Data for case study 6.4

249


F.1. Problem data

Table F.1.1: Data of hot utilities for case study

Parameter Unit cost Heat load* (MW) Flue gas (1500 – 800 oC) 150.0 $/kW.yr 80.281 HP steam# (400.0 – 399.9 oC) 69.5 $/kW.yr 9.375 Stripping steam+ (100 psig) 3.77 $/1000 lb 9.344 Cold water (10 – 40 oC) 5.25 $/kW.yr 83.340

*: total load = 99.0 MW; +: 260 oC and 4.5 bar; HP: high pressure; #: 42 bar

Table F.1.2: Data for heating fuels in CO2 emissions

NHV (kJ/kg) Carbon % Heavy fuel oil 39,771 86.5 Natural gas 51,600 75.4

NHV: net heating value

F.2. Results for retrofit with integrated gas turbine

The calculations in the following table are obtained by using the models presented in

Section 4.5, Chapter 4). Cost parameters used in calculations are given in Table F.2.2.

Note that the total heat load of the flue gas, including that for stripping steam, is

distributed on the gas turbine and the furnace.

Table F.2.1: CO2 emissions from optimum unit with integrated gas turbine


Total heat energy consumption MW 79.780 Flue gas heat load MW 67.136 Stripping steam heat load MW 6.557 Utility steam heat load MW 9.375 Heat load distributed on gas turbine MW 15.588 Heat load distributed on furnace MW 58.105 CO2 emissions from steam boiler kg/h 2,698 CO2 emissions from gas turbine kg/h 9,092 CO2 emissions from furnace kg/h 18,552 Total local CO2 emissions kg/h 30,342 Fuel consumption save at power station MW 46.669 CO2 emissions saved at power station kg/h -13,411 Total global CO2 emissions kg/h 16,931 Flue gas flow rate t/h 146.2 Exhaust temperature from gas turbine oC 499.0


250

Gas turbine efficiency % 73.7 Power generated in gas turbine MW 14.0 Capital cost of gas turbine MM$ 5.261 Price of power generated MM$/yr 3.920 Stripping steam flow rate kmol/h 1255.0 Total operating costs* MM$/yr 13.232 HEN capital investment MM$ 0.429 Crude oil temperature before furnace oC 230.7 Total operating cost saving+ MM$/yr 6.951 Total capital investment MM$ 5.690 Payback yr 0.82 ∆Tmin in existing HEN oC 25.0

*: including utilities and steam; +: including price of power generated

Table F.2.2: Cost and economic parameters for CO2 emissions calculation

Parameter Value Power electricity $/MW.h 35 Carbon tax $/t CO2 15 Gas turbine capital cost k$ 195.1 × (Power*) + 2529.2 Power station efficiency % 30 Furnace efficiency % 90 Boiler efficiency % 80 Atmospheric temperature oC 25 Flue gas temperature oC 1800 Stack temperature oC 150 Operation time yr/h 8600

*: in MW

Table F.2.3: Optimum values of operating conditions


Optimum value


1


2


3


kmol/h 1067.48 1095.35



251

Table F.2.4: Additional area and cost for optimum unit

Exchanger No.

Existing area (m2)


Capital cost ($)

1 333.6 278.321* 62889.5 2 316.9 308.9* 66281.7 3 20.7 66.6* 33196.5 4 1.68 331.0* 68652.9 5 147.9 28.8823+ 11995.4 9 15.8 36.5* 26812.7 10 37.7 67.5* 33381.9 13 254.6 109.3+ 27745.4

16hu 25.2 18.2+ 21867.4 18 28.8 101.5* 39385.1 22 159.9 69.5+ 20865.9

26cu 117.6 43.2+ 15448.8 Total ($) 428,523.0



3

1

1 82 2

1 5 91 3 2 6 8 1 04 1 2

10

41 41 13

2 1 1 5

2 7

44

4 42 2

Figure F.2.1: Relocation of exchanger 4 for optimum network

Table F.2.5: Process stream data for optimum unit

No. Stream name



Enthalpy change(MW)




252

F.3. Results for retrofit without integrated gas turbine

Table F.3.1: Optimum values of operating conditions


Optimum value


1


2


3


kmol/h 1067.48 1382.58


Table F.3.2: Additional area and cost for optimum unit

Exchanger No.

Existing area (m2)


Capital cost ($)

1 333.6 216.9+ 42717.8 2 316.9 331.9* 68748.2 3 20.7 59.1* 31736.6 4 1.7 305.5* 65915.7 9 15.8 41.2* 27898.7 10 37.7 72.4* 34297.2 13 254.6 28.2+ 11812.1

16hu 25.2 19.0* 22103.9 18 28.8 88.7* 37221.8 22 159.9 81.5+ 23064.6 Total ($) 365,516.3



3

1

1 82 2

1 5 91 3 2 6 8 1 04 1 2

10

41 41 13

2 1 1 5

2 7

44

4 42 2

Figure F.3.1: Relocation of exchanger 4 for optimum network (with integrated gas turbine)


253

Table F.3.3: Process stream data for optimum unit

No. Stream name



Enthalpy change(MW)



Appendix G: Data for case study 6.5

254


Table G.1: Operating conditions for new product yields

Parameter* Base case

New product

yield Steam flow in column 2 kmol/h 250 600 Flow of pump-around liquid in column 2 kmol/h 2305.7 3225.3 Flow rate of liquid between columns 2 and 3

kmol/h 167.7 232.2

Flow of pump-around liquid in column 3 kmol/h 5790.9 5015.9 Flow rate of liquid between columns 3 and 4

kmol/h 1067.5 896.2

Reflux ratio in column 4 4.78 4.40 variables are given for decomposed sequence (see Figure 6.1, Chapter 4) *: other operating conditions are same for both cases

Table G.2: Additional area and cost for unit with new product yields

Exchanger No.

Existing area (m2)


Capital cost ($)

2 316.9 14.9+ 7889.9 3 20.7 14.0+ 7593.2 4 1.6 2.6* 15553.2 5 147.9 40.1+ 14756.7 6 152.5 59.0+ 18815.6 9 15.8 5.7+ 4305.0 10 37.7 10.6+ 6386.1 11 19.0 0.4+ 832.8 15 39.6 15.5+ 8112.4

19hu 4.6 6.5* 17573.3 21hu 6.8 3.0+ 2910.2 23cu 45.4 4.3+ 3610.7 24cu 1146.2 53.2+ 17615.9 25cu 57.4 35.0+ 13531.4




255

Table G.3: Process stream data for unit with new product yields

No. Stream name



Enthalpy change(MW)



Table G.4: Optimum operating conditions for unit with new product yields

Colno.

Optimisation variable Base case

New product

yield

Optimum unit

Feed preheating temperature oC 365.0 365.0 367.8 Flow rate of pump-around liquid kmol/h 2186.5 2186.5 2662.5 Temperature drop across pump-around oC 30.00 30.00 25.05

1

Flow rate of stripping steam kmol/h 1200.0 1200.0 1110.5 Flow rate of pump-around liquid kmol/h 2305.7 3225.3 3151.0 Temperature drop across pump-around oC 50.00 50.00 41.99

2

Flow rate of stripping steam kmol/h 250.0 600.0 546.4 Flow rate of pump-around liquid kmol/h 5790.9 5016.0 5775.2 Temperature drop across pump-around oC 20.00 20.00 24.09

3


kmol/h 1067.5 896.2 749.6

4 Reflux ratio 4.78 4.40 3.54


256

Table G.5: Additional area and cost for optimum unit with new product yields

Exchanger No.

Existing area (m2)


Capital cost ($)

3 20.7 6.1+ 4502.4 5 147.9 28.2+ 11818.5 6 152.5 39.8+ 14685.2 9 15.8 11.0+ 6522.5 10 37.7 18.6+ 9092.1 18 28.8 102.8* 39594.6

19hu 4.6 5.9* 17304.9 21hu 6.8 0.2+ 559.2 26cu 117.6 23.4+ 10492.6



Table G.6: Process stream data for optimum unit with new product yields

No. Stream name



Enthalpy change(MW)

1 Crude oil feed 25.00 367.82 156.09 2 Bottom PA 300.47 275.42 -13.15 3 Bottom steam 20.00 260.00 16.69 4 Residue 338.13 100.00 -50.24 5 Middle PA 240.75 198.76 -19.38 6 HD steam 20.00 260.00 8.21 7 Heavy distillate 238.18 50.00 -7.57 8 Top PA 166.33 142.24 -13.35 9 LD reboiler 268.36 275.75 5.30 10 Light distillate 275.75 40.00 -15.71 11 Condenser 97.88 76.98 -46.69 12 Light naphtha 76.98 40.00 -1.33 13 HN reboiler 184.49 189.78 3.46 14 Heavy naphtha 189.78 40.00 -6.30


Appendix H: Data for case study 6.6.1

257


Table H.1: Process stream data for base case with preflash

No. Stream name



Enthalpy change(MW)

1 Preflash feed 25.00 190.00 60.39 2 Crude oil feed 190.00 365.00 89.91 3 Bottom PA 293.11 263.11 -10.49 4 Bottom steam 20.00 260.00 18.04 5 Residue 324.51 100.00* -47.92 6 Middle PA 244.71 194.71 -17.73 7 HD steam 20.00 260.00 3.76 8 Heavy distillate 247.62 50.00* -4.49 9 Top PA 174.28 154.28 -4.44 10 LD reboiler 271.89 281.53 6.45 11 Light distillate 281.53 40.00* -19.77 12 Condenser 101.94 76.97 -53.30 13 Light naphtha 76.97 40.00* -1.33 14 HN reboiler 182.82 189.80 6.91 15 Heavy naphtha 189.80 40.00* -6.19


Table H.2: Additional area and cost for base case with preflash

Exchanger No.

Existing area (m2)


Capital cost ($)

1 333.6 3.3+ 3059.9 2 317.0 10.4+ 6286.5 4 1.7 7.3* 17927.5 5 148.0 8.5+ 5533.2 7 313.6 28.0+ 11762.4 10 37.7 11.9+ 6849.5 15 39.7 7.5+ 5133.3

17hu 14.3 0.8+ 1254.2 19hu 4.7 0.1+ 335.0 21hu 6.8 0.3+ 707.2 22 160.0 14.7+ 7840.8 24 1146.2 22.0+ 10105.8 Total ($) 76,795.3




258

Table H.3: Optimum operating conditions for unit with preflash drum

Column Optimisation variable Base case with preflash

Optimum unit with preflash

Preflash Feed preheating temperature oC 190.0 178.9 1 Flow rate of pump-around liquid kmol/h 1812.8 2008.2

Flow rate of pump-around liquid kmol/h 2328.9 6427.8 Temperature drop across pump-around

oC 50.00 15.10 2

Flow rate of stripping steam kmol/h 250.0 220.1 Flow rate of pump-around liquid kmol/h 2260.6 300.97 Temperature drop across pump-around

oC 20.00 11.98 3


kmol/h 1113.8 1541.3


The small flow rate (301 kmol/h) of the pump-around of column 3 indicates that at these

operating conditions, this pump-around is not beneficial to the energy saving. Therefore,

the duty of that pump-around can be reduced as much as possible.

Table H.4: Process stream data for optimum unit with preflash

No. Stream name



Enthalpy change(MW)




259

Table H.5: Additional area and cost for optimum unit with preflash

Exchanger No.

Existing area (m2)


Capital cost ($)

2 317.0 52.6+ 16195.4 4 1.7 7.6* 17990.8 5 148.0 19.6+ 8867.8 7 313.6 6.9+ 4700.5 8 307.2 10.3+ 6007.3 9 15.8 2.1+ 2259.5 10 37.7 1.3+ 1696.0 13 254.7 112.7+ 25780.8 18 28.9 145.0+ 43076.9 22 160.0 40.6+ 13829.9

27cu 233.4 44.7+ 14661.3 Total ($) 155,066.5



Table H.6: Optimum operating conditions for unit with preflash drum

Column Optimisation variable Base case

Optimum unit

Preflash Feed preheating temperature oC - 203.8 Feed preheating temperature oC 365.0 368.2 Flow rate of pump-around liquid kmol/h 2186.56 3196.69 Temperature drop across pump-around

oC 30.00 17.07

1


oC 50.00 24.74 2


oC 20.00 13.51 3


kmol/h 1067.48 2534.29


Table H.7: Product flow rates of optimum unit with preflash



% Difference (mole basis)

Light naphtha (LN) 680.69 746.30 +9.6 Heavy naphtha (HN) 493.45 433.65 -12.1 Light distillate (LD) 652.84 643.51 -1.4 Heavy distillate (HD) 149.82 143.95 -3.9 Residue (RES) 633.89 643.29 +1.5


260

Table H.8: Additional area and cost for optimum unit with preflash

Exchanger No.

Existing area (m2)


Capital cost ($)

1 333.6 134.9+ 31670.4 2 316.9 160.2+ 35291.8 4 1.68 550.7* 89743.2 5 147.9 261.0* 60905.2 7 313.5 326.5* 68177.5 8 307.1 101.6+ 26493.8 9 15.8 27.6* 24546.6 10 37.7 103.6* 39725.3 13 254.6 159.9+ 35263.3 15 39.6 22.1+ 10140.1

17hu 14.2 2.1+ 2303.8 18 28.8 23.7* 23477.3

21hu 6.8 4.0+ 3440.8 23cu 45.4 38.7* 27317.8



Table H.9: Process stream data for optimum unit with preflash

No. Stream name



Enthalpy change(MW)




261

1

1 5 7 9

1 3 2 6 8 1 0 1 24

1 13

4

5 1 5

87

21

56

8

8

Figure H.1: Relocation of exchanger 8 for optimum unit with preflash

0

0.2

0.4

0.6

0.8

1

1.2

0 5 10 15 20 25 30 35 40 45

Stage number

FUA

With preflash_baseWith preflash_38%

Figure H.2: FUA curve for existing unit with preflash (stages from column top to bottom)

Appendix I: Data for case study 6.6.2

262


Steam

Steam

Residue

X1

3

4

5

Main column

TPA

MPA

BPA

2

Upper SS

Middle SS

Lower SS

Light distillate

Heavy distillate

Water

Heavy naphtha

Light distillate

Heavy distillate

Residue

Steam

Steam

1

2

3

4

5

+

BPA

MPA Middle SS

Lower SS

TPA

Figure I.1: Redistribution of stages into sections of decomposed column sequence of main distillation column

Table I.1: Number of stages and of main distillation tower sections

Parameter Column section 5 4 3 2 1

Middle SS

Lower SS

Number of stages 5 9 10 8 9 7 5 Diameter (m) 5.5 8.0 8.0 7.5 7.0* 3.5 3.0

*: taken as average diameter for sections 1 and 2

Note that for the new column section, which includes sections 1 and 2, the column

diameter is taken as the smaller one in the optimisation.

Table I.2: Design specifications of prefractionator column

Parameter Specification

Light key component 4 Heavy key component 6 Recovery of light key % 99.81* Recovery of heavy key % 98.66* Reflux ratio R/Rmin 1.01 Mechanism of reboiling Reboiler Temperature of feed oC 230.0 Column pressure bar 2.5

*: values are based on crude oil feed, obtained from base case (Table 6.4, Chapter 4)


263

Table I.3: Operating conditions of main column of unit with prefractionator

Parameter Column 1

Column 2

Column 3

Feed preheating temperature (oC) 365 Column pressure (bar) 2.5 2.5 2.5 Steam flow rate (kmol/h) 1200 200 Pump-around ∆T (oC) 30 50 Pump-around flow rate (kmol/h) 1675.28 1808.40 Pump-around duty (MW) 9.828 14.300 Liquid flow rate between any column and subsequent column (kmol/h)

331.38 142.14

Reflux ratio 2.78 Condenser duty (MW) 22.770 Reboiler duty (MW) 7.698

Table I.4: Product flow rates of unit with prefractionator


Unit with prefractionator

(kmol/h) Light naphtha (LN) 680.7 642.6 Heavy naphtha (HN) 493.5 525.0 Light distillate (LD) 652.8 653.2 Heavy distillate (HD) 149.8 148.2 Residue (RES) 633.9 641.8

Table I.5: Key component recoveries of products for unit with prefractionator

Recovery*of key component of product

Existing unit Unit with prefractionator




264

Table I.6: Process streams for base case and unit with prefractionator

Stream No.

Base case Stream No.


1 Crude oil feed 1 Prefractionator feed 2 Crude oil feed 2 Bottom PA 3 Bottom PA 3 Bottom steam 4 Bottom steam 4 Residue 5 Residue 5 Middle PA 6 Middle PA 6 HD steam 7 HD steam 7 Heavy distillate 8 Heavy distillate 8 Top PA 9 Main column condenser 9 LD reboiler 10 LD reboiler 10 Light distillate 11 Light distillate 11 Condenser 12 Prefractionator condenser 12 Light naphtha 13 Light naphtha 13 HN reboiler 14 Prefractionator reboiler 14 Heavy naphtha 15 Heavy naphtha

4

3

1

8 2 5

1 82 2

5 7 92 6 8 1 0

12

13

15

9

10

7

5

6

14

4 1 2

11

41 13

2 1

1 9

1 6

1 7

1 5

2 6

2 7

2 4

2 3

2 8

2

11 31 4

Figure I.2: Heat exchanger network for unit with prefractionator (see Table I.2, for stream references)


265

Table I.7: Process stream data for unit with prefractionator

No. Stream name

Supply temperature

(oC)

Target temperature

(oC)


1 Prefractionator feed 25.00 230.00 80.63 2 Crude oil feed 251.47 365.00 60.39 3 Bottom PA 303.18 273.18 -9.85 4 Bottom steam 20.00 260.00 18.04 5 Residue 335.11 100.00* -49.84 6 Middle PA 258.98 208.98 -14.32 7 HD steam 20.00 260.00 3.01 8 Heavy distillate 263.07 50.00* -5.31 9 Main column condenser 146.17 126.17 -22.79 10 LD reboiler 272.31 282.19 7.70 11 Light distillate 282.19 40.00* -20.24 12 Prefractionator condenser 110.39 82.32 -20.85 13 Light naphtha 82.32 40.00* -1.43 14 Prefractionator reboiler 224.73 251.47 18.07 15 Heavy naphtha 146.17 40.00* -28.74


Table I.8: Additional area and cost for unit with prefractionator

Exchanger No.

Existing area (m2)


Capital cost ($)

2 316.9 418.1* 77496.7 5 147.9 629.5* 96499.2 7 313.5 348.5* 70498.6 8 307.1 16.9+ 8547.7 9 15.8 468.8* 82332.8 10 37.7 115.3* 41588.2 11 19.0 19.8* 22367.1 13 254.6 104.3+ 26937.7

17hu 14.2 24.4* 23692.5 18 28.8 171.2* 49704.0 22 159.9 48.3+ 16583.2

26cu 117.6 179.5* 50819.3 27cu 233.3 7.7+ 5212.2 28cu 82.4 355.4* 71208.7




266

Table I.9: Operating conditions of main column of optimum unit with prefractionator

Parameter Column 1

Column 2

Column 3

Feed preheating temperature (oC) 365 Column pressure (bar) 2.5 2.5 2.5 Steam flow rate (kmol/h) 1200 200 Pump-around ∆T (oC) 30 50 Pump-around flow rate (kmol/h) 1690.08 1825.38 Pump-around duty (MW) 9.921 14.430 Liquid flow rate between any column and subsequent column (kmol/h)

333.56 143.12

Reflux ratio 2.73 Condenser duty (MW) 22.890 Reboiler duty (MW) 7.717

Table I.10: Product flow rates of optimum with prefractionator

Unit with prefractionator (kmol/h) Product Existing unit (kmol/h) Base Optimum

Light naphtha (LN) 680.7 642.6 630.1 Heavy naphtha (HN) 493.5 525.0 537.5 Light distillate (LD) 652.8 653.2 653.6 Heavy distillate (HD) 149.8 148.2 150.0 Residue (RES) 633.9 641.8 640.6

Table I.11: Key component recoveries for optimum unit with prefractionator

Unit with prefractionator Recovery*of key component of product

Existing unit Base Optimum




267


No. Stream name

Supply temperature

(oC)

Target temperature

(oC)




Table I.13: Additional area and cost for optimum unit with prefractionator

Exchanger No.

Existing area (m2)


Capital cost ($)

2 316.9 228.0* 56983.2 5 147.9 188.8* 52042.8 7 313.5 397.8* 75505.3 9 15.8 0.6+ 1028.2 10 37.7 602.5* 94218.9 12 14.3 28.7* 24836.9 13 254.6 85.1+ 23690.7 15 39.6 61.3* 32168.1

17hu 14.2 28.9* 24890.4 18 28.8 28.1* 24680.6 22 159.9 67.4+ 20457.4

26cu 117.6 187.8* 51916.0 28cu 82.4 396.9* 75413.9




268

Table I.14: Optimum operating conditions for unit with prefractionator


Column

Optimisation variable

Base Optimum

Prefract-ionator

Feed preheating temperature oC 230.0 215.7

Flow rate of pump-around liquid kmol/h 1675.3 1224.1 Flow rate of stripping steam kmol/h 1200.0 1082.9

1

∆T across pump-around oC 30.00 40.11 Flow rate of pump-around liquid kmol/h 1808.4 2101.4 Flow rate of stripping steam kmol/h 200.0 219.9

2

∆T across pump-around oC 50.00 37.25 3 Reflux ratio 2.78 2.70

∆T: temperature drop

Table I.15: Product flow rates of optimum with prefractionator

Unit with prefractionator (kmol/h) Product Existing unit (kmol/h) Base Optimum


Table I.16: Key component recoveries for optimum unit with prefractionator

Unit with prefractionator Recovery*of key component of product

Existing unit Base Optimum




269


No. Stream name

Supply temperature

(oC)

Target temperature

(oC)




Table I.18: Additional area and cost for optimum unit with prefractionator

Exchanger No.

Existing area (m2)


Capital cost ($)

3 20.7 20.9* 22684.7 5 147.9 25.8 11170.5 7 313.5 1006.2* 125231.9 8 307.1 457.0* 81225.0 9 15.8 247.2* 59286.9 10 37.7 586.3* 92833.0 12 14.3 45.6* 28888.6 15 39.6 54.9* 30869.0

17hu 14.2 23.9* 23547.5 22 159.9 64.8 19957.9

25cu 57.4 7.3 5032.0 26cu 117.6 175.0v 50219.7 28cu 82.4 265.0* 61368.3




270

1

1 5 7 9

1 3 2 6 8 1 04 1 24

1 13

4

72

1

8

7

7

Figure I.3: Relocation of exchanger 7 in existing for optimum unit with prefractionator

0

1

2

3

4

5

6

7

8

9

0 10 20 30 40

Stage number

Stag

e di

amet

er (m

)

With prefractionator

Existing diameter

Figure I.4: Stage diameter of unit with prefractionator (stages from column top to

bottom)

Table I.19: Optimum operating conditions for unit with prefractionator (59% increase)

59% Increase in capacity

Column

Optimisation variable

Base Optimum

Prefract-ionator

Feed preheating temperature oC 230.0 218.5

Flow rate of pump-around liquid kmol/h 2663.7 2413.0 1 ∆T across pump-around oC 30.00 33.39 Flow rate of pump-around liquid kmol/h 2875.3 3849.3 2 ∆T across pump-around oC 50.00 47.87

3 Reflux ratio 2.78 2.10 ∆T: temperature drop


271

Table I.20: Optimum design specifications of unit with prefractionator (59% increase)

Parameter Base Optimum

Feed preheat temperature oC 230.0 218.5 Theoretical stages in rectifying section - 4.7 6.2 Theoretical stages in stripping section - 12.1 16.3 Column diameter m 4.97 4.96 Column efficiency % 59.26* 59.26* Reflux ratio - 2.72 1.72# Condenser duty MW 33.15 24.13 Reboiler duty MW 29.21 29.20

*: calculated from Equation 4.62 (Chapter 4); average viscosity = 0.203 cP; #: R/Rmin = 1.01

Table I.21: Product flow rates of optimum with prefractionator (59% increase)

50% Increase on unit with prefractionator (kmol/h)


Base Optimum Light naphtha (LN) 680.7 1021.7 1008.4 Heavy naphtha (HN) 493.5 834.7 841.9 Light distillate (LD) 652.8 1038.5 1039.5 Heavy distillate (HD) 149.8 235.7 237.7 Residue (RES) 633.9 1020.41 1019.5

Table I.22: Recoveries of products for unit with prefractionator (59% increase)

50% Increase on unit with prefractionator

Recovery*of key component of product

Existing unit

Base Optimum




272

Table I.23: Additional area for optimum unit with prefractionator (59% increase)

Exchanger No.

Existing area (m2)


Capital cost ($)

1 333.6 121.0* 29575.6 5 147.9 853.6* 114175.6 7 313.5 648.1* 98040.6 8 307.1 1044.9* 127937.5 9 15.8 750.1* 106255.5 10 37.7 141.2* 45502.5 11 19 77.4* 35220.1 13 254.6 145.7+ 33248.9 15 39.6 139.8* 45295.3

17hu 14.2 48.9* 29606.0 18 28.8 61.9* 32287.1

19hu 4.6 0.7+ 1099.0 22 159.9 200.4* 53540.9

23cu 45.4 22.9+ 10362.5 25cu 57.4 35.6+ 13676.4 26cu 117.6 227.2* 56887.4 27cu 233.3 141.3+ 32611.1 28cu 82.4 695.3* 101900.3



Table I.24: Process stream data for optimum unit with prefractionator (59% increase)

No. Stream name

Supply temperature

(oC)

Target temperature

(oC)




Appendix J: Data for case study 6.7

273


1 5 7 9

1 3 2 6 8 1 04 1 24

1 13

Crude oil streamBranch 1

Branch 1

Figure J.1: Existing exchangers to be enhanced, crude oil is split into 2 branches

Table J.1: Data for exchanger units

Exchanger No.

Area (m2) hshell / htube UA (kW/oC)

1 333.6 1.8 166.821 2 316.9 1.9 158.481 3 20.7 1.2 10.377 4 1.68 1.4 0.844 5 147.9 1.9 73.979 6 152.5 1.3 76.261 7 313.5 2.0 156.780 8 307.1 1.3 153.577 9 15.8 1.8 7.902 10 37.7 1.7 18.873 11 19.0 1.3 9.535 12 14.3 1.5 7.155 13 254.6 2.3 127.328

hshell: shell-side heat transfer coefficient htube: tube-side heat transfer coefficient

Table J.2: Maximum area for heat transfer enhancement

Exchanger No.

Area (m2) hshell / htube Maximum* area (m2)

Total effective area (m2)

1 333.6 1.8 133.4 467.0 2 316.9 1.9 142.6 459.5 3 20.7 1.2 2.1 22.8 4 1.68 1.4 0.3 2.0 5 147.9 1.9 66.6 214.5 6 152.5 1.3 22.9 175.4 7 313.5 2.0 156.8 470.3 8 307.1 1.3 46.1 353.2 9 15.8 1.8 6.3 22.1 10 37.7 1.7 13.2 50.9 11 19.0 1.3 2.9 21.9 12 14.3 1.5 3.6 17.9 13 254.6 2.3 165.5 420.1

*: calculated from equation 2.10 (Chapter 2)


274

Table J.3: Calculations of model parameters for retrofit with enhancement


99.0 96.6 83.8 82.5 75.5 68.7 Total additional

area (m2) 4015.8* 85.1 1625.6 1864.3 2379.1 3279.3 Retrofit area

(m2) 4015.8* 4100.9 5641.4 5880.1 6394.9 7295.1 Exchanger No. Exchanger total additional area required (m2)

12 14.3* 10 37.7* 20.6 495.3 506.8 8 307.1* 6 152.5* 4 1.7* 1.6 300.1 324.7 197.4 2 316.9* 298.3 311.4 252.6 230.0 11 19.0* 9 15.8* 32.0 49.7 146.3 146.3 126.1 7 313.5* 77.4 123.1 208.7 5 147.9* 425.1 597.4 620.1 495.6 3 20.7* 48.9 1 333.6* 147.6 55.4 13 254.6* 155.6 164.7 306.6 563.8

*: existing exchanger area

Table J.4: Calculations of model parameters for retrofit with enhancement


99.0 96.6 83.8 82.5 75.5 68.7 Additional area

(m2) 4015.8* 85.1 1625.6 1864.3 2379.1 3279.3 Retrofit area

(m2) 4015.8* 4100.9 5641.4 5880.1 6394.9 7295.1 Exchanger No. Additional area after excluding area with enhancement (m2)

12 10.7 0.0 0.0 0.0 0.0 0.0 10 24.5 7.4 0.0 0.0 482.1 493.6 8 245.7 0.0 0.0 0.0 0.0 0.0 6 129.6 0.0 0.0 0.0 0.0 0.0 4 1.3 1.3 299.8 324.4 197.0 0.0 2 174.3 0.0 155.7 168.8 110.0 87.4 11 16.2 0.0 0.0 0.0 0.0 0.0 9 9.5 25.7 43.4 140.0 140.0 119.8 7 156.8 0.0 0.0 0.0 0.0 52.0 5 81.3 0.0 358.6 530.9 553.5 429.1 3 18.6 0.0 0.0 0.0 46.8 0.0 1 200.2 0.0 14.1 0.0 0.0 0.0


275

13 89.1 0.0 0.0 0.0 141.2 398.3

Added area after HTE 1157.8 34.4 871.5 1164.1 1670.6 1580.2

Net+ total area (m2) 3238.3 4081.0 5136.5 5366.9 5875.2 6744.2

*: existing exchanger area; HTE: heat transfer enhancement; +: physical area only

The types of modifications to the existing network and the locations of the modified

exchanger units for this study can be seen in Section C.2 (Appendix C).

Table J.3 gives the total additional area required by each exchanger unit on the crude oil

stream for every energy consumption level. These exchangers area are obtained from

the same retrofit study carried out in Section C.2 (Appendix C). However, Table J.4

gives the physical exchanger required after exploiting heat transfer enhancement (HTE)

capabilities. The exchanger additional area is then compared with the maximum area

(Table J.2) that can be achieved if the exchanger is enhanced.

For specific exchanger unit, if the total area required is larger than the maximum

amount, heat transfer enhancement is applied to this exchanger and extra physical area

will be necessary. This extra area is the difference between the total area required and

the maximum amount that can be provided through enhancement. Typical example for

this condition is exchanger unit number 2 and energy consumption of 83.8 MW. Total

exchanger area required is 298.3 m2 (Table J.3); while maximum area available with

enhancement is 142.6 m2 (Table J.2). Therefore, enhancement is applied for the

maximum area; then, extra physical area of 155.7 m2 (= 298.3 – 142.6) (Table J.4) will

be needed to provide the required area difference.

On the other hand, if the area required is less or equal to the maximum area available

with enhancement, no physical area is needed and only heat transfer enhancement is

sufficient to provide the total required area (e.g. exchange unit number 7; energy

consumption of 82.5 MW).

The above procedure is applied to all exchanger units alongside for all energy

consumption levels. Finally, for all energy consumption levels, the total exchanger area

that is required in addition to heat transfer enhancement is determined. Then, the net

area required by exchangers after applying heat transfer enhancement can be evaluated

(see Table J.4).


276

Data of net retrofit area and energy consumption are plotted (Figure J.2), and compared

with the data for existing HEN without enhancement. The data are then regressed to

obtain the retrofit model parameters m′ and c′ (see equation 4.48, Chapter 4). The

regressed model parameters m′ and c′, are 1.5145751·107 (m2/MW) and -1.8138

respectively.

y = 15145751.6090x-1.8138

y = 9883123.3138x-1.6959

2000

3000

4000

5000

6000

7000

8000

35 45 55 65 75 85 95 105


Ret

rofit

are

a (m

2)

Existing unit

With HTE

Existing HEN

HEN with HTE

Figure J.2: Retrofit curve and data regression for existing HEN with heat transfer enhancement (HTE) (horizontal dotted line represents the existing exchanger total area)

Table J.5: Optimum operating conditions for unit with heat transfer enhancement


Optimum value


1


2


3


kmol/h 1067.48 632.58



277

Table J.6: Product flow rates of optimum unit with enhancement




Table J.7: Key component recoveries of products for optimum unit with enhancement

Recovery* of key component of product

Existing unit Optimum unit



Table J.8: Additional and enhancement area for optimum unit with enhancement

Distribution of required area (m2)

Unit No.

Existing area (m2)


Enhanced New area

Capital cost of new ($)

1 333.6 83.1 83.1 0.0 0.0 2 316.9 123.3 123.3 0.0 0.0 4 1.7 345.7 0.3 345.3* 70168.7 5 147.9 546.7 66.6 480.2* 83383.6 7 313.5 252.5 156.8 95.7+ 25515.6 9 15.8 31.7 6.3 25.4* 23955.3 10 37.7 23.3 13.2 10.1+ 6175.1 13 254.6 216.0 165.5 50.5+ 17048.8

16hu 25.2 5.5 5.5+ 4225.4 18 28.8 201.1 201.1* 53631.0

19hu 4.6 8.3 8.3* 18353.0 22 159.9 49.6 49.6+ 16859.5

26cu 117.6 20.6 20.6+ 9708.1 Total ($) 329,024.1 *: Capital cost ($) = 13000 + 1530 × (additional area) 0.63



278

Table J.9: Process stream data for optimum unit with heat transfer enhancement

No. Stream name






Figure J.3: Retrofit modifications to existing preheat train, showing types of exchanger additional area

1

3

8

9

7

6

2

2 5

1 82 2

1 3 6 8

11

12

14

4

5

13

1 2

10

1 41 13

2 1

1 9

1 6

1 7

1 5

2 6

2 7

2 4

2 3

2 8

Additional area

4

Relocation

42

Additional and enhancedareaEnhanced areaX

1X

X

X

5X

7X

4X

9X

1 0X

X

Doctor of Philosophy Mamdouh Ayad Gadalla Prof. Robin ...

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