MODELING OF VACUUM GAS OIL HYDROCRACKING...
Transcript of MODELING OF VACUUM GAS OIL HYDROCRACKING...
MODELING OF VACUUM GAS OIL HYDROCRACKING PROCESS
USING LUMPING APPROACH
SEPEHR SADIGHI
A thesis submitted in fulfilment of the
requirements for the award of the degree of
Doctor of Philosophy (Chemical Engineering)
Faculty of Chemical Engineering
Universiti Teknologi Malaysia
SEPTEMBER 2012
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Dedicated to:
My father and mother For their love and concerns.
To my supervisor,
Professor Dr. Arshad Ahmad, Thank you for your kindness and your helpful guidelines.
For all my kind-hearted friends, Thank you for being so much kind and helpful,
Allah will bless all of u.
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ACKNOWLEDGEMENTS
First and foremost, in a humble way I would like to give all the Praise to
Allah, the Almighty God for with his mercy has given me the strengths and time to
complete my research.
I am deeply indebted to my supervisor, Prof. Dr. Arshad Ahmad for his great
guidance and endless supports throughout completing this project. His guidance,
helps and patience are actually encouraging me more in finishing and pursuing
further in this field.
Thanks also to Dr. Akbar Irandoukht from Research Institute of Petroleum
Industry (RIPI) and Dr. Mansoor Shirvani from Iran University of Science and
Technology (IUST) in helping and consulting me to conduct my PhD project.
Additionally, I acknowledge Dr. Mehdi Rashidzadeh who is the head of Catalysis
Research Center of Research Institute of Petroleum Industry (RIPI) because of his
supports. Not forgotten, I also acknowledge Mr. Reza Mohaddecy and Mr. Kamal
Masoudian for helping me to gather the required data to do this project.
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ABSTRACT
Conversion of heavy feedstock into lighter products in a hydrocracking
process has a strong influence on the profitability of petroleum refining. To facilitate
initiatives toward improving the process efficiency, a good process model is
required. This thesis discusses the development and application of lump modeling
strategies in a pilot plant and industrial scale hydrocracking processes. For the pilot
plant, two different four-lump models are considered, i.e., combined bed and dual
bed models. Unlike the simpler combined bed model, the dual bed model includes
hydrogen consumption and hydrotreating reactions, and the reactor is subdivided into
two different layers so that the effect of hydrotreating reactions can be integrated. To
extend the application to a commercial refinery, a full-lump model is configured to
predict the product yields of a commercial hydrocracking unit known as Isomax.
Using bed temperatures, flow rate of fresh vacuum gas oil (VGO), recycle rate and
catalyst life as process variables, the model is proven capable of predicting the yield
of all products. The model also provides improvement to previous works by
considering liquefied petroleum gas (LPG), light gases, fresh VGO and recycle feed
as separate lumps. This model is then used in optimizing the reactor operation where
the bed temperature, flow rate of fresh VGO and combined feed ratio are adjusted to
increase the plant profitability whilst maintaining all process limitations and
operating constraints. To extend the model for dynamic applications, a modified
space-time conservation element and solution element method (CE-SE) is
introduced. The results obtained from simulation of the pilot plant based on four-
lump model using CE-SE method are comparable to those obtained using finite
difference method, thus providing opportunities for further works involving dynamic
behavior of the process. Although the study is focussed on hydrocracking, this thesis
has proved the practicality of using lumped modeling technique to address model
development requirement for complex industrial processes.
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ABSTRAK
Penukaran bahan mentah berat kepada produk yang lebih ringan dalam proses
pemecahan-hidro mempunyai pengaruh yang kuat ke atas keuntungan proses
penapisan petroleum. Untuk memudahkan inisiatif ke arah meningkatkan kecekapan
proses, satu model yang baik diperlukan. Tesis ini membincangkan pembangunan
dan aplikasi strategi permodelan tergumpal dalam proses pemecahan-hidro berskala
loji pandu dan industri. Untuk proses berskala loji pandu, dua model empat-gumpal
dipertimbangkan, iaitu model lapisan-tergabung dan model dwi-lapisan. Berbeza
dengan model lapisan-tergabung yang lebih mudah, model dwi-lapisan mengambil-
kira penggunaan hidrogen dan tindakbalas hidrorawat, dan reaktor dibahagikan
kepada dua bahagian yang berbeza supaya kesan daripada tindakbalas hidrorawat
boleh disepadukan. Bagi membolehkan penggunaan untuk penapisan komersil,
model gumpal-penuh telah dibentuk untuk meramalkan hasil produk unit
pemecahan-hidro yang dipanggil Isomax. Dengan menggunakan suhu lapisan, kadar
aliran minyak gas vakum (VGO) segar, kadar kitar semula dan hayat pemangkin
sebagai input kepada model, peramalan yang baik untuk semua produk telah
diperolehi. Model ini juga menyediakan penambahbaikan kepada kerja-kerja
sebelumnya dengan mempertimbangkan VGO, LPG, gas ringan, suapan segar dan
kitar semula sebagai gumpalan berasingan. Seterusnya, ia digunakan dalam
mengoptimumkan operasi reaktor di mana suhu lapisan, kadar alir VGO segar dan
nisbah suapan tergabung diselaraskan untuk meningkatkan keuntungan loji dengan
mengambilkira semua batasan proses dan kekangan operasi. Untuk menambah
penggunaan model gumpalan kepada aplikasi dinamik, teknik permodelan ruang-
masa elemen keabadian elemen penyelesaian (CE-SE) diperkenalkan. Keputusan
yang diperolehi dari simulasi loji pandu berasaskan empat-gumpal adalah setanding
dengan apa yang diperolehi dengan menggunakan kaedah beza terhingga, sekaligus
membuka peluang kepada kajian lanjut yang melibatkan tingkah laku dinamik.
Walaupun kajian ini memfokus kepada proses pemecahan-hidro, tesis ini telah
membuktikan kesesuaian penggunaan teknik permodelan tergumpal bagi memenuhi
keperluan permodelan bagi proses-proses yang kompleks di industri.
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TABLE OF CONTENTS
CHAPTER TITLE PAGE
DECLARATION
DEDICATION
ACKNOWLEDGEMENTS
ABSTRACT
ABSTRAK
TABLE OF CONTENTS
LIST OF TABLES
LIST OF FIGURES
LIST OF ABBREVIATIONS
LIST OF SYMBOLS
LIST OF APPENDICES
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1 INTRODUCTION
1.1 Background of Research
1.2 Problem Statement
1.3 Research Objectives
1.4 Scope of Study
1.5 Significant of the Study
1.6 Layout of the Thesis
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2 LITERATURE REVIEW
2.1 Introduction
2.2 Hydrocracking process
2.2.1 Process Description
2.2.2 Feedstock and Products
2.2.3 Hydrocracking Reactor
2.2.4 Hydrocracking Catalysis
2.2.5 Process Variables
2.2.6 Chemical Reactions
2.3 Steady State Modeling Methodology
2.3.1 Continuous Lumping Models
2.3.2 Discrete Lumping Models
2.4 Dynamic Modeling
2.4.1 Application of the Dynamic Model
2.5 Process Optimization
2.6 Summary
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3 RESEARCH METHODOLOGY
3.1 Introduction
3.2 Data Gathering from Pilot Scale Plant
3.2.1 Pilot Scale Hydrocracking Plant
3.2.2 Pilot Scale Catalyst
3.2.3 Feed and Product Characterization
3.2.4 Test Conditions
3.3 Data Gathering from Industrial Scale Unit
3.4 Modeling Methodology
3.4.1 Steady State Model
3.3.2 Dynamic Model
3.5 Simulation Studies
3.5.1 Software Used
3.5.2 Simulations
3.5.3 Determination of Model Accuracy
3.6 Summary
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4 MODELING A PILOT SCALE HYDROCRACKING
REACTOR
4.1 Introduction
4.2 Experimental Results
4.3 Modeling a Pilot Scale Hydrocracking Reactor
4.3.1 Reaction Models
4.3.2 Combined Bed Model
4.3.3 Dual Bed Model
4.4 Model Development
4.5 Results and Discussions
4.6 Summary
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MODELING AND OPTIMIZATION OF THE
I NDUSTRIAL VGO HYDROCRACKING PROCESS
5.1 Introduction
5.2 Industrial Data
5.3 Modeling of VGO Hydrocracking Process
5.3.1 Reactor Model
5.3.2 Separation Section
5.3.3 Parameter Estimation, Simulation and
Evaluation
5.3.4 Results and Discussion
5.4 Process Optimization
5.4.1 Profit Function
5.4.2 Developing the Optimizer
5.5 Results and Discussions
5.5.1 Profit Achievements
5.5.2 Optimal Decision Variables
5.6 Summary
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DYNAMIC MODELING OF THE PILOT SCALE
REACTOR
6.1 Introduction
6.2 Developing Dynamic model
6.2.1 Dynamic Mass Conservation
6.2.2 Numerical Methods
6.2.2.1 Finite Difference Method (FDM)
6.2.2.2 The Space-time Conservation Element
and Solution Element Method (CE-SE)
6.3 Results and Discussions
6.3.1 Steady State Analysis
6.3.2 Stability Analysis
6.3.3 Dynamic Simulation
6.4 Summary
CONCLUSIONS AND RECOMMENDATIONS
7.1 Summary
7.2 Conclusions
7.3 Recommendations
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REFERENCES
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Appendices A-E 154-174
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LIST OF TABLES
TABLE NO. TITLE PAGE
2.1
3.1
3.2
3.3
3.4
3.5
4.1
4.2
4.3
4.4
4.5
4.6
4.7
4.8
4.9
4.10
Typical feedstocks of hydrocrackers
Characteristics of HDT&HDC catalyst
Properties of fresh VGO and recycle feed
Average properties of hydrocracking product
Variation in properties of fresh vacuum gas oil in
industrial Case
Catalyst Specifications of hydrocracking process in
industrial case
Yield percent of hydrocracking process basis on the fresh
feed
Average properties of hydrocracking product
Major reactions in the hydrotreating of VGO
Coefficient values for the hydrogen consumption
Kinetic parameters for complete network of combined
bed model
Kinetic parameters for reduced network of combined bed
model
Kinetic parameters for the complete network of the dual
bed model
Kinetic parameters for the reduced network of the dual
bed model
The AAD% for the different lumping models
ANOVA results of the developed model
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5.1
5.2
5.3
5.4
5.5
5.6
5.7
5.8
5.9
6.1
Bed temperature in reactors vs. operating time
Feed and recycle flowrates of Isomax plant vs. operating
time
Average properties of hydrocracking product
The yield of the hydrocracking products for Isomax case
studies
Kinetic parameters for the complete model
Kinetic and deactivation parameters for the reduced
model
Electrical and heating equipment of Isomax unit
Price of Energy, feed and products in the target refinery
Profit and energy expenses of Isomax unit
Comparison between the AAD% of CE-SE & FDM for
the case studies
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LIST OF FIGURES
FIGURE NO. TITLE PAGE
1.1
2.1
2.2
2.3
2.4
2.5
2.6
2.7
3.1
3.2
3.3
3.4
3.5
3.6
3.7
3.8
3.9
4.1
4.2
4.3
4.4
Flowchart of the research design
Schematic flow plan for single-stage hydrocracking plant
Schematic Flow Plan for Two Stage Hydrocracking Unit
Trickle-bed flow
Schematic diagram of a commercial hydrocracking
reactor
Steps of modeling hydrocracking process
Description of a feed stock as discrete components
Five-lump discrete-lump model proposed by Sanchez
Flowchart of the research methodology of Chapter 4
Flowchart of the research methodology of Chapter 5
Flowchart of the research methodology of Chapter 6
Simplified process flow diagram of hydrocracking set up
Sections of catalytic reactor bed
Block flow diagram of the hydrocracking process
A simplified scheme of the hydrocracking process in the
commercial case
Schematic representation of series mixed cells
Control volume of the catalyst inside the catalytic bed
Gas yield vs. Temperature.
Naphtha yield vs. Temperature
Distillate yield vs. Temperature
Unconverted oil yield vs. Temperature
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4.5
4.6
4.7
4.8
4.9
4.10
4.11
4.12
4.13
5.1a
5.1b
5.2
5.3a
5.3b
5.4
5.5a
5.5b
5.5c
5.6
5.7
Aromatic content vs. Temperature
The complete four-lump kinetic model
Schematic representations of the developed lumping
models (A) Combined bed model (B) Dual bed model
The reduced 4-lump kinetic network for the combined bed
model
The reduced 4-lump kinetic network for the dual bed
model
Predicted yields, measured yields and deviation plots for
the prediction of gas lump
Predicted yields, measured yields and deviation plots for
the prediction of naphtha lump
Predicted yields, measured yields and deviation plots for
the prediction of distillate lump
Predicted yields, measured yields and deviation plots for
the prediction of VGO lump
(a) The complete eight-lump kinetic model
(b) The reduced eight-lump kinetic model
The scheme of the process flow diagram of Isomax
optimizer
Parity plot for the predicted gas, LPG, LN and HN yields
Parity plot for the predicted Ker., Dis. and offtest yields
Comparison of the optimized and the actual yields of
Isomax plant
Comparison of the optimized and the actual yields of gas
in Isomax plant
Comparison of the optimized and the actual yields of
LPG, LN and HN in Isomax plant
Comparison of the optimized and the actual yields of
Kersosene, Diesel and Offtest in Isomax plant
Comparison of the optimized and the actual WABTS
Comparison of the optimized and the actual CFR of
Isomax plant
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5.8a
5.8b
5.8c
6.1
6.2
6.3
6.4
6.5
6.6
6.7
6.8
6.9
6.10
Comparison of the optimized and the actual bed
temperatures of the first Isomax reactor
Comparison of the optimized and the actual bed
temperatures of the second Isomax reactor
Comparison of the optimized and the actual bed
temperatures of the third Isomax reactor
The space-time mesh for the domain time versus the
catalyst volume
Solving VGO hydrocracking dynamic model using CE-
SE
The response of the dynamic model solved by CE-SE
from (LHSV=0.8 h-1, T=360ºC) to (LHSV=1.05 h-1,
T=390ºC), Co<1
The response of the dynamic model solved by CE-SE
from (LHSV=0.8 h-1, T=3600C) to (LHSV=1.05 h-1,
T=390ºC), Co~1.047
The response of the dynamic model solved by CE-SE
from (LHSV=0.8 h-1, T=3600C) to (LHSV=1.05 h-1,
T=390ºC), Co ~1.057
The response of the dynamic model solved by CE-SE &
FDM from (LHSV=0.8 h-1, T=3600C) to (LHSV= 0.8
h-1, T=3900C), CO max=0.989
The response of the dynamic model solved by CE-SE &
FDM from (LHSV=0.8 h-1, T=3600C) to (LHSV=1.05
h-1, T=3900C), CO max=0.979
The response of the dynamic model solved by CE-SE &
FDM from (LHSV=1.05 h-1, T=3900C) to (LHSV=0.8
h-1, T=3600C), CO max=0.93
Convergence time and courant number of case studies
vs. time-step-size
Convergence time (■) and AAD% (▲) of case studies
vs. dissipation factor
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LIST OF ABBREVIATIONS
AAD - Absolute Average Deviation, %
ABT - Average bed temperature
ACM - Aspen Custom Modeler
ANOVA - Analysis of variance
AGO - Atmospheric gas oil
BASF - Badische Anilin und Soda Fabrik
CE-SE - Conservation element-solution element
CFR - Combined feed ratio
Co - Courant number
DF - Degree of freedom
FBP - Final boiling point, °C
FCC - Fluid catalytic cracking
FD - Flash drum
FDM - Finite difference method
FEM - Finite element method
FVM - Finite volume method
Frac - Fractionation tower
GS - Stabilizer tower
HCGO - Heavy cycle gas oil
HDA - Hydrodearomatization
HDC - Hydrocracking
HDN - Hydrodenitrogenation
HDS - Hydrodesulfurization
HDT - Hydrotreating
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HPS - High pressure separator
IBP - Initial boiling point, °C
LCO - Light cycle oil
LCGO - Light cycle gas oil
LHSV - Liquid Hourly Space Velocity, h-1
LPG - Liquefied petroleum gas
LPS - Low pressure separator
ODE - Ordinary differential equation
SE - Solution element
SPA - Sponge absorber
Sp.gr - Specific gravity
SQE - Sum of squared error
SRD - Straight Run Diesel
Stab Gas - Stabilizer gas
VGO - Vacuum Gas Oil
WABT - Weighted average bed temperature
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LIST OF SYMBOLS
A - Space-time region A in the domain
B - Price of the unit of product, feed or electricity
C - Mass concentration, kg/m3
C' - Functional mass concentration, kg/m3
Cp - Heat capacity of lumps, kJ/kg.°C
Cpf - Heat capacity of VGO feed, kJ/kg.°C
Cph - Heat capacity of hydrogen, 14.21 kJ/kg.°C
C1 - Methane in light gas
C2 - Ethane in light gas
C3 - Hydrocarbons with 3 carbons in LPG
C4 - Hydrocarbons with 4 carbons in LPG
C5 - Hydrocarbons with 5 carbons in LPG
C6 - Hydrocarbons with 6 carbons in LN
D - Distillate or Diesel
E - Apparent activation energy, kcal/mol
Efht - Fuel consumption of the furnace, m3/h
Efhx - Fuel consumption of the reactor furnace, m3/h
EE-l - Fuel consumption of reboiler, m3/h
Ep-l - Electrical consumption of the pump, kW
ET-502 - Fuel consumption of the turbine 502 in Isomax plant, m3/h
ET-531 - Fuel consumption of the turbine 531 in Isomax plant, m3/h
Effht - Thermal efficiency of the furnace, 0.735
Effhx - Thermal efficiency of the reactor furnace, 0.685
Expen - Energy consumption of Isomax unit
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f - Concentration-flow function
F - Feed lump
Fm - Mass flow rate of stream through the reactor, kg/h
G - Gas
hr
- Current density vector in the domain
H2 - Hydrogen
H2S - Hydrogen sulfide
Hf - Heating value of the fuel gas, 5.327´104 kJ/kg
HN - Heavy Naphtha
i - Cell number
j - Discretized volume of the catalyst
k - Reaction rate constant, m3/(h.m3 cat)
kE-l - Energy coefficient of reboiler, kJ/m3
kh - Consumed mass of hydrogen per converted mass of VGO
kp-l - Energy coefficient of pump, kW.h/m3
kT-502 - Fuel coefficient of H2 turbine 502 in Isomax plant, m3/h
kT-531 - Fuel coefficient of feed turbine 531 in Isomax plant, m3/h
k0 - Frequency factor, m3/(h.m3 cat)
K - Kerosene
Kc - Proportional gain, °C/%)],
LN - Light Naphtha
meas - Measured yield 0m - Mass flow rate, kg/h
M - Element of catalyst in the commercial reactor
Mw - Molecular weight
Mws - Molecular weight of sulfur (32)
MwN - Molecular weight of nitrogen (14)
n - Time step-size
nr
- Outward unit normal of a surface element
N - Naphtha
NH3 - Ammonia
Nv - Number of cells (200)
Nt - Number of total experiments
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Opc - Operating charges of Isomax unit
p - Number of test runs 0
mP - Mass flow rate of product leaving fractionator, kg/h
Pred - Predicted yield
Profit - Profit of the Isomax plant
Q - Volume flow rate
QE-l - Fluid flow rate of the heavy naphtha reboiler, m3/h
Qp-l - Fluid flow rate of the product in Isomax , m3/h
r - Rate of VGO converted to the hydrocracking products, kg/h
R - Ideal gas constant, 1.987 kcal.kmol-1.K-1
Rl - Reaction rate of lump l, kg/(h.m3 cat)
R1-3 - Reactors of Isomax plant (three parallel reactors)
S - Surface region or Real plant selectivity
Ssp - Set point selectivity
S - Surface region
t - Time domain (h)
tc - Accumulated feed
T - Temperature, K or R
Teff - Temperature of the effluent exchanger, 363°C
TH - Output stream of the furnace, 120°C
u - Feed, distillate, naphtha and gas lumps
UN - Unconverted oil
V - An arbitrary but constant volume element of the domain
Vb - Total Volume of bed, m3
Vcat - Volume of catalyst per cell, m3
W - Weight of catalyst
wt% - Weight percent
x - Catalyst volume domain; Number of Isomax reactor
X - Mass fraction of lumps
Xc - Weight fraction of catalyst in Isomax bed.
Y - Yield of products
a - Consumed mass of hydrogen per mass of VGO (chapter 4) or Catalyst deactivation (chapter 5)
120 ,..bb - Coefficient values for the hydrogen consumption
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s - Area unit normal of a surface element
D - Difference between actual and optimized values
tD - Time-step
vD - Volume step
e - Dissipation factor
'e - Catalyst void fraction
h - Effectiveness factor
n - Catalyst volume element
r - Density, kg/m3
j - Time-Space function in the integral (chapter 6) or Decay function (Chapter 5)
t - Stiff function
actual - Actual value of Isomax plant
Af - Aromatic in feed
Ap - Aromatic in product
b - Catalytic bed
cat - Catalyst
D - Diesel
'Dj - Distillate to lighter lumps
Dh - Diesel in the output stream of hydrotreating bed
e - Electricity
fh - VGO feed or residue in the output stream of hydrotreating bed
f - VGO feed
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LIST OF APPENDICES
APPENDIX. TITLE PAGE
A
B
C
D
E
Hydrogen Consumption for the Pilot Scale VGO
Hydrocracker
AVOVA Analysis
List of Publications in Journals with Impact Factor (ISI)
Abstract of Publications in Journals with Impact Factor
(ISI)
List of Cited Publications in Journals with the Impact
Factor (ISI)
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CHAPTER 1
INTRODUCTION
1.1 Background of Research
The needs for processing heavy feedstock into high value products are
exacerbated by the increasing demand of light oil fractions and decreasing reserves
of light crude oils. This steady growth in fuel demands can only be fulfilled by the
inclusion of heavier feedstock into refinery operations (Hsu and Robinson, 2006),
which can be converted to lighter ones using thermal and/or catalytic processing in
the absence or presence of hydrogen pressure (Gary and Handwerk, 2001). This has
been responded by the introduction of several process licenses for industrial use.
Considering economic aspects, hydrocracking is seen as a suitable option as it
improves the quality and quantity of the refined petroleum products simultaneously
(Balasubramanian and Pushpavanam, 2008), especially if optimum design and
operations are realized. The versatility and flexibility of the process makes it
economically attractive to convert different types of feedstocks into various products
including gas, LPG, naphtha, kerosene and diesel, leading to its widespread
applications.
To enhance the efficiency of the hydrocracking process, the kinetics of the
reactions involved must be fully defined so that the process behavior can be
predicted. This can only be achieved if both the kinetic model and its corresponding
parameters are correctly estimated. Having accurate models, the reactor operations
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can be configured so that desirable product yield distribution was obtained in the
most economical manner.
Ideally, the kinetic model should take into account all elementary reactions,
which include all the different components in the feedstock (Balasubramanian and
Pushpavanam, 2008). However, the complexity of hydrocracking feed makes it
extremely difficult to characterize and describe its kinetic at a molecular level
(Ancheyta et al., 1999). This has given rise to the use of simplified approach by
combining several similar components into groups called lumps. Using “black-box”
concept, the input-output relationships among the lumps are configured to form a
network of reactions that define the process. These models can then be extended for
variety of tasks to facilitate the hydrocracking process including (Valavarasu et al.,
2005):
1) Design tool
2) Automation and optimizing control
3) Finding the optimum operation
4) Catalyst deactivation
In hydrocracking research, two major lumping methods have been used, i.e.,
continuous and discrete lumping approaches. In the first method, the reactive mixture
is considered to form a continuum mixture with respect to its properties such as
boiling point, molecular weight, carbon number or chemical species (Basak et al.,
2004; Elizalde et al., 2009; Elizade and Ancheyta, 2011); but, in the discrete lumping
approach, the reaction network is reduced to the limited number of reactions among
the lumped components. The lumps, based on compound types present in feedstock
and products (e.g., lumps of diesel, kerosene, gasoline, etc.), are often defined by
boiling point ranges. This approach is attractive for kinetic modeling of complex
mixtures because of its simplicity (Ancheyta et al., 2005).
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1.2 Problem Statement
Optimal operating conditions are required in an industrial process operation
to ensure profitability. This is nevertheless hampered by the fact that the plant is
subjected to various uncertainties such as variability in feedstock, changing
environmental condition and upsets in the process itself. To effectively handle these
difficulties, the process behavior must be understood and predictable using some
form of process models. Using these models product yields are estimated and
sensitivity analyses can be carried out so that the effect of operating parameters like
reactor temperature, pressure and space velocity on product yields can be sensed.
The model can also be extended for the use in optimization, control, design of new
plants and selection of appropriate hydrocracking catalysts.
A number of works have been presented in the literatures, but a full lump
model for an industrial hydrocracking plant that predicts and optimizes the yield of
all products has not been reported. Furthermore, if the steady state model can be
extended to a dynamic form, it can be used in dynamic optimization and control.
Additionally the prediction of the dynamic behavior of the reactor during start up or
shut down can be facilitated. It is essential that the chemical engineer have a better
understanding from the dynamic relationship among process and operating variables.
In this instance, little knowledge is a dangerous matter for the process operation
(Wozny and Jeromin, 1994). These serve as motivations for this work.
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1.3 Research Objectives
The main objectives of this research are as follows:
i. Developing a kinetic based steady state model for a pilot scale vacuum gas oil
(VGO) hydrocracking unit by using the discrete-lumping approach.
ii. Developing a practical steady state model and simulator for a commercial VGO
hydrocracking unit.
iii. Applying discrete-lumping approach in the constrained optimization of a
commercial VGO hydrocracking plant.
iv. Developing a new dynamic model for a pilot scale VGO hydrocracking reactor
by applying the “conservation element-solution element” or “CE-SE” method.
1.4 Scope of Study
The scope of this work consists of the following actions:
I. Gathering the experimental data from a pilot scale VGO hydrocracking plant
II. Developing a steady state lumped kinetic-based model for the pilot scale
plant and comparing the effect of hydrogen consumption on the accuracy of
the yield prediction.
III. Gathering the industrial data from a commercial VGO hydrocracking plant.
IV. Developing a commercial full lump kinetic-based model including of catalyst
deactivation to simulate the yield of all precious products such as diesel and
gasoline.
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V. Optimizing the operating parameters of an industrial VGO hydrocracking
plant to maximize the profit of the process.
VI. Developing a dynamic model for the pilot scale plant using the lumping
approach. To simulate and verify the dynamic behavior of the pilot plant.
Figure 1.1 illustrates the flow of this research for covering all the mentioned
scopes of study.
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Figure 1.1 Flowchart of the research design
MODELING OF VACUUM GAS OIL HYDROCRACKING PROCESS BY USING LUMPING APPROACH
Literature Review
i) Studying hydrocracking and hydrotreating process ii) Studying modeling and optimizing approaches in literature
Data Gathering
i) Pilot scale experiments to develop an improved lumped model ii) Gathering actual data from refineries to develop practical lumped models
Pilot scale model
i) 4-lump combined-bed model ii) 4-lump dual-bed model
Commercial scale model
8-lump practical model (full lump model) including catalyst deactivation
Dynamic model
i) Developing 4-lump dynamic model
ii) Solving the dynamic model using conventional approach
iii) Solving the dynamic model using CE-SE approach
Process Optimization
i) Adding cost elements to the 8-lump practical model
ii) Profit maximization of the commercial plant by using the practical model
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1.5 Significance of the study
The work proved the applicability of lump modeling approach in representing
industrial hydrocracking effectively. Two model configurations for the pilot scale
plant have been investigated and their applications in process analysis and
optimization have been conducted. A simple and practical optimization strategy has
been put forward which can serve as a useful guide in plant operation. The model has
also been extended to include dynamic capability using a powerful, but less common,
CE-SE method. This allows further investigations that require dynamic process
information such as process control, dynamic optimization, alarm analysis and other
control-safety related studies.
In short, this work has brought forward the following contributions:
i. Introducing discrete lumping models for a pilot scale hydrocracking plant.
The main advantage of that over those previously reported in the literatures is
consideration of the hydrogen consumption in kinetic equations and overall
mass balance. This model can predict the yield of hydrocracking products and
hydrogen consumption reasonably well.
ii. Introducing an applicable approach to simulate the product yields of
commercial hydrocracking units during their cyclic operation.
iii. Investigating selective modeling of the catalyst life by using the commercial
data.
iv. Introducing a simple and applicable approach to access the maximum profit
in a commercial hydrocracking unit and/or the similar refinery processes.
v. Incorporating a discrete-lumped kinetic approach to simulate the dynamic
behavior of hydrocracking plants with accurate and robust results.
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vi. Developing CE-SE solving approach to be used for simulating the dynamic
behavior of a VGO hydrocracking reactor. Its accuracy is studied against the
conventional solving method.
1.6 Layout of the Thesis
This thesis is divided into 7 chapters. Following this introductory chapter,
literature review is presented in chapter 2. Then in chapter 3, the research
methodology and the required data to develop pilot and industrial scale lumping
models have been presented.
In chapter 4, after discussing on the results of the pilot scale experiments, it is
explained how by using lumping approach, 4-lump models are developed for the
pilot scale plant. Two different steady state strategies (combined-bed and dual-bed)
have been developed and compared together in this chapter.
Chapter 5 is allocated to develop an applicable discrete lumping model for
being used to predict the product yields of an industrial plant. At first the gathered
data from the understudy commercial plant is presented. Then, a full lump industrial
model is developed, and finally it is used to optimize the profit of the commercial
hydrocracking plant by manipulating the significant process variables.
Then in chapter 6, due to the importance of the dynamic model to control the
performance of hydrocracking process, the steady state results of chapter 4 are
applied to develop a dynamic lumped model. It is shown that this model is capable of
simulating the response of the pilot scale model to the process disturbances. To solve
the developed dynamic model, modern of art conservation element-solution element
(CE-SE) method is used and its accuracy is compared against a conventional solving
approach.
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