THESIS
TECHNO-ECONOMIC FOR BIOETHANOL FROM
LIGNOCELLULOSIC
MONSIKAN VILAIPAN
GRADUATE SCHOOL, KASETSART UNIVERSITY
Academic Year 2019
THESIS APPROVAL
GRADUATE SCHOOL, KASETSART UNIVERSITY
DEGREE:
Master of Engineering (Sustainable Energy and Resources
Engineering)
MAJOR FIELD: Sustainable Energy and Resources Engineering
FACULTY: Engineering
TITLE: Techno-Economic for Bioethanol from Lignocellulosic
NAME: Miss Monsikan Vilaipan
THIS THESIS HAS BEEN ACCEPTED BY
(Assistant Professor Maythee Saisriyoot, Dr.Techn.)
THESIS ADVISOR
(Associate Professor Thongchai Rohitatisha Srinophakun,
Ph.D.)
GRADUATE COMMITTEE
CHAIRMAN
APPROVED BY THE GRADUATE SCHOOL ON
(Associate Professor Srijidtra Charoenlarpnopparut,
Ph.D.)
DEAN
THESIS
TECHNO-ECONOMIC FOR BIOETHANOL FROM LIGNOCELLULOSIC
MONSIKAN VILAIPAN
A Thesis Submitted in Partial Fulfillment of
the Requirements for the Degree of
Master of Engineering (Sustainable Energy and Resources Engineering)
Graduate School, Kasetsart University
Academic Year 2019
C
ABST RACT Monsikan Vilaipan : Techno-Economic for Bioethanol from Lignocellulosic.
Master of Engineering (Sustainable Energy and Resources Engineering), Major
Field: Sustainable Energy and Resources Engineering, Faculty of Engineering.
Thesis Advisor: Assistant Professor Maythee Saisriyoot, Dr.Techn. Academic Year
2019
The techno-economic analysis of bioethanol production plants from
EFB, OPT and various ratios of EFB:OPT is evaluated in this work. The simulation
model and evaluation of economic feasibility were carried out using Aspen Plus,
Aspen Adsorption, Aspen Economic Analyzer software, and Microsoft Office
Excel. The conditions and results to produce an ethanol base on the preliminary
experiment. The evaluation of mass balance, equipment design, scheduling unit
utilization was performed to assess to accomplish the objective. The bioethanol
production process consists of three sections: Pretreatment, Simultaneous
saccharification and fermentation (SSF), and Purification process. The specified
first pretreatment technology was employed for each feedstock to obtain high
breakdown efficiency of their structure. The highest ethanol yield from OPT and
EFB is 3.311 and 3.4 wt%, respectively. The combination of the distillation and
dehydration is considered as a purification process. Two candidate dehydration
technologies, PV and PSA, are compared in order to determine the suitable
technology for bioethanol plants.
The results represent that the bioethanol production plant with PV gives
better economic results compared with PSA. The bioethanol plants required very
high investment cost to produce 99.5 wt% of ethanol, 10,000 L/day. Therefore, the
small plant capacity with large capital and operating costs results in negative NPV.
The highest NPV is -11,247,848 $ with 0.8831 $/unit for OPT plant with
PV. Therefore, these plants are nonprofitable and not recommended for investment.
To increase NPV to positive, the sensitivity analysts suggest increasing ethanol
concentration in fermentation broth by remaining the same condition.
_________________ _______________________________ ____ / ____ / ____
Student's signature Thesis Advisor's signature
D
ACKNOWLEDGEMENT S
ACKNOWLEDGEMENTS
This thesis becomes a reality with the kind support and help of many
individuals. I would like to extend my sincere thanks to all of them.
First of all, I would like to grateful to my thesis advisor, Assistant Professor Dr.
Maythee Saisriyoot, for time, knowledgeable guidance, encouragement, supporting and
attentiveness on my research. I could not have imagined having a better advisor for my
research.
Besides my advisor, I would like to thank the committee members, Associate
Professor Dr. Penjit Srinophakun, Associate Professor Dr. Thongchai Rohitatisha
Srinophakun, and external committee, Assistant Professor Dr. Veerayut
Lersbamrungsuk, for their encouragement, insightful comment, and point of my
mistake.
In addition, a thank you for the scholarship from faculty of Engineering,
Kasetsart University and Thailand Advanced Institute of Science and Technology and
Tokyo Institute of Technology (TAIST-Tokyo Tech) program, under National Science
and Technology Development Agency (NSTDA) for supporting to complete the
research.
Finally, I owe my deepest gratitude to my lovely family, brother and best friend
for the enthusiasm, support, love, and cheerfulness. They all kept me going, and this
research would not have been possible without them.
Monsikan Vilaipan
TABLE OF CONTENTS
Page
ABSTRACT .................................................................................................................. C
ACKNOWLEDGEMENTS .......................................................................................... D
TABLE OF CONTENTS .............................................................................................. E
LIST OF TABLES ........................................................................................................ H
LIST OF FIGURES ....................................................................................................... J
INTRODUCTION ......................................................................................................... 1
OBJECTIVES ................................................................................................................ 3
SCOPES OF WORK...................................................................................................... 3
LITERATURE REVIEW .............................................................................................. 4
1. Ethanol ................................................................................................................... 4
2. Biomass .................................................................................................................. 4
3. Palm oil residue ..................................................................................................... 4
4. Empty Fruit Bunch (EFB) and Oil palm trunk (OPT) ........................................... 4
5. Lignocellulose ........................................................................................................ 5
6. Bioethanol production ........................................................................................... 6
6.1. Pretreatment process ..................................................................................... 6
6.1.1. Mechanical/physical pretreatment .................................................... 7
6.1.2. Chemical pretreatment ..................................................................... 7
6.1.3. Physico-chemical pretreatment ........................................................ 8
6.1.4. Biological pretreatment .................................................................... 8
6.1.5. Hydrothermal pretreatment .............................................................. 8
6.2. Hydrolysis .................................................................................................... 9
6.3. Fermentation ................................................................................................. 9
6.4. Separate hydrolysis and fermentation and Simultaneous saccharification
and fermentation ....................................................................................... 10
7. Purification .......................................................................................................... 11
F
8. Economic Analysis .............................................................................................. 12
8.1. Cost-benefit analysis .................................................................................. 12
8.1.1. Cost analysis ................................................................................... 12
8.1.2. Benefit analysis .............................................................................. 13
8.2. Total Capital Investment ............................................................................ 13
8.3. Net Present Value (NPV) ........................................................................... 14
8.4. Internal rate of return (IRR) ....................................................................... 14
8.5. Payback Period (PB) .................................................................................. 15
8.6. Salvage Value ............................................................................................. 15
8.7. Depreciation ............................................................................................... 15
9. Literature review .................................................................................................. 16
MATERIALS AND METHODS ................................................................................. 22
Materials .................................................................................................................. 22
Methods ................................................................................................................... 22
1. Concept to produce bioethanol from lignocellulose ...................................... 22
2. Mass balance ................................................................................................. 23
3. Concept to design size of equipment ............................................................. 26
4. Scheduling flow process ................................................................................ 26
5. Simulation process of bioethanol by Aspen Plus software ........................... 26
5.1. Pretreatment process .......................................................................... 27
5.2. Simultaneous saccharification and fermentation (SSF) .................... 28
5.3. Purification ........................................................................................ 28
5.3.1. Dehydration ........................................................................ 29
6. Economic assessment .................................................................................... 31
6.1. Total Capital cost ............................................................................... 31
6.2. Total operating cost .......................................................................... 32
7. Sensitivity Analysis ....................................................................................... 34
7.1. The concentration of ethanol from SSF ............................................ 34
7.2. Chemical cost .................................................................................... 35
G
RESULTS AND DISCUSSION .................................................................................. 36
1. Mass balance calculation ..................................................................................... 36
2. Equipment design ................................................................................................ 36
3. Scheduling ........................................................................................................... 37
4. Simulation model ................................................................................................. 42
4.1. Pretreatment model ..................................................................................... 42
4.2. SSF model .................................................................................................. 43
4.3. Purification model ...................................................................................... 43
4.3.1. Optimize parameter of Distillation column .................................... 44
4.3.2. Dehydration with Pervaporation .................................................... 68
4.3.3. Dehydration with Pressure swing adsorption ................................. 70
5. Economic analysis ............................................................................................... 79
5.1. OPT plant ................................................................................................... 79
5.2. EFB plant .................................................................................................... 84
5.3. Two feedstocks plant .................................................................................. 86
6. Sensitivity ............................................................................................................ 87
7. Conclusion ........................................................................................................... 89
LITERATURE CITED ................................................................................................ 91
APPENDICES ............................................................................................................. 94
CURRICULUM VITAE ............................................................................................ 146
LIST OF TABLES
Page
Table 1 The chemical characteristic of raw material and pretreated EFB ................... 19
Table 2 The composition of OPT in each process ....................................................... 23
Table 3 The composition of EFB in each process ....................................................... 24
Table 4 The composition of the 80:20 ratio of EFB:OPT in each process .................. 24
Table 5 The composition of the 50:50 ratio of EFB:OPT in each process .................. 25
Table 6 The composition of the 20:80 ratio of EFB:OPT in each process .................. 25
Table 7 Parameter for economic assessment ............................................................... 34
Table 8 Escalation assumption..................................................................................... 34
Table 9 Mass input and mass output of three plants .................................................... 36
Table 10 The operating time of upstream and SSF and SSF tank utilization for OPT
plant.............................................................................................................................. 39
Table 11 The operating time of upstream and SSF and SSF tank utilization for EFB
and various ratios of feedstocks plants ........................................................................ 41
Table 12 Configuration of a combination of the distillation and pervaporation model
for OPT plant ............................................................................................................... 69
Table 13 Configuration of the combination of distillation and pressure swing
adsorption model for OPT plant .................................................................................. 71
Table 14 Configuration of a combination of the distillation and pervaporation model
for EFB plant................................................................................................................ 72
Table 15 Configuration of a combination of the distillation and pervaporation model
for the two feedstocks plant ......................................................................................... 72
Table 16 Configuration of the combination of distillation and pervaporation model for
distillate various ethanol concentrations in fermentation broth to 80 wt% for OPT ... 73
Table 17 Configuration of the combination of distillation and pervaporation model for
distillate various ethanol concentrations in fermentation broth to 85 wt% for OPT ... 73
Table 18 Configuration of the combination of distillation and pervaporation model for
distillate various ethanol concentrations in fermentation broth to 90 wt% for OPT ... 74
Table 19 The component of total capital cost for the OPT plant ................................. 81
I
Table 20 The component of total operating cost for the OPT plant ............................ 82
Table 21 Economic result for OPT plant ..................................................................... 82
Table 22 The component of total capital cost for EFB plant ....................................... 84
Table 23 The component of total operating cost for EFB plant .................................. 85
Table 24 Economic result for EFB plant ..................................................................... 85
Table 25 The component of total capital cost for two feedstocks plant ...................... 86
Table 26 The component of total operating cost for two feedstocks plant .................. 86
Table 27 Economic result for two feedstocks plant ..................................................... 87
Table 28 Economic result for various Ctec2 price ....................................................... 87
Table 29 Economic result for distillation 4, 6, 8 wt% to 80 wt% ................................ 88
Table 30 Economic result for distillation 4, 6, 8 wt% to 85 wt% ................................ 88
Table 31 Economic result for distillation 4, 6, 8 wt% to 90 wt% ................................ 89
LIST OF FIGURES
Page
Figure 1 Concept for bioethanol production .................................................................. 6
Figure 2 Pretreatment of lignocellulosic materials before bioethanol and biogas
production ...................................................................................................................... 7
Figure 3 The process for ethanol production from lignocellulosic biomass ................ 10
Figure 4 Energy demand .............................................................................................. 16
Figure 5 Multistage pervaporation process .................................................................. 17
Figure 6 The total annual cost in the purification process ........................................... 17
Figure 7 Flow diagram of batch fermentation ............................................................. 20
Figure 8 Flow diagram of continuous fermentation–pervaporation process ............... 20
Figure 9 Flow diagram of steam explosion .................................................................. 21
Figure 10 The scheme of the production process ........................................................ 23
Figure 11 Overview of the bioethanol production process from OPT......................... 27
Figure 12 Overview of the bioethanol production process from EFB ......................... 27
Figure 13 Overview of the bioethanol production process from various ratios of OPT
and EFB ....................................................................................................................... 27
Figure 14 Purification section ...................................................................................... 29
Figure 15 Operating time of the initial SSF tank of OPT plant ................................... 38
Figure 16 Equipment utilization for two consecutive batches for OPT plant .............. 38
Figure 17 Operating time of the initial SSF tank of EFB and two feedstocks plants .. 40
Figure 18 Equipment utilization for two consecutive batches for EFB and two
feedstocks plants .......................................................................................................... 40
Figure 19 Simulation model of the three pretreatment processes for OPT.................. 42
Figure 20 Simulation model of the three pretreatment processes for EFB .................. 42
Figure 21 Simulation model of the media preparing, sterilizing and SSF process ...... 43
Figure 22 Sensitivity number of stages of distillation column for case 1 of OPT plant
with pervaporation technology .................................................................................... 45
K
Figure 23 Sensitivity molar reflux ratio of distillation column for case 1 of OPT plant
with pervaporation technology .................................................................................... 45
Figure 24 Sensitivity distillation to feed of distillation column for case 1 of OPT plant
with pervaporation technology .................................................................................... 45
Figure 25 Sensitivity feed stage of the distillation column for case 1 of OPT plant
with pervaporation technology .................................................................................... 45
Figure 26 Sensitivity number of stages of distillation column for case 2 of OPT plant
with pervaporation technology .................................................................................... 46
Figure 27 Sensitivity molar reflux ratio of distillation column for case 2 of OPT plant
with pervaporation technology .................................................................................... 46
Figure 28 Sensitivity distillation to feed of distillation column for case 2 of OPT plant
with pervaporation technology .................................................................................... 46
Figure 29 Sensitivity feed stage of the distillation column for case 2 of OPT plant
with pervaporation technology .................................................................................... 46
Figure 30 Sensitivity number of stages of distillation column for case 3 of OPT plant
with pervaporation technology .................................................................................... 47
Figure 31 Sensitivity molar reflux ratio of distillation column for case 3 of OPT plant
with pervaporation technology .................................................................................... 47
Figure 32 Sensitivity distillation to feed of distillation column for case 3 of OPT plant
with pervaporation technology .................................................................................... 47
Figure 33 Sensitivity feed stage of the distillation column for case 3 of OPT plant
with pervaporation technology .................................................................................... 47
Figure 34 Sensitivity number of stages of distillation column for case 4 of OPT plant
with pervaporation technology .................................................................................... 48
Figure 35 Sensitivity molar reflux ratio of distillation column for case 4 of OPT plant
with pervaporation technology .................................................................................... 48
Figure 36 Sensitivity distillation to feed of distillation column for case 4 of OPT plant
with pervaporation technology .................................................................................... 48
Figure 37 Sensitivity feed stage of the distillation column for case 4 of OPT plant
with pervaporation technology .................................................................................... 48
Figure 38 Sensitivity number of stages of distillation column for case 1 of OPT plant
with pressure swing adsorption technology ................................................................. 49
Figure 39 Sensitivity molar reflux ratio of distillation column for case 1 of OPT plant
with pressure swing adsorption technology ................................................................. 49
L
Figure 40 Sensitivity distillation to feed of distillation column for case 1 of OPT plant
with pressure swing adsorption technology ................................................................. 49
Figure 41 Sensitivity feed stage of distillation column for case 1 of OPT plant with
pressure swing adsorption technology ......................................................................... 49
Figure 42 Sensitivity number of stages of distillation column for case 2 of OPT plant
with pressure swing adsorption technology ................................................................. 50
Figure 43 Sensitivity molar reflux ratio of distillation column for case 2 of OPT plant
with pressure swing adsorption technology ................................................................. 50
Figure 44 Sensitivity distillation to feed of distillation column for case 2 of OPT plant
with pressure swing adsorption technology ................................................................. 50
Figure 45 Sensitivity feed stage of the distillation column for case 2 of OPT plant
with pressure swing adsorption technology ................................................................. 50
Figure 46 Sensitivity number of stages of distillation column for case 3 of OPT plant
with pressure swing adsorption technology ................................................................. 51
Figure 47 Sensitivity molar reflux ratio of distillation column for case 3 of OPT plant
with pressure swing adsorption technology ................................................................. 51
Figure 48 Sensitivity distillation to feed of distillation column for case 3 of OPT plant
with pressure swing adsorption technology ................................................................. 51
Figure 49 Sensitivity feed stage of distillation column for case 3 of OPT plant with
pressure swing adsorption technology ......................................................................... 51
Figure 50 Sensitivity number of stages of distillation column for case 4 of OPT plant
with pressure swing adsorption technology ................................................................. 52
Figure 51 Sensitivity molar reflux ratio of distillation column for case 4 of OPT plant
with pressure swing adsorption technology ................................................................. 52
Figure 52 Sensitivity distillation to feed of distillation column for case 4 of OPT plant
with pressure swing adsorption technology ................................................................. 52
Figure 53 Sensitivity feed stage of distillation column for case 4 of OPT plant with
pressure swing adsorption technology ......................................................................... 52
Figure 54 Sensitivity number of stages of distillation column for EFB plant with
pervaporation technology............................................................................................. 53
Figure 55 Sensitivity molar reflux ratio of distillation column for EFB plant with
pervaporation technology............................................................................................. 53
Figure 56 Sensitivity distillation to feed of distillation column for EFB plant with
pervaporation technology............................................................................................. 53
M
Figure 57 Sensitivity feed stage of distillation column for EFB plant with
pervaporation technology............................................................................................. 53
Figure 58 Sensitivity number of stages of distillation column for 100:0 ratio of EFB
and OPT plant with pervaporation technology ............................................................ 54
Figure 59 Sensitivity molar reflux ratio of distillation column for 100:0 ratio of EFB
and OPT plant with pervaporation technology ............................................................ 54
Figure 60 Sensitivity distillation to feed of distillation column for 100:0 ratio of EFB
and OPT plant with pervaporation technology ............................................................ 54
Figure 61 Sensitivity feed stage of distillation column for 100:0 ratio of EFB and OPT
plant with pervaporation technology ........................................................................... 54
Figure 62 Sensitivity number of stages of distillation column for 80:20 ratio of EFB
and OPT plant with pervaporation technology ............................................................ 55
Figure 63 Sensitivity molar reflux ratio of distillation column for 80:20 ratio of EFB
and OPT plant with pervaporation technology ............................................................ 55
Figure 64 Sensitivity distillation to feed of distillation column for 80:20 ratio of EFB
and OPT plant with pervaporation technology ............................................................ 55
Figure 65 Sensitivity feed stage of distillation column for 80:20 ratio of EFB and OPT
plant with pervaporation technology ........................................................................... 55
Figure 66 Sensitivity number of stages of distillation column for 50:50 ratio of EFB
and OPT plant with pervaporation technology ............................................................ 56
Figure 67 Sensitivity molar reflux ratio of distillation column for 50:50 ratio of EFB
and OPT plant with pervaporation technology ............................................................ 56
Figure 68 Sensitivity distillation to feed of distillation column for 50:50 ratio of EFB
and OPT plant with pervaporation technology ............................................................ 56
Figure 69 Sensitivity feed stage of distillation column for 50:50 ratio of EFB and OPT
plant with pervaporation technology ........................................................................... 56
Figure 70 Sensitivity number of stages of distillation column for 20:80 ratio of EFB
and OPT plant with pervaporation technology ............................................................ 57
Figure 71 Sensitivity molar reflux ratio of distillation column for 20:80 ratio of EFB
and OPT plant with pervaporation technology ............................................................ 57
Figure 72 Sensitivity distillation to feed of distillation column for 20:80 ratio of EFB
and OPT plant with pervaporation technology ............................................................ 57
Figure 73 Sensitivity feed stage of distillation column for 20:80 ratio of EFB and OPT
plant with pervaporation technology ........................................................................... 57
N
Figure 74 Sensitivity number of stages of distillation column for 0:100 ratio of EFB
and OPT plant with pervaporation technology ............................................................ 58
Figure 75 Sensitivity molar reflux ratio of distillation column for 0:100 ratio of EFB
and OPT plant with pervaporation technology ............................................................ 58
Figure 76 Sensitivity distillation to feed of distillation column for 0:100 ratio of EFB
and OPT plant with pervaporation technology ............................................................ 58
Figure 77 Sensitivity feed stage of distillation column for 0:100 ratio of EFB and OPT
plant with pervaporation technology ........................................................................... 58
Figure 78 Sensitivity number of stages of distillation column for distillate 4 wt% of
fermentation broth to 80 wt% and dehydrate with pervaporation technology............. 59
Figure 79 Sensitivity molar reflux ratio of distillation column for distillate 4 wt% of
fermentation broth to 80 wt% and dehydrate with pervaporation technology............. 59
Figure 80 Sensitivity distillation to feed of distillation column for distillate 4 wt% of
fermentation broth to 80 wt% and dehydrate with pervaporation technology............. 59
Figure 81 Sensitivity feed stage of distillation column for distillate 4 wt% of
fermentation broth to 80 wt% and dehydrate with pervaporation technology............. 59
Figure 82 Sensitivity number of distillation column for distillate 6 wt% of
fermentation broth to 80 wt% and dehydrate with pervaporation technology............. 60
Figure 83 Sensitivity molar reflux ratio of distillation column for distillate 6 wt% of
fermentation broth to 80 wt% and dehydrate with pervaporation technology y.......... 60
Figure 84 Sensitivity distillation to feed of distillation column for distillate 6 wt% of
fermentation broth to 80 wt% and dehydrate with pervaporation technology............. 60
Figure 85 Sensitivity feed stage of distillation column for distillate 6 wt% of
fermentation broth to 80 wt% and dehydrate with pervaporation technology............. 60
Figure 86 Sensitivity number of stages of distillation column for distillate 8 wt% of
fermentation broth to 80 wt% and dehydrate with pervaporation technology............. 61
Figure 87 Sensitivity molar reflux ratio of distillation column for distillate 8 wt% of
fermentation broth to 80 wt% and dehydrate with pervaporation technology............. 61
Figure 88 Sensitivity distillation to feed of distillation column for distillate 8 wt% of
fermentation broth to 80 wt% and dehydrate with pervaporation technology............. 61
Figure 89 Sensitivity feed stage of distillation column for distillate 8 wt% of
fermentation broth to 80 wt% and dehydrate with pervaporation technology............. 61
Figure 90 Sensitivity number of stages of distillation column for distillate 4 wt% of
fermentation broth to 85 wt% and dehydrate with pervaporation technology............. 62
O
Figure 91 Sensitivity molar reflux ratio of distillation column for distillate 4 wt% of
fermentation broth to 85 wt% and dehydrate with pervaporation technology............. 62
Figure 92 Sensitivity distillation to feed of distillation column for distillate 4 wt% of
fermentation broth to 85 wt% and dehydrate with pervaporation technology............. 62
Figure 93 Sensitivity feed stage of distillation column for distillate 4 wt% of
fermentation broth to 85 wt% and dehydrate with pervaporation technology............. 62
Figure 94 Sensitivity number of stages of distillation column for distillate 6 wt% of
fermentation broth to 85 wt% and dehydrate with pervaporation technology............. 63
Figure 95 Sensitivity molar reflux ratio of distillation column for distillate 6 wt% of
fermentation broth to 85 wt% and dehydrate with pervaporation technology............. 63
Figure 96 Sensitivity distillation to feed of distillation column for distillate 6 wt% of
fermentation broth to 85 wt% and dehydrate with pervaporation technology............. 63
Figure 97 Sensitivity feed stage of distillation column for distillate 6 wt% of
fermentation broth to 85 wt% and dehydrate with pervaporation technology............. 63
Figure 98 Sensitivity number of stages of distillation column for distillate 8 wt% of
fermentation broth to 85 wt% and dehydrate with pervaporation technology............. 64
Figure 99 Sensitivity molar reflux ratio of distillation column for distillate 8 wt% of
fermentation broth to 85 wt% and dehydrate with pervaporation technology............. 64
Figure 100 Sensitivity distillation to feed of distillation column for distillate 8 wt% of
fermentation broth to 85 wt% and dehydrate with pervaporation technology............. 64
Figure 101 Sensitivity feed stage of distillation column for distillate 8 wt% of
fermentation broth to 85 wt% and dehydrate with pervaporation technology............. 64
Figure 102 Sensitivity number o of distillation column for distillate 4 wt% of
fermentation broth to 90 wt% and dehydrate with pervaporation technology............. 65
Figure 103 Sensitivity molar reflux ratio of distillation column for distillate 4 wt% of
fermentation broth to 90 wt% and dehydrate with pervaporation technology............. 65
Figure 104 Sensitivity distillation to feed of distillation column for distillate 4 wt% of
fermentation broth to 90 wt% and dehydrate with pervaporation technology............. 65
Figure 105 Sensitivity feed stage of distillation column for distillate 4 wt% of
fermentation broth to 90 wt% and dehydrate with pervaporation technology............. 65
Figure 106 Sensitivity number of stages of distillation column for distillate 6 wt% of
fermentation broth to 90 wt% and dehydrate with pervaporation technology............. 66
Figure 107 Sensitivity molar reflux ratio of distillation column for distillate 6 wt% of
fermentation broth to 90 wt% and dehydrate with pervaporation technology............. 66
P
Figure 108 Sensitivity distillation to feed of distillation column for distillate 6 wt% of
fermentation broth to 90 wt% and dehydrate with pervaporation technology............. 66
Figure 109 Sensitivity feed stage of distillation column for distillate 6 wt% of
fermentation broth to 90 wt% and dehydrate with pervaporation technology............. 66
Figure 110 Sensitivity number of stages of distillation column for distillate 8 wt% of
fermentation broth to 90 wt% and dehydrate with pervaporation technology............. 67
Figure 111 Sensitivity molar reflux ratio of distillation column for distillate 8 wt% of
fermentation broth to 90 wt% and dehydrate with pervaporation technology............. 67
Figure 112 Sensitivity distillation to feed of distillation column for distillate 8 wt% of
fermentation broth to 90 wt% and dehydrate with pervaporation technology............. 67
Figure 113 Sensitivity feed stage of distillation column for distillate 8 wt% of
fermentation broth to 90 wt% and dehydrate with pervaporation technology............. 67
Figure 114 Relationship of energy demand in dotted line and recovery efficiency in a
solid line with various separation factor and difference inlet concentration applying
the hydrophilic membrane ........................................................................................... 68
Figure 115 Flowsheet for the distillation and dehydration process using PV method 69
Figure 116 Flowsheet of pressure swing adsorption in Aspen Adsorption software .. 70
Figure 117 Flowsheet for the distillation and dehydration process using PSA method
...................................................................................................................................... 71
Figure 118 The simulation model of bioethanol production from OPT by using Aspen
plus with distillation and pervaporation technology .................................................... 75
Figure 119 The simulation model of bioethanol production from OPT by using Aspen
plus with distillation and pressure swing adsorption technology ................................ 76
Figure 120 The simulation model of bioethanol production from EFB by using Aspen
plus with distillation and pervaporation technology .................................................... 77
Figure 121 The simulation model of bioethanol production from two feedstocks by
using Aspen plus with distillation and pervaporation technology ............................... 78
Figure 122 Comparison of the total capital cost of 4 cases ......................................... 83
Figure 123 Comparison of operating costs of 4 cases ................................................. 83
Figure 124 Distribution element of operating costs for OPT plant with 85 wt% in the
overhead stream before dehydrating with PV and PSA ............................................... 83
Figure 125 Distribution chemical costs for OPT plant ................................................ 84
1
INTRODUCTION
The energy consumption in Thailand has increased over the years, and the
world's demand for energy trends to continuously increase with expected population
growth. On the other hand, the supply of fossil fuel, crude oil, and coal is a limited
resource and going to deplete. These fuel sources cause negative effects on the
environment such as the greenhouse effect, global warming. Therefore, renewable
energy from biomass has become a popular alternative energy source to reduce
dependence on fossil fuel, crude oil, and coal. Bioethanol derived from food sources
such as sucrose or starch called first-generation bioethanol has the potential to become
the major source of energy supply to replace fossil fuels. Nevertheless, the main
disadvantage of first-generation is the loss of a food source problem. Therefore,
nonfood sources such as wood, glass, agriculture residual, and lignocellulosic biomass
called second-generation become the promising raw material to produce bioethanol.
Lignocellulosic biomass is considered as promising renewable raw material to
produce energy source. The obvious advantages of lignocellulosic biomass are more
abundant and less expensive than food crops (Derman et al., 2018). Bioethanol
derived from lignocellulosic biomass is an environmentally friendly, clean, and
renewable energy fuel because biomass consumes as much carbon dioxide when it
was growing as it forms during the combustion of bioethanol. Resulting in the net
contribution to the greenhouse effect is zero. Therefore, this work decided to study
about bioethanol derived from lignocellulosic biomass that has a great possibility to
replace fossil fuels.
Ethanol can be produced from lignocellulosic such as corn, cassava, sugar
cane, molasses, oil palm, empty fruit bunch, and rice. They are a mixture of natural
polymers based on cellulose, hemicellulose, and lignin. The sources of cellulose are
generally abundant and available. Therefore, there is a various price of lignocellulose
feedstock. The production cost of bioethanol has increased as well as the price of the
feedstock increase. So, it is important to find out the suitable lignocellulose feedstock
with low feedstock price to produce high ethanol yield. As Thailand is a third-largest
oil palm producer, the Empty fruit bunch (EFB) and oil palm trunk (OPT) are a large
amount of waste from the oil palm extraction and harvesting. Therefore, they were
considered as feedstock to produce bioethanol in this work. However, the
lignocellulose has a strong structure because of covering with lignin as a cell wall. In
order to obtain the product, they must first be breakdown their structure before the
conversion process. The pretreatment process was required to breakdown feedstock’s
structure, and eliminate lignin, which resistant to fermentation ability to increase
yields of fermentable cellulose.
The information to produce the highest ethanol yield from preliminary work
was required. The bioethanol production plant consists of 4 main methods, which are
1) pre-treatment 2) hydrolysis 3) fermentation, and 4) purification. The first step,
EFB, and OPT must be chop to a smaller size, 20×20×5 mm. The small size of two
feedstocks is conveyed to the pretreatment process. In the pretreatment process,
specified first pretreatment techniques are applied for each feedstock. EFB is
2
pretreated with Hot compress water (HCW) at 200oC, 30 bar, 15 min. OPT is
pretreated with Steam explosion (SE) at 210oC, 18.6 bar, 4 min. Then, the treated
OPT and EFB are operated under the same condition and technology. Hot water
washing at 80 oC, 30 minutes, and Hydrogen peroxide digestion (H2O2) at 70oC, 30
minutes are required as second and third pretreatment processes. These processes
have a high potential to destroy the structure and remove the lignin. Pretreatment can
be removing structural and compositional impediments to hydrolysis to improve the
rate of enzyme hydrolysis and increase yields of fermentable. The treated feedstocks
with less amount of lignin are transferred to the simultaneous saccharification and
fermentation (SSF) process. After SSF, most of the cellulose is converted to sugars
and glucose, respectively. The bioethanol from SSF is stored in the buffer tank before
feeding to the purification process. As the SSF process can produce only 3 wt% of
ethanol. Therefore, it must be purified to fuel grade, about 99.5 wt%.
Aspen Plus software V8.8 was performed to simulate the bioethanol
production process from OPT, EFB, and two feedstocks by using the preliminary’s
condition. Aspen Plus is software for simulation in chemical engineering. It can
generate a simulation process and evaluate the total cost investment (TCI) in terms of
equipment and total production cost (TPC). The simulation model was transferred to
Aspen Economic analyzer software to evaluate the utility of the plant.
This work aims to study the technology and evaluate the economic feasibility
of the bioethanol production process from lignocellulose. To accomplish the
objective, the following tasks, including understanding the bioethanol process,
simulation of the bioethanol process and purification process, equipment design,
economic analysis of the bioethanol plant, and a sensitivity analysis was performed in
this work.
3
OBJECTIVES
1. To study and design the bioethanol production process from oil palm trunk (OPT),
Empty fruit bunch (EFB), and various ratios of OPT and EFB.
2. To analyze the economic feasibility of three ethanol production plants.
3. To analyze sensitivity for different concentrations of ethanol before the purification
process and effect of Ctec2 on the production cost of OPT plant.
SCOPES OF WORK
1. Oil palm trunk (OPT) and empty fruit bunch (EFB) are used as a raw material to
produce 10000 liters/day of ethanol.
2. The purity of ethanol is 99.5 wt%
3. Use Aspen Plus software to simulate the production process.
4. Evaluate the total capital investment, total operating cost, the net present value
(NPV), Internal rate of return (IRR), payback period (PB), and production cost per
unit.
4
LITERATURE REVIEW
1. Ethanol
Ethanol is the alcohol and the most promising biofuels for renewable energy.
It is extensively used as fuel in transportation. The physical properties of ethanol are
volatile and colorless. The boiling and melting point of ethanol are 78.5 and -114 °C
at atmospheric pressure (Zaini, 2006). The chemical formula of ethanol is
CH3CH2OH. Ethanol is a polar solvent. Ethanol can be produced by ferment sugar.
For the fermentation process, ethanol and carbon dioxide are produced by fermentable
sugar with yeast or bacteria.
Bioethanol from lignocellulose is produced by using familiar methods, such as
fermentation, and it can be used for transportation systems as fuel. Additional main
two reactions/methods including hydrolysis and fermentation are key processes to
converted to bioethanol from lignocellulose.
2. Biomass
Biomass is one of the most important renewable energy resources. Using
biomass to produce bioethanol can solve the biomass disposal problems from
agricultural industries. Especially, biomass produced from the palm oil industries.
3. Palm oil residue
The palm oil industry especially generates a huge amount of oil palm biomass
residues after harvesting of oil palm fruits, replantation of the trees, and oil palm
processing. The oil palm residues are empty fruit bunches (EFBs), palm oil mill
effluent (POME), palm kernel shell (PKS), oil palm trunks (OPT), oil palm leaves
(OPL), oil palm fronds (OPF) and mesocarp fiber (MF). Disposal of these oil palm
residues is critical for both environmental protection and agricultural profitability.
Therefore, utilizing huge biomass wastes to produce clean and sustainable fuel for the
future is a good solution.
4. Empty Fruit Bunch (EFB) and Oil palm trunk (OPT)
Empty fruit brunch and Oil palm trunk are available and abundance of fibrous
material from biological origin. Empty fruit bunches (EFB), palm oil mill effluent
(POME) and palm kernel shell (PKS) are biomass wastes form palm oil plantation.
They contain a lot of cellulose. Those celluloses in EFB and OPT are the potential to
be converted into sugar and bioethanol, respectively. Cellulose is the major
component of EFB and OPT that can be converted into glucose and bioethanol by
hydrolysis and fermentation process, respectively. They have the highest fiber yield,
and it is the only material utilized commercially for fiber extraction. It is realized as a
promising raw material for bioethanol production due to its potential as a source of
glucose. On the other hand, lignocellulose requires the pretreatment process due to
5
increase yields of fermentable cellulose and eliminate lignin or other resistant
substance in the fermentation process.
5. Lignocellulose
It is the most abundantly available raw material on the Earth for the
production of biofuels. It is composed of cellulose, hemicellulose, and lignin.
Lignocellulosic are promising feedstock because of high yields, low costs, good
suitability for low-quality field, and low environmental impact. These reasons make it
more easily decide to utilize it as raw material for producing energy. Most ethanol
conversion systems have been based on a single feedstock. But considering the
hydrolysis and fermentation process, it is possible to use multiple feedstock types.
This method may even be accomplishing a suitable method for a large scale.
Cellulose contains 40–60% of the dry biomass, is a linear polymer of glucose–
glucose dimer. Cellulose is the major component of the plant. It is a β-1,4- linked
linear polymer of glucose units and is insoluble in water, dilutes acidic solutions, and
dilute alkaline solutions at normal temperatures. The structure is difficult to break due
to the orientation of the linkages, and additional hydrogen bonding makes the polymer
rigid. In the hydrolysis process, the polysaccharide is broken down to free sugar
molecules by the property of water, called saccharification. The glucose, is a six-
carbon sugar or hexose, was produced.
Hemicellulose contains 20–40% of the dry biomass. It consists of short a lot of
branched chains of various sugars such as mainly xylose, and arabinose, galactose,
glucose, and mannose. The degree of polymerization (DP) of hemicellulose is on
average about 100-200 and the molecules can be highly branched called amorphous
structure. Because of the low DP and amorphous structures, hemicellulose is more
easily degraded in dilute acidic than cellulose and easy to hydrolyze. It also contains
smaller amounts of non-sugars such as acetyl groups.
Lignin contains 10–25% of the dry biomass. It is a basic component in all
lignocellulosic biomass. Lignin is one of the most abundant organic polymers in
plants. Therefore, any ethanol production process will have lignin as a residue. It is a
large complex polymer of phenylpropane and methoxy groups, a non-carbohydrate
polyphenolic substance that coat the cell walls and join the cells together. The
chemical properties of lignin include halogenation, nitration, and oxidation reactions.
Lignin is an amorphous thermoplastic polymer, so it has a slight friability under high
temperature. It is degradable by only a few organisms, into higher-value products
such as organic acids, phenols, and vanillin. By using chemical processes valuable
fuel additives may be produced.
6
6. Bioethanol production
The normal concept to produce bioethanol, the bioethanol as the product is
produced from a variety of biomass material. The first step is to mill the biomass to
reduce the size and increase surface area called mechanical pretreatment. It can be
done by dry milling or wet milling. The second step is pretreatment to change the
structure of biomass to make it more accessible for the subsequent process. There are
many types of pretreatment methods, such as chemical, physicochemical, biological
techniques. The third step is the hydrolysis process by using enzymes to convert the
cellulose into glucose and the last step is further fermented to ethanol using glucose-
fermenting microorganisms. After fermentation, ethanol in the mixture is separated
from water called the purification process. There are many methods for purified
ethanol.
Figure 1 Concept for bioethanol production
6.1. Pretreatment process
Pretreatment is an important step for lignocellulosic materials to
produce bioethanol. Lignocellulosic materials contain polysaccharides, such as
cellulose and hemicelluloses. Pretreatment is mainly necessary to change the structure
of cellulosic biomass to make cellulose more accessible to the enzymes that convert
the carbohydrate polymers into fermentable sugars. Pretreatment is an additional
process to produce ethanol. The pretreatment step is considered as the most expensive
processing step or an additional cost in cellulosic biomass-to-fermentable sugars
conversion. So, there are many researchers have focused on this step to make a cost-
effective conversion of cellulose to bioethanol. An effective pretreatment should have
following characteristics (1) maximum cellulose or hemicellulose fractions so that it
can also be converted into fermentable sugar which can further be converted into
ethanol, (2) minimize the formation of inhibitors during pretreatment due to it can be
degradation products, (3) minimize loss of carbohydrate due to it can affect the
conversion of fermentable sugar and (4) minimizing energy input, and the process is
economically efficient as well as cost-effective. To achieve all the criteria mentioned
above should be comprehensively considered as a basis to achieve maximal end
product of interest. Pretreatment technology can be classified into biological,
physical, chemical, and physicochemical pretreatments. However, energy consumed
in the pretreatment process is an important factor to consider.
Pretreatment is the primary process for biomass materials such as EFB
and OPT for improving the cellulose reactivity with cellulase enzymes and for
increasing the yield of fermentable sugars. Pretreatment can increase bioethanol yield
Size
reduction Pretreatment hydrolysis Purification fermentation
7
and productivity significantly. The pretreatment process is required to break down the
lignin structure and disturb the crystalline structure of cellulose. After pretreatment,
the acids or enzymes can easily access the cellulose to hydrolyze into monomers.
Pretreatment allows changing the structure of the lignocelluloses such as increasing
the surface area and porosity of biomass, modifying and removing the lignin, some
part of polymerizes, and removes the hemicelluloses, and reduces the crystallinity of
cellulose. The pretreatment processes or combination can increase the potential to
convert the wastes to biofuel production and increase accessibility to the enzymes
(Behera et al., 2014).
Figure 2 Pretreatment of lignocellulosic materials before bioethanol and biogas
production
Source: Behera et al. (2014)
6.1.1. Mechanical/physical pretreatment
Mechanical pretreatment for lignocellulosic biomass. The
common purpose of mechanical is size reduction processes by using dry milling,
compression milling, vibratory ball milling, and wet milling. Consumption of energy
for size reduction depends on machine variables, rate of feed material, material
properties, moisture content, and initial particle size. Normally, size reduction can be
accomplished by splitting or shearing with sharp knives in which the geometry of
particles is damaged due to impact or compression.
6.1.2. Chemical pretreatment
Chemical pretreatment has considered one of the most
promising methods to increase the biodegradability of cellulose by removing lignin
and hemicelluloses to decrease the degree of polymerization and crystallinity of the
cellulosic component in lignocelluloses. There are many chemicals, which have been
experimented and reported to have a significant effect on the structure of
lignocellulosic biomass. Some chemical does not produce toxic residues after
pretreatment processes.
8
Alkaline hydrogen peroxide pretreatment (AHP), the use of
H2O2 for the pretreatment of lignocellulosic biomass is based on the chemical
reactions that this oxidizing agent undergoes in the alkaline liquid medium. AHP
pretreatments could enhance the enzymatic digestibility by swelling fibers, breaking
ester bonds of lignin-carbohydrate complexes, solubilizing lignin molecules,
increasing the surface area, and providing more cellulose for the accessibility of
enzyme. AHP pretreatment was appealing to the effective degradation of lignin from
lignocellulosic biomass because H2O2 could degrade to oxygen.
6.1.3. Physico-chemical pretreatment
Pretreatments that combine both the chemical and physical
processes are of importance in dissolving hemicellulose and the conversion of lignin
structure. This pretreatment provides improved accessibility of the cellulose for
hydrolytic enzymes.
6.1.4. Biological pretreatment
Biological pretreatment is mostly associated with the action of
fungi that are capable of producing enzymes to degrade lignin, hemicelluloses, and
polyphenols present in the biomass. Biological pretreatment has attracted interest
because of its advantages over physical/chemical pretreatments such as substrate and
reaction specificity, low energy requirements, no generation of toxic compounds, and
high yield of desired products. However, its disadvantages are as possible as its
advantages, due to biological pretreatment is a very slow process and requires
accurate control of growth conditions.
6.1.5. Hydrothermal pretreatment
Pretreatments utilizing primarily steam or liquid water at high
temperatures can efficiently convert biomass to a form that can be easily digested by
enzymes by facilitating autohydrolysis reactions within the biomass. Processes
utilizing hot water or steam as the primary chemical are known as hydrothermal
pretreatments.
Steam explosion (SE) is pretreatment processes that low use of
chemicals and limited energy consumption. With this method, high pressure saturated
steam is injected into a batch or continuous reactor filled with biomass. During the
steam injection, the temperature rises to 160-260 ºC. Subsequently, the pressure is
suddenly reduced, and the biomass goes to an explosive decompression with
hemicellulose degradation and lignin matrix disruption as a result. Results of steam-
explosion pretreatment depend on residence time, temperature, particle size, and
moisture content
9
Hot-compressed water (HCW) pretreatment, which biomass is
exposed to pressured hot water, is a pretreatment method that uses water under
pressure penetrates the cell structure of biomass, hydrates cellulose, and dissolves
hemicellulose and lignin at high temperature (around 200 °C). HCW pretreatment
does not require the addition of any chemical and generates little inhibition to
subsequent hydrolysis and fermentation.
6.2. Hydrolysis
Hydrolysis is the process that converts polysaccharides into
monomeric sugars. The fermentable sugars obtained from hydrolysis can be
fermented into ethanol. Lignocellulose can be hydrolytically broken down into simple
sugars with enzymatically by cellulolytic enzymes or chemically by sulfuric or other
acids. In this process, cellulose is hydrolyzed to glucose, whereas hemicellulose gives
rise to several pentose and hexoses. The advantage of enzymatic hydrolysis is low
toxicity, low utility costs and low corrosion compared to acid or alkaline hydrolysis
The hydrolysis process is hydrolyzed cellulose to glucose by either
acid or enzymatic hydrolysis. The main disadvantage of acid hydrolysis processes is
severely limit for commercial application, which affects the economic feasibility of
dilute acid hydrolysis due to sugar degradation and consequence, low process yields.
Therefore, enzymatic hydrolysis was utilized in this project. Enzymatic hydrolysis of
cellulose is carried out by cellulase enzymes. The utility costs of enzymatic hydrolysis
are low compared to acid or alkaline hydrolysis because enzyme hydrolysis is usually
conducted at conditions (pH 4.8 and temperature 45– 50 °C) and does not have a
corrosion problem (Duff et al., 1996). The products of the hydrolysis are usually
reducing sugars including glucose. Both bacteria and fungi can produce cellulases for
the hydrolysis of lignocellulosic materials.
6.3. Fermentation
The final process for achieving the ethanol production, yeast
fermentation is considered to be an interesting technology, which is improved to gain
the high efficiency due to the traditional yeast fermentations are not ideally suited to
the unique fermentation requirements of cellulose hydrolysates. The fermentation
operates under an anaerobic condition at a temperature of about 32-35 C° and pH
around 4.2-4.5. Fermentable sugar is converted to ethanol as a main product and
carbon dioxide as a by-product. These byproducts need to be removed to obtain pure
ethanol. Batch fermentation, fed-batch, and continuous fermentation are the
commonly adopted industrial methods.
Batch fermentation, all material/substance is introduced into the tank
with fixed volume and retention time for the operation. The sugar in the fermenter is
continuously converting to ethanol until the end of time. After the fermentation, the
residues are taken out from the fermentation tank, then cleaned and sterilized before
10
the next batch of fermentation. Therefore, batch fermentation should be large
scale/volume to operate per time/cycle. On the other hand, the continuous
fermentation never stops. It continues to run for a long period with the addition of
nutrients and collecting the product at regular intervals.
Fed-batch fermentation is an operational technique in biotechnological
processes where one or more nutrients are fed to the reactor during cultivation and in
which the product remains in the bioreactor until the end of the run. The process of
fed-batch fermentation is an effective method in reducing growth inhibition caused by
high substrate concentration. In the fed-batch fermentation process, a suitable feeding
strategy is crucial to achieving both high productivity and yield of the product.
6.4. Separate hydrolysis and fermentation and Simultaneous saccharification
and fermentation
There are two different processes in ethanol production. In the case of
pretreatment, cellulose hydrolysis, fermentation, and product recovery take place in
different reactors, the process is called separate hydrolysis and fermentation (SHF). In
the SHF process, cellulases from the enzyme production are added to the pretreated
material to convert cellulose to glucose, called the hydrolysis process. After
hydrolysis, the microorganism/yeast is added to convert glucose to ethanol, called
fermentation process. All process is separated.
For simultaneous saccharification and fermentation (SSF), enzymatic
cellulose hydrolysis and sugar fermentation are operated in one reactor. Cellulases are
added to the pretreated materials to hydrolyze the cellulose to glucose, while the
microorganism converts glucose into biofuels in the same reactor. Because glucose,
an inhibitor of cellulase, is converted by the microorganism into ethanol. SSF can
efficiently remove or reduce the inhibitory effect of glucose on cellulases, thus
achieving faster biomass hydrolysis rates and higher ethanol yields as compared to
SHF. The SHF and SSF were shown in Figure 3.
Figure 3 The process for ethanol production from lignocellulosic biomass
11
Source: Fan (2014)
7. Purification
After fermentation bioethanol contains a low concentration of ethanol, so it
has to increase concentration by purifying process. The boiling point of water (100
°C) is higher than the ethanol boiling point (78 °C). In this process, ethanol is purified
around 95% because of the azeotropic mixture between water and ethanol. But for
industries require the concentration of ethanol more than 99%. Therefore, the special
process for removal of water is required such as azeotropic distillation, extractive
distillation, and molecular sieve adsorption and pervaporation.
Azeotropic distillation uses a solvent with an intermediate boiling point to
introduce new azeotropes to the mixture and at the same time to generate two liquid
phases that allow, in a combined way, separating ethanol from water. This technique
has lost acceptance due to its poor stability and high energy consumption.
Extractive distillation is a partial vaporization process in the presence of a
non-volatile and high boiling point entrainer which does not form any azeotropes with
the original components of the azeotropic mixture. The extractive distillation requires
entrainer or solvent. The entrainer is much lower than the azeotropic case and
additionally, the quantity of entrainer is lower which affects the diameter of the
columns. It can be observed that the column diameters are smaller in the extractive
distillation systems and also the energy consumption in the columns. On the other
hand, the most important variables used to achieve the desired ethanol concentration
are the entrainer to feed molar ratio and the reflux ratio. The former has a little effect
over the energy consumption compared with the reflux ratio impact on the reboiler
duty.
The molecular sieve dehydration technology utilizes the adsorption
phenomenon, and this is used to produce anhydrous ethanol. These Molecular Sieves
for Ethanol drying is made of popular materials called zeolites. The industrial process
essentially involved passing the vapor form of ethanol through a column of the
molecular sieve, which separates any water present in the vapor form from the ethanol
by trapping its molecules onto its surface while allowing molecules of ethanol to pass
through. Two different subzones of operation are provided within a master transfer
zone so that the two liquids part ways without any further interaction. This process
also does not use any other chemical, hence eliminating the chance of contaminating
ethanol with any other substance completely. The costs of the usage of such chemicals
are also thus automatically eliminated altogether. On the other hand, it recovers higher
alcohol from the process. Therefore, Molecular Sieve Technology offers cost
advantages and higher alcohol recovery rates.
Pressure swing adsorption is a method for the separation of some gas from a
mixture of gases under different pressure levels. It is a widely used technology for the
purification of gases. Specific adsorbent materials such as zeolites, activated carbon,
molecular sieves are used as a trap, preferentially adsorbing the target gas species at
12
high pressure and desorb gas at low pressure. Adsorbents are porous solids, preferably
having a large surface area per unit mass. Since different molecules have different
interactions with the surface of the adsorbent, it is eventually possible to separate
them.
Pervaporation is a membrane process and combines permeation and
vaporization. Pervaporation is used to separate a liquid mixture. The used membrane
is a dense non-porous membrane or a very finely porous ceramic membrane that
depends on what kind of component wants to remove. Specifically, for this process,
the permeating component is converted into an evaporation phase, due to the low
vapor pressure on the permeate-side. This low vapor pressure is normally achieved by
setting a vacuum on the permeate side of the membrane. In most cases, the permeate
is re-condensed. The pervaporation process contains 3 steps (1) Selective sorption in
the membrane on the influent-side, (2) Selective diffusion through the membrane, (3)
Desorption in the gas phase on the permeate-side.
8. Economic Analysis
An economic analysis is a process which makes a clear picture of the existing
economic climate, as it relates to the company’s ability to succeed. There are several
tools for economic evaluation that can be used to gain a comprehensive view of how
the company will manage in the future. The economic analysis takes into account the
opportunity costs of resources employed and attempts to measure in terms of costs
and benefits of a project. The main objective of conducting a project economic
analysis is to help assess the sustainability of investment projects. Therefore, it is best
undertaken at the early stages of the project. The tools for a measure of project worth
including Net Present Value (NPV), Internal Rate of Return (IRR), Payback period
(PB), and production cost per unit.
8.1. Cost-benefit analysis
Cost-benefit analysis in project management is a process used to
analyze decisions. It has been devised to evaluate the costs versus the benefits in the
project proposal. It begins with a list. There is a list of every project expense and what
the benefits will be achieved after successfully executing the project. After that use
these data to calculate total capital investment cost (TCI), net present value (NPV),
internal rate of return (IRR), and the payback period (PB). These variables are used to
determine the worth of the project.
8.1.1. Cost analysis
Investment cost
It includes any expenses during the first step of the project. The
main cost of investment costs is construction costs. To calculate the total investment
13
in addition to the construction, there are many other factors to consider. The costs of
land, machine, installation and equipment costs, water supply, electric fee, telephone
fee, expert employment costs, technical know-how employment.
Operating cost
It is the expenses that are related to the operation of a business,
or the operation of a device, component, piece of equipment or facility. The operation
costs include raw material costs, energy, and water supply for equipment, chemical
costs, consulting costs, worker employment, transportation costs.
Maintenance costs
Maintenance expenses are the costs incurred to keep
equipment, engine, and building in good condition or good working order during the
project life.
8.1.2. Benefit analysis
The benefit of the project is all the outcomes of the project. The
benefit of the project is comprised of direct benefits including goods and services and
indirect benefits.
Direct benefit
Direct benefits are the outcome according to the aim of the
project. Normally, they are profit from goods and services.
Indirect benefit
Indirect benefits are profit beyond the main purpose of the
project.
8.2. Total Capital Investment
Investment is needed as the capital costs to begin product manufacture.
Total Capital Investment (TCI) of a chemical plant includes the purchase of the land,
building, offsite, supporting facilities, utility installation, market research, licensing,
and contractor’s fee. The investment costs are needed to supply the necessary
manufacturing and plant facilities.
Fix capital cost (FCI) is an expense or cost that does not change with
an increase or decrease in the number of goods or services produced or sold. For the
calculation of FCI can be following.
14
FCI = Direct costs + Indirect costs (1)
Direct costs are expenses that directly go into producing goods or
providing service including purchased equipment (Columns, Heat Exchangers,
pumps, tanks), Equipment Installation, Piping (includes insulation), Instruments and
Control, Electrical Equipment, Buildings (Process, Administration, Maintenance
shops), Site Preparation, Service Facilities (steam, water, air, fuel), Waste treatment,
fire control. Offices, Land.
Indirect costs are expenses that keep operating including Engineering
and Supervision (Administrative and Design), Supervision and Inspection,
Construction Expenses, Contractor's fee, Contingency, Start-up expenses.
8.3. Net Present Value (NPV)
The NPV of a project or investment reflects the degree to which cash
inflow, or revenue, equals or exceeds the amount of investment capital required to
fund it. To decide the worth of the project is up to NPV. If the NPV is a positive
value, it is worth to invest. A negative value of NPV is not worth in invest. For the
calculation of NPV, it estimated from cash inflow and outflow. For the calculation of
NPV can be following.
=+
+−= Ti ir
iC
CNPV1
)1(0
(2)
Where r is the discount rate
T is the number of time periods
C0 is Initial Investment
Ci is Cash flow at year i
i is Project start time
8.4. Internal rate of return (IRR)
The internal rate of return (IRR) is a metric used in capital budgeting
to estimate the profitability of potential investments. The internal rate of return is a
discount rate that makes the net present value (NPV) of all cash flows from a
particular project equal to zero. A business needs to look at the IRR as the plan for
future growth and expansion. It is necessary to know the discount rate (r) in the
market. If the market interest rate is lower than IRR, it is worth to invest. If there are
many projects/choices the project with the highest IRR should be considered first. For
the calculation of IRR can be following.
When NPV = 0; =+
= Ti ir
iC
C 1)1(
0 (3)
15
Where r is the discount rate
T is the number of time periods
C0 is Initial Investment
Ci is Cash flow at year i
i is Project start time
If, IRR > r, accept the project
IRR< r, reject the project
8.5. Payback Period (PB)
The payback period is the length of time required to recover the costs
of an investment. The payback period of the investment is an important determinant
of whether to undertake the position or project, as long payback periods are typically
not desirable for investment positions. For the calculation of PB can be the following:
Payback Period = Cost of the investment (4)
Annual net cash inflow
The payback period ignores the time value of money, unlike other
methods. There are two significant problems with this method; it ignores the time
value of money and ignores any benefits that occur after the payback period. So, it
does not measure profitability.
8.6. Salvage Value
Salvage value is the estimated value that paid when the item is sold at
the end of its useful life and is used to determine annual depreciation. It is net cash
obtainable from the sale of used property.
8.7. Depreciation
The monetary value of an asset decreases over time due to use, wear,
and tear or obsolescence. This decrease is measured as depreciation. Types of
depreciation include physical such as wear and tear, corrosion, accidents, age
deterioration, functional, obsolescence and depletion such as loss from materials
consumed and applicable to natural resources (timber, mineral, oil deposits).
Depreciation = Total equipment - Salvage Value (5)
Project Life
16
9. Literature review
Nagy et al. (2015) studied the energy demand of increased concentration for
ethanol solution up to the fuel-grade quality and also discussed how energy
consumption could be reduced by applying the pervaporation (PV) process with the
different operating patterns as showed in Figure 5. All processes were simulated with
Chemcad software and evaluated the energy consumption of each unit. For
distillation, they experimented with various concentrations of ethanol in feed to
achieve 50 wt%, 70 wt%, and 93 wt %. The energy demand strongly increased with
decreasing the ethanol concentration in the feed-in Figure 4.
Figure 4 Energy demand
Source: Nagy et al. (2015)
In the case of PV, the multistage PV had effective due to only one stage of PV
cannot reach the fuel grade product. The 5wt% of ethanol was introduced into
multistage PV to increase concentration up to 99.5% as the product. Process A, the
three-stage membrane, which two hydrophobic and one hydrophilic, can reduce
energy consumption by about 13%. Process B, the two-stage membrane with
hydrophobic and one hydrophilic, can reduce energy consumption by 40% but the
membrane in this process B not available for commercial scale. Moreover, the single
technique and hybrid technique (Distillation+PV) were compared and the result
showed that the hybrid process has efficient higher than a single distillation and one
or multi-stage pervaporation process. Application of pervaporation with hydrophilic
membrane module in a hybrid process can especially be advantageous to further
concentration product of distillation with 50–70 wt% ethanol.
17
Figure 5 Multistage pervaporation process
Source: Nagy et al. (2015)
Valentínyi et al. (2018) studied alternatives for the separation of diluted
ethanol-n-butanol-water mixture that was proposed and simulated in ChemCAD
software. The objective was the minimization of the total annual cost (TAC) to
produce ethanol with a purity of 99.7 wt%. They compared the capital costs and
operating costs between extractive distillation and pervaporation process. The first
distillation column (C1) was the common first step of all alternatives. The 2 wt%
ethanol was fed into the first column (C1). Then ethanol flow to dehydration
including extractive distillation column and pervaporation process to increase
concentration up to 99.7wt%. In the case of pervaporation, alternative separated with
multiple hydrophilic pervaporation modules connected in series, can reduce the
investment costs and operating costs as shown in Figure 6. However, the main
disadvantage is membrane must be replaced every 2.5 years. While extractive
distillation required chemical and high energy consumption to separate ethanol and
water.
Figure 6 The total annual cost in the purification process
Source: Valentínyi et al. (2018)
18
Ebrahimiaqda et al. (2017) studied ethanol production from several varieties
of sweet sorghum (Dale, T-Sugar, M81E, and 350FS). They found that after the
fermentation process, the sweet sorghum fermentation broth has a high-water content.
Therefore, it is necessary to analyze on simulating and optimizing the distillation and
purification processes to achieve 99.8 wt% ethanol as a product. They compared two
methods including extractive distillation and pressure swing adsorption (PSA) with 3
A molecular sieve and also optimized facility sizing, operating conditions, and total
annualized cost. The result showed that applying an extractive distillation method
results in a cost that is, on average, 12% less than that of PSA using molecular sieves.
Comparing the operating and capital costs for each variety shows that the operating
costs for either method were approximately the same. However, the capital costs for
the PSA method was notably higher than the extractive distillation process. Moreover,
the feed entering the distillation column contains small amounts of ethanol and to
obtain a high purity product. A significant amount of energy was required to
concentrate the ethanol to its azeotrope. Therefore, the capital and operating costs of
the first column accounted for between 70 and 80% of the TAC in both methods.
Bastidas et al. (2010) studied and compared the three main ethanol
dehydration technologies including azeotropic, extractive, and adsorption processes,
and also determined the main operating conditions to produce 300 cubic meters per
day of 99.5 mole % ethanol by using Aspen plus. The total cost was implemented
considering the total investment and operating costs of each technology. For
adsorption processes, the final product of the anhydrous ethanol produced is lower
than the obtained in the distillation. This is due to the high ethanol recycle required to
regenerate the second bed. This affects the efficiency of the process importantly and
increases the total energy consumption. The total cost of the three technologies was
azeotropic > adsorption > extractive. This result presents that the equipment costs of
extractive distillation with ethylene glycol was the lowest option to dehydrate ethanol.
The extractive distillation with ethylene glycol represents the most interesting
alternative because the energy consumptions and capital investment costs were
competitive and represent important savings in the final costs of ethanol produced.
Kamarludin et al. (2014) reviewed the mechanical pretreatment of
lignocellulose by focusing mainly on the size reduction technique by grinding process
and they studied combination method, chemical-mechanical pretreatment, was
considered whereby a green ionic liquid (IL) solvent was introduced. The size
reduction of the lignocellulose particles by mechanical pretreatment had been shown
to improve the performance during the subsequent lignocellulose conversion steps due
to the increase in the surface area of the biomass particles and the decrease of
cellulose crystallinity. However, mechanical pretreatment generates costly due to high
energy consumption. Therefore, the combined mechanical and ionic liquid
pretreatment may reduce energy consumption and hence reduce the overall processing
costs.
Pangsang et al. (2018) studied the pretreatment by using Hot compress water
(HCW) for breakdown the structure and eliminate hemicellulose and lignin of oil
palm empty fruit bunch (EFB). The EFB has experienced two different processes
19
including Separate hydrolysis and fermentation (SHF) and simultaneous
saccharification and fermentation (SSF) by Saccharomyces cerevisiae TISTR 5606.
The EFB was pretreated with Hot compress water at the temperature of 200ºC at 30
bars for 15 minutes. Moreover, they experimented by using different temperatures (30
and 35°C) and enzyme loadings (100 and 300 FPU/g dry pretreated EFB) on the
hydrolysis and fermentation process. After Hot compress water pretreatment, they can
increase the cellulose content from 38.50% to 69.27% due to the elimination of
hemicellulose and lignin. As showed in Table 1, the composition of EFB pretreated
with Hot compress water. The best result, with the highest concentration of ethanol
obtained, was at 30°C in terms of both SHF and SSF processes with the range of 53.4-
54.4 g/L ethanol and 0.74-0.75 g/L⋅h.
Table 1 The chemical characteristic of raw material and pretreated EFB
Lignin (%) Cellulose (%) Hemicellulose (%)
EFB 11.63 38.50 26.12
EFB with HCW 3.77 69.27 8.63
(% dry weight)
Source: Pangsang et al. (2018)
Upajak et al. (2018) studied the pretreatment process. The lignocellulosic
biomass was pretreated by Alkaline hydrogen peroxide (AHP). The biomass was
pretreated under a condition with temperatures (30-120°C), H2O2 concentration (2.5-
10%), residence times (1, 2, and 4 h), 10 % (w/v) substrate loading and the initial
pressure was at 20 bars under nitrogen. After the pretreatment, the pretreated solids
were separated using filtration and then thoroughly washed with DI. The optimal
condition for H2O2 pretreatment of biomass was H2O2 concentration of 5% using 60 oC for 2 hours. Under optimal conditions can increase hemicellulose solubilization
into the aqueous phase and also lead to the enhanced glucose yield from enzymatic
hydrolysis and a small amount of formation of inhibitory by-products. After Alkaline
hydrogen peroxide pretreatment, they can increase in surface area, breaking structural
intermolecular bonds between carbohydrates and lignin, disordering the lignin
structure, and isolating lignin from the biomass. Therefore, H2O2 pretreatment led to
higher sugar yield after enzymatic hydrolysis of the pretreated solids.
O’Brien et al. (2000) studied the continuous fermentation process and purify
with the pervaporation method for fuel ethanol production. The data and performance
of membrane-based on previous, a flux of 0.15 kg/m2/h, and a selectivity of 10.3. For
a baseline, membrane cost is $200 per m2. Moreover, they compared the batch
fermentation process as the base case and continuous fermentation–pervaporation
process with the same dehydration method. The advantage of continuous
fermentation–pervaporation may be simplicity, toxicity to fermenting organisms, and
recovery of ethanol, requiring less distillation capacity and energy consumption. As
shown in Figures 7 and 8. They designed the equipment, sized, and costs estimated
for the fermentation, pervaporation, distillation, and dehydration process of a
commercial-scale fuel ethanol plant by using the data from a simulation by Aspen
plus software. The result showed that using commercial organophilic membranes can
20
achieve 42 wt% of ethanol concentration for a continuous fermentation–pervaporation
system. Therefore, the equipment sizes and utility requirements for the continuous
fermentation–pervaporation lower than the base case.
Figure 7 Flow diagram of batch fermentation
Figure 8 Flow diagram of continuous fermentation–pervaporation process
Source: O’Brien et al. (2000)
Medina et al. (2016) studied the effect of the steam explosion (SE)
pretreatment under autocatalytic conditions on EFB. Temperature and reaction time
were the operational variables studied. The SE pretreatment was carried out in a
stainless-steel reactor with a 10 L capacity. Pretreatment was performed with 300 g of
dried EFB, containing 2.0% ± 0.7% moisture. The material was introduced in the
reactor vessel, and saturated steam was fed until reach desired temperature, the
heating time was around 2 minutes. The reaction time was controlled after the
temperature was reached. The sudden decompression released the material into a
cyclone and the vapor was liberated to the atmosphere. The pretreated material was
neutralized with water and solids were recovered by centrifugation. The solids were
21
oven-dried, and a fraction was milled for carbohydrate and lignin analyses. The
process flow diagram is shown in Figure 9. The best pretreatment performance was
obtained at 195oC for 6 min, with an increase of 24% in cellulose and 68% reduction
in hemicellulose.
Figure 9 Flow diagram of steam explosion
Source: Medina et al. (2016)
22
MATERIALS AND METHODS
Materials
1. Personal computer (Lenovo)
1.1 Windows edition: Windows 10 Pro 64-bit Operating System
1.2 System processor: Intel ® Core ™ i5-6200U CPU @ 2.3 GHz
1.3 System installed memory (RAM): 8.00 GB
2. Microsoft Office (Excel) version 2016
3. Simulation software (Aspen Technology)
3.1 Aspen Plus V8.8
3.2 Aspen Economic Analyzer V8.8
3.3 Aspen adsorption V8.8
3.4 Aspen properties V8.8
Methods
In this work, a techno-economic analysis of the three-bioethanol production
plant from lignocellulosic feedstock was performed. Aspen Plus, Aspen Adsorption,
Aspen Economic analyzer, and Microsoft Office Excel carried out the simulation and
the economic evaluation, respectively. The information from a preliminary
experiment was required to simulate the industrial scale of the bioethanol production
process. Temperature, pressure, operating time, composition of feedstock and solid
yield determined from experiment was employed to develop the simulation model by
Aspen plus software.
Three bioethanol plants were designed to produce high purity of bioethanol as
fuel products from different feedstock including OPT, EFB, and various ratios of two
feedstocks. The different kinds of feedstock with different compositions influence the
ethanol yield in SSF process. The methodology to design three bioethanol plants
consists of many steps including understanding the necessary information required to
design the production plant, mass balance calculation, scheduling flow process,
equipment design, simulation plant, and economic assessment.
1. Concept to produce bioethanol from lignocellulose
Lignocellulose is considered as the second generation to produce
biofuel. Its main advantage is noncompetition food raw material. EFB is waste from a
palm oil extraction plant and OPT is waste from replantation that harvested every 25
years. Their component consists of cellulose, hemicellulose, lignin, ash, and other
contents. The composition of OPT and EFB in each process was obtained from
preliminary experiments as determined in Tables 2 and 3. Meanwhile, the
composition of various ratios of two feedstock did not provide. Therefore, eq. 6 was
applied to the calculated mass fraction of various ratios of two feedstocks. The
23
common process to produce ethanol is hydrolysis and fermentation. In order to obtain
high ethanol yield, three pretreatment steps, SSF and purify process were employed in
this work. The scheme of the production process comprised five main processes
including three pretreatment techniques, SSF, and purification, as shown in Figure 10.
Three-section called upstream process covered three-step pretreatment, middle stream
covered after the pretreatment process to SSF, and downstream covered purification
process. In upstream, steam explosion and hot compress water were the suitable first
pretreatment technology to pretreat OPT and EFB, respectively. After that, the same
technology and condition were conducted for both feedstocks. For OPT plant, two
dehydration technologies were studied to determine the suitable technology for
dehydrate the dilute ethanol to fuel grade. The proper technology will be employed
for other plants.
Figure 10 The scheme of the production process
2. Mass balance
Mass balance calculation is elementary work before simulation to
verify the simulation result from Aspen plus. The mass balance template was
developed by Microsoft Office (Excel) version 2016 to follow the distribution of mass
in each process faithfully. The experiment's data including the mass fraction of
cellulose, hemicellulose, lignin, ash, other, total mass recovery, and ethanol yield,
were applied to create mass balance flowsheet. Since there is no information in some
parts, the assumption by a specialist was required. The 6to create the mass balance
flowsheet was presented in Tables 2 to 6.
Table 2 The composition of OPT in each process
OPT SE Hot water H2O2 Neutralize
Composition % dry % dry % dry % dry % dry
Cellulose 0.3867 0.439 0.6176 0.6786 0.7396
Hemicellulose 0.233 0.232 0.041 0.02635 0.0117
Lignin 0.2376 0.116 0.2032 0.16 0.1168
Ash 0.0162 0.0162 0.0116 0.01055 0.0095
Other 0.1265 0.1968 0.1266 0.1245 0.1224
Pretreatment
2nd SSF Purification
Pretreatment
3rd
Pretreatment
1st
Upstream Middlestream Downstream
24
Total 1 1 1 1 1
% recovery 1 0.894 0.791 0.731 0.982
% moisture 0.37 0.82 0.84 0.12
Table 3 The composition of EFB in each process
EFB HCW Hot water H2O2 Neutralize
Composition % dry % dry % dry % dry % dry Cellulose 0.3885 0.5690 0.6927 0.7057 0.7057
Hemicellulose 0.2614 0.1345 0.0863 0.0136 0.0136
Lignin 0.1162 0.1302 0.0377 0.1490 0.1490
Ash 0.014 0.0128 0.0144 0.0178 0.0178
Other 0.2199 0.1532 0.1689 0.1137 0.1137
Total 1 1 1 1 1
% recovery 1 0.908 0.769 0.866 0.997
% moisture 0.37 0.82 0.84 0.12
Because of non-available the composition of the various ratios of two
feedstocks results, eq 6 will be used to calculate mass fraction for each ratio. Tables 4
to 6 show the composition of various ratios of EFB:OPT in each process.
Mass fraction of i = (X × xi) + (Y × yi) (6)
Where X is ratio of EFB
Y is ratio of OPT
xi is the composition i of EFB mass fraction
yi is the composition i of OPT mass fraction
i is the composition of feedstock
Table 4 The composition of the 80:20 ratio of EFB:OPT in each process
EFB OPT HCW SE Hot
water
H2O2 Neutralize
Composition % dry % dry % dry % dry % dry % dry % dry Cellulose 0.3885 0.3885 0.5690 0.439 0.6777 0.7003 0.7125
Hemicellulose 0.2614 0.2614 0.1345 0.232 0.0772 0.0162 0.0133
Lignin 0.1162 0.1162 0.1302 0.116 0.0708 0.1512 0.1426
Ash 0.014 0.014 0.0128 0.0162 0.0138 0.0164 0.0162
25
Other 0.2199 0.2199 0.1532 0.1968 0.1604 0.1159 0.1155
Total 1 1 1 1 1 1 1
% recovery 1 1 0.908 0.894 0.7734 0.839 0.994
% moisture 0.37 0.37 0.82 0.84 0.12
Table 5 The composition of the 50:50 ratio of EFB:OPT in each process
EFB OPT HCW SE Hot
water
H2O2 Neutralize
Composition % dry % dry % dry % dry % dry % dry % dry Cellulose 0.3885 0.3885 0.5690 0.439 0.6552 0.6922 0.7227
Hemicellulose 0.2614 0.2614 0.1345 0.232 0.0637 0.0200 0.0127
Lignin 0.1162 0.1162 0.1302 0.116 0.1205 0.1545 0.1329
Ash 0.014 0.014 0.0128 0.0162 0.0130 0.0142 0.0137
Other 0.2199 0.2199 0.1532 0.1968 0.1478 0.1191 0.1181
Total 1 1 1 1 1 1 1
% recovery 1 1 0.908 0.894 0.78 0.7985 0.9895
% moisture 0.37 0.37 0.82 0.84 0.12
Table 6 The composition of the 20:80 ratio of EFB:OPT in each process
EFB OPT HCW SE Hot
water
H2O2 Neutralize
Composition % dry % dry % dry % dry % dry % dry % dry Cellulose 0.3885 0.3885 0.5690 0.439 0.6326 0.6840 0.7328
Hemicellulose 0.2614 0.2614 0.1345 0.232 0.0501 0.0238 0.0121
Lignin 0.1162 0.1162 0.1302 0.116 0.1701 0.1578 0.1232
Ash 0.014 0.014 0.0128 0.0162 0.0122 0.0120 0.0112
Other 0.2199 0.2199 0.1532 0.1968 0.1351 0.1223 0.1207
Total 1 1 1 1 1 1 1
% recovery 1 1 0.908 0.894 0.7866 0.758 0.985
% moisture 0.37 0.37 0.82 0.84 0.12
The composition of 100:0 and 0:100 of EFB:OPT is the same as the
composition of EFB and OPT. In this work, mass input was determined 47,208 kg/day
of feedstock for three plants to produce more than 10,000 L/day of 99.5 wt% ethanol.
26
3. Concept to design size of equipment
The basic concept to design the size of equipment was to cover 47,208
kg/day of feedstock. The maximum size of the reactor base on the available maximum
size, 50 m3. Since upstream processes took a shorter time compared with the SSF
process, they could be designed to be a cyclic operation of small units to minimize the
equipment purchasing costs and avoid the bottleneck before feeding to the SSF. The
mass flow in each process was applied to access the size of equipment in each
process. Mass flow in/out and operating time of each process determine the size of the
reactor, which is the main factor impacting the equipment purchasing costs and
directly influences the total capital cost. The concept to design the size of equipment
for the various ratios of OPT and EFB plant was to cover maximum capacity
production for 100:0, 80:20, 50:50, 20:80, 0:100 of EFB:OPT. Most reactors can be
simply a tank e.g. agitated tank, heat jacket tank, mixing heat jacket tank.
4. Scheduling flow process
The operating time of the upstream process was assumed to 30 minutes
excluding the sterilized unit took only 20 minutes. Scheduling aims to avoid the
bottleneck and run a continuous production because of completely different operating
times of the upstream process and individual SSF. The first pretreatment tank was
filled with small pieces of feedstock and then operated for some time. After the
required time has elapsed, the treated feedstock in the reactor was drained out and the
fed to further reactor by a screw pump. Moreover, most processes were batch
operating and neglected time to transfer during the process. Therefore, it is important
to design the size, amount of the reactor, and sequence of unit utilization to avoid the
bottleneck and produce bioethanol more than 10,000 liters every day. As the SSF
process required the longest operating time for 60 hours. So, it was considered as a
bottleneck point and the maximum volume of reactor (50 m3) was employed. Most of
the previous equipment was utilized for shorter periods compared to the SSF process.
Therefore, a new cycle of the first pretreatment step was initiated every 30 minutes.
5. Simulation process of bioethanol by Aspen Plus software
This section describes the condition and required information in the
simulation section. The simulation was performed in Aspen Plus and Adsorption
software to simulate the entire process and PSA performance. Figures 11 to 13
illustrate the framework of three production plants.
27
Figure 11 Overview of the bioethanol production process from OPT
Figure 12 Overview of the bioethanol production process from EFB
Figure 13 Overview of the bioethanol production process from various ratios of OPT
and EFB
5.1. Pretreatment process
The feedstocks were milled to small sizes of about 20×20×5
mm and removed moisture by sundry before pretreatment. In pretreatment focus on 4
techniques including Hot Compress Water (HCW) (at 200oC, 30 bar, 15 min), Steam
explosion (SE) (at 210oC, 18.6 bar, 4 min), Hot water (at 80 oC, 30 min) and hydrogen
peroxide digestion (H2O2) (at 70oC, 30 min). HCW and SE methods were primary
pretreatments for EFB and OPT, respectively. The condition for each pretreatment
step was as follows.
5.1.1. Hot compress water (HCW), 1:10 of OPT:water
was added into a heat jacket agitated vessel and operated under 200 ºC, 30 bar for 15
min.
28
5.1.2. Steam explosion (SE), small particle sizes of EFB
were added into a SE reactor and input hot stream until reached 210 ºC, 18.6 bar and
wait for 4 min.
5.1.3. Hot water washing, 1:8 of OPT:water was added
into a heat jacket agitated vessel and operated under 80ºC for 30 min.
5.1.4. Hydrogen peroxide (H2O2) digestion, the treated
OPT and EFB were added to a digestion vessel containing 10:1 of water:solid,
3.5:10,000 of NaOH:solid and 3:100 of 3 wt% of H2O2:solid. All components were
mixed together and operate under 80ºC for 30 min.
The purpose of the pretreatment process is for enhancing the
cellulose reactivity with cellulase enzymes and increasing the yield of fermentable
sugars. Pretreatment can increase bioethanol yield and productivity significantly.
5.2. Simultaneous saccharification and fermentation (SSF)
SSF is a combination process of hydrolysis and fermentation to
produce ethanol from lignocellulose. The principle of performing is the enzymatic
hydrolysis together with the fermentation, instead of the hydrolysis before
fermentation. The main advantages of SSF are shorter operating times and higher
productivity compared with SHF. On the other hand, disadvantages are the need to
find a suitable condition for both the enzymatic hydrolysis and the fermentation such
as temperature and the difficulty to recycle the yeast and the enzymes.
In SSF process required enzyme Ctec2 10 FPU/g fiber and S.
cerevisiae. They were added together and operated under 40°C. It can produce the
highest ethanol yield of about 34 g/L of ethanol for OPT and 33.11 g/L for EFB at 60
hr. The ethanol yield of various ratios of feedstocks was presented in Table 9.
5.3. Purification
The mixture from SSF, remained a quantity of water, need to
remove impurity and increase ethanol concentration to fuel grade by removing the
water contain. The common method to separated is distillation, involving separation
mixtures of liquids by exploiting differences in the boiling points of the different
components then subsequently condensed back to a liquid phase. The distillation
process works by the different boiling point of the water and ethanol. As ethanol has a
lower boiling point compared to water, the ethanol will become the vapor phase
before the water. It goes up to the top of the column and is condensed and separated.
In this work, the distillation column was represented by the Radfrac model to operate
under vacuum pressure and implemented by Aspen Plus 8.8V. The dilute ethanol
from SSF process was fed to the vacuum distillation column to produce high ethanol
concentration at top of the column called overhead stream. The ethanol concentration
in the overhead stream directly affects the performance and costs of the dehydration
section. Therefore, the 4 cases of various ethanol concentrations in the overhead
29
stream were carried out to determine the suitable concentration with the lowest
production cost per unit. The parameters of the distillation column including molar
reflux ratio, number of stages, feed stage and distillation to feed were optimized to
produce the purpose overhead product e.g. 50, 80, 85, 90 wt% of ethanol for
pervaporation (PV) technology and 80, 85, 90, 94 wt% for pressure swing adsorption
(PSA) technology. The sensitivity of four parameters of the distillation column was
studied to determine the proper values to produce various ethanol concentrations with
minimum energy demand and the quantity of ethanol loss in the waste stream must
lower than 1 kg/hr.
Figure 14 Purification section
5.3.1. Dehydration
Pervaporation
Pervaporation is the methodology to separate the liquid mixture
by property of membrane. The membrane is a dense non-porous polymeric membrane
or inorganic membrane that allow removing specific component to pass through the
membrane. This work, hydrophilic was applied to remove water content less than
ethanol. The water will be evaporated into the vapor phase as a result of the vacuum
pressure on the permeate side, contain a high fraction of water and a few ethanols are
condensed to the liquid phase and pump to distillate as recover stream. The final
products, high ethanol is obtained from the retentate side. The calculation method of
the energy demand of pervaporation was provided in this section. The energy needed
for the evaporation of water per unit of permeated is Q* (MJ/kgw). Hi is the heat of
pervaporation of species i (Et, W denoted Ethanol and water, respectively).
𝑄∗ = 𝐻𝐸𝑡𝐽𝐸𝑡
𝐽𝑊 + 𝐻𝑊 (7)
In the case of vacuum pervaporation, the value of Q* was taken
into account to calculate the specific energy demand related to that of product 1 kg of
ethanol with 𝐶𝑊,𝑜𝑢𝑡𝐿 the concentration of the water inlet phase. The energy required is
calculated as follows eq 8.
PSA Ethanol
99.5 wt%
Ethanol
3 wt%
Dehydration
PV Distillation
Column
Number of stages
Molar reflux ratio
Distillation to feed
Feed stage
30
𝑄 = 𝑄∗(𝐶𝑊,𝑖𝑛
𝐿 − 𝐶𝑊,𝑜𝑢𝑡𝐿 )
𝐶𝐸𝑡,𝑖𝑛𝐿 −(𝐶𝑊,𝑖𝑛
𝐿 − 𝐶𝑊,𝑜𝑢𝑡𝐿 )
𝐽𝐸𝑡𝐽𝑤
(8)
The 𝐽𝑖 is mass transfer or permeation rate of species i, was used
to calculating the specific energy eq 7.
𝐽𝑤
𝐽𝐸𝑡= [
𝐶𝑤
𝐶𝐸𝑡]𝐺 (9)
Eq 9. is a mass transfer ratio that can be rewritten as eq 10.
𝐽𝑤
𝐽𝐸𝑡=
𝐶𝑤−𝐿
𝛼𝐶𝐸𝑡−𝐿 (10)
Where 𝐶𝐸𝑡−𝐿 and 𝐶𝑤
−𝐿 are the logarithmic mean value of ethanol
and water in the feed phase, which can be calculated as follow eq 11.
𝐶𝐸𝑡−𝐿 =
𝐶𝐸𝑡,𝑖𝑛𝐿 − 𝐶𝐸𝑡,𝑜𝑢𝑡
𝐿
𝐼𝑛(𝐶𝐸𝑡,𝑖𝑛𝐿 /𝐶𝐸𝑡,𝑜𝑢𝑡
𝐿 ) (11)
As knowing the ratio of permeation rates of i, the concentration
of liquid permeate can be predicted by eq 12.
𝐶𝐸𝑡𝐺 =
1
1+𝐶𝑤
−𝐿
𝛼𝐶𝐸𝑡−𝐿
(12)
The recovery efficiency of fuel-grade quality (𝜂) can be
calculated by eq 13.
𝜂 = (1 −𝑉𝑝
𝑉𝑖𝑛)
𝐶𝐸𝑡,𝑜𝑢𝑡𝐿
𝐶𝐸𝑡,𝑖𝑛𝐿 (13)
Pressure swing adsorption
Aspen Adsorption was used to simulate the performance of
cyclic PSA. The two columns were considered as absorption and desorption working
in the cycle. The PSA cycle consists of two stages. 1) Adsorption which operates
under high pressure, gases water will be attracted to the adsorbent surface. 2)
Desorption which operates under low pressure to release adsorbed water. The result of
various ethanol concentrations in the overhead stream from Aspen pen software was
transferred to Aspen adsorption software. The overhead stream was fed to the bottom
of the PSA column, packed with Zeolite 3A in the form of spheres with properties as
shown in Table 14. Mathematic Modeling of Pressure swing absorption as follows eq.
14 was used to analyze the ethanol dehydration production process.
31
𝑄𝑖 = 𝐼𝑃1𝑖𝐼𝑃2𝑖𝑃𝑦𝑖
1+Σ𝑘(𝐼𝑃2𝑘𝑃𝑦𝑘) (14)
Where 𝑄𝑖 𝑖𝑠 Amount adsorbed for component i,(kmol/kg
adsorbent), 𝐼𝑃1,2 𝑖𝑠 Isotherm parameters for component i, 𝑃 is Gas Pressure,(bar) and
yi is Gas-phase mole fraction for component i
In this work, three bioethanol production plants were simulated and evaluated
economic feasibility. The first plant was designed for only OPT as feedstock. For
OPT plant, two dehydration technologies including PV and PSA were compared. The
best candidate for technology to purify 3 wt% of the ethanol from OPT plant was
employed for other plants. The second plant was designed for only EFB as feedstock.
EFB has the high potential to produce more ethanol yield according to high cellulose
contain. However, OPT has an obvious limitation in terms of the seasonally harvested
oil palm trunk. So, the third plant was designed for two feedstocks, EFB and OPT.
This plant has more flexibility in operation which can work with various ratios of
EFB and OPT. The period for operating was 7,200 hours/year. The life of the plant
was 20 years with a capacity of more than 10,000 Liters/day of 99.5 wt% ethanol.
6. Economic assessment
The economic evaluation of each alternative bioethanol plant was
calculated in order to determine the feasibility of a project. In this work, Direct fixed
capital (DFC) was represented as the total capital cost. The methodology to calculate
DFC is reported in Petrides (2000). DFC was estimated based on the total equipment
purchasing cost (PC) in a plant. This section showed the element for the calculation of
the total capital cost. The costs of common equipment such as mixing tank, heat
jacket tank, pump were estimated by Vendors' citation. Costs for some equipment
such as cooler, heaters, distillation column were calculated by the Aspen process
economic analyzer V8.8. The total operating cost consists of raw materials, labor,
chemical substance, plant overhead, G and A expenses, utilities, maintenance,
miscellaneous, and membrane/molecular sieve replacement. The detail of total capital
and the operating cost was explained as follows.
6.1. Total Capital cost
The method to calculate total capital cost comprises of 4 main
elements.
1. Total plant direct cost (TPDC) including
1.1 Equipment Purchase Cost (PC) is the major factor in DFC
calculation.
1.2 Installation costs were according to 50 % of PC.
1.3 Piping was according to 40 % of PC. The expense of piping
including purchasing and layout design represented in this part.
32
1.4 Instrumentation costs were estimated at 35 % of PC.
1.5 Insulation is the necessary item for the safety of plant was
estimated at 3 % of PC
1.6 Electrical consists of the infrastructure of the plant such as
lighting, wiring in the plant were estimated at 15 % of PC
1.7 Building costs requires the control room, laboratory, office,
and plant. the costs were estimated at 45 % of PC.
1.8 Land was decided to locate in the south of Thailand where
is the main region for palm production. So, located nearby the feedstock source can
reduce transportation costs. The land value was estimated at 4% of PC, and profit
from land after the end of the plant’s life is 20% per year.
2. Total plant indirect cost (TPIC) including
2.1 Engineering costs including design plant layout, draft and
green print of plant, was estimated at 25 % of TPDC
2.2 Construction costs of the plant were estimated at 35% of
TPDC
3. The contractor’s fee, including during construction and business,
was estimated at 5% of the summation of TPDC and TPIC.
4. A contingency is an expense for a potentially negative event that
may occur in the future, such as natural disasters, economic fluctuation. It was
estimated at 10% of summation of TPDC and TPI
6.2. Total operating cost
In this section, the expenses were divided into 9 elements.
1. Raw materials
The bioethanol plant has a capacity of more than 10,000 L/day
of ethanol. The feedstock, OPT, EFB, and various ratios of OPT and EFB, was
required 47,208 kg/day. The price of OPT is 0.001 $/kg and 0.0015625 $/kg for EFB
which base on the retailer citation in the south region of Thailand.
2. Labor
The operating labor per shift is 12 labors. This plant has 3 shifts
in one day, 8 hours per shift. The local labor costs in the south region of Thailand on
average about 1.2 $/day.
3. Chemical substance
33
The amount of chemical was required to produce bioethanol.
The price of enzyme Ctec2 is 0.53 $/kg, 0.81 $/kg for hydrogen peroxide, 0.09 $/kg
for ammonium sulfate, 0.10 $/kg for urea, 0.20 $/kg for sodium hydroxide, and
S.Cerevisae cultivation was accounted for 10% of total chemical price for one SSF
tank.
4. Plant overhead
Plant Overheads are the charge which cannot directly determine
or traced with any element costs. Plant overhead was estimated at 25% of the
summation of labor and maintenance costs (Goldthorpe et al. 2014).
5. G and A expenses
G and A expenses costs are necessary costs to maintain a
company's daily operations. G and A expenses were accounted for 4 % of summation
of labor, plant overhead, and maintenance costs (Goldthorpe et al. 2014).
6. Utilities
The utility consists of electric city, steam, and water. All utility
prices base on the price in Aspen Plus 8.8V. The electricity is 7.75x10-2 $/kW, water
is 2.12x10-7 $/KJ/hr, and steam which is the main utility of plant is 1.90x10-6 $/KJ/hr.
7. Maintenance
Maintenance costs were accounted for 10% per 8,000 hr of
purchasing equipment costs. This parameter base on the database in Aspen Plus 8.8V.
8. Miscellaneous
Miscellaneous is a special expense excluding the above
element. It was assumed at 1000 $ per year (Diopenes and Laptaned 2009).
9. Membrane/molecular sieve replacement
Membrane and Zeolite 3A were assumed to replace every 5
years. The price of the membrane on average is 200 $/m2 with 100 $ replacement
cost. The price of adsorbent (Zeolite 3A) is 1.3 $/ kg, 4639 kg per one column
(O’Brien et al. 2000).
Table 7 displays the related parameters to evaluate the economic
feasibility of the plant. Discount rate bases on the minimum loan rate of Krungthai
bank (7 Feb 2020). The selling price of the final product was assumed to 0.781$/L
base on the average price of ethanol in Thailand. Depreciation expense with the
straight-line depreciation method bases on Thailand Tax Depreciation Rates. The end
34
of plants’ life, revenue from salvage value of equipment, and profit from land will be
received. Table 8 displays the escalation cost defines as changes in the costs of
product, raw material, labor, and utilities in a given economy over a period (Diopenes
and Laptaned 2009).
Table 7 Parameter for economic assessment
Parameters Values Ref.
Working time 7200 hours N/A
Raw material 1967 kg/hour N/A
Discount rate 5.775% Krungthai bank’s
report
Proposed product price 0.781 $/L N/A
Tax rate 20 %/year Thailand Corporate
Tax Rate
Economic life of the project 20 years N/A
Depreciation method Straight Line N/A
Depreciation Expense 20% Thailand Tax
Depreciation Rates
Land 20% TerraBKK Research
Savage value 20% TerraBKK Research
Table 8 Escalation assumption
Percent/year
Products Escalation 1
Raw Material Escalation 1
Operating and Maintenance Labor Escalation 1
Utility Escalation 1
The parameters represented in Table 7 and 8 were used to calculate cost
analysis, total capital investment (TCI), the net present value (NPV) Internal rate of
return (IRR), and Payback Period (PB) by Microsoft Office Excel 2016 to assess the
feasibility of investment in this project.
7. Sensitivity Analysis
In this section, the sensitivity of two variables was studied for the lowest
production cost of OPT plant. Since the purification was mentioned as the highest
energy consumption part and also generate a high production cost. The costs of
purification strongly rely on ethanol concentration in the feed. Therefore, the various
ethanol concentration in the fermentation broth was assumed and studied in this
section to determine the effect of ethanol concentration on production cost.
7.1. The concentration of ethanol from SSF
• 4 wt% ethanol in a fermentation broth
35
• 6 wt% ethanol in a fermentation broth
• 8 wt% ethanol in a fermentation broth
As different ethanol concentrations in the fermentation broth, the various
ethanol concentrations in feed were increased to 80, 85, 90 wt% to determine the
proper ethanol concentration in the overhead stream before feeding to dehydration
section with pervaporation technology.
Moreover, the effect of the highest portion of chemical price was analyzed.
Enzyme Ctec2 was the highest portion accounting for 40 % of the chemical costs. The
price of enzyme Ctec2 was reduced by 15, 30, 45, 60, 75% from the initial price (0.53
$/kg) to calculate the production cost per unit.
7.2. Chemical cost
• 15 % reduction of enzyme Ctec2 price (0.45 $/kg)
• 30 % reduction of enzyme Ctec2 price (0.37 $/kg)
• 45 % reduction of enzyme Ctec2 price (0.29 $/kg)
• 60 % reduction of enzyme Ctec2 price (0.21 $/kg)
• 75 % reduction of enzyme Ctec2 price (0.13 $/kg)
.
36
RESULTS AND DISCUSSION
The result of three bioethanol production plants including OPT, EFB, and
various ratios of two feedstocks plants has presented. The first plant focuses on the
determination of the proper technology in the purification section to obtain the lowest
production cost per unit by comparison of the combination of optimized distillation
column with pervaporation (PV) and optimized distillation column with pressure
swing adsorption (PSA). The second (EFB) and third (two feedstocks) plant have
studied the effect on the economic results when utilizing the EFB and various ratios of
two feedstocks by remaining the best candidate for purification technology from OPT
plant.
1. Mass balance calculation
A mass balance flowsheet was developed by Microsoft excel 2016. Mass
balance calculation is essential for verifying the consistency of the compositional
analysis data after the pretreatment and SSF process. The amount of ethanol in the
final process can be estimated by the material balance calculation. The mass transfer
result of each unit was applied to calculate the size, number, and unit utilization of the
reactor in each unit. Table 9 displays the amount of mass input (feedstock), output
(final product), and ethanol concentration from SSF. EFB contains the higher
cellulose fraction with lowers major resistant composition, e.g. lignin compared with
OPT. So, EFB and high EFB ratio in two feedstocks plants can produce a higher
ethanol yield when using the same amount of input (feedstock).
Table 9 Mass input and mass output of three plants
Feedstock Mass input
(kg)
Ethanol
(Liters)
ethanol concentration
(w/v%)
OPT 47,208 11,404.33 3.311
EFB 47,208 13,880.55 3.4
EFB:OPT ratio
100:20 47,208 13,880.55 3.4
80:20 47,208 13,377.41 3.3822
50:50 47,208 12,629.98 3.3555
20:80 47,208 11,891.52 3.3288
0:100 47,208 11,404.33 3.311
2. Equipment design
The mass transfer in each unit determines the size of the rector. The size and
type of main equipment for OPT, EFB, and two feedstock plants were displayed in
Appendix Table A1 to A3, A4, and A5 to A6. The main equipment of various ratios
of feedstocks plant was designed to cover the maximum capacity (100:0 of
EFB:OPT). So, the equipment size of two plants, EFB and various ratios of
feedstocks, was the same.
37
3. Scheduling
The appropriate scheduling can increase productivity without further
investment. Therefore, scheduling is significantly influential to investment costs. As
each plant operates with different sizes and amounts of a reactor, lead to a different
schedule must be designed. The schedule aims to produce bioethanol for more than
10,000 liters every day. Figures 15 and 17 display the equipment utilization to feed
the pretreated feedstock to one SSF tank. EFB and two feedstocks plants can operate
with the same schedule because of utilizing the same size and amount of equipment.
Due to the maximum size of the reactor was 50 m3, 6 and 8 tanks of SSF were
required for OPT and EFB plant to cover 10,000 L of ethanol called a batch. Figures
16 and 18 display the equipment utilization for two consecutive batches. The
operating time of the upstream process is 6 and 5 hours for OPT and EFB plant. This
is the time required to go from the preparation of feedstock until fulfilling the first
SSF tank. Then, the pretreated feedstock in SSF tank was sterilized for 20 minutes
before SSF process, For SSF process required 3 hours to produce the highest ethanol
yield. Therefore, the first SSF tank produced ethanol at 66 hours 20 minutes and 65
hours 20 minutes for OPT and EFB plant. However, since most of the upstream
process was utilized for shorter periods compare with SSF, a new first pretreatment
process is initiated every 30 minutes. Therefore, the next SSF can complete in 3 and 4
hours for OPT and EFB plant.
The upstream process of OPT plant was operated for eight rounds before
feeding to one tank of SSF as presented in Figure 15. One batch demands 6 tanks of
SSF to produced 10,000 L of ethanol as shown in Table 10. For EFB and two
feedstocks plants, upstream processes operated for six rounds before feeding to one
tank of SSF as shown in Figure 17. One batch demands 8 tanks of SSF to produced
10,000 L of ethanol as shown in Table 11. The total processing time is approximately
5,180 minutes to produce the first 10,000 L of ethanol which runs from the first
pretreatment step to the SSF process. The next batch can complete in 24 hr to produce
10,000 L of ethanol. To continue the production process and avoid the bottleneck, 16
and 21 of SSF tanks were required for OPT and EFB plant.
38
Figure 15 Operating time of the initial SSF tank of OPT plant
Figure 16 Equipment utilization for two consecutive batches for OPT plant
0 500 1,000 1,500 2,000 2,500 3,000 3,500 4,000
Round 1
Round 2
Round 3
Round 4
Round 5
Round 6
Round 7
Round 8
Time (min)
SE
HOT
H2O2
Neutralize
Media
Sterilize
SSF
0 1,000 2,000 3,000 4,000 5,000 6,000 7,000
Round 1
Round 7
Round 13
Round 19
Round 25
Round 31
Round 37
Round 43
Round 49
Round 55
Round 61
Round 67
Round 73
Round 79
Round 85
Round 91
Time (min)
SE
HOT
H2O2
Neutralize
Media
Sterilize
SSF
39
Table 10 The operating time of upstream and SSF and SSF tank utilization for OPT
plant
SSF
tank no.
Upstream SSF
Start (min) End (min) Start (min) End (min)
First batch (10,000 L of ethanol)
1 0 360 360 3980
2 240 600 600 4220
3 480 840 840 4460
4 720 1080 1080 4700
5 960 1320 1320 4940
6 1200 1560 1560 5180
Second batch (10,000 L of ethanol)
7 1440 1800 1800 5420
8 1680 2040 2040 5660
9 1920 2280 2280 5900
10 2160 2520 2520 6140
11 2400 2760 2760 6380
12 2640 3000 3000 6620
Third batch (10,000 L of ethanol)
13 2880 3240 3240 6860
14 3120 3480 3480 7100
15 3360 3720 3720 7340
16 3600 3960 3960 7580
1 3840 4200 4200 7820
2 4080 4440 4440 8060
Fourth batch (10,000 L of ethanol)
3 4320 4680 4680 8300
4 4560 4920 4920 8540
5 4800 5160 5160 8780
6 5040 5400 5400 9020
7 5280 5640 5640 9260
8 5520 5880 5880 9500
Fifth batch (10,000 L of ethanol)
9 5760 6120 6120 9740
10 6000 6360 6360 9980
11 6240 6600 6600 10220
12 6480 6840 6840 10460
13 6720 7080 7080 10700
14 6960 7320 7320 10940
40
Figure 17 Operating time of the initial SSF tank of EFB and two feedstocks plants
Figure 18 Equipment utilization for two consecutive batches for EFB and two
feedstocks plants
0 500 1,000 1,500 2,000 2,500 3,000 3,500 4,000 4,500 5,000 5,500 6,000 6,500 7,000
Round 1
Round 6
Round 11
Round 16
Round 21
Round 26
Round 31
Round 36
Round 41
Round 46
Round 51
Round 56
Round 61
Round 66
Round 71
Round 76
Round 81
Round 86
Round 91
Round 96
Time (min)
SE&HCW
Hot water
H2O2
Neutralize
Media
Sterilize
SSF
0 500 1,000 1,500 2,000 2,500 3,000 3,500 4,000
Round 1
Round 2
Round 3
Round 4
Round 5
Round 6
Time (min)
SE&HCW
Hot water
H2O2
Neutralize
Media
Sterilize
SSF
41
Table 11 The operating time of upstream and SSF and SSF tank utilization for EFB
and various ratios of feedstocks plants
SSF
tank no.
Upstream SSF
Start (min) End (min) Start (min) End (min)
First batch (10,000 L of ethanol)
1 0 300 300 3920
2 180 480 480 4100
3 360 660 660 4280
4 540 840 840 4460
5 720 1020 1020 4640
6 900 1200 1200 4820
7 1080 1380 1380 5000
8 1260 1560 1560 5180
Second batch (10,000 L of ethanol)
9 1440 1740 1740 5360
10 1620 1920 1920 5540
11 1800 2100 2100 5720
12 1980 2280 2280 5900
13 2160 2460 2460 6080
14 2340 2640 2640 6260
15 2520 2820 2820 6440
16 2700 3000 3000 6620
Third batch (10,000 L of ethanol)
17 2880 3180 3180 6800
18 3060 3360 3360 6980
19 3240 3540 3540 7160
20 3420 3720 3720 7340
21 3600 3900 3900 7520
1 3780 4080 4080 7700
2 3960 4260 4260 7880
3 4140 4440 4440 8060
Fourth batch (10,000 L of ethanol)
4 4320 4620 4620 8240
5 4500 4800 4800 8420
6 4680 4980 4980 8600
7 4860 5160 5160 8780
8 5040 5340 5340 8960
9 5220 5520 5520 9140
10 5400 5700 5700 9320
11 5580 5880 5880 9500
42
4. Simulation model
4.1. Pretreatment model
The completed simulation model of three bioethanol production plants
was illustrated in Figures 118 to 121. In the first step, feedstocks were fed to the
crusher machine to reduce the size before the pretreatment method and remove
moisture by sundry. The small pieces of feedstock were introduced to the
pretreatment section, consists of 3 steps to change the structure of the feedstock and
make cellulose more accessible in the subsequent process. Figures 19 and 20 illustrate
the model of the pretreatment section of OPT and EFB plant.
Figure 19 Simulation model of the three pretreatment processes for OPT
Figure 20 Simulation model of the three pretreatment processes for EFB
Three pretreatment units were represented in RYield model in Aspen
Plus 8.8V. The first pretreatment for OPT was a steam explosion (SE), the small
pieces of OPT were added into the reactor and input the hot steam to increase
temperature and pressure to desire condition (210 oC, 18.6 bar), then open the valve to
decrease pressure to atmospheric pressure rapidly. The total operating time of SE
process was assumed to 30 min. The OPT fibers exploded at atmospheric pressure and
the structure of biomass was changed. Meanwhile, small pieces of EFB were
introduced to the high-pressure agitated vessel jacket, operated under high pressure
HCW HOTWATER
HYDROGEN
NEUTRAL
PUMP2 PUMP3PUMP4
CENTRIFU
PUMP1
EFB
WATER1
H2O2
WATER2
NAOHWATER3
WATER
INPUMP3
S17INPUMP5
SE HOTWATER
HYDROGEN
NEUTRAL
PUMP2 PUMP3PUMP4
CENTRIFU
PUMP1
OPT
WATER1
H2O2
WATER2
NAOHWATER3
STEAM
INPUMP2
INPUMP3
S17
S1
INPUMP5
43
and temperature (200 oC, 30 bar, 30 min). The second pretreatment aims to remove
the hemicellulose by washing with hot water at 80 oC for 30 min. The last step was
pretreatment with hydrogen peroxide digestion (H2O2). The 50 wt% of H2O2 solution
was diluted to 3 wt% before fed in the reactor. This step, water, NaOH, and 3 wt% of
H2O2 were added in one reactor and operated at 70 oC for 30 min. The purpose of
H2O2 digestion is removing the lignin which is an inhibitor for hydrolysis and
fermentation process. After pretreatment, the feedstock contains high cellulose
fraction and low hemicellulose, lignin, ash, and other fraction. The next step is to
adjust pH by adding water to neutralize. Since soaked feedstock in the neutralizing
process was centrifuged by the centrifugal machine to remove water form feedstock.
4.2. SSF model
After the pretreatment process, the treated feedstocks were sent to the
mixing tank by screw pump as shown in Figure 21. This process aims to prepare the
suitable surrounding condition for fermentation by adding water, urea, and
ammonium sulfate. These chemicals were mixed together called media. Mixing of
treated feedstock and media was sterilized at 121 oC for 20 min before sending it to
SSF process. In SSF process, enzyme Ctec2 and yeast Saccharomyces cerevisiae were
simultaneously added into the SSF reactor. Simultaneous saccharification and
fermentation (SSF) is one process option to produce ethanol from lignocellulose. The
principal benefits of performing the enzymatic hydrolysis together with the
fermentation, instead of in a separate step after the hydrolysis, are the reduced end-
product inhibition of the enzymatic hydrolysis, and the reduced investment costs.
From our preliminary, the highest ethanol yield from OPT and EFB was 3.311 w/v%
and 3.4 w/v%. The highest ethanol yield can be achieved at 60 hours after this time
the concentration continuously decreases. Plenty of chemical substances were
required for producing 10,000 L/day of ethanol. So, sensitivity analysis of chemical
costs will be concerned in the sensitivity section.
Figure 21 Simulation model of the media preparing, sterilizing and SSF process
4.3. Purification model
For OPT plant, the diluted ethanol (3.311 w/v%) was fed to the
purification section to remove impurity and increase concentration of ethanol to fuel
MIXT ANK SS FAUTOCLAV
PUMP5PUMP6
PUMP7
UREA
AMMONIUM
YE AST
CT EC2
INSS F
INAUTOCL
INPUMP6INPUMP8
INPUMP7
WAT ER4
INPUMP5
44
grade. In purification section carried out the combination of the distillation column
and dehydration technology. The four-parameter of the distillation column was
optimized to produce high ethanol concentration before feeding to the dehydration
process. In the dehydration process, two technologies including pervaporation (PV)
and pressure swing adsorption (PSA) were compared to find out the proper
technology to increase ethanol concentration to 99.5 wt%. The waste stream from the
dehydration model contained dilute ethanol, was recycled to a distillation column to
minimize ethanol loss. The simulation model of OPT plant with pervaporation and
pressure swing adsorption is illustrated in Figure 118 and 119, respectively. The best
candidate for dehydration technology was employed for EFB and two feedstocks
plants due to containing similar ethanol yield in the SSF process.
4.3.1. Optimize parameter of Distillation column
Figures 22 to 113 present the effect of four-parameters on the
mass flow of ethanol, ethanol loss in the waste stream in the blue line ( ) and
ethanol concentration, reboiler duty in the orange line ( ).
The number of stages and molar reflux ratio were increased to
rise purity of the overhead product. The reflux ratio not only increases purity but also
increases the quantity of liquid condensed and returning to the column. Distillation to
feed directly affects the amount of mass flow in the overhead stream. Consequently,
cost increases for reboiler and condenser. Figures 22 to 113 present the effect of 4
parameters on mass fraction, the mass flow rate of ethanol, reboiler duty, and the
mass flow of ethanol in the waste stream. The suitable values ware selected to
produce the purposed ethanol concentration (50-94 wt%) and mass flow with low
reboiler duty and ethanol in the waste stream must less than 1 kg/hr. Radfrac column
was performed to increase ethanol concentration to 50, 80, 85, 90 wt% before sending
to pervaporation model and 80, 85, 90, 94 wt% before pressure swing adsorption
model.
45
4.3.1.1. Sensitivity analysis four-parameter of distillation column for
OPT pant with pervaporation technology
Distillate to 50 wt%
Figure 22 Sensitivity number of stages of distillation column for case 1 of OPT plant
with pervaporation technology
Figure 23 Sensitivity molar reflux ratio of distillation column for case 1 of OPT plant
with pervaporation technology
Figure 24 Sensitivity distillation to feed of distillation column for case 1 of OPT plant
with pervaporation technology
Figure 25 Sensitivity feed stage of the distillation column for case 1 of OPT plant
with pervaporation technology
0
0.2
0.4
0.6
0.8
1
0
100
200
300
400
500
4 6 8 10 12
Eth
anol
conce
ntr
atio
n (
%w
t)
Mas
s fl
ow
of
ethan
ol
(kg/h
r)
Numer of stage
0
20
40
60
80
100
120
5.00E+05
5.05E+05
5.10E+05
5.15E+05
4 6 8 10 12
Eth
anol
was
te (
kg/h
r)
Reb
oil
er d
uty
(ca
l/se
c)
Number of stage
0
1
2
3
4
5
6
7
8
3.70E+05
4.20E+05
4.70E+05
5.20E+05
5.70E+05
6.20E+05
2 2.5 3 3.5 4
Eth
ano
l w
aste
(k
g/h
r)
Reb
oil
er d
uty
(ca
l/se
c)
Molar reflux ratio
0
0.2
0.4
0.6
0.8
1
426
427
428
429
430
431
432
433
434
2 2.5 3 3.5 4
Eth
ano
l co
nce
ntr
atio
n (
%w
t)
Mas
s fl
ow
of
ethan
ol
(kg/h
r)
Molar reflux ratio
0
0.2
0.4
0.6
0.8
1
0
100
200
300
400
500
0.01 0.03 0.05 0.07 0.09
Eth
ano
l co
nce
ntr
atio
n (
%w
t)
Mas
s fl
ow
of
ethan
ol
(kg/h
r)
Distillation to feed
0
50
100
150
200
250
300
350
400
1.70E+05
4.70E+05
7.70E+05
1.07E+06
0.01 0.03 0.05 0.07 0.09
Eth
ano
l w
aste
(k
g/h
r)
Reb
oil
er d
uty
(ca
l/se
c)
Distillation to feed
0
0.5
1
1.5
2
2.5
3
5.00E+05
5.05E+05
5.10E+05
5.15E+05
2 3 4 5 6
Eth
anol
was
te (
kg/h
r)
Reb
oil
er d
uty
(ca
l/se
c)
Feed stage
0
0.2
0.4
0.6
0.8
1
430.5
431
431.5
432
432.5
433
433.5
434
2 3 4 5 6
Eth
anol
conce
ntr
atio
n (
%w
t)
Mas
s fl
ow
of
ethan
ol
(kg/h
r)
Feed stage
46
Distillate to 80 wt%
Figure 26 Sensitivity number of stages of distillation column for case 2 of OPT plant
with pervaporation technology
Figure 27 Sensitivity molar reflux ratio of distillation column for case 2 of OPT plant
with pervaporation technology
Figure 28 Sensitivity distillation to feed of distillation column for case 2 of OPT plant
with pervaporation technology
Figure 29 Sensitivity feed stage of the distillation column for case 2 of OPT plant
with pervaporation technology
0
2
4
6
8
10
12
2.58E+05
2.58E+05
2.58E+05
2.58E+05
2.58E+05
2.58E+05
2.58E+05
2.58E+05
15 17 19 21 23 25
Eth
anol
was
te (
kg/h
r)
Reb
oil
er d
uty
(ca
l/se
c)
Number of stage
0
0.2
0.4
0.6
0.8
1
376
378
380
382
384
386
388
15 17 19 21 23 25
Eth
anol
conce
ntr
atio
n (
%w
t)
Mas
s fl
ow
of
ethan
ol
(kg/h
r)
Number of stage
0
0.2
0.4
0.6
0.8
1
300
320
340
360
380
400
2 2.5 3 3.5 4
Eth
ano
l co
nce
ntr
atio
n (
%w
t)
Mas
s fl
ow
of
ethan
ol
(kg/h
r)
Molar reflux ratio
0102030405060708090
2.00E+05
2.20E+05
2.40E+05
2.60E+05
2.80E+05
3.00E+05
2 2.5 3 3.5 4
Eth
ano
l w
aste
(kg/h
r)
Reb
oil
er d
uty
(ca
l/se
c)
Molar reflux ratio
0
50
100
150
200
1.70E+05
2.70E+05
3.70E+05
4.70E+05
5.70E+05
6.70E+05
7.70E+05
8.70E+05
9.70E+05
0.01 0.03 0.05 0.07 0.09
Eth
ano
l w
aste
(k
g/h
r)
Reb
oil
er d
uty
(ca
l/se
c)
Distillation to feed
0
0.2
0.4
0.6
0.8
1
0
50
100
150
200
250
300
350
400
450
0.01 0.03 0.05 0.07 0.09
Eth
ano
l co
nce
ntr
atio
n (
%w
t)
Mas
s fl
ow
of
ethan
ol
(kg/h
r)
Distillation to feed
0
50
100
150
200
250
2.50E+05
2.52E+05
2.54E+05
2.56E+05
2.58E+05
2.60E+05
2 4 6 8 10
Eth
anol
was
te (
kg/h
r)
Reb
oil
er d
uty
(ca
l/se
c)
Feed stage
0
0.2
0.4
0.6
0.8
1
0
50
100
150
200
250
300
350
400
450
2 4 6 8 10
Eth
anol
conce
ntr
atio
n (
%w
t)
Mas
s fl
ow
of
ethan
ol
(kg/h
r)
Feed stage
47
Distillate to 85 wt%
Figure 30 Sensitivity number of stages of distillation column for case 3 of OPT plant
with pervaporation technology
Figure 31 Sensitivity molar reflux ratio of distillation column for case 3 of OPT plant
with pervaporation technology
Figure 32 Sensitivity distillation to feed of distillation column for case 3 of OPT plant
with pervaporation technology
Figure 33 Sensitivity feed stage of the distillation column for case 3 of OPT plant
with pervaporation technology
0
2
4
6
8
10
12
2.61E+05
2.61E+05
2.61E+05
2.61E+05
2.61E+05
2.61E+05
2.61E+05
2.61E+05
2.62E+05
15 17 19 21 23 25
Eth
anol
was
te (
kg/h
r)
Reb
oil
er d
uty
(ca
l/se
c)
Number of stage
0.84
0.86
0.88
0.9
0.92
0.94
0.96
0.98
1
376
378
380
382
384
386
388
390
15 17 19 21 23 25
Eth
anol
conce
ntr
atio
n (
%w
t)
Mas
s fl
ow
of
ethan
ol
(kg/h
r)
Number of stage
0
50
100
150
200
250
2.50E+05
2.52E+05
2.54E+05
2.56E+05
2.58E+05
2.60E+05
2.62E+05
2.64E+05
2 4 6 8 10
Eth
anol
was
te (
kg/h
r)
Reb
oil
er d
uty
(ca
l/se
c)
Feed stage
0
0.2
0.4
0.6
0.8
1
150
200
250
300
350
400
450
2 4 6 8 10
Eth
anol
conce
ntr
atio
n (
%w
t)
Mas
s fl
ow
of
ethan
ol
(kg/h
r)
Feed stage
0102030405060708090100110120
1.90E+05
2.10E+05
2.30E+05
2.50E+05
2.70E+05
2 2.5 3 3.5 4
Eth
ano
l w
aste
(kg/h
r)
Reb
oil
er d
uty
(ca
l/se
c)
Molar reflux ratio
0
0.2
0.4
0.6
0.8
1
270
290
310
330
350
370
390
410
2 2.5 3 3.5 4
Eth
ano
l co
nce
ntr
atio
n (
%w
t)
Mas
s fl
ow
of
ethan
ol
(kg/h
r)
Molar reflux ratio
0
60
120
180
1.70E+05
3.70E+05
5.70E+05
7.70E+05
9.70E+05
0.01 0.03 0.05 0.07 0.09
Eth
ano
l w
aste
(k
g/h
r)
Reb
oil
er d
uty
(ca
l/se
c)
Distillation to feed
0
0.2
0.4
0.6
0.8
1
200
250
300
350
400
0.01 0.03 0.05 0.07 0.09
Eth
ano
l co
nce
ntr
atio
n (
%w
t)
Mas
s fl
ow
of
ethan
ol
(kg/h
r)
Distillation to feed
48
Distillate to 90 wt%
Figure 34 Sensitivity number of stages of distillation column for case 4 of OPT plant
with pervaporation technology
Figure 35 Sensitivity molar reflux ratio of distillation column for case 4 of OPT plant
with pervaporation technology
Figure 36 Sensitivity distillation to feed of distillation column for case 4 of OPT plant
with pervaporation technology
Figure 37 Sensitivity feed stage of the distillation column for case 4 of OPT plant
with pervaporation technology
0
2
4
6
8
10
2.78E+05
2.78E+05
2.78E+05
2.78E+05
2.78E+05
2.78E+05
2.78E+05
2.78E+05
15 17 19 21 23 25
Eth
anol
was
te (
kg/h
r)
Reb
oil
er d
uty
(ca
l/se
c)
Number of stage
0.88
0.9
0.92
0.94
0.96
0.98
1
370
372
374
376
378
380
15 17 19 21 23 25
Eth
anol
conce
ntr
atio
n (
%w
t)
Mas
s fl
ow
of
ethan
ol
(kg/h
r)
Number of stage
0
10
20
30
40
50
60
70
80
1.90E+05
2.10E+05
2.30E+05
2.50E+05
2.70E+05
2.90E+05
3.10E+05
3 4 5 6
Eth
ano
l w
aste
(k
g/h
r)
Reb
oil
er d
uty
(ca
l/se
c)
Molar reflux ratio
0
0.2
0.4
0.6
0.8
1
270
290
310
330
350
370
390
3 4 5 6
Eth
ano
l co
nce
ntr
atio
n (
%w
t)
Mas
s fl
ow
of
ethan
ol
(kg/h
r)
Molar reflux ratio
0
30
60
90
120
1.70E+05
3.70E+05
5.70E+05
7.70E+05
9.70E+05
1.17E+06
0.01 0.03 0.05 0.07 0.09
Eth
ano
l w
aste
(k
g/h
r)
Reb
oil
er d
uty
(ca
l/se
c)
Distillation to feed
0
0.2
0.4
0.6
0.8
1
200
250
300
350
400
0.01 0.03 0.05 0.07 0.09
Eth
ano
l co
nce
ntr
atio
n (
%w
t)
Mas
s fl
ow
of
ethan
ol
(kg/h
r)
Distillation to feed
0
50
100
150
200
250
2.70E+05
2.73E+05
2.76E+05
2.79E+05
2 4 6 8 10
Eth
anol
was
te (
kg/h
r)
Reb
oil
er d
uty
(ca
l/se
c)
Feed stage
0
0.2
0.4
0.6
0.8
1
140
190
240
290
340
390
440
2 4 6 8 10
Eth
anol
conce
ntr
atio
n (
%w
t)
Mas
s fl
ow
of
ethan
ol
(kg/h
r)
Feed stage
49
4.3.1.2 Sensitivity analysis four-parameter of distillation column for
OPT pant with pressure swing adsorption technology
Distillate to 80 wt%
Figure 38 Sensitivity number of stages of distillation column for case 1 of OPT plant
with pressure swing adsorption technology
Figure 39 Sensitivity molar reflux ratio of distillation column for case 1 of OPT plant
with pressure swing adsorption technology
Figure 40 Sensitivity distillation to feed of distillation column for case 1 of OPT plant
with pressure swing adsorption technology
Figure 41 Sensitivity feed stage of distillation column for case 1 of OPT plant with
pressure swing adsorption technology
0
10
20
30
40
2.50E+05
3.00E+05
3.50E+05
4.00E+05
2 3 4 5
Eth
ano
l w
aste
(k
g/h
r)
Reb
oil
er d
uty
(ca
l/se
c)
Molar reflux ratio
0
0.2
0.4
0.6
0.8
1
500
510
520
530
540
2 3 4 5
Eth
ano
l co
nce
ntr
atio
n (
%w
t)
Mas
s fl
ow
of
ethan
ol
(kg/h
r)
Molar reflux ratio
0306090120150180210240270300330
1.60E+05
2.60E+05
3.60E+05
4.60E+05
5.60E+05
6.60E+05
7.60E+05
8.60E+05
0.01 0.03 0.05 0.07 0.09
Eth
ano
l w
aste
(k
g/h
r)
Reb
oil
er d
uty
(ca
l/se
c)
Distillation to feed
0
0.2
0.4
0.6
0.8
1
200
250
300
350
400
450
500
550
0.01 0.03 0.05 0.07 0.09
Eth
ano
l co
nce
ntr
atio
n (
%w
t)
Mas
s fl
ow
of
ethan
ol
(kg/h
r)
Distillation to feed
0
0.2
0.4
0.6
0.8
1
140
190
240
290
340
390
440
490
540
590
2 4 6 8 10
Eth
anol
conce
ntr
atio
n (
%w
t)
Mas
s fl
ow
of
ethan
ol
(kg/h
r)
Feed stage
0
50
100
150
200
250
300
2.80E+05
2.83E+05
2.86E+05
2.89E+05
2.92E+05
2 4 6 8 10
Eth
anol
was
te (
kg/h
r)
Reb
oil
er d
uty
(ca
l/se
c)
Feed stage
0
1
2
3
4
5
6
2.90E+05
2.90E+05
2.90E+05
2.90E+05
2.90E+05
2.90E+05
15 17 19 21 23 25
Eth
anol
was
te (
kg/h
r)
Reb
oil
er d
uty
(ca
l/se
c)
Number of stage
0
0.2
0.4
0.6
0.8
1
525
526
527
528
529
530
531
532
15 17 19 21 23 25
Eth
anol
conce
ntr
atio
n (
%w
t)
Mas
s fl
ow
of
ethan
ol
(kg/h
r)
Number of stage
50
Distillate to 85 wt%
Figure 42 Sensitivity number of stages of distillation column for case 2 of OPT plant
with pressure swing adsorption technology
Figure 43 Sensitivity molar reflux ratio of distillation column for case 2 of OPT plant
with pressure swing adsorption technology
Figure 44 Sensitivity distillation to feed of distillation column for case 2 of OPT plant
with pressure swing adsorption technology
Figure 45 Sensitivity feed stage of the distillation column for case 2 of OPT plant
with pressure swing adsorption technology
0102030405060708090
2.20E+05
2.70E+05
3.20E+05
3.70E+05
2 3 4 5
Eth
ano
l w
aste
(kg/h
r)
Reb
oil
er d
uty
(ca
l/se
c)
Molar reflux ratio
0
0.2
0.4
0.6
0.8
1
420430440450460470480490500510520
2 3 4 5
Eth
ano
l co
nce
ntr
atio
n (
%w
t)
Mas
s fl
ow
of
ethan
ol
(kg/h
r)
Molar reflux ratio
0306090120150180210240270300330
1.60E+05
2.60E+05
3.60E+05
4.60E+05
5.60E+05
6.60E+05
7.60E+05
8.60E+05
0.01 0.03 0.05 0.07 0.09
Eth
ano
l w
aste
(k
g/h
r)
Reb
oil
er d
uty
(ca
l/se
c)
Distillation to feed
0
0.2
0.4
0.6
0.8
1
200
250
300
350
400
450
500
550
0.01 0.03 0.05 0.07 0.09
Eth
ano
l co
nce
ntr
atio
n (
%w
t)
Mas
s fl
ow
of
ethan
ol
(kg/h
r)
Distillation to feed
0
50
100
150
200
250
300
2.70E+05
2.73E+05
2.76E+05
2.79E+05
2 4 6 8 10
Eth
anol
was
te (
kg/h
r)
Reb
oil
er d
uty
(ca
l/se
c)
Feed stage
0
0.2
0.4
0.6
0.8
1
140
190
240
290
340
390
440
490
540
2 4 6 8 10
Eth
anol
conce
ntr
atio
n (
%w
t)
Mas
s fl
ow
of
ethan
ol
(kg/h
r)
Feed stage
0
2
4
6
8
10
12
14
2.76E+05
2.76E+05
2.76E+05
2.76E+05
2.76E+05
2.76E+05
2.76E+05
15 17 19 21 23 25
Eth
anol
was
te (
kg/h
r)
Reb
oil
er d
uty
(ca
l/se
c)
Number of stage
0.82
0.85
0.88
0.91
0.94
0.97
1
496
498
500
502
504
506
508
510
512
15 17 19 21 23 25
Eth
anol
conce
ntr
atio
n (
%w
t)
Mas
s fl
ow
of
ethan
ol
(kg/h
r)
Number of stage
51
Distillate to 90 wt%
Figure 46 Sensitivity number of stages of distillation column for case 3 of OPT plant
with pressure swing adsorption technology
Figure 47 Sensitivity molar reflux ratio of distillation column for case 3 of OPT plant
with pressure swing adsorption technology
Figure 48 Sensitivity distillation to feed of distillation column for case 3 of OPT plant
with pressure swing adsorption technology
Figure 49 Sensitivity feed stage of distillation column for case 3 of OPT plant with
pressure swing adsorption technology
0
2
4
6
8
10
3.01E+05
3.01E+05
3.01E+05
3.01E+05
3.01E+05
3.01E+05
3.01E+05
3.01E+05
15 17 19 21 23 25
Eth
anol
was
te (
kg/h
r)
Reb
oil
er d
uty
(ca
l/se
c)
Number of stage
0.88
0.91
0.94
0.97
1
483484485486487488489490491492493
15 17 19 21 23 25
Eth
anol
conce
ntr
atio
n (
%w
t)
Mas
s fl
ow
of
ethan
ol
(kg/h
r)
Number of stage
0
30
60
90
120
2.00E+05
2.50E+05
3.00E+05
3.50E+05
2 3 4 5
Eth
ano
l w
aste
(kg/h
r)
Reb
oil
er d
uty
(ca
l/se
c)
Molar reflux ratio
0
0.2
0.4
0.6
0.8
1
350
370
390
410
430
450
470
490
510
2 3 4 5
Eth
ano
l co
nce
ntr
atio
n (
%w
t)
Mas
s fl
ow
of
ethan
ol
(kg/h
r)
Molar reflux ratio
0306090120150180210240270300330
1.60E+05
3.60E+05
5.60E+05
7.60E+05
9.60E+05
1.16E+06
0.01 0.03 0.05 0.07 0.09
Eth
ano
l w
aste
(k
g/h
r)
Reb
oil
er d
uty
(ca
l/se
c)
Distillation to feed
0
0.2
0.4
0.6
0.8
1
200
250
300
350
400
450
500
550
0.01 0.03 0.05 0.07 0.09
Eth
ano
l co
nce
ntr
atio
n (
%w
t)
Mas
s fl
ow
of
ethan
ol
(kg/h
r)
Distillation to feed
0
0.2
0.4
0.6
0.8
1
140
190
240
290
340
390
440
490
540
2 4 6 8 10
Eth
anol
conce
ntr
atio
n (
%w
t)
Mas
s fl
ow
of
ethan
ol
(kg/h
r)
Feed stage
0
50
100
150
200
250
300
2.70E+05
2.80E+05
2.90E+05
3.00E+05
3.10E+05
2 4 6 8 10
Eth
anol
was
te (
kg/h
r)
Reb
oil
er d
uty
(ca
l/se
c)
Feed stage
52
Distillate to 95 wt%
Figure 50 Sensitivity number of stages of distillation column for case 4 of OPT plant
with pressure swing adsorption technology
Figure 51 Sensitivity molar reflux ratio of distillation column for case 4 of OPT plant
with pressure swing adsorption technology
Figure 52 Sensitivity distillation to feed of distillation column for case 4 of OPT plant
with pressure swing adsorption technology
Figure 53 Sensitivity feed stage of distillation column for case 4 of OPT plant with
pressure swing adsorption technology
0
0.5
1
1.5
2
2.5
2.92E+05
2.92E+05
2.92E+05
2.92E+05
25 27 29 31 33 35
Eth
anol
was
te (
kg/h
r)
Reb
oil
er d
uty
(ca
l/se
c)
Number of stage
0.91
0.94
0.97
1
478.5
479
479.5
480
480.5
481
25 27 29 31 33 35
Eth
anol
conce
ntr
atio
n (
%w
t)
Mas
s fl
ow
of
ethan
ol
(kg/h
r)
Number of stage
0
30
60
90
120
150
180
210
2.00E+05
4.00E+05
6.00E+05
8.00E+05
1.00E+06
1.20E+06
0.01 0.03 0.05 0.07 0.09
Eth
ano
l w
aste
(k
g/h
r)
Reb
oil
er d
uty
(ca
l/se
c)
Distillation to feed
0
0.2
0.4
0.6
0.8
1
200
250
300
350
400
450
500
0.01 0.03 0.05 0.07 0.09
Eth
ano
l co
nce
ntr
atio
n (
%w
t)
Mas
s fl
ow
of
ethan
ol
(kg/h
r)
Distillation to feed
0
30
60
90
120
150
1.90E+05
2.10E+05
2.30E+05
2.50E+05
2.70E+05
2.90E+05
2 3 4 5
Eth
ano
l w
aste
(kg/h
r)
Reb
oil
er d
uty
(ca
l/se
c)
Molar reflux ratio
0
0.2
0.4
0.6
0.8
1
310
330
350
370
390
410
430
450
470
490
2 3 4 5
Eth
ano
l co
nce
ntr
atio
n (
%w
t)
Mas
s fl
ow
of
ethan
ol
(kg/h
r)
Molar reflux ratio
0
10
20
30
40
50
60
70
80
2.70E+05
2.80E+05
2.90E+05
3.00E+05
5 7 9 11 13 15
Eth
anol
was
te (
kg/h
r)
Reb
oil
er d
uty
(ca
l/se
c)
Feed stage
0.86
0.88
0.9
0.92
0.94
0.96
0.98
1
140
190
240
290
340
390
440
490
540
5 7 9 11 13 15
Eth
anol
conce
ntr
atio
n (
%w
t)
Mas
s fl
ow
of
ethan
ol
(kg/h
r)
Feed stage
53
4.3.1.3 Sensitivity analysis four-parameter of distillation column for
EFB pant with pervaporation technology
Distillate to 85 wt%
Figure 54 Sensitivity number of stages of distillation column for EFB plant with
pervaporation technology
Figure 55 Sensitivity molar reflux ratio of distillation column for EFB plant with
pervaporation technology
Figure 56 Sensitivity distillation to feed of distillation column for EFB plant with
pervaporation technology
Figure 57 Sensitivity feed stage of distillation column for EFB plant with
pervaporation technology
0
1
2
3
4
5
6
7
8
3.25E+05
3.26E+05
3.26E+05
3.27E+05
3.27E+05
15 17 19 21 23 25
Eth
anol
was
te (
kg/h
r)
Reb
oil
er d
uty
(ca
l/se
c)
Number of stage
0.82
0.84
0.86
0.88
0.9
0.92
0.94
0.96
0.98
1
466
467
468
469
470
471
472
473
474
475
15 17 19 21 23 25
Eth
anol
conce
ntr
atio
n (
%w
t)
Mas
s fl
ow
of
ethan
ol
(kg/h
r)
Number of stage
0
5
10
15
20
25
30
2.80E+05
3.00E+05
3.20E+05
3.40E+05
3.60E+05
3.80E+05
3 3.5 4 4.5 5
Eth
ano
l w
aste
(k
g/h
r)
Reb
oil
er d
uty
(ca
l/se
c)
Molar reflux ratio
0
0.2
0.4
0.6
0.8
1
440
450
460
470
480
3 3.5 4 4.5 5
Eth
ano
l co
nce
ntr
atio
n (
%w
t)
Mas
s fl
ow
of
ethan
ol
(kg/h
r)
Molar reflux ratio
0
50
100
150
200
2.00E+05
5.00E+05
8.00E+05
1.10E+06
1.40E+06
0.01 0.03 0.05 0.07 0.09
Eth
ano
l w
aste
(k
g/h
r)
Reb
oil
er d
uty
(ca
l/se
c)
Distillation to feed
0
0.2
0.4
0.6
0.8
1
0
100
200
300
400
500
0.01 0.03 0.05 0.07 0.09
Eth
ano
l co
nce
ntr
atio
n (
%w
t)
Mas
s fl
ow
of
ethan
ol
(kg/h
r)
Distillation to feed
0
50
100
150
200
250
300
3.00E+05
3.05E+05
3.10E+05
3.15E+05
3.20E+05
3.25E+05
3.30E+05
2 4 6 8 10
Eth
anol
was
te (
kg/h
r)
Reb
oil
er d
uty
(ca
l/se
c)
Feed stage
0
0.2
0.4
0.6
0.8
1
0
100
200
300
400
500
2 4 6 8 10
Eth
anol
conce
ntr
atio
n (
%w
t)
Mas
s fl
ow
of
ethan
ol
(kg/h
r)
Feed stage
54
4.3.1.4 Sensitivity analysis four-parameter of distillation column for
100:0 ratio of EFB and OPT plant with pervaporation technology
Distillate to 85 wt%
Figure 58 Sensitivity number of stages of distillation column for 100:0 ratio of EFB
and OPT plant with pervaporation technology
Figure 59 Sensitivity molar reflux ratio of distillation column for 100:0 ratio of EFB
and OPT plant with pervaporation technology
Figure 60 Sensitivity distillation to feed of distillation column for 100:0 ratio of EFB
and OPT plant with pervaporation technology
Figure 61 Sensitivity feed stage of distillation column for 100:0 ratio of EFB and
OPT plant with pervaporation technology
0
1
2
3
4
5
6
7
8
3.25E+05
3.26E+05
3.26E+05
3.27E+05
3.27E+05
15 17 19 21 23 25
Eth
anol
was
te (
kg/h
r)
Reb
oil
er d
uty
(ca
l/se
c)
Number of stage
0.82
0.84
0.86
0.88
0.9
0.92
0.94
0.96
0.98
1
466
467
468
469
470
471
472
473
474
475
15 17 19 21 23 25
Eth
anol
conce
ntr
atio
n (
%w
t)
Mas
s fl
ow
of
ethan
ol
(kg/h
r)
Number of stage
0
5
10
15
20
25
30
2.80E+05
3.00E+05
3.20E+05
3.40E+05
3.60E+05
3.80E+05
3 3.5 4 4.5 5
Eth
ano
l w
aste
(k
g/h
r)
Reb
oil
er d
uty
(ca
l/se
c)
Molar reflux ratio
0
0.2
0.4
0.6
0.8
1
440
450
460
470
480
3 3.5 4 4.5 5
Eth
ano
l co
nce
ntr
atio
n (
%w
t)
Mas
s fl
ow
of
ethan
ol
(kg/h
r)
Molar reflux ratio
0
50
100
150
200
2.00E+05
5.00E+05
8.00E+05
1.10E+06
1.40E+06
0.01 0.03 0.05 0.07 0.09
Eth
ano
l w
aste
(k
g/h
r)
Reb
oil
er d
uty
(ca
l/se
c)
Distillation to feed
0
0.2
0.4
0.6
0.8
1
0
100
200
300
400
500
0.01 0.03 0.05 0.07 0.09
Eth
ano
l co
nce
ntr
atio
n (
%w
t)
Mas
s fl
ow
of
ethan
ol
(kg/h
r)
Distillation to feed
0
50
100
150
200
250
300
3.00E+05
3.05E+05
3.10E+05
3.15E+05
3.20E+05
3.25E+05
3.30E+05
2 4 6 8 10
Eth
anol
was
te (
kg/h
r)
Reb
oil
er d
uty
(ca
l/se
c)
Feed stage
0
0.2
0.4
0.6
0.8
1
0
100
200
300
400
500
2 4 6 8 10
Eth
anol
conce
ntr
atio
n (
%w
t)
Mas
s fl
ow
of
ethan
ol
(kg/h
r)
Feed stage
55
4.3.1.5 Sensitivity analysis four-parameter of distillation column for
80:20 ratio of EFB and OPT plant with pervaporation technology
Distillate to 85 wt%
Figure 62 Sensitivity number of stages of distillation column for 80:20 ratio of EFB
and OPT plant with pervaporation technology
Figure 63 Sensitivity molar reflux ratio of distillation column for 80:20 ratio of EFB
and OPT plant with pervaporation technology
Figure 64 Sensitivity distillation to feed of distillation column for 80:20 ratio of EFB
and OPT plant with pervaporation technology
Figure 65 Sensitivity feed stage of distillation column for 80:20 ratio of EFB and
OPT plant with pervaporation technology
0
2
4
6
8
10
3.10E+05
3.11E+05
3.12E+05
3.13E+05
3.14E+05
15 17 19 21 23 25
Eth
anol
was
te (
kg/h
r)
Reb
oil
er d
uty
(ca
l/se
c)
Number of stage
0.84
0.86
0.88
0.9
0.92
0.94
0.96
0.98
1
448449450451452453454455456457458
15 17 19 21 23 25
Eth
anol
conce
ntr
atio
n (
%w
t)
Mas
s fl
ow
of
ethan
ol
(kg/h
r)
Number of stage
0
50
100
150
200
2.00E+05
5.00E+05
8.00E+05
1.10E+06
1.40E+06
0.01 0.03 0.05 0.07 0.09
Eth
ano
l w
aste
(k
g/h
r)
Reb
oil
er d
uty
(ca
l/se
c)
Distillation to feed
0
0.2
0.4
0.6
0.8
1
250
300
350
400
450
500
0.01 0.03 0.05 0.07 0.09
Eth
ano
l co
nce
ntr
atio
n (
%w
t)
Mas
s fl
ow
of
ethan
ol
(kg/h
r)
Distillation to feed
0
50
100
150
200
250
300
3.00E+05
3.05E+05
3.10E+05
3.15E+05
2 4 6 8 10
Eth
anol
was
te (
kg/h
r)
Reb
oil
er d
uty
(ca
l/se
c)
Feed stage
0
0.2
0.4
0.6
0.8
1
180
230
280
330
380
430
480
2 4 6 8 10
Eth
anol
conce
ntr
atio
n (
%w
t)
Mas
s fl
ow
of
ethan
ol
(kg/h
r)
Feed stage
0
30
60
90
120
2.20E+05
2.50E+05
2.80E+05
3.10E+05
3.40E+05
3.70E+05
2 3 4 5
Eth
ano
l w
aste
(k
g/h
r)
Reb
oil
er d
uty
(ca
l/se
c)
Molar reflux ratio
0
0.2
0.4
0.6
0.8
1
330
350
370
390
410
430
450
470
2 3 4 5
Eth
ano
l co
nce
ntr
atio
n (
%w
t)
Mas
s fl
ow
of
ethan
ol
(kg/h
r)
Molar reflux ratio
56
4.3.1.6 Sensitivity analysis four-parameter of distillation column for
50:50 ratio of EFB and OPT plant with pervaporation technology
Distillate to 85 wt%
Figure 66 Sensitivity number of stages of distillation column for 50:50 ratio of EFB
and OPT plant with pervaporation technology
Figure 67 Sensitivity molar reflux ratio of distillation column for 50:50 ratio of EFB
and OPT plant with pervaporation technology
Figure 68 Sensitivity distillation to feed of distillation column for 50:50 ratio of EFB
and OPT plant with pervaporation technology
Figure 69 Sensitivity feed stage of distillation column for 50:50 ratio of EFB and
OPT plant with pervaporation technology
0
2
4
6
8
10
2.90E+05
2.93E+05
2.96E+05
2.99E+05
15 17 19 21 23 25
Eth
anol
was
te (
kg/h
r)
Reb
oil
er d
uty
(ca
l/se
c)
Number of stage
0.84
0.86
0.88
0.9
0.92
0.94
0.96
0.98
1
423
424
425
426
427
428
429
430
431
432
15 17 19 21 23 25
Eth
anol
conce
ntr
atio
n (
%w
t)
Mas
s fl
ow
of
ethan
ol
(kg/h
r)
Number of stage
0
50
100
150
200
2.00E+05
5.00E+05
8.00E+05
1.10E+06
1.40E+06
0.01 0.03 0.05 0.07 0.09
Eth
ano
l w
aste
(k
g/h
r)
Reb
oil
er d
uty
(ca
l/se
c)
Distillation to feed
0
0.2
0.4
0.6
0.8
1
250
300
350
400
450
0.01 0.03 0.05 0.07 0.09
Eth
ano
l co
nce
ntr
atio
n (
%w
t)
Mas
s fl
ow
of
ethan
ol
(kg/h
r)
Distillation to feed
0
50
100
150
200
250
300
2.90E+05
2.93E+05
2.96E+05
2.99E+05
2 4 6 8 10
Eth
anol
was
te (
kg/h
r)
Reb
oil
er d
uty
(ca
l/se
c)
Feed stage
0
0.2
0.4
0.6
0.8
1
180
230
280
330
380
430
480
2 4 6 8 10
Eth
anol
conce
ntr
atio
n (
%w
t)
Mas
s fl
ow
of
ethan
ol
(kg/h
r)
Feed stage
0
30
60
90
120
2.10E+05
2.40E+05
2.70E+05
3.00E+05
3.30E+05
2 3 4 5
Eth
ano
l w
aste
(k
g/h
r)
Reb
oil
er d
uty
(ca
l/se
c)
Molar reflux ratio
0
0.2
0.4
0.6
0.8
1
300
320
340
360
380
400
420
440
2 3 4 5
Eth
ano
l co
nce
ntr
atio
n (
%w
t)
Mas
s fl
ow
of
ethan
ol
(kg/h
r)
Molar reflux ratio
57
4.3.1.7 Sensitivity analysis four-parameter of distillation column for
20:80 ratio of EFB and OPT plant with pervaporation technology
Distillate to 85 wt%
Figure 70 Sensitivity number of stages of distillation column for 20:80 ratio of EFB
and OPT plant with pervaporation technology
Figure 71 Sensitivity molar reflux ratio of distillation column for 20:80 ratio of EFB
and OPT plant with pervaporation technology
Figure 72 Sensitivity distillation to feed of distillation column for 20:80 ratio of EFB
and OPT plant with pervaporation technology
Figure 73 Sensitivity feed stage of distillation column for 20:80 ratio of EFB and
OPT plant with pervaporation technology
0
1
2
3
4
5
6
7
8
2.80E+05
2.81E+05
2.81E+05
2.82E+05
15 17 19 21 23 25
Eth
anol
was
te (
kg/h
r)
Reb
oil
er d
uty
(ca
l/se
c)
Number of stage
0.84
0.86
0.88
0.9
0.92
0.94
0.96
0.98
1
398
399
400
401
402
403
404
405
406
407
15 17 19 21 23 25
Eth
anol
conce
ntr
atio
n (
%w
t)
Mas
s fl
ow
of
ethan
ol
(kg/h
r)
Number of stage
020406080100120140160180
1.90E+05
4.90E+05
7.90E+05
1.09E+06
0.01 0.06
Eth
ano
l w
aste
(k
g/h
r)
Reb
oil
er d
uty
(ca
l/se
c)
Distillation to feed
0
0.2
0.4
0.6
0.8
1
230
280
330
380
430
0.01 0.03 0.05 0.07 0.09
Eth
ano
l co
nce
ntr
atio
n (
%w
t)
Mas
s fl
ow
of
ethan
ol
(kg/h
r)
Distillation to feed
0
50
100
150
200
250
2.70E+05
2.73E+05
2.76E+05
2.79E+05
2.82E+05
2 4 6 8 10
Eth
anol
was
te (
kg/h
r)
Reb
oil
er d
uty
(ca
l/se
c)
Feed stage
0
0.2
0.4
0.6
0.8
1
170
220
270
320
370
420
2 4 6 8 10
Eth
anol
conce
ntr
atio
n (
%w
t)
Mas
s fl
ow
of
ethan
ol
(kg/h
r)
Feed stage
0
30
60
90
120
2.00E+05
2.30E+05
2.60E+05
2.90E+05
3.20E+05
2 3 4 5
Eth
ano
l w
aste
(k
g/h
r)
Reb
oil
er d
uty
(ca
l/se
c)
Molar reflux ratio
0
0.2
0.4
0.6
0.8
1
290
310
330
350
370
390
410
430
2 3 4 5
Eth
ano
l co
nce
ntr
atio
n (
%w
t)
Mas
s fl
ow
of
ethan
ol
(kg/h
r)
Molar reflux ratio
58
4.3.1.8 Sensitivity analysis four-parameter of distillation column for
0:100 ratio of EFB and OPT plant with pervaporation technology
Distillate to 85 wt%
Figure 74 Sensitivity number of stages of distillation column for 0:100 ratio of EFB
and OPT plant with pervaporation technology
Figure 75 Sensitivity molar reflux ratio of distillation column for 0:100 ratio of EFB
and OPT plant with pervaporation technology
Figure 76 Sensitivity distillation to feed of distillation column for 0:100 ratio of EFB
and OPT plant with pervaporation technology
Figure 77 Sensitivity feed stage of distillation column for 0:100 ratio of EFB and
OPT plant with pervaporation technology
0
2
4
6
8
10
2.63E+05
2.64E+05
2.64E+05
2.65E+05
2.65E+05
2.66E+05
15 17 19 21 23 25
Eth
anol
was
te (
kg/h
r)
Reb
oil
er d
uty
(ca
l/se
c)
Number of stage
0.84
0.86
0.88
0.9
0.92
0.94
0.96
0.98
1
378
380
382
384
386
388
390
15 17 19 21 23 25
Eth
anol
conce
ntr
atio
n (
%w
t)
Mas
s fl
ow
of
ethan
ol
(kg/h
r)
Number of stage
0
20
40
60
80
100
120
140
160
1.80E+05
3.80E+05
5.80E+05
7.80E+05
9.80E+05
1.18E+06
0.01 0.06
Eth
ano
l w
aste
(k
g/h
r)
Reb
oil
er d
uty
(ca
l/se
c)
Distillation to feed
0
0.2
0.4
0.6
0.8
1
230
250
270
290
310
330
350
370
390
410
0.01 0.03 0.05 0.07 0.09
Eth
ano
l co
nce
ntr
atio
n (
%w
t)
Mas
s fl
ow
of
ethan
ol
(kg/h
r)
Distillation to feed
0
50
100
150
200
250
2.50E+05
2.53E+05
2.56E+05
2.59E+05
2.62E+05
2.65E+05
2.68E+05
2 4 6 8 10
Eth
anol
was
te (
kg/h
r)
Reb
oil
er d
uty
(ca
l/se
c)
Feed stage
0
0.2
0.4
0.6
0.8
1
150
200
250
300
350
400
2 4 6 8 10
Eth
anol
conce
ntr
atio
n (
%w
t)
Mas
s fl
ow
of
ethan
ol
(kg/h
r)
Feed stage
0
30
60
90
120
1.90E+05
2.20E+05
2.50E+05
2.80E+05
3.10E+05
2 3 4 5
Eth
ano
l w
aste
(k
g/h
r)
Reb
oil
er d
uty
(ca
l/se
c)
Molar reflux ratio
0
0.2
0.4
0.6
0.8
1
260
280
300
320
340
360
380
400
2 3 4 5
Eth
ano
l co
nce
ntr
atio
n (
%w
t)
Mas
s fl
ow
of
ethan
ol
(kg/h
r)
Molar reflux ratio
59
4.3.1.9 Sensitivity analysis four-parameter of distillation column for
distillate 4 wt% of fermentation broth to 80 wt%
Figure 78 Sensitivity number of stages of distillation column for distillate 4 wt% of
fermentation broth to 80 wt% and dehydrate with pervaporation technology
Figure 79 Sensitivity molar reflux ratio of distillation column for distillate 4 wt% of
fermentation broth to 80 wt% and dehydrate with pervaporation technology
Figure 80 Sensitivity distillation to feed of distillation column for distillate 4 wt% of
fermentation broth to 80 wt% and dehydrate with pervaporation technology
Figure 81 Sensitivity feed stage of distillation column for distillate 4 wt% of
fermentation broth to 80 wt% and dehydrate with pervaporation technology
0.7
0.75
0.8
0.85
0.9
0.95
1
504.5
505
505.5
506
506.5
507
15 17 19 21 23 25
Eth
anol
conce
ntr
atio
n (
%w
t)
Mas
s fl
ow
of
ethan
ol
(kg/h
r)
Number of stage
0.7
0.75
0.8
0.85
0.9
0.95
1
420430440450460470480490500510520
2 3 4 5
Eth
ano
l co
nce
ntr
atio
n (
%w
t)
Mas
s fl
ow
of
ethan
ol
(kg/h
r)
Molar reflux ratio
0
0.2
0.4
0.6
0.8
1
190
240
290
340
390
440
490
540
0.01 0.03 0.05 0.07 0.09
Eth
ano
l co
nce
ntr
atio
n (
%w
t)
Mas
s fl
ow
of
ethan
ol
(kg/h
r)
Distillation to feed
0
0.2
0.4
0.6
0.8
1
140
190
240
290
340
390
440
490
540
2 4 6 8 10
Eth
anol
conce
ntr
atio
n (
%w
t)
Mas
s fl
ow
of
ethan
ol
(kg/h
r)
Feed stage
0
0.5
1
1.5
2
2.5
3.07E+05
3.07E+05
3.07E+05
3.07E+05
3.07E+05
3.07E+05
3.07E+05
3.07E+05
15 17 19 21 23 25
Eth
anol
was
te (
kg/h
r)
Reb
oil
er d
uty
(ca
l/se
c)
Number of stage
0
20
40
60
80
2.20E+05
2.50E+05
2.80E+05
3.10E+05
3.40E+05
3.70E+05
4.00E+05
4.30E+05
2 3 4 5
Eth
ano
l w
aste
(kg/h
r)
Reb
oil
er d
uty
(ca
l/se
c)
Molar reflux ratio
0
50
100
150
200
250
300
1.60E+05
3.60E+05
5.60E+05
7.60E+05
9.60E+05
0.01 0.03 0.05 0.07 0.09
Eth
ano
l w
aste
(k
g/h
r)
Reb
oil
er d
uty
(ca
l/se
c)
Distillation to feed
0
50
100
150
200
250
3.00E+05
3.03E+05
3.06E+05
3.09E+05
2 4 6 8 10
Eth
anol
was
te (
kg/h
r)
Reb
oil
er d
uty
(ca
l/se
c)
Feed stage
60
4.3.1.10 Sensitivity analysis four-parameter of distillation column for
distillate 6 wt% of fermentation broth to 80 wt%
Figure 82 Sensitivity number of distillation column for distillate 6 wt% of
fermentation broth to 80 wt% and dehydrate with pervaporation technology
Figure 83 Sensitivity molar reflux ratio of distillation column for distillate 6 wt% of
fermentation broth to 80 wt% and dehydrate with pervaporation technology y
Figure 84 Sensitivity distillation to feed of distillation column for distillate 6 wt% of
fermentation broth to 80 wt% and dehydrate with pervaporation technology
Figure 85 Sensitivity feed stage of distillation column for distillate 6 wt% of
fermentation broth to 80 wt% and dehydrate with pervaporation technology
0
0.2
0.4
0.6
0.8
1
735
740
745
750
755
760
765
10 12 14 16 18 20
Eth
anol
conce
ntr
atio
n (
%w
t)
Mas
s fl
ow
of
ethan
ol
(kg/h
r)
Number of stage
0
0.2
0.4
0.6
0.8
1
750
755
760
765
2 3 4 5
Eth
ano
l co
nce
ntr
atio
n (
%w
t)
Mas
s fl
ow
of
ethan
ol
(kg/h
r)
Molar reflux ratio
0
0.2
0.4
0.6
0.8
1
190
290
390
490
590
690
790
0.01 0.03 0.05 0.07 0.09
Eth
ano
l co
nce
ntr
atio
n (
%w
t)
Mas
s fl
ow
of
ethan
ol
(kg/h
r)
Distillation to feed
0
0.2
0.4
0.6
0.8
1
140
240
340
440
540
640
740
840
2 4 6 8 10
Eth
anol
conce
ntr
atio
n (
%w
t)
Mas
s fl
ow
of
ethan
ol
(kg/h
r)
Feed stage
0
5
10
15
20
25
3.35E+05
3.35E+05
3.35E+05
3.35E+05
3.36E+05
3.36E+05
10 12 14 16 18 20
Eth
anol
was
te (
kg/h
r)
Reb
oil
er d
uty
(ca
l/se
c)
Number of stage
0
0.5
1
1.5
2
2.90E+053.20E+053.50E+053.80E+054.10E+054.40E+054.70E+055.00E+055.30E+05
2 3 4 5
Eth
ano
l w
aste
(kg/h
r)
Reb
oil
er d
uty
(ca
l/se
c)
Molar reflux ratio
0
70
140
210
280
350
420
490
560
1.40E+05
3.40E+05
5.40E+05
7.40E+05
9.40E+05
0.01 0.03 0.05 0.07 0.09
Eth
ano
l w
aste
(k
g/h
r)
Reb
oil
er d
uty
(ca
l/se
c)
Distillation to feed
0
50
100
150
200
250
300
350
3.20E+05
3.23E+05
3.26E+05
3.29E+05
3.32E+05
3.35E+05
3.38E+05
2 4 6 8 10
Eth
anol
was
te (
kg/h
r)
Reb
oil
er d
uty
(ca
l/se
c)
Feed stage
61
4.3.1.11 Sensitivity analysis four-parameter of distillation column for
distillate 8 wt% of fermentation broth to 80 wt%
Figure 86 Sensitivity number of stages of distillation column for distillate 8 wt% of
fermentation broth to 80 wt% and dehydrate with pervaporation technology
Figure 87 Sensitivity molar reflux ratio of distillation column for distillate 8 wt% of
fermentation broth to 80 wt% and dehydrate with pervaporation technology
Figure 88 Sensitivity distillation to feed of distillation column for distillate 8 wt% of
fermentation broth to 80 wt% and dehydrate with pervaporation technology
Figure 89 Sensitivity feed stage of distillation column for distillate 8 wt% of
fermentation broth to 80 wt% and dehydrate with pervaporation technology
0
0.2
0.4
0.6
0.8
1
860
880
900
920
940
960
980
1000
1020
1040
10 12 14 16 18 20
Eth
anol
conce
ntr
atio
n (
%w
t)
Mas
s fl
ow
of
ethan
ol
(kg/h
r)
Number of stage
0
0.2
0.4
0.6
0.8
1
1000
1005
1010
1015
2 3 4 5
Eth
ano
l co
nce
ntr
atio
n (
%w
t)
Mas
s fl
ow
of
ethan
ol
(kg/h
r)
Molar reflux ratio
0
0.2
0.4
0.6
0.8
1
200
300
400
500
600
700
800
900
1000
1100
0.01 0.03 0.05 0.07 0.09
Eth
ano
l co
nce
ntr
atio
n (
%w
t)
Mas
s fl
ow
of
ethan
ol
(kg/h
r)
Distillation to feed
0
0.2
0.4
0.6
0.8
1
500
600
700
800
900
1000
1100
2 4 6 8 10
Eth
anol
conce
ntr
atio
n (
%w
t)
Mas
s fl
ow
of
ethan
ol
(kg/h
r)
Feed stage
0
20
40
60
80
100
120
140
160
3.63E+05
3.64E+05
3.64E+05
3.65E+05
3.65E+05
3.66E+05
3.66E+05
10 12 14 16 18 20
Eth
anol
was
te (
kg/h
r)
Reb
oil
er d
uty
(ca
l/se
c)
Number of stage
0
2
4
6
8
10
3.50E+05
4.00E+05
4.50E+05
5.00E+05
5.50E+05
6.00E+05
6.50E+05
7.00E+05
7.50E+05
2 3 4 5
Eth
ano
l w
aste
(kg/h
r)
Reb
oil
er d
uty
(ca
l/se
c)
Molar reflux ratio
0
100
200
300
400
500
600
700
800
1.50E+05
3.50E+05
5.50E+05
7.50E+05
9.50E+05
0.01 0.03 0.05 0.07 0.09
Eth
ano
l w
aste
(k
g/h
r)
Reb
oil
er d
uty
(ca
l/se
c)
Distillation to feed
0
50
100
150
200
250
300
350
400
3.60E+05
3.61E+05
3.62E+05
3.63E+05
3.64E+05
3.65E+05
3.66E+05
2 4 6 8 10
Eth
anol
was
te (
kg/h
r)
Reb
oil
er d
uty
(ca
l/se
c)
Feed stage
62
4.3.1.12 Sensitivity analysis four-parameter of distillation column for
distillate 4 wt% of fermentation broth to 85 wt%
Figure 90 Sensitivity number of stages of distillation column for distillate 4 wt% of
fermentation broth to 85 wt% and dehydrate with pervaporation technology
Figure 91 Sensitivity molar reflux ratio of distillation column for distillate 4 wt% of
fermentation broth to 85 wt% and dehydrate with pervaporation technology
Figure 92 Sensitivity distillation to feed of distillation column for distillate 4 wt% of
fermentation broth to 85 wt% and dehydrate with pervaporation technology
Figure 93 Sensitivity feed stage of distillation column for distillate 4 wt% of
fermentation broth to 85 wt% and dehydrate with pervaporation technology
0.7
0.75
0.8
0.85
0.9
0.95
1
420430440450460470480490500510520
2 3 4 5
Eth
ano
l co
nce
ntr
atio
n (
%w
t)
Mas
s fl
ow
of
ethan
ol
(kg/h
r)
Molar reflux ratio
0
0.2
0.4
0.6
0.8
1
200
250
300
350
400
450
500
550
0.01 0.03 0.05 0.07 0.09
Eth
ano
l co
nce
ntr
atio
n (
%w
t)
Mas
s fl
ow
of
ethan
ol
(kg/h
r)
Distillation to feed
0
0.2
0.4
0.6
0.8
1
140
190
240
290
340
390
440
490
540
2 4 6 8 10
Eth
anol
conce
ntr
atio
n (
%w
t)
Mas
s fl
ow
of
ethan
ol
(kg/h
r)
Feed stage
0
1
2
3
4
5
6
7
2.90E+05
2.90E+05
2.90E+05
2.90E+05
2.90E+05
2.90E+05
2.90E+05
15 17 19 21 23 25
Eth
anol
was
te (
kg/h
r)
Reb
oil
er d
uty
(ca
l/se
c)
Number of stage
0
20
40
60
80
2.20E+05
2.50E+05
2.80E+05
3.10E+05
3.40E+05
3.70E+05
4.00E+05
2 3 4 5
Eth
ano
l w
aste
(kg/h
r)
Reb
oil
er d
uty
(ca
l/se
c)
Molar reflux ratio
0
70
140
210
280
1.60E+05
3.60E+05
5.60E+05
7.60E+05
9.60E+05
0.01 0.03 0.05 0.07 0.09
Eth
ano
l w
aste
(k
g/h
r)
Reb
oil
er d
uty
(ca
l/se
c)
Distillation to feed
0
50
100
150
200
250
300
2.80E+05
2.85E+05
2.90E+05
2.95E+05
2 4 6 8 10
Eth
anol
was
te (
kg/h
r)
Reb
oil
er d
uty
(ca
l/se
c)
Feed stage
0.8
0.85
0.9
0.95
1
504
505
506
507
508
509
510
511
512
15 17 19 21 23 25
Eth
anol
conce
ntr
atio
n (
%w
t)
Mas
s fl
ow
of
ethan
ol
(kg/h
r)
Number of stage
63
4.3.1.13 Sensitivity analysis four-parameter of distillation column for
distillate 6 wt% of fermentation broth to 85 wt%
Figure 94 Sensitivity number of stages of distillation column for distillate 6 wt% of
fermentation broth to 85 wt% and dehydrate with pervaporation technology
Figure 95 Sensitivity molar reflux ratio of distillation column for distillate 6 wt% of
fermentation broth to 85 wt% and dehydrate with pervaporation technology
Figure 96 Sensitivity distillation to feed of distillation column for distillate 6 wt% of
fermentation broth to 85 wt% and dehydrate with pervaporation technology
Figure 97 Sensitivity feed stage of distillation column for distillate 6 wt% of
fermentation broth to 85 wt% and dehydrate with pervaporation technology
0
10
20
2.90E+05
3.20E+05
3.50E+05
3.80E+05
4.10E+05
4.40E+05
4.70E+05
5.00E+05
5.30E+05
2 3 4 5
Eth
ano
l w
aste
(k
g/h
r)
Reb
oil
er d
uty
(ca
l/se
c)
Molar reflux ratio
0
70
140
210
280
350
420
490
1.60E+05
3.60E+05
5.60E+05
7.60E+05
9.60E+05
1.16E+06
0.01 0.03 0.05 0.07 0.09
Eth
ano
l w
aste
(k
g/h
r)
Reb
oil
er d
uty
(ca
l/se
c)
Distillation to feed
0
50
100
150
200
250
300
350
3.80E+05
3.81E+05
3.82E+05
3.83E+05
3.84E+05
3.85E+05
3.86E+05
2 4 6 8 10
Eth
anol
was
te (
kg/h
r)
Reb
oil
er d
uty
(ca
l/se
c)
Feed stage
0
5
10
15
20
25
3.84E+05
3.85E+05
3.85E+05
3.85E+05
3.85E+05
3.85E+05
3.85E+05
3.85E+05
3.85E+05
10 12 14 16 18 20
Eth
anol
was
te (
kg/h
r)
Reb
oil
er d
uty
(ca
l/se
c)
Number of stage
0.82
0.84
0.86
0.88
0.9
0.92
0.94
0.96
0.98
1
730
740
750
760
770
2 3 4 5
Eth
ano
l co
nce
ntr
atio
n (
%w
t)
Mas
s fl
ow
of
ethan
ol
(kg/h
r)
Molar reflux ratio
0
0.2
0.4
0.6
0.8
1
200
300
400
500
600
700
800
0.01 0.03 0.05 0.07 0.09
Eth
ano
l co
nce
ntr
atio
n (
%w
t)
Mas
s fl
ow
of
ethan
ol
(kg/h
r)
Distillation to feed
0
0.2
0.4
0.6
0.8
1
140
240
340
440
540
640
740
840
2 4 6 8 10
Eth
anol
conce
ntr
atio
n
(%w
t)
Mas
s fl
ow
of
ethan
ol
(kg/h
r)
Feed stage
0.82
0.84
0.86
0.88
0.9
0.92
0.94
0.96
0.98
1
740
745
750
755
760
765
770
10 12 14 16 18 20
Eth
anol
conce
ntr
atio
n (
%w
t)
Mas
s fl
ow
of
ethan
ol
(kg/h
r)
Number of stage
64
4.3.1.14 Sensitivity analysis four-parameter of distillation column for
distillate 8wt% of fermentation broth to 85 wt%
Figure 98 Sensitivity number of stages of distillation column for distillate 8 wt% of
fermentation broth to 85 wt% and dehydrate with pervaporation technology
Figure 99 Sensitivity molar reflux ratio of distillation column for distillate 8 wt% of
fermentation broth to 85 wt% and dehydrate with pervaporation technology
Figure 100 Sensitivity distillation to feed of distillation column for distillate 8 wt% of
fermentation broth to 85 wt% and dehydrate with pervaporation technology
Figure 101 Sensitivity feed stage of distillation column for distillate 8 wt% of
fermentation broth to 85 wt% and dehydrate with pervaporation technology
0.84
0.86
0.88
0.9
0.92
0.94
0.96
0.98
1
1008
1010
1012
1014
1016
1018
1020
1022
10 12 14 16 18 20
Eth
anol
conce
ntr
atio
n (
%w
t)
Mas
s fl
ow
of
ethan
ol
(kg/h
r)
Number of stage
0.84
0.86
0.88
0.9
0.92
0.94
0.96
0.98
1
1000
1005
1010
1015
1020
1025
2 3 4 5
Eth
ano
l co
nce
ntr
atio
n (
%w
t)
Mas
s fl
ow
of
ethan
ol
(kg/h
r)
Molar reflux ratio
0
0.2
0.4
0.6
0.8
1
200
300
400
500
600
700
800
900
1000
1100
0.01 0.03 0.05 0.07 0.09
Eth
ano
l co
nce
ntr
atio
n (
%w
t)
Mas
s fl
ow
of
ethan
ol
(kg/h
r)
Distillation to feed
0
0.2
0.4
0.6
0.8
1
500
600
700
800
900
1000
1100
2 4 6 8 10
Eth
anol
conce
ntr
atio
n (
%w
t)
Mas
s fl
ow
of
ethan
ol
(kg/h
r)
Feed stage
0
2
4
6
8
10
12
4.76E+05
4.76E+05
4.76E+05
4.76E+05
4.76E+05
10 12 14 16 18 20
Eth
anol
was
te (
kg/h
r)
Reb
oil
er d
uty
(ca
l/se
c)
Number of stage
0
2
4
6
8
10
3.50E+05
4.00E+05
4.50E+05
5.00E+05
5.50E+05
6.00E+05
6.50E+05
2 3 4 5
Eth
ano
l w
aste
(kg/h
r)
Reb
oil
er d
uty
(ca
l/se
c)
Molar reflux ratio
0
100
200
300
400
500
600
700
1.50E+05
3.50E+05
5.50E+05
7.50E+05
9.50E+05
0.01 0.03 0.05 0.07 0.09
Eth
ano
l w
aste
(k
g/h
r)
Reb
oil
er d
uty
(ca
l/se
c)
Distillation to feed
0
50
100
150
200
250
300
350
400
4.70E+05
4.71E+05
4.72E+05
4.73E+05
4.74E+05
4.75E+05
4.76E+05
4.77E+05
2 4 6 8 10
Eth
anol
was
te (
kg/h
r)
Reb
oil
er d
uty
(ca
l/se
c)
Feed stage
65
4.3.1.15 Sensitivity analysis four-parameter of distillation column for
distillate 4 wt% of fermentation broth to 90 wt%
Figure 102 Sensitivity number o of distillation column for distillate 4 wt% of
fermentation broth to 90 wt% and dehydrate with pervaporation technology
Figure 103 Sensitivity molar reflux ratio of distillation column for distillate 4 wt% of
fermentation broth to 90 wt% and dehydrate with pervaporation technology
Figure 104 Sensitivity distillation to feed of distillation column for distillate 4 wt% of
fermentation broth to 90 wt% and dehydrate with pervaporation technology
Figure 105 Sensitivity feed stage of distillation column for distillate 4 wt% of
fermentation broth to 90 wt% and dehydrate with pervaporation technology
0.7
0.75
0.8
0.85
0.9
0.95
1
350
370
390
410
430
450
470
490
510
2 3 4 5
Eth
ano
l co
nce
ntr
atio
n (
%w
t)
Mas
s fl
ow
of
ethan
ol
(kg/h
r)
Molar reflux ratio
0
0.2
0.4
0.6
0.8
1
190
240
290
340
390
440
490
540
0.01 0.03 0.05 0.07 0.09
Eth
ano
l co
nce
ntr
atio
n (
%w
t)
Mas
s fl
ow
of
ethan
ol
(kg/h
r)
Distillation to feed
0
0.2
0.4
0.6
0.8
1
140
190
240
290
340
390
440
490
540
2 4 6 8 10
Eth
anol
conce
ntr
atio
n (
%w
t)
Mas
s fl
ow
of
ethan
ol
(kg/h
r)
Feed stage
0
10
20
30
40
50
3.17E+05
3.17E+05
3.18E+05
3.18E+05
3.18E+05
3.18E+05
3.18E+05
3.18E+05
10 12 14 16 18 20
Eth
anol
was
te (
kg/h
r)
Reb
oil
er d
uty
(ca
l/se
c)
Number of stage
0
20
40
60
80
100
120
140
2.00E+05
2.30E+05
2.60E+05
2.90E+05
3.20E+05
3.50E+05
2 3 4 5
Eth
ano
l w
aste
(kg/h
r)
Reb
oil
er d
uty
(ca
l/se
c)
Molar reflux ratio
0
50
100
150
200
250
300
1.60E+05
3.60E+05
5.60E+05
7.60E+05
9.60E+05
0.01 0.03 0.05 0.07 0.09
Eth
ano
l w
aste
(k
g/h
r)
Reb
oil
er d
uty
(ca
l/se
c)
Distillation to feed
0
50
100
150
200
250
300
3.00E+05
3.03E+05
3.06E+05
3.09E+05
3.12E+05
3.15E+05
3.18E+05
3.21E+05
2 4 6 8 10
Eth
anol
was
te (
kg/h
r)
Reb
oil
er d
uty
(ca
l/se
c)
Feed stage
0.7
0.75
0.8
0.85
0.9
0.95
1
440
450
460
470
480
490
500
10 12 14 16 18 20
Eth
anol
conce
ntr
atio
n (
%w
t)
Mas
s fl
ow
of
ethan
ol
(kg/h
r)
Number of stage
66
4.3.1.16 Sensitivity analysis four-parameter of distillation column for
distillate 6 wt% of fermentation broth to 90 wt%
Figure 106 Sensitivity number of stages of distillation column for distillate 6 wt% of
fermentation broth to 90 wt% and dehydrate with pervaporation technology
Figure 107 Sensitivity molar reflux ratio of distillation column for distillate 6 wt% of
fermentation broth to 90 wt% and dehydrate with pervaporation technology
Figure 108 Sensitivity distillation to feed of distillation column for distillate 6 wt% of
fermentation broth to 90 wt% and dehydrate with pervaporation technology
Figure 109 Sensitivity feed stage of distillation column for distillate 6 wt% of
fermentation broth to 90 wt% and dehydrate with pervaporation technology
0.84
0.86
0.88
0.9
0.92
0.94
0.96
0.98
1
650
670
690
710
730
750
770
2 3 4 5
Eth
ano
l co
nce
ntr
atio
n (
%w
t)
Mas
s fl
ow
of
ethan
ol
(kg/h
r)
Molar reflux ratio
0
0.2
0.4
0.6
0.8
1
190
290
390
490
590
690
790
0.01 0.03 0.05 0.07 0.09
Eth
ano
l co
nce
ntr
atio
n (
%w
t)
Mas
s fl
ow
of
ethan
ol
(kg/h
r)
Distillation to feed
0
0.2
0.4
0.6
0.8
1
140
240
340
440
540
640
740
840
2 4 6 8 10
Eth
anol
conce
ntr
atio
n (
%w
t)
Mas
s fl
ow
of
ethan
ol
(kg/h
r)
Feed stage
0
10
20
30
40
50
60
70
80
3.91E+05
3.91E+05
3.91E+05
3.92E+05
3.92E+05
10 12 14 16 18 20
Eth
anol
was
te (
kg/h
r)
Reb
oil
er d
uty
(ca
l/se
c)
Number of stage
0
10
20
30
40
50
60
70
80
2.60E+05
3.00E+05
3.40E+05
3.80E+05
4.20E+05
2 3 4 5
Eth
ano
l w
aste
(kg/h
r)
Reb
oil
er d
uty
(ca
l/se
c)
Molar reflux ratio
0
70
140
210
280
350
420
490
1.80E+05
3.80E+05
5.80E+05
7.80E+05
9.80E+05
1.18E+06
0.01 0.03 0.05 0.07 0.09
Eth
ano
l w
aste
(k
g/h
r)
Reb
oil
er d
uty
(ca
l/se
c)
Distillation to feed
0
50
100
150
200
250
300
350
400
3.80E+05
3.83E+05
3.86E+05
3.89E+05
3.92E+05
3.95E+05
2 4 6 8 10
Eth
anol
was
te (
kg/h
r)
Reb
oil
er d
uty
(ca
l/se
c)
Feed stage
0.84
0.86
0.88
0.9
0.92
0.94
0.96
0.98
1
660670680690700710720730740750760
10 12 14 16 18 20
Eth
anol
conce
ntr
atio
n (
%w
t)
Mas
s fl
ow
of
ethan
ol
(kg/h
r)
Number of stage
67
4.3.1.17 Sensitivity analysis four-parameter of distillation column for
distillate 8 wt% of fermentation broth to 90 wt%
Figure 110 Sensitivity number of stages of distillation column for distillate 8 wt% of
fermentation broth to 90 wt% and dehydrate with pervaporation technology
Figure 111 Sensitivity molar reflux ratio of distillation column for distillate 8 wt% of
fermentation broth to 90 wt% and dehydrate with pervaporation technology
Figure 112 Sensitivity distillation to feed of distillation column for distillate 8 wt% of
fermentation broth to 90 wt% and dehydrate with pervaporation technology
Figure 113 Sensitivity feed stage of distillation column for distillate 8 wt% of
fermentation broth to 90 wt% and dehydrate with pervaporation technology
0
0.2
0.4
0.6
0.8
1
500
600
700
800
900
1000
1100
2 4 6 8 10
Eth
anol
conce
ntr
atio
n (
%w
t)
Mas
s fl
ow
of
ethan
ol
(kg/h
r)
Feed stage
0
100
200
300
400
500
600
700
800
1.50E+05
3.50E+05
5.50E+05
7.50E+05
9.50E+05
0.01 0.03 0.05 0.07 0.09
Eth
ano
l w
aste
(k
g/h
r)
Reb
oil
er d
uty
(ca
l/se
c)
Distillation to feed
0
100
200
300
400
500
4.40E+05
4.43E+05
4.46E+05
4.49E+05
4.52E+05
2 4 6 8 10
Eth
anol
was
te (
kg/h
r)
Reb
oil
er d
uty
(ca
l/se
c)
Feed stage
0
10
20
30
40
50
60
70
80
4.48E+05
4.48E+05
4.48E+05
4.49E+05
4.49E+05
4.49E+05
4.49E+05
4.49E+05
4.50E+05
10 12 14 16 18 20
Eth
anol
was
te (
kg/h
r)
Reb
oil
er d
uty
(ca
l/se
c)
Number of stage
0.86
0.88
0.9
0.92
0.94
0.96
0.98
1
910
920
930
940
950
960
970
980
990
1000
10 12 14 16 18 20
Eth
anol
conce
ntr
atio
n (
%w
t)
Mas
s fl
ow
of
ethan
ol
(kg/h
r)
Number of stage
0
5
10
15
20
25
30
35
3.20E+05
3.70E+05
4.20E+05
4.70E+05
5.20E+05
5.70E+05
2 3 4 5
Eth
ano
l w
aste
(kg/h
r)
Reb
oil
er d
uty
(ca
l/se
c)
Molar reflux ratio
0.88
0.9
0.92
0.94
0.96
0.98
1
950955960965970975980985990995
1000
2 3 4 5
Eth
ano
l co
nce
ntr
atio
n (
%w
t)
Mas
s fl
ow
of
ethan
ol
(kg/h
r)
Molar reflux ratio
0
0.2
0.4
0.6
0.8
1
200
300
400
500
600
700
800
900
1000
1100
0.01 0.03 0.05 0.07 0.09
Eth
ano
l co
nce
ntr
atio
n (
%w
t)
Mas
s fl
ow
of
ethan
ol
(kg/h
r)
Distillation to feed
68
4.3.2. Dehydration with Pervaporation
The energy consumption and recovery efficiency were
calculated from eq 7. and 13. respectively with a width range of separation factor 50
to 1000 of hydrophilic membrane and plotted graph to show the relation of septation
factor (dotted line) and ethanol concentration in feed (solid line) to recovery
efficiency and energy demand as shown in Figure 114. This graph demonstrated that
higher concentration in feed and high separation factor significantly reduce energy
demand to purify with high recovery efficiency. Moreover, it shows that the
separation factor of more than 300 provided a high percent recovery of almost 100%
of the maximum ethanol concentration in feed (95 wt%) and about 99 % of percent
recovery for 85 and 90 wt%. But membrane with very high separation factor is
unavailable in the commercial. Therefore, the membrane with separation factor lower
than 300 was studied and applied in this work. The ethanol concentration before
feeding to pervaporation model was selected to 50, 80, 85, and 90 wt%.
Figure 114 Relationship of energy demand in dotted line and recovery efficiency in a
solid line with various separation factor and difference inlet concentration applying
the hydrophilic membrane
Four different membrane types, PVA/PAA, PVA, 6FDA-
NDA/DABA, Polyimide (BMTCHDA) from Lee et al., (1995), Li et al., (2006), Le et
al., (2014), and Kim et al., (2000) were selected to dehydrated 50, 80, 85, 90 wt%
ethanol to fuel grade, respectively. The separation factor and permeate rate of each
membrane as shown in Table 12 was applied to calculate the energy demand in
dehydration and membrane area. Figure 115 presents the scheme for the purification
section. As the amount of ethanol loss in the waste stream from the distillation
column was less than 1 kg/hr. Therefore, we can assume that there is an equal
quantity of ethanol in the overhead stream was fed to the dehydration process. The
temperature was increased to 80 oC before passing through the membrane as
suggested in Nagy et al., (2015). The permeate stream from PV model was condensed
and recovered to a distillation column to minimize the ethanol loss.
0.82
0.84
0.86
0.88
0.9
0.92
0.94
0.96
0.98
1
00.20.40.60.8
11.21.41.61.8
22.22.42.62.8
3
50 200 350 500 650 800 950
reco
ver
y e
ffic
iency
(%
)
The
Ener
gy d
eman
d (
MJ/
kgE
t)
Separation factor
50
60
70
80
85
90
95
50
60
70
80
85
90
95
69
Figure 115 Flowsheet for the distillation and dehydration process using PV method
Table 12 Configuration of a combination of the distillation and pervaporation model
for OPT plant
Ethanol concentration
in an overhead stream (%)
50 80 85 90
Optimized parameters
of distillation column
Number of stages 10 22 22 22
Feed stage 2 5 6 7
Reflux ratio 3.44 3.22 3.89 5.22
Distillation to feed 0.047 0.02 0.0178 0.0155
Membrane parameter
Type of membrane PVA/PAA PVA 6FDA-NDA/DABA BMTCHDA
Energy demand (MJ/kg eth) 1181.56 226.87 163.09 92.67
Permeate flux (kg/m2.hr) 2.8 1.5 2.7 1.7
Area (m2) 169.64 67.76 28.35 24.84
Table 12 shows the optimized parameters of the distillation
column to produce the purposed overhead product and parameter of the selected
membrane for each case. Different types of membranes were matched for each
concentration. The efficiency to purify relies on not only overhead products but also
the performance of the membrane. High permeated rate and selectivity of the
membrane have more influential to the performance of the membrane, noticeable
from the amount of ethanol loss in the permeated stream and ethanol yield in the
retentate stream. A high separation factor leads to high ethanol recovery efficiency in
the retentate side and low energy consumption to separate. While permeate flux
directly affects the membrane area which mainly causes the investment costs of the
PV model. The distillation column and membrane were integrated and considered as
single technology for purification. In this section, the sensitivity and optimized four-
parameters of distillation were analyzed to obtain the minimum production cost.
70
4.3.3. Dehydration with Pressure swing adsorption
In this section, the cyclic operation of PSA was performed by
Aspen Adsorption V8.8 software as shown in Figure 116. The model is developed
from the original EthanolDehyd in the program by applying Extend Langmuire1
isotherm. The final product was used to purge in the depressurizing step. Therefore,
waste in depressurizes step contained the amount of ethanol. Waste stream needs to
recover to obtain high production efficiency and minimize ethanol loss. The data of
the overhead stream was transferred from Aspen plus to Aspen Adsorption software
to simulate the trapping water in two columns. These columns contained molecular
sieve namely Zeolite 3A. Figure 117 presents the scheme for the purification section
by using two-column to allows the continuous production of high ethanol yield. The
related parameters to simulate and evaluate the costs were presented in Table 13. The
adsorption stage took about 450 seconds for the production stage and purge out 20-
30% of the product at 1.2 bar from the top. The desorption stage operated after the
adsorption stage completed under pressure 0.2 bar. The recovery efficiency of PSA in
the range of 70-78%. Higher ethanol concentration obtained higher percent ethanol
recovery and high purity in the final product. The result from Aspen adsorption was
performed to Aspen plus software to simulate the PSA performance as shown in
Figure 119 to complete the bioethanol production process and evaluate the economics
of this plant.
Figure 116 Flowsheet of pressure swing adsorption in Aspen Adsorption software
71
Figure 117 Flowsheet for the distillation and dehydration process using PSA method
Table 13 Configuration of the combination of distillation and pressure swing
adsorption model for OPT plant
Ethanol concentration
in an overhead stream (%)
80 85 90 94
Optimized parameters
of distillation column
Number of stages 22 22 22 30
Feed stage 5 6 7 14
Reflux ratio 2.67 3 4.44 4.83
Distillation to feed 0.0272 0.0235 0.02 0.0179
Adsorb/Desorb column parameter
Adsorbent porosity 0.21
Bed void fraction 0.37
Pressure (bar) 0.2 – 1.2
Bulk density (kg/m3) 750
Particle radius (mm) 2
Dimension of adsorbent(m) 3.5
Height of adsorbent (m) 1.5
Amount of absorbent (kg) 12061
According to, the 85 wt% of the overhead product dehydrated
with pervaporation technology has the lowest production as present in the economic
section. It was considered as the best candidate for purification technology.
Distillation to 85 wt% in the overhead stream and dehydrate with pervaporation was
the best way to purify because of the lowest production cost per unit. This technology
was employed for the other plants because of similar ethanol concentration about 3
wt% in the fermentation broth. The configuration of EFB plant and two feedstock
plants was shown in Tables 14 and 15.
72
Table 14 Configuration of a combination of the distillation and pervaporation model
for EFB plant
Ethanol concentration in an overhead stream (%) 85
Optimized parameters
of distillation column
Number of stages 20
Feed stage 6
Reflux ratio 4.1
Distillation to feed 0.0186
Membrane parameter
Type of membrane 6FDA-NDA/DABA
Energy demand (MJ/kg eth) 209.26
Permeate flux (kg/m2.hr) 2.7
Area (m2) 36.21
Table 15 Configuration of a combination of the distillation and pervaporation model
for the two feedstocks plant
EFB:OPT ratio 100:0 80:20 50:50 20:80 0:100
Optimized parameters
of distillation column
Number of stages 20 20 20 20 21
Feed stage 6 6 6 6 6
Reflux ratio 4.1 4.1 4.1 4.1 4
Distillation to feed 0.0186 0.0183 0.0182 0.0181 0.0178
Membrane parameter
Type of membrane 6FDA-NDA/DABA
Energy demand (MJ/kg eth) 209.26 194.79 185.63 176.42 163.13
Permeate flux (kg/m2.hr) 2.7 2.7 2.7 2.7 2.7
Area (m2) 36.21 33.80 32.19 30.57 28.35
In the sensitivity section, the ethanol concentration in the
fermentation broth was increased to study the effect of various ethanol concentrations
on production cost. The various ethanol concentration in the fermentation broth was
assumed to 4, 6, 8 wt% and distilled to 80, 85, 90 wt% to find out the suitable ethanol
concentration in the overhead stream. Then best candidate for the dehydration section,
the pervaporation technology, was employe to increase ethanol concentration to 99.5
wt%.
Tables 16 to 18 display the optimized distillation and
membrane parameters for various ethanol concentrations in the fermentation broth
distilled to 80, 85, 90 wt% for OPT plant. These parameters will be applied in the
sensitivity analysis section to explain the relationship between concentration in feed
and production cost.
73
Table 16 Configuration of the combination of distillation and pervaporation model
for distillate various ethanol concentrations in fermentation broth to 80 wt% for OPT
Ethanol concentration
of fermentation broth
4 6 8
Optimized parameters
of distillation column
Number of stages 18 17 15
Feed stage 4 4 4
Reflux ratio 3.2
2.2 1.7
Distillation to feed 0.0265 0.04 0.054
Membrane parameter
Type of membrane 6FDA-NDA/DABA
Energy demand (MJ/kg eth) 303.65 454.17 614.32
Permeate flux (kg/m2.hr) 1.5 1.5 1.5
Area (m2) 79.93 119.57 183.15
Table 17 Configuration of the combination of distillation and pervaporation model
for distillate various ethanol concentrations in fermentation broth to 85 wt% for OPT
Ethanol concentration
of fermentation broth
4 6 8
Optimized parameters
of distillation column
Number of stages 20 15 13
Feed stage 6 6 6
Reflux ratio 3.33 3.32 3.31
Distillation to feed 0.0238 0.0238 0.047
Membrane parameter
Type of membrane 6FDA-NDA/DABA
Energy demand (MJ/kg eth) 224.50 336.28 435.55
Permeate flux (kg/m2.hr) 2.7 2.7 2.7
Area (m2) 38.86 58.22 75.59
74
Table 18 Configuration of the combination of distillation and pervaporation model
for distillate various ethanol concentrations in fermentation broth to 90 wt% for OPT
Ethanol concentration
of fermentation broth
4 6 8
Optimized parameters
of distillation column
Number of stages 18 17 15
Feed stage 7 8 8
Reflux ratio 4.8 4.2 3.65
Distillation to feed 0.0205 0.031 0.042
Membrane parameter
Type of membrane 6FDA-NDA/DABA
Energy demand (MJ/kg eth) 123.88 182.81 251.07
Permeate flux (kg/m2.hr) 1.7 1.7 1.7
Area (m2) 33.17 48.99 67.18
75
Fig
ure
118 T
he
sim
ula
tion m
od
el o
f bio
ethanol
pro
duct
ion f
rom
OP
T b
y usi
ng A
spen
plu
s w
ith d
isti
llati
on a
nd p
erva
pora
tion t
echnolo
gy
SE
HO
TW
AT
ER
HY
DR
OG
EN
NE
UT
RA
LM
IXT
AN
KS
SF
CE
NT
RIF
2
ST
OR
AG
E
SEP
2
ME
MB
RA
NE
CO
LUM
N
AU
TO
CL
AV
MIX
ER
PU
MP
11
HX
1
CO
OLE
R1
PU
MP
12
PU
MP
2P
UM
P3
PU
MP
5P
UM
P6
PU
MP
8
PU
MP
9
PU
MP
4
CE
NT
RIF
U
PU
MP
10
PU
MP
1
PU
MP
7O
PT
WA
TE
R1
H2
O2
WA
TE
R2
NA
OH
WA
TE
R3
UR
EA
AM
MO
NIU
M
WA
TE
R4
YE
AS
T
CT
EC
2
SO
LID
WA
S
ST
EA
M
PE
RM
EA
TE
PR
OD
UC
T
WA
ST
EC
OL
INS
SF
INA
UT
OC
LIN
MIX
ER
INC
OLU
MN
RE
CY
CL
E
INP
UM
P3
INP
UM
P5
INP
UM
P6
INP
UM
P8
S1
7IN
PU
MP
10
INP
UM
P7
S2
76
Fig
ure
119 T
he
sim
ula
tion m
od
el o
f bio
ethanol
pro
duct
ion f
rom
OP
T b
y usi
ng A
spen
plu
s w
ith d
isti
llati
on a
nd p
ress
ure
sw
ing
adso
rpti
on t
echnolo
gy
SE
HO
TW
AT
ER
HY
DR
OG
EN
NE
UT
RA
LM
IXT
AN
KS
SF
CE
NT
RIF
2
ST
OR
AG
E
AU
TO
CLA
VP
UM
P2
PU
MP
3P
UM
P5
PU
MP
6P
UM
P8
PU
MP
9
PU
MP
4
CE
NT
RIF
U
PU
MP
10
DE
SO
RB
AD
SO
RB
CO
LU
MN
OP
T
WA
TE
R1
H2
O2
WA
TE
R2
NA
OH
WA
TE
R3
UR
EA
AM
MO
NIU
M
WA
TE
R4
YE
AS
T
CT
EC
2
SO
LID
WA
S
ST
EA
M
INS
SF
INA
UT
OC
LIN
MIX
ER
INP
UM
P2
INP
UM
P3
INP
UM
P5
INP
UM
P6
INP
UM
P8
INP
UM
P9
INP
UM
P4
S1
7IN
PU
MP
10
RE
CY
CLE
S8
ET
H99
PR
OD
UC
T
S1
1S
1S
19
S3
WA
ST
E
77
Fig
ure
120 T
he
sim
ula
tion m
od
el o
f bio
ethanol
pro
duct
ion f
rom
EF
B b
y usi
ng A
spen
plu
s w
ith d
isti
llati
on a
nd p
erva
pora
tion t
echnolo
gy
HC
WH
OT
WA
TE
R
HY
DR
OG
EN
NE
UT
RA
LM
IXT
AN
KS
SF
CE
NT
RIF
2
ST
OR
AG
E
SEP
2
ME
MB
RA
NE
CO
LUM
N
AU
TO
CL
AV
MIX
ER
PU
MP
11
HX
1
CO
OLE
R1
PU
MP
12
PU
MP
2P
UM
P3
PU
MP
5P
UM
P6
PU
MP
8
PU
MP
9
PU
MP
4
CE
NT
RIF
U
PU
MP
10
PU
MP
1
PU
MP
7E
FB
WA
TE
R1
H2
O2
WA
TE
R2
NA
OH
WA
TE
R3
UR
EA
AM
MO
NIU
M
WA
TE
R4
YE
AS
T
CT
EC
2
SO
LID
WA
S
WA
TE
R
PE
RM
EA
TE
PR
OD
UC
T
WA
ST
EC
OL
INS
SF
INA
UT
OC
LIN
MIX
ER
INC
OLU
MN
RE
CY
CL
E
INP
UM
P3
INP
UM
P5
INP
UM
P6
INP
UM
P8
S1
7IN
PU
MP
10IN
PU
MP
7
78
Fig
ure
121 T
he
sim
ula
tion m
od
el o
f bio
ethanol
pro
duct
ion f
rom
tw
o f
eedst
ock
s by
usi
ng A
spen
plu
s w
ith d
isti
llati
on a
nd p
erva
pora
tion
tech
nolo
gy
SE
HO
TWA
TER
HY
DR
OG
EN
NE
UTR
AL
ME
DIA
SS
FS
TOR
AG
E
SE
P2
PE
RV
AP
OR
CO
LUM
N
AU
TOC
LA
VC
EN
TRIF
U
CE
NTR
IF2
WA
TER
2
H2O
2
WA
TER
3
NA
OH
WA
TER
4
PE
PT
ON
E
BU
FF
ER
YE
AS
T
CTE
C2
PR
OD
UC
T
OP
TEF
B
S10
RE
CY
CLE
B1
S2
HC
W
STE
AM
WA
TER
1
S7
79
5. Economic analysis
The preliminary economic evaluation of a project for production plant usually
associates with the estimation of capital investment, operating costs, and analysis of
profitability. The economic evaluation was performed in order to determine the
feasibility of each project. When the parameter and assumptions to calculate regarding
Table 7. The economic results of three bioethanol production plants were presented in
Table 19 to 27. The simulation model of bioethanol production in the Aspen Plus
software was transferred to the Aspen Process Economic Analyzer program to
calculate the utility costs and some equipment price. The economic result and
production cost per unit are helpful information for project decisions.
5.1. OPT plant
The distribution capital costs and operating costs of 4 cases for PV and
PSA were presented in Tables 19 and 20 respectively. The calculation of total capital
investment (TCI) found that plants dehydrated with pervaporation (PV) use less of
total capital investment (TCI) compared with pressure swing absorption (PSA). As a
result of higher equipment purchasing cost of PSA cause higher investment costs. The
total capital cost for OPT plants is in the range of 22x106$ to 24x106$. The equipment
purchasing costs affected the total capital cost significantly because the various
elements of total capital cost are estimated based on the total equipment purchase
costs (PC) by using several multipliers as shown in the economic assessment section.
The graph for the comparison of the total capital cost of 4 cases showed in Figure
122. Detailed definitions of the main equipment can be found in Appendix Table A1
to A3. The operating costs to run a bioethanol plant are the sum of all expenses
including raw materials, labor, chemical substance, plant overhead, G and A
expenses, utilities, maintenance, miscellaneous, membrane/molecular sieve
replacement. Figure 124 illustrated that utility cost is the highest element, accounted
for 32%, contributed to operating cost. The bioethanol plant with PSA technology
consumes higher utility costs to produce high purity of ethanol. Therefore, the
operating costs of PSA higher than PV for all cases, except case 1 because case 1 of
OPT plant with PV had more amount of mass flow in the overhead stream. The mass
flow in the overhead stream directly impacts to energy demand in distillation and
dehydration. The graph for the comparison of the operating costs of 4 cases showed in
Figure 123. From these results, we can state that the bioethanol plant with
pervaporation seems to dominate dehydration because of lower production cost in all
cases compared with pressure swing absorption. Moreover, the result demonstrated
that increasing dilute ethanol to 85 wt% in the overhead stream before dehydrating
with PV and PSA have more economically effective. The pervaporation technology
was the best candidate for dehydration technology because of the lowest production
cost per unit. So, we can conclude that distillate dilute ethanol to 85 wt% before
feeding to Polyimide (BMTCHDA) membrane to dehydrate and produce 99.5wt% of
ethanol is the best way for purification.
However, these projects required large investment costs to produce a small
capacity plant that can produce the final product only 10,000 L/day. It resulted in
80
NPV of all cases was a negative value. It means this project is not suitable for
investment. Table 21 provides a summary of the economic result for the OPT plant.
Tables A12 to A27 in Appendix show the annual cash flow and the net present value
of the OPT plant.
81
Tab
le 1
9 T
he
com
ponen
t of
tota
l ca
pit
al c
ost
for
the
OP
T p
lant
P
V
PS
A
Eth
an
ol
con
cen
trati
on
in o
ver
hea
d s
trea
m (
%)
50
80
85
90
80
85
90
94
Equip
men
t purc
has
e co
st
4224522
4248147
4240364
42399
63
4464556
4463356
4464756
4496856
Inst
alla
tion
2112261
2124074
2120182
21199
81
2232278
2231678
2232378
2248428
Pro
cess
pip
ing
1689809
1699259
1696146
16959
85
1785822
1785342
1785902
1798742
Inst
rum
enta
tion
1478583
1486852
1484128
14839
87
1562595
1562175
1562665
1573900
Insu
lati
on
126736
127444
127211
12719
9
133937
133901
133943
134906
Ele
ctri
cal
633678
637222
636055
63599
4
669683
669503
669713
674528
Buil
din
gs
1901035
1911666
1908164
19079
83
2009050
2008510
2009140
2023585
Lan
d
168981
169926
169615
16959
9
178582
178534
178590
179874
Tota
l p
lan
t d
irec
t co
st (
TP
DC
) 12335605
12404590
12381864
12380
691
13036503
13032999
13037087
13130819
Engin
eeri
ng
3083901
3101148
3095466
30951
73
3259126
3258250
3259272
3282705
Con
stru
ctio
n
4317462
4341607
4333652
43332
42
4562776
4561550
4562980
4595787
Tota
l p
lan
t in
dir
ect
cost
(T
PIC
)
7401363
7442754
7429118
74284
14
7821902
7819799
7822252
7878491
Tota
l p
lan
t co
st (
TP
C)
19736968
19847344
19810983
19809
105
20858405
20852798
20859339
21009310
Con
trac
tor’
s fe
e 986848
992367
990549
99045
5
1042920
1042640
1042967
1050466
Con
tingen
cy
1973697
1984734
1981098
19809
11
2085840
2085280
2085934
2100931
Tota
l ca
pit
al
cost
22697513
22824446
22782630
22780
471
23987165
23980718
23988240
24160707
82
Tab
le 2
0 T
he
com
ponen
t of
tota
l oper
atin
g c
ost
fo
r th
e O
PT
pla
nt
P
V
PS
A
50
80
85
90
80
85
90
94
Raw
mat
eria
ls
14162
14162
14162
14162
14162
14162
14162
14162
Lab
or
259200
259200
259200
259200
259200
259200
259200
259200
Chem
ical
subst
ance
456183
456183
456183
456183
456183
456183
456183
456183
Pla
nt
over
hea
d
159852
160383
160208
160199
165253
165226
165257
165979
G a
nd A
Expen
ses
31970
32077
32042
32040
33051
33045
33051
33196
Uti
liti
es
646549
585139
585877
589463
617726
612499
616496
613218
Mai
nte
nan
ce
380207
382333
381633
381597
401810
401702
401828
404717
Mis
cell
aneo
us
1000
1000
1000
1000
1000
1000
1000
1000
Tota
l op
erati
ng c
ost
1949123
1890477
1890305
1893844
1948385
1943016
1947177
1947655
Tab
le 2
1 E
conom
ic r
esult
for
OP
T p
lant
P
V
PS
A
50
80
85
90
80
85
90
94
NP
V (
$)
-11,8
13,9
44
-11,2
95,4
41
-1
1,2
47,8
48
-11,2
75,5
04
-1
2,9
18,5
08
-12,8
90,9
11
-12,9
10,6
45
-1
4,0
39,9
17
IRR
(%
) -0
.08%
0.2
4%
0.2
5%
0.2
3%
-0
.31%
-0
.30%
-0
.31%
-0
.99%
PB
(yea
r)
N/A
N
/A
N/A
N
/A
N/A
N
/A
N/A
N
/A
Pro
duct
ion
cost
per
unit
($/u
nit
)
0.8
99
0.8
8355
0.8
831
0.8
84
0.9
190
0.9
184
0.9
1879
0.9
209
83
Figure 122 Comparison of the total capital cost of 4 cases
Figure 123 Comparison of operating costs of 4 cases
Figure 124 Distribution element of operating costs for OPT plant with 85 wt% in the
overhead stream before dehydrating with PV and PSA
2.2E+07
2.2E+07
2.3E+07
2.3E+07
2.4E+07
2.4E+07
2.5E+07
case 1 case 2 case 3 case 4
To
tal
ca
pit
al
cost
($
)
PV
PSA
1.80E+06
1.84E+06
1.88E+06
1.92E+06
1.96E+06
2.00E+06
case 1 case 2 case 3 case 4
To
tal
op
erat
ing c
ost
($
/yea
r) PV
PSA
1%
13-14%
23%
8%
2%
31-32%
21%
0%
Raw Materials
Labor
Chamical supstance
Plant Overhead
G and A Expenses
Utilities
maintenance
Miscellaneous
84
Figure 125 Distribution chemical costs for OPT plant
As a result of the highest portion of operating costs is utility costs account for
32 % and chemical costs account for 23 % of total operating cost. In this work, the
best candidate plant, OPT plant with PV technology, will be studied the sensitivity of
the highest element contributed to the chemical costs. Figure 125 shows the highest
element contributed to chemical costs was enzyme Ctec2, accounted for 47%. The
additional information about the raw material and chemical costs of OPT plant can be
found in Appendix Table A7.
5.2. EFB plant
The best way to purification of OPT plant was employed to other
plants. Tables 22 to 24 provide the economic results of EFB plant with PV
technology. The dilute ethanol in the fermentation broth was increased to 85 wt% by
the Radfrac column and remove the water in an overhead stream by PV technology to
achieve 99.5 wt% of ethanol.
Table 22 The component of total capital cost for EFB plant
Ethanol concentration
in an overhead stream (%)
85
Equipment purchase cost 5503432
Installation 2751716
Process piping 2201373
Instrumentation 1926201
Insulation 165103
Electrical 825515
Buildings 2476544
Land 220137
Total plant direct cost (TPDC) 16070020
Engineering 4017505
Construction 5624507
47%
3%
25%
2%
15%
0%8%
CTec2
H2O2
Ammonium sulfate
S. cerevisiae
Urea
NaOH
Water
85
Total plant indirect cost (TPIC) 9642012
Total plant cost (TPC) 25712033
Contractor’s fee 1285602
Contingency 2571203
Total capital cost 29568837
Table 23 The component of total operating cost for EFB plant
Ethanol concentration
in the overhead stream (%)
85
Raw materials 22129
Labor 259200
Chemical substance 536537
Plant overhead 188627
G and A Expenses 37725
Utilities 707048
Maintenance 495309
Miscellaneous 1000
Total operating cost 2247574
Table 24 Economic result for EFB plant
Ethanol concentration
in the overhead stream (%)
85
NPV ($) -14,762,153
IRR (%) 0.18%
PB (year) N/A
Production cost per unit ($/unit) 0.892
For EFB plant, it had a high potential to produce bioethanol because of the
highest ethanol yield compared with OPT as shown in Table 9. OPT had more lignin
fraction and caused less efficiency in ethanol conversion. Therefore, EFB plant can
produce more products and obtain higher incomes. But there is the main disadvantage
of EFB plant that is it must operate under high pressure (30 bar) in the first
pretreatment process. It results in the requirement of high equipment purchasing costs
for the high-pressure agitated vessel in a hot compress water process (HCW). The
total equipment costs of EFB plant are 30 % more than OPT plant. Even though
higher annual income but EFB plant had a higher production cost compare with OPT
plant. The Net Present Value (NPV) of EFB plant is negative, meaning not suitable
for investment. The detailed definitions of the main equipment, additional information
on raw material and chemical costs, and annual cash flow and net present value of
EFB plant can be found in Appendix, Tables A4, A8, and A27 to A28.
86
5.3. Two feedstocks plant
The distribution of capital costs, operating costs and economic results
of various ratios of two feedstocks plants were present in Tables 25 to 27.
Table 25 The component of total capital cost for two feedstocks plant
EFB:OPT ratio 100:0 80:20 50:50 20:80 0:100
Equipment purchase cost 5553432 5552726 5536729 5536096 5555868
Installation 2776716 2776363 2768364 2768048 2777934
Process piping 2221373 2221090 2214692 2214439 2222347
Instrumentation 1943701 1943454 1937855 1937634 1944554
Insulation 166603 166582 166102 166083 166676
Electrical 833015 832909 830509 830414 833380
Buildings 2499044 2498727 2491528 2491243 2500141
Land 222137 222109 221469 221444 222235
Total plant
direct cost
(TPDC)
16216020 16213959 16167248 16165401 16223134
Engineering 4054005 4053490 4041812 4041350 4055783
Construction 5675607 5674886 5658537 5657890 5678097
Total plant
indirect cost
(TPIC)
9729612 9728376 9700349 9699241 9733880
Total plant
cost (TPC)
25945633 25942335 25867597 25864642 25957014
Contractor’s fee 1297282 1297117 1293380 1293232 1297851
Contingency 2594563 2594233 2586760 2586464 2595701
Total capital cost 29837477 29833685 29747737 29744338 29850566
Table 26 The component of total operating cost for two feedstocks plant
EFB:OPT ratio 100:0 80:20 50:50 20:80 0:100
Raw materials 22129 20535 18146 15756 14162
Labor 259200 259200 259200 259200 259200
Chemical substance 536537 521454 497393 471975 454346
Plant overhead 189752 189736 189376 189362 189807
G and A Expenses 37950 37947 37875 37872 37961
Utilities 707048 706751 674441 640704 602530
Maintenance 499809 499745 498306 498249 500028
Miscellaneous 1000 1000 1000 1000 1000
Total operating cost 2253424 2236370 2175737 2114118 2059035
87
Table 27 Economic result for two feedstocks plant
EFB:OPT
ratio
100:0 80:20 50:50 20:80 0:100
NPV ($) -15,038,365 -16,080,237 -17,072,550 -18,267,834 -19,053,929
IRR (%) 0.12% -0.32% -0.77% -1.29% -1.61%
PB (year) N/A N/A N/A N/A N/A
Production
Cost per
unit ($/unit)
0.897 0.926 0.960 1.004 1.036
For two feedstocks plant, the main unit operations were designed to cover the
maximal capacity, 100:0 of EFB:OPT. So, the equipment purchasing costs of all
ratios were approximately the same except for the equipment in the purification
process. SSF process produces different ethanol yield. The fermentation broth
released from the SSF units in each ratio of two feedstocks had different ethanol
yield, therefore the purification process had to operate under different conditions to
produce the same quality of the final product. The two feedstocks plant had the
highest equipment purchasing costs compared with OPT and EFB plant because of
two technologies in the first pretreatment process. The total capital cost of all ratio
was approximately the same. While a high EFB ratio provides high operating costs
because of higher mass flow in the process. The highest ethanol yield was produced
from the production plant with EFB:OPT equal to 100:0 yielding the lowest
production cost per unit. Meanwhile, the production plant with EFB:OPT equal to
0:100 produced the lowest ethanol yield contributing to the highest production cost
per unit compared with other plants. The result indicated that the Net Present Value
(NPV) of all ratios of two feedstocks plant was negative. The detailed definitions of
the main equipment, additional information on raw material and chemical costs, and
annual cash flow and net present value of EFB plant can be found in Appendix,
Tables A5, A6 to A11, and A30 to A39.
6. Sensitivity
In the last section, sensitivity analysis has studied the effect of two variables
on production cost for the best candidate of OPT plant with PV method. As
considered in the operation portion, Figure 125 shows that enzyme Ctec2 was the
main factor contributed to the total chemical costs. So, the various Ctec2 price
including 0.53, 0.45, 0.37, 0.29, 0.21, 0.13 $ was studied. The result indicated that
decreasing Ctec2 price by 70% provides a lower 7.4% of production cost per unit as
shown in Table 28.
Table 28 Economic result for various Ctec2 price Ctec2 price
($/kg)
0.53 0.45 0.37 0.29 0.21 0.13
NPV ($) -
11,247,9
21
-
10,943,6
86
-
10,639,4
50
-
10,335,2
15
-
10,030,9
80
-
9,726,7
45
88
IRR (%) 0.25 0.41 0.57 0.73 0.89 1.05
PB (year) N/A N/A N/A N/A N/A N/A
Production
Cost per
unit ($/unit)
0.8831
0.8736
0.8641
0.8546
0.8452
0.8357
Moreover, the ethanol concentration released from SSF strongly influenced to
the purification section. The various ethanol concentration in fermentation broth has
studied the effect on production cost. In this section, the bioethanol concentration was
varied between 4 to 8 wt%. The same amount of feedstock, 47,208 kg/day, was fed
into the process to produce various ethanol concentrations in the SSF process.
Moreover, the various ethanol concentration in the overhead stream has studied to
determine the proper concentration for each ethanol concentration in the fermentation
broth. The various ethanol concentration was distilled to 80, 85, 90 wt% before
feeding to PV model. The parameter for various ethanol concentration plants was
shown in Table 18. The economic result of various ethanol concentrations was
presented in Tables 29 to 31. These results demonstrated that a higher concentration
can be purified with a lower number of stages. Therefore, capital costs and operating
costs consistently decreased. For 4 to 8 wt%, distillates to 85 wt% provide the lowest
production cost per unit with the highest NPV. It requited the lowest total capital and
operating cost compared with 80 and 90 wt%. The 6 and 8 wt% had a positive value
of NPV. Therefore, we can conclude that the high concentration in the fermentation
broth is preferable to minimize production cost for bioethanol plants. The annual cash
flow and the net present value of sensitivity analysis were shown in the Appendix,
Tables A40 to A57.
Table 29 Economic result for distillation 4, 6, 8 wt% to 80 wt%
Ethanol concentration (wt%) 4 6 8
NPV ($) -2,868,446 14,638,350 31,936,117
IRR (%) 4.45% 11.93% 18.48%
PB (year) 16.66 9.97 5.45
Production
Cost per
unit ($/unit)
0.6743 0.45241 0.34210
Table 30 Economic result for distillation 4, 6, 8 wt% to 85 wt%
Ethanol concentration (wt%) 4 6 8
NPV ($) -2,825,501 14,733,890 32,231,073
IRR (%) 4.47% 11.99% 18.65%
PB (year) 15.29 8.01 5.4
Production
Cost per
unit ($/unit)
0.6739 0.4523 0.3420
89
Table 31 Economic result for distillation 4, 6, 8 wt% to 90 wt%
Ethanol concentration (wt%) 4 6 8
NPV ($) -2,942,067 14,674,717 32,223,988
IRR (%) 4.42% 11.95% 18.64%
PB (year) 15.38 8.01 5.41
Production
Cost per
unit ($/unit)
0.6762 0.4530 0.3417
7. Conclusion
This study evaluated the economic feasibility of bioethanol production from
OPT, EFB, and various ratios of two feedstocks to produce 99.5 wt% of ethanol,
10000 L/day. The specified first pretreatment technology was employed for each
feedstock to produce the highest ethanol yield. The Hot compressor water (HCW),
Steam explosion (SE) were used to destroy feedstock’s structure, then hot water
washing, and hydrogen peroxide digestion process were used to increase cellulose
fraction for both feedstocks before feeding to simultaneous saccharification
fermentation (SSF) process. SSF was considered as the conversion process to produce
ethanol about 3 wt%. The dilute ethanol must be raised purity by purification. In
purification, PV and PSA were performed to increase 3 wt% of ethanol to fuel grade,
99.5 wt%. The mass balance throughout the bioethanol production plant was
calculated by developing the mass flow sheet in Microsoft excel 2016. The economic
evaluation was performed by Aspen Process Economic Analyzer and Microsoft excel
2016 to evaluate the price of some equipment, utility of the plant, and evaluated the
economic result. The economic result including NPV, IRR, PB, production cost per
unit was selected as the indicator for an investment decision. The sensitivity analysis
of various ethanol concentrations in the fermentation broth was studied.
The result showed that large investment costs were required to produce a small
capacity of bioethanol products, resulting in high production cost per unit, and NPV is
negative. Even though EFB can produce higher ethanol yield but the HCW, the first
pretreatment step, required the expensive equipment. It causes higher investment costs
and production cost per unit. Therefore, OPT plant pretreated with SE is promising
bioethanol production plant to produce bioethanol with lower production cost. The
economic analyst demonstrated that the best candidate for OPT plant with
pervaporation (PV) technology needs the total capital investment (TCI) equal to
22,782,630 $ and total operating cost equal to 1,890,305 $ with 0.8831 $/unit of
production cost per unit. While the best candidate for OPT plant with pressure swing
adsorption technology needs the total capital investment (TCI) equal to 23,980,718 $
and total operating cost equal to 1,943,016 $ with 0.9184 $/unit of production cost per
unit. EFB plant needs the total capital investment equal to 29,568,837 $ and total
operating costs equal to 2,247,474 $ with 0.892 $/unit of production cost per unit.
And the highest ethanol yield from two feedstocks, 100:0 of EFB:OPT needs the total
capital investment (TCI) equal to 29,837,477 $ and total operating cost equal to
2,253,424 $ with 0.897 $/unit of production cost per unit. This result confirms that all
projects are not feasible and nonprofitable because of net present worth is negative,
90
the internal rate of return is lower than the interest rate. Therefore, the bioethanol
plants were not recommended to develop. The sensitivity analysis suggested that the
NPV became positive when ethanol concentration in the fermentation broth was
increased higher than 6 wt% by remaining the same amount of input and conditions to
produce bioethanol.
LITERATURE CITED
LITERATURE CITED
Achinas, S., Leenders, N., Krooneman, J., & Euverink, G. J. W. (2019). Feasibility
Assessment of a Bioethanol Plant in the Northern Netherlands. Applied Sciences,
9(21), 4586.
Alfani, F., Gallifuoco, A., Saporosi, A., Spera, A., & Cantarella, M. (2000). Comparison
of SHF and SSF processes for the bioconversion of steam-exploded wheat straw.
Journal of Industrial Microbiology and Biotechnology, 25(4), 184-192.
Bastidas, P. A., Gil, I. D., & Rodríguez, G. (2010). Comparison of the main ethanol
dehydration technologies through process simulation. Paper presented at the
European Symposium on Computer-Aided Process Engineering, 20th.
Behera, S., Arora, R., Nandhagopal, N., & Kumar, S. (2014). Importance of chemical
pretreatment for bioconversion of lignocellulosic biomass. Renewable and
Sustainable Energy Reviews, 36, 91-106.
Da Silva, A. R. G., Ortega, C. E. T., & Rong, B.-G. (2016). Techno-economic analysis
of different pretreatment processes for lignocellulosic-based bioethanol
production. Bioresource Technology, 218, 561-570.
Dahnum, D., Tasum, S. O., Triwahyuni, E., Nurdin, M., & Abimanyu, H. (2015).
Comparison of SHF and SSF processes using enzyme and dry yeast for
optimization of bioethanol production from empty fruit bunch. Energy Procedia,
68, 107-116.
Derman, E., Abdulla, R., Marbawi, H., & Sabullah, M. K. (2018). Oil palm empty fruit
bunches as a promising feedstock for bioethanol production in Malaysia.
Renewable Energy, 129, 285-298.
Diopenes, R. G., & Laptaned, U. (2011). Supply Chain Management cost analysis: a
case study of bioethanol production from cassava in Thailand. International
Journal of Logistics Systems and Management, 9(3), 296-314.
Duff, S. J., & Murray, W. D. (1996). Bioconversion of forest products industry waste
cellulosics to fuel ethanol: a review. Bioresource Technology, 55(1), 1-33.
Ebrahimiaqda, E., & Ogden, K. L. (2017). Simulation and cost analysis of distillation
and purification step in the production of anhydrous ethanol from sweet
sorghum. ACS Sustainable Chemistry & Engineering, 5(8), 6854-6862.
Fan, Z. (2014). Consolidated Bioprocessing for Ethanol Production. In Biorefineries
(pp. 141-160): Elsevier.
Goldthorpe, S. (2014). Aspen Simulation and Evaluation of Economic Feasibility of
CO2 Capture for Gaojing Gas-Fired Power Plant. ADB Technical Assistance
Project. Available: https://www.adb.org/sites/default/files/project-
document/81987/45096-001-tacr-02.pdf
Gomar-Madriz, L. E., Luna, J. S., Serna-González, M., Hernández-Castro, S., & Castro-
Montoya, A. J. (2016). Dehydration of Ethanol by PSA Process with Pressure
Equalization Step Added. Bioethanol, 2(1).
Kamarludin, C., Norsyarahah, S., Jainal, M. S., Azizan, A., Safaai, N. S. M., Daud, M.,
& Rafizan, A. (2014). Mechanical pretreatment of lignocellulosic biomass for
biofuel production. Paper presented at the Applied Mechanics and Materials.
Kang, K. E., Chung, D.-P., Kim, Y., Chung, B.-W., & Choi, G.-W. (2015). High-titer
ethanol production from simultaneous saccharification and fermentation using a
92
continuous feeding system. Fuel, 145, 18-24.
Kim, J.-H., Lee, K.-H., & Kim, S. Y. (2000). Pervaporation separation of water from
ethanol through polyimide composite membranes. Journal of Membrane
Science, 169(1), 81-93.
Kim, S., & Kim, C. H. (2013). Bioethanol production using the sequential acid/alkali-
pretreated empty palm fruit bunch fiber. Renewable Energy, 54, 150-155.
Kristiani, A., Effendi, N., Aristiawan, Y., Aulia, F., & Sudiyani, Y. (2015). Effect of
combining chemical and irradiation pretreatment process to the characteristic of
oil palm's empty fruit bunches as raw material for second-generation bioethanol.
Energy Procedia, 68, 195-204.
KrungthaixBank.xAvailable:xhttps://krungthai.com/Download/rateFee/RateFeeDownlo
ad_4380loan_07_02_63.pdf.
Kucharska, K., Rybarczyk, P., Hołowacz, I., Łukajtis, R., Glinka, M., & Kamiński, M.
(2018). Pretreatment of lignocellulosic materials as substrates for fermentation
processes. Molecules, 23(11), 2937.
Le, N. L., & Chung, T.-S. (2014). High-performance sulfonated
polyimide/polyimide/polyhedral oligosilsesquioxane hybrid membranes for
ethanol dehydration applications. Journal of Membrane Science, 454, 62-73.
Lee, K. H., Kim, H. K., & Rhim, J. W. (1995). Pervaporation separation of binary
organic–aqueous liquid mixtures using crosslinked PVA membranes. III.
Ethanol–water mixtures. Journal of applied polymer science, 58(10), 1707-1712.
Li, B.-B., Xu, Z.-L., Qusay, F. A., & Li, R. (2006). Chitosan-poly (vinyl alcohol)/poly
(acrylonitrile)(CS–PVA/PAN) composite pervaporation membranes for the
separation of ethanol-water solutions. Desalination, 193(1-3), 171-181.
Medina, J. D. C., Woiciechowski, A., Zandona Filho, A., Nigam, P. S., Ramos, L. P., &
Soccol, C. R. (2016). Steam explosion pretreatment of oil palm empty fruit
bunches (EFB) using autocatalytic hydrolysis: A biorefinery approach.
Bioresource Technology, 199, 173-180.
Nagy, E., & Boldyryev, S. (2013). Energy demand for biofuel production applying
distillation and/or pervaporation. Chem. Eng. Trans, 35, 265-270.
Nagy, E., Mizsey, P., Hancsók, J., Boldyryev, S., & Varbanov, P. (2015). Analysis of
energy saving by a combination of distillation and pervaporation for biofuel
production. Chemical Engineering Processing: Process Intensification, 98, 86-
94.
O’Brien, D. J., Roth, L. H., & McAloon, A. J. (2000). Ethanol production by continuous
fermentation–pervaporation: a preliminary economic analysis. Journal of
Membrane Science, 166(1), 105-111.
Öhgren, K., Bura, R., Lesnicki, G., Saddler, J., & Zacchi, G. (2007). A comparison
between simultaneous saccharification and fermentation and separate hydrolysis
and fermentation using steam-pretreated corn stover. Process Biochemistry,
42(5), 834-839.
Pangsang, N., Sakdaronnarong, C., Thanapimmetha, A., & Srinophakun, P. (2018).
High ethanol production from oil palm empty fruit bunch pretreated by hot-
compressed water technique. Paper presented at the 2018 IEEE 5th International
Conference on Engineering Technologies and Applied Sciences (ICETAS).
Petrides, D. (2000) Bioprocess design and economics. Bioseparations Science and
Engineering, 1-83.
93
Somnuek, S., Slingerland, M. M., & Grünbühel, C. M. (2016). The introduction of oil
palm in Northeast Thailand: A new cash crop for smallholders? Asia Pacific
Viewpoint, 57(1), 76-90.
Suttikul, S., Srinorakutara, T., Butivate, E., & Orasoon, K. (2016). Comparison of SHF
and SSF processes for ethanol production from alkali-acid pretreated sugarcane
trash. Asia-Pacific Journal of Science and Technology, 21(2), 229-235.
TerraBKK Research. Available: http://research.terrabkk.com/th/
Tgarguifa, A., & Abderafi, S. (2016). A comparative study of separation processes for
bioethanol production. Paper presented at the 2016 International Renewable and
Sustainable Energy Conference (IRSEC).
ThailandxBiofuelxReferencexPrice.xAvailable:xhttps://www.ceicdata.com/en/thailand/
biofuel-reference-price
ThailandxCorporatexTaxxRate.xAvailable:https://tradingeconomics.com/thailand/corpo
rate-tax-rate.
Thailand Tax Depreciation Rates. Available:https://sherrings.com/depreciation-tax-
rates-thailand.html
Upajak, S., Laosiripojana, N., Champreda, V., Kreethachart, T., & Imman, S. (2018).
EFFECT OF COMBINATION OF LIQUID HOT WATER SYSTEM AND
HYDROGEN PEROXIDE PRETREATMENT ON ENZYMATIC
SACCHARIFICATION OF CORN COB. INTERNATIONAL JOURNAL OF
GEOMATE, 15(51), 31-38.
Valentínyi, N., Gáspár, M., Tóth, A. J., Haáz, E., André, A., & Mizsey, P. (2018).
Investigation of process alternatives for the separation of ethanol, n-butanol and
water ternary mixture. Chemical Engineering Transactions, 69, 13.
Vane, L. M. J. J. o. C. T., Biotechnology: International Research in Process, E., &
Technology, C. (2005). A review of pervaporation for product recovery from
biomass fermentation processes. Journal of Chemical Technology &
Biotechnology: International Research in Process, Environmental & Clean
Technology, 80(6), 603-629.
Wyman, C. E., Spindler, D. D., & Grohmann, K. (1992). Simultaneous saccharification
and fermentation of several lignocellulosic feedstocks to fuel ethanol. Biomass
Bioenergy, 3(5), 301-307.
Zaini, H. H. H. (2006). Bioethanol Production from Empty Fruit Bunch (EFB) of Oil
Palm. KUKTEM.
94
APPENDICES
95
Table A1 List of main equipment for OPT plant
Model Type of equipment Unit Volume
(m3)
Price
per
unit
(USD)
Total
price
(USD)
Crusher Woodchipper machine 1 - 13000 13000
SE HP jacketed tank 1 3 50000 50000
Hot water Agitated vessel jacket 1 10 40000 40000
H2O2 Agitated vessel jacket 1 12 48000 48000
Neutralize Agitated vessel jacket 1 14 56000 56000
Centrifuge Horizontal Spiral
Centrifuge
2 - 15,400 30800
Media tank Agitated vessel 1 6 24000 24000
Storage tank Vertical vessel 1 50 20000
0
200000
SSF tank Agitated vessel jacket 16 50 20000
0
320000
0
Dilute tank Agitated vessel 1 3 12000 12000
Yeast cultivation
tank
Agitated vessel 16 5 20000 320000
Yeast cultivation
tank
Agitated vessel 16 0.5 2000 32000
Yeast cultivation
tank
Agitated vessel 16 0.05 200 3200
Yeast cultivation
tank
Small vessel 16 0.005 20 320
Table A2 List of equipment cost in pervaporation technology for OPT plant
Model Unit Case 1 Case 2 Case 3 Case 4
Distillation column 1 133200 178900 179000 179900
Membrane 1 33927 13552 5669 4968
Cooler 1 1 10400 8700 8700 8100
Hx 1 1 8100 8100 8100 8100
Vacuum pump 1 2500 2500 2500 2500
Pump 2 2000 2000 2000 2000
Screw pump 7 5075 5075 5075 5075
Table A3 List of equipment cost in pressure swing adsorption technology for OPT
plant
Model Unit Case 1 Case 2 Case 3 Case 4
Distillation column 1 179900 179800 181300 225700
Adsorb column 1 100000 100000 100000 100000
Desorb column 1 100000 100000 100000 100000
Zeolite 3A 2 12061 12061 12061 12061
Cooler 1 1 8700 8700 8700 8600
96
Cooler 2 1 8900 8800 8700 8700
Cooler 3 1 8700 8700 8700 8600
Hx 1 1 9900 8900 8900 8800
Pump 4 2000 2000 2000 2000
Screw pump 7 5075 5075 5075 5075
Table A4 List of equipment for EFB plant
Model Type of equipment Unit Volume
(m3)
Price
per
unit
(USD)
Total
price
(USD)
Crusher Woodchipper machine 1 N/A 13000 13000
HCW HP jacketed tank 1 12 17760
0
177600
Hot water Agitated vessel jacket 1 10 40000 40000
H2O2 Agitated vessel jacket 1 12 48000 48000
Neutralize Agitated vessel jacket 1 17 68000 68000
Centrifuge Horizontal Spiral
Centrifuge
2 N/A 15400 30800
Media tank Agitated vessel 1 7 28000 28000
Storage tank Vertical vessel 1 50 20000
0
200000
SSF tank Agitated vessel jacket 16 50 20000
0
420000
0
Dilute tank Agitated vessel 1 3 12000 12000
Yeast cultivation
tank
Agitated vessel 16 5 20000 420000
Yeast cultivation
tank
Agitated vessel 16 0.5 2000 42000
Yeast cultivation
tank
Agitated vessel 16 0.05 200 4200
Yeast cultivation
tank
Small vessel 16 0.005 20 420
Distillation column 1 N/A 18580
0
185800
Membrane 1 N/A 7237 7237
Cooler 1 1 N/A 8700 8700
Hx 1 1 N/A 8100 8100
Vacuum pump 1 N/A 2500 2500
Pump 4 N/A 500 2000
Screw pump 7 N/A 725 5075
97
Table A5 List of equipment for two feedstocks plant
Model Type of equipment Unit Volume
(m3)
Price
per
unit
(USD)
Total
price
(USD)
Crusher Woodchipper machine 1 N/A 13000 13000
SE 1 3 50000 50000
HCW HP jacketed tank 1 12 17760
0
177600
Hot water Agitated vessel jacket 1 10 40000 40000
H2O2 Agitated vessel jacket 1 12 48000 48000
Neutralize Agitated vessel jacket 1 17 68000 68000
Centrifuge Horizontal Spiral
Centrifuge
2 N/A 15400 30800
Media tank Agitated vessel 1 7 28000 28000
Storage tank Vertical vessel 1 50 20000
0
200000
SSF tank Agitated vessel jacket 16 50 20000
0
420000
0
Dilute tank Agitated vessel 1 3 12000 12000
Yeast cultivation
tank
Agitated vessel 16 5 20000 420000
Yeast cultivation
tank
Agitated vessel 16 0.5 2000 42000
Yeast cultivation
tank
Agitated vessel 16 0.05 200 4200
Yeast cultivation
tank
Small vessel 16 0.005 20 420
Table A6 List of equipment cost in pervaporation technology for two feedstocks plant
Model Unit EFB:OPT
100:0 80:20 50:50 20:80 0:100
Distillation column 1 185800 185600 170000 169700 176800
Membrane 1 7237 6731 6434 6101 5673
Cooler 1 1 8700 8700 8600 8600 8700
Hx 1 1 8100 8100 8100 8100 8100
Vacuum pump 1 2500 2500 2500 2500 2500
Pump 2 2000 2000 2000 2000 2000
Screw pump 7 5075 5075 5075 5075 5075
98
Table A7 Additional information for raw material and chemical substance for OPT
plant and 100:0 EFB:OPT plant
Chemical substance Unit/hr Cost/unit Total
cost
($/hr)
Total cost
($/year)
OPT 1967 0.001 2 14162
Enzyme Ctec2
H2O2 50 wt%
Ammonium sulfate
Saccharomyces cerevisiae
Urea
Sodium hydroxide (NaOH)
Water
55.94 0.53 63.36 456183
2.50 0.81
190.19 0.09
1084.08 0.00094
95.10 0.10
0.49 0.20
9985 0.00049
Table A8 Additional information for raw material and chemical substance for EFB
plant and 0:100 EFB:OPT plant
Chemical substance Unit/hr Cost/unit Total
cost ($/hr)
Total cost
($/year)
EFB 1967 0.0016 3 22129
Enzyme Ctec2
H2O2 50 wt%
Ammonium sulfate
Saccharomyces cerevisiae
Urea
Sodium hydroxide (NaOH)
Water
66.43 0.53 74 532928
2.47 0.81
225.88 0.09
1287.49 0.00094
112.94 0.10
0.48 0.20 11858 0.00049
Table A9 Additional information for raw material and chemical substance for 80:20
EFB:OPT plant
Chemical substance Unit/hr Cost/unit Total
cost ($/hr)
Total cost
($/year)
OPT
EFB
393
1574
0.0010
0.0016
0.4
2.5
2832
17703
Enzyme Ctec2
H2O2 50 wt%
Ammonium sulfate
Saccharomyces cerevisiae
Urea
Sodium hydroxide (NaOH)
Water
64.34 0.53 72 521454
2.48 0.81
218.75 0.09
1246.86 0.00094
109.37 0.10
0.48 0.20
11858 0.00049
Table A10 Additional information for raw material and chemical substance for 50:50
EFB:OPT plant
Chemical substance Unit/hr Cost/unit Total Total cost
99
cost
($/hr)
($/year)
OPT
EFB
984
984
0.0010
0.0016
0.4
2.5
2832
17703
Enzyme Ctec2
H2O2 50 wt%
Ammonium sulfate
Saccharomyces cerevisiae
Urea
Sodium hydroxide (NaOH)
Water
61.43 0.53 69 497393
2.50 0.81
208.85 0.09
1190.46 0.00094
104.43 0.10
0.49 0.20
10965 0.00049
Table A11 Additional information for raw material and chemical substance for 20:80
EFB:OPT plant
Chemical substance Unit/hr Cost/unit Total
cost
($/hr)
Total cost
($/year)
OPT
EFB
1574
393
0.0010
0.0016
0.4
2.5
2832
17703
Enzyme Ctec2
H2O2 50 wt%
Ammonium sulfate
Saccharomyces cerevisiae
Urea
Sodium hydroxide (NaOH)
Water
58.18 0.53 66 471975
2.50 0.81
197.83 0.09
1127.61 0.00094
98.91 0.10
0.49 0.20 10386 0.00049
100
Tab
le A
12 A
nnual
cas
h f
low
and n
et p
rese
nt
val
ue
for
case
1 o
f O
PT
pla
nt
wit
h p
ervap
ora
tion t
echn
olo
gy
Y
ears
R
even
ue
Exp
end
itu
re
Inco
me
bef
ore
tax
T
ax
In
com
e aft
er t
ax
C
ash
flo
w
0
2
2,6
97,5
13
-
2
2,6
97,5
13
1
2
,681,4
35
1
,949,1
23
732,3
12
112,6
66
619,6
46
- 2
2,0
77,8
67
2
2
,708,2
49
1
,958,3
22
749,9
27
116,1
89
633,7
38
- 2
1,4
44,1
30
3
2
,735,3
31
1
,967,6
13
767,7
18
119,7
48
647,9
71
- 2
0,7
96,1
59
4
2
,762,6
85
1
,976,9
97
785,6
88
123,3
41
662,3
46
- 2
0,1
33,8
12
5
2
,790,3
12
2
,037,3
66
752,9
46
116,7
93
636,1
53
- 1
9,4
97,6
59
6
2
,818,2
15
1
,996,0
48
822,1
67
130,6
37
691,5
30
- 1
8,8
06,1
29
7
2
,846,3
97
2
,005,7
16
840,6
81
134,3
40
706,3
41
- 1
8,0
99,7
88
8
2
,874,8
61
2
,015,4
81
859,3
80
138,0
80
721,3
00
- 1
7,3
78,4
88
9
2
,903,6
09
2
,025,3
44
878,2
66
141,8
57
736,4
09
- 1
6,6
42,0
80
10
2
,932,6
46
2
,086,1
96
846,4
50
135,4
94
710,9
56
- 1
5,9
31,1
23
11
2
,961,9
72
2
,045,3
66
916,6
06
149,5
25
767,0
81
- 1
5,1
64,0
42
12
2
,991,5
92
2
,055,5
27
936,0
64
153,4
17
782,6
48
- 1
4,3
81,3
95
13
3
,021,5
08
2
,065,7
91
955,7
17
157,3
47
798,3
70
- 1
3,5
83,0
25
14
3
,051,7
23
2
,076,1
56
975,5
66
161,3
17
814,2
49
- 1
2,7
68,7
76
15
3
,082,2
40
2
,137,5
17
944,7
23
155,1
48
789,5
75
- 1
1,9
79,2
01
16
3
,113,0
62
2
,097,2
00
1
,015,8
62
169,3
76
846,4
86
- 1
1,1
32,7
15
101
Tab
le A
13 A
nnual
cas
h f
low
and n
et p
rese
nt
val
ue
for
case
1 o
f O
PT
pla
nt
wit
h p
erv
apora
tion t
echn
olo
gy (
conti
nues
)
Yea
rs
Rev
enu
e
Exp
end
itu
re
Inco
me
bef
ore
tax
T
ax
In
com
e aft
er t
ax
C
ash
flo
w
17
3
,144,1
93
2
,107,8
80
1
,036,3
13
1
73,4
66
862,8
47
- 1
0,2
69,8
68
18
3
,175,6
35
2
,118,6
67
1
,056,9
68
1
77,5
98
879,3
71
- 9
,390,4
97
19
3
,207,3
91
2
,129,5
61
1
,077,8
30
1
81,7
70
896,0
60
- 8
,494,4
37
20
3
,239,4
65
2
,140,5
65
1
,098,9
01
1
85,9
84
8
,236,1
43
- 258,2
94
NP
V
-1
1,8
13,9
44
102
Tab
le A
14 A
nnual
cas
h f
low
and n
et p
rese
nt
val
ue
for
case
2 o
f O
PT
pla
nt
wit
h p
ervap
ora
tion t
echn
olo
gy
Y
ears
R
even
ue
Exp
end
itu
re
Inco
me
bef
ore
tax
T
ax
In
com
e aft
er t
ax
C
ash
flo
w
0
2
2,8
24,4
46
-2
2,8
24,4
46
1
2,6
80,6
80
1,8
90,4
77
790,2
02
124,0
55
666,1
47
-22,1
58,2
99
2
2,7
07,4
86
1,8
99,0
62
808,4
24
127,7
00
680,7
24
-21,4
77,5
74
3
2,7
34,5
61
1,9
07,7
33
826,8
28
131,3
80
695,4
48
-20,7
82,1
27
4
2,7
61,9
07
1,9
16,4
91
845,4
16
135,0
98
710,3
18
-20,0
71,8
08
5
2,7
89,5
26
1,9
45,6
64
843,8
62
134,7
87
709,0
74
-19,3
62,7
34
6
2,8
17,4
21
1,9
34,2
70
883,1
52
142,6
45
740,5
07
-18,6
22,2
27
7
2,8
45,5
95
1,9
43,2
92
902,3
03
146,4
75
755,8
28
-17,8
66,4
00
8
2,8
74,0
51
1,9
52,4
06
921,6
46
150,3
44
771,3
02
-17,0
95,0
98
9
2,9
02,7
92
1,9
61,6
10
941,1
82
154,2
51
786,9
31
-16,3
08,1
67
10
2,9
31,8
20
1,9
91,2
35
940,5
85
154,1
32
786,4
53
-15,5
21,7
14
11
2,9
61,1
38
1,9
80,2
96
980,8
42
162,1
83
818,6
59
-14,7
03,0
55
12
2,9
90,7
49
1,9
89,7
79
1,0
00,9
71
166,2
09
834,7
62
-13,8
68,2
93
13
3,0
20,6
57
1,9
99,3
57
1,0
21,3
00
170,2
75
851,0
25
-13,0
17,2
68
14
3,0
50,8
64
2,0
09,0
31
1,0
41,8
33
174,3
81
867,4
51
-12,1
49,8
16
15
3,0
81,3
72
2,0
39,1
30
1,0
42,2
43
174,4
63
867,7
79
-11,2
82,0
37
16
3,1
12,1
86
2,0
28,6
69
1,0
83,5
16
182,7
18
900,7
98
-10,3
81,2
39
103
Tab
le A
15 A
nnual
cas
h f
low
and n
et p
rese
nt
val
ue
for
case
2 o
f O
PT
pla
nt
wit
h p
erv
apora
tion t
echn
olo
gy (
conti
nues
)
Yea
rs
Rev
enu
e
Exp
end
itu
re
Inco
me
bef
ore
tax
T
ax
In
com
e aft
er t
ax
C
ash
flo
w
17
3,1
43,3
08
2,0
38,6
36
1,1
04,6
71
186,9
49
917,7
22
-9,4
63,5
17
18
3,1
74,7
41
2,0
48,7
03
1,1
26,0
38
191,2
22
934,8
15
-8,5
28,7
01
19
3,2
06,4
88
2,0
58,8
70
1,1
47,6
18
195,5
38
952,0
80
-7,5
76,6
22
20
3,2
38,5
53
2,0
69,1
39
1,1
69,4
14
199,8
98
8,3
33,6
97
757,0
75
NP
V
-1
1,2
95,4
41
104
Tab
le A
16 A
nnual
cas
h f
low
and n
et p
rese
nt
val
ue
for
case
3 o
f O
PT
pla
nt
wit
h p
ervap
ora
tion t
echn
olo
gy
Y
ears
R
even
ue
Exp
end
itu
re
Inco
me
bef
ore
tax
T
ax
In
com
e aft
er t
ax
C
ash
flo
w
0
2
2,7
82,6
30
-2
2,7
82,6
30
1
2,6
80,0
02
1,8
90,3
05
789,6
97
124,0
17
665,6
81
-22,1
16,9
49
2
2,7
06,8
02
1,8
98,8
97
807,9
05
127,6
58
680,2
47
-21,4
36,7
03
3
2,7
33,8
70
1,9
07,5
75
826,2
94
131,3
36
694,9
58
-20,7
41,7
44
4
2,7
61,2
08
1,9
16,3
40
844,8
68
135,0
51
709,8
17
-20,0
31,9
27
5
2,7
88,8
20
1,9
33,6
97
855,1
23
137,1
02
718,0
22
-19,3
13,9
05
6
2,8
16,7
09
1,9
34,1
34
882,5
74
142,5
92
739,9
82
-18,5
73,9
23
7
2,8
44,8
76
1,9
43,1
65
901,7
11
146,4
19
755,2
91
-17,8
18,6
31
8
2,8
73,3
25
1,9
52,2
86
921,0
38
150,2
85
770,7
54
-17,0
47,8
78
9
2,9
02,0
58
1,9
61,4
98
940,5
60
154,1
89
786,3
71
-16,2
61,5
07
10
2,9
31,0
78
1,9
79,3
07
951,7
72
156,4
31
795,3
40
-15,4
66,1
67
11
2,9
60,3
89
1,9
80,2
00
980,1
89
162,1
15
818,0
74
-14,6
48,0
93
12
2,9
89,9
93
1,9
89,6
91
1,0
00,3
02
166,1
37
834,1
64
-13,8
13,9
28
13
3,0
19,8
93
1,9
99,2
78
1,0
20,6
15
170,2
00
850,4
15
-12,9
63,5
13
14
3,0
50,0
92
2,0
08,9
60
1,0
41,1
32
174,3
04
866,8
29
-12,0
96,6
84
15
3,0
80,5
93
2,0
27,2
43
1,0
53,3
50
176,7
47
876,6
03
-11,2
20,0
81
16
3,1
11,3
99
2,0
28,6
15
1,0
82,7
83
182,6
34
900,1
50
-10,3
19,9
32
105
Tab
le A
17 A
nnual
cas
h f
low
and n
et p
rese
nt
val
ue
for
case
3 o
f O
PT
pla
nt
wit
h p
erv
apora
tion t
echn
olo
gy (
conti
nues
)
Yea
rs
Rev
enu
e
Exp
end
itu
re
Inco
me
bef
ore
tax
T
ax
In
com
e aft
er t
ax
C
ash
flo
w
17
3,1
42,5
13
2,0
38,5
91
1,1
03,9
22
186,8
61
917,0
60
-9,4
02,8
71
18
3,1
73,9
38
2,0
48,6
66
1,1
25,2
72
191,1
31
934,1
40
-8,4
68,7
31
19
3,2
05,6
77
2,0
58,8
42
1,1
46,8
35
195,4
44
951,3
91
-7,5
17,3
40
20
3,2
37,7
34
2,0
69,1
20
1,1
68,6
14
199,8
00
8,3
19,5
03
802,1
63
NP
V
-1
1,2
47,8
48
106
Tab
le A
18 A
nnual
cas
h f
low
and n
et p
rese
nt
val
ue
for
case
4 o
f O
PT
pla
nt
wit
h p
ervap
ora
tion t
echn
olo
gy
Y
ears
R
even
ue
Exp
end
itu
re
Inco
me
bef
ore
tax
T
ax
In
com
e aft
er t
ax
C
ash
flo
w
0
2
2,7
80,4
71
-2
2,7
80,4
71
1
2,6
80,4
81
1,8
93,8
44
786,6
36
123,4
08
663,2
29
-22,1
17,2
42
2
2,7
07,2
86
1,9
02,4
73
804,8
13
127,0
43
677,7
70
-21,4
39,4
72
3
2,7
34,3
58
1,9
11,1
87
823,1
71
130,7
15
692,4
57
-20,7
47,0
15
4
2,7
61,7
02
1,9
19,9
89
841,7
13
134,4
23
707,2
90
-20,0
39,7
25
5
2,7
89,3
19
1,9
36,3
30
852,9
89
136,6
78
716,3
11
-19,3
23,4
14
6
2,8
17,2
12
1,9
37,8
57
879,3
55
141,9
51
737,4
04
-18,5
86,0
10
7
2,8
45,3
84
1,9
46,9
25
898,4
59
145,7
72
752,6
87
-17,8
33,3
23
8
2,8
73,8
38
1,9
56,0
85
917,7
54
149,6
31
768,1
23
-17,0
65,2
01
9
2,9
02,5
77
1,9
65,3
35
937,2
41
153,5
29
783,7
13
-16,2
81,4
88
10
2,9
31,6
02
1,9
82,1
30
949,4
73
155,9
75
793,4
98
-15,4
87,9
90
11
2,9
60,9
18
1,9
84,1
15
976,8
03
161,4
41
815,3
62
-14,6
72,6
28
12
2,9
90,5
28
1,9
93,6
46
996,8
82
165,4
57
831,4
25
-13,8
41,2
03
13
3,0
20,4
33
2,0
03,2
72
1,0
17,1
61
169,5
12
847,6
48
-12,9
93,5
55
14
3,0
50,6
37
2,0
12,9
95
1,0
37,6
42
173,6
09
864,0
34
-12,1
29,5
21
15
3,0
81,1
44
2,0
30,2
66
1,0
50,8
78
176,2
56
874,6
22
-11,2
54,8
99
16
3,1
11,9
55
2,0
32,7
32
1,0
79,2
22
181,9
25
897,2
98
-10,3
57,6
02
107
Tab
le A
19 A
nnual
cas
h f
low
and n
et p
rese
nt
val
ue
for
case
4 o
f O
PT
pla
nt
wit
h p
erv
apora
tion t
echn
olo
gy (
conti
nues
)
Yea
rs
Rev
enu
e
Exp
end
itu
re
Inco
me
bef
ore
tax
T
ax
In
com
e aft
er t
ax
C
ash
flo
w
17
3,1
43,0
74
2,0
42,7
50
1,1
00,3
25
186,1
45
914,1
80
-9,4
43,4
22
18
3,1
74,5
05
2,0
52,8
67
1,1
21,6
38
190,4
08
931,2
30
-8,5
12,1
92
19
3,2
06,2
50
2,0
63,0
85
1,1
43,1
65
194,7
13
948,4
52
-7,5
63,7
40
20
3,2
38,3
13
2,0
73,4
06
1,1
64,9
07
199,0
62
8,3
15,8
37
752,0
97
NP
V
-1
1,2
75,5
04
108
Tab
le A
20 A
nnual
cas
h f
low
and n
et p
rese
nt
val
ue
for
case
1 o
f O
PT
pla
nt
wit
h p
ress
ure
sw
ing a
dso
rpti
on t
echnolo
gy
Y
ears
R
even
ue
Exp
end
itu
re
Inco
me
bef
ore
tax
T
ax
In
com
e aft
er t
ax
C
ash
flo
w
0
2
3,9
87,1
65
-2
3,9
87,1
65
1
2,6
75,8
92
1,9
48,3
85
727,5
07
109,7
85
617,7
22
-23,3
69,4
43
2
2,7
02,6
51
1,9
57,2
95
745,3
55
113,3
55
632,0
01
-22,7
37,4
43
3
2,7
29,6
77
1,9
66,2
95
763,3
82
116,9
60
646,4
22
-22,0
91,0
21
4
2,7
56,9
74
1,9
75,3
85
781,5
88
120,6
01
660,9
87
-21,4
30,0
34
5
2,7
84,5
44
1,9
96,6
27
787,9
16
121,8
67
666,0
50
-20,7
63,9
84
6
2,8
12,3
89
1,9
93,8
39
818,5
50
127,9
94
690,5
56
-20,0
73,4
28
7
2,8
40,5
13
2,0
03,2
05
837,3
08
131,7
45
705,5
63
-19,3
67,8
65
8
2,8
68,9
18
2,0
12,6
64
856,2
54
135,5
34
720,7
20
-18,6
47,1
45
9
2,8
97,6
07
2,0
22,2
17
875,3
90
139,3
62
736,0
28
-17,9
11,1
16
10
2,9
26,5
83
2,0
43,9
27
882,6
56
140,8
15
741,8
41
-17,1
69,2
75
11
2,9
55,8
49
2,0
41,6
12
914,2
37
147,1
31
767,1
06
-16,4
02,1
69
12
2,9
85,4
08
2,0
51,4
55
933,9
52
151,0
74
782,8
78
-15,6
19,2
91
13
3,0
15,2
62
2,0
61,3
97
953,8
65
155,0
56
798,8
08
-14,8
20,4
83
14
3,0
45,4
14
2,0
71,4
38
973,9
76
159,0
79
814,8
98
-14,0
05,5
85
15
3,0
75,8
68
2,0
93,6
40
982,2
28
160,7
29
821,4
99
-13,1
84,0
86
16
3,1
06,6
27
2,0
91,8
22
1,0
14,8
05
167,2
45
847,5
60
-12,3
36,5
26
109
Tab
le A
21 A
nnual
cas
h f
low
and n
et p
rese
nt
val
ue
for
case
1 o
f O
PT
pla
nt
wit
h p
ress
ure
sw
ing a
dso
rpti
on t
echnolo
gy
(co
nti
nues
)
Yea
rs
Rev
enu
e
Exp
end
itu
re
Inco
me
bef
ore
tax
T
ax
In
com
e aft
er t
ax
C
ash
flo
w
17
3,1
37,6
93
2,1
02,1
67
1,0
35,5
26
171,3
89
864,1
37
-11,4
72,3
88
18
3,1
69,0
70
2,1
12,6
16
1,0
56,4
54
175,5
74
880,8
80
-10,5
91,5
09
19
3,2
00,7
61
2,1
23,1
69
1,0
77,5
92
179,8
02
897,7
90
-9,6
93,7
19
20
3,2
32,7
69
2,1
33,8
28
1,0
98,9
40
184,0
72
8,6
54,1
94
-1,0
39,5
25
NP
V
-1
2,9
18,5
08
110
Tab
le A
22 A
nnual
cas
h f
low
and n
et p
rese
nt
val
ue
for
case
2 o
f O
PT
pla
nt
wit
h p
ress
ure
sw
ing a
dso
rpti
on t
echnolo
gy
Y
ears
R
even
ue
Exp
end
itu
re
Inco
me
bef
ore
tax
T
ax
In
com
e aft
er t
ax
C
ash
flo
w
0
2
3,9
80,7
18
-2
3,9
80,7
18
1
2,6
72,7
06
1,9
43,0
16
729,6
89
110,2
31
619,4
58
-23,3
61,2
60
2
2,6
99,4
33
1,9
51,8
75
747,5
58
113,8
05
633,7
53
-22,7
27,5
07
3
2,7
26,4
27
1,9
60,8
22
765,6
05
117,4
14
648,1
91
-22,0
79,3
16
4
2,7
53,6
91
1,9
69,8
59
783,8
32
121,0
60
662,7
73
-21,4
16,5
44
5
2,7
81,2
28
1,9
91,0
47
790,1
81
122,3
29
667,8
52
-20,7
48,6
92
6
2,8
09,0
40
1,9
88,2
04
820,8
36
128,4
60
692,3
76
-20,0
56,3
16
7
2,8
37,1
31
1,9
97,5
15
839,6
16
132,2
16
707,4
00
-19,3
48,9
16
8
2,8
65,5
02
2,0
06,9
18
858,5
84
136,0
10
722,5
74
-18,6
26,3
43
9
2,8
94,1
57
2,0
16,4
16
877,7
41
139,8
41
737,9
00
-17,8
88,4
43
10
2,9
23,0
99
2,0
38,0
69
885,0
29
141,2
99
743,7
30
-17,1
44,7
13
11
2,9
52,3
30
2,0
35,6
97
916,6
33
147,6
20
769,0
13
-16,3
75,7
00
12
2,9
81,8
53
2,0
45,4
83
936,3
70
151,5
67
784,8
03
-15,5
90,8
97
13
3,0
11,6
72
2,0
55,3
66
956,3
06
155,5
54
800,7
51
-14,7
90,1
45
14
3,0
41,7
88
2,0
65,3
48
976,4
40
159,5
81
816,8
59
-13,9
73,2
86
15
3,0
72,2
06
2,0
87,4
91
984,7
15
161,2
36
823,4
79
-13,1
49,8
07
16
3,1
02,9
28
2,0
85,6
13
1,0
17,3
16
167,7
56
849,5
59
-12,3
00,2
48
111
Tab
le A
23 A
nnual
cas
h f
low
and n
et p
rese
nt
val
ue
for
case
2 o
f O
PT
pla
nt
wit
h p
ress
ure
sw
ing a
dso
rpti
on t
echnolo
gy
(co
nti
nues
)
Yea
rs
Rev
enu
e
Exp
end
itu
re
Inco
me
bef
ore
tax
T
ax
In
com
e aft
er t
ax
C
ash
flo
w
17
3,1
33,9
57
2,0
95,8
97
1,0
38,0
60
171,9
05
866,1
55
-11,4
34,0
92
18
3,1
65,2
97
2,1
06,2
85
1,0
59,0
12
176,0
96
882,9
17
-10,5
51,1
76
19
3,1
96,9
50
2,1
16,7
76
1,0
80,1
74
180,3
28
899,8
46
-9,6
51,3
30
20
3,2
28,9
20
2,1
27,3
72
1,1
01,5
47
184,6
03
8,6
54,1
90
-997,1
40
NP
V
-1
2,8
90,9
11
112
Tab
le A
24 A
nnual
cas
h f
low
and n
et p
rese
nt
val
ue
for
case
3 o
f O
PT
pla
nt
wit
h p
ress
ure
sw
ing a
dso
rpti
on t
echnolo
gy
Y
ears
R
even
ue
Exp
end
itu
re
Inco
me
bef
ore
tax
T
ax
In
com
e aft
er t
ax
C
ash
flo
w
0
2
3,9
88,2
40
-2
3,9
88,2
40
1
2,6
75,5
54
1,9
47,1
77
728,3
77
109,9
57
618,4
20
-23,3
69,8
20
2
2,7
02,3
10
1,9
56,0
76
746,2
34
113,5
29
632,7
05
-22,7
37,1
15
3
2,7
29,3
33
1,9
65,0
64
764,2
69
117,1
36
647,1
34
-22,0
89,9
81
4
2,7
56,6
26
1,9
74,1
41
782,4
85
120,7
79
661,7
06
-21,4
28,2
75
5
2,7
84,1
93
1,9
95,3
70
788,8
23
122,0
46
666,7
76
-20,7
61,4
99
6
2,8
12,0
35
1,9
92,5
69
819,4
65
128,1
75
691,2
90
-20,0
70,2
09
7
2,8
40,1
55
2,0
01,9
22
838,2
33
131,9
29
706,3
05
-19,3
63,9
04
8
2,8
68,5
56
2,0
11,3
68
857,1
89
135,7
20
721,4
69
-18,6
42,4
35
9
2,8
97,2
42
2,0
20,9
08
876,3
34
139,5
49
736,7
85
-17,9
05,6
50
10
2,9
26,2
14
2,0
42,6
05
883,6
10
141,0
04
742,6
06
-17,1
63,0
44
11
2,9
55,4
77
2,0
40,2
76
915,2
00
147,3
22
767,8
78
-16,3
95,1
66
12
2,9
85,0
31
2,0
50,1
06
934,9
25
151,2
67
783,6
58
-15,6
11,5
07
13
3,0
14,8
82
2,0
60,0
34
954,8
48
155,2
52
799,5
96
-14,8
11,9
11
14
3,0
45,0
31
2,0
70,0
61
974,9
70
159,2
76
815,6
94
-13,9
96,2
17
15
3,0
75,4
81
2,0
92,2
49
983,2
32
160,9
28
822,3
03
-13,1
73,9
14
16
3,1
06,2
36
2,0
90,4
17
1,0
15,8
19
167,4
46
848,3
73
-12,3
25,5
41
113
Tab
le A
25 A
nnual
cas
h f
low
and n
et p
rese
nt
val
ue
for
case
3 o
f O
PT
pla
nt
wit
h p
ress
ure
sw
ing a
dso
rpti
on t
echnolo
gy
(co
nti
nues
)
Yea
rs
Rev
enu
e
Exp
end
itu
re
Inco
me
bef
ore
tax
T
ax
In
com
e aft
er t
ax
C
ash
flo
w
17
3,1
37,2
98
2,1
00,7
48
1,0
36,5
50
171,5
92
864,9
58
-11,4
60,5
83
18
3,1
68,6
71
2,1
11,1
82
1,0
57,4
89
175,7
80
881,7
09
-10,5
78,8
74
19
3,2
00,3
58
2,1
21,7
21
1,0
78,6
37
180,0
09
898,6
27
-9,6
80,2
46
20
3,2
32,3
61
2,1
32,3
65
1,0
99,9
96
184,2
81
8,6
55,3
87
-1,0
24,8
59
NP
V
-1
2,9
10,6
45
114
Tab
le A
26 A
nnual
cas
h f
low
and n
et p
rese
nt
val
ue
for
case
4 o
f O
PT
pla
nt
wit
h p
ress
ure
sw
ing a
dso
rpti
on t
echnolo
gy
Y
ears
R
even
ue
Exp
end
itu
re
Inco
me
bef
ore
tax
T
ax
In
com
e aft
er t
ax
C
ash
flo
w
0
2
4,1
60,7
07
-2
4,1
60,7
07
1
2,6
77,2
59
1,9
47,6
55
729,6
04
109,9
46
619,6
58
-23,5
41,0
49
2
2,7
04,0
32
1,9
56,5
21
747,5
11
113,5
27
633,9
83
-22,9
07,0
66
3
2,7
31,0
72
1,9
65,4
76
765,5
96
117,1
44
648,4
52
-22,2
58,6
14
4
2,7
58,3
83
1,9
74,5
20
783,8
63
120,7
98
663,0
65
-21,5
95,5
48
5
2,7
85,9
66
1,9
95,7
15
790,2
52
122,0
75
668,1
76
-20,9
27,3
72
6
2,8
13,8
26
1,9
92,8
80
820,9
46
128,2
14
692,7
32
-20,2
34,6
40
7
2,8
41,9
64
2,0
02,1
98
839,7
67
131,9
78
707,7
88
-19,5
26,8
52
8
2,8
70,3
84
2,0
11,6
09
858,7
75
135,7
80
722,9
95
-18,8
03,8
57
9
2,8
99,0
88
2,0
21,1
14
877,9
73
139,6
20
738,3
54
-18,0
65,5
04
10
2,9
28,0
79
2,0
42,7
76
885,3
03
141,0
86
744,2
17
-17,3
21,2
86
11
2,9
57,3
60
2,0
40,4
11
916,9
48
147,4
15
769,5
34
-16,5
51,7
53
12
2,9
86,9
33
2,0
50,2
05
936,7
29
151,3
71
785,3
58
-15,7
66,3
95
13
3,0
16,8
02
2,0
60,0
96
956,7
07
155,3
66
801,3
40
-14,9
65,0
55
14
3,0
46,9
71
2,0
70,0
86
976,8
84
159,4
02
817,4
82
-14,1
47,5
73
15
3,0
77,4
40
2,0
92,2
37
985,2
03
161,0
66
824,1
37
-13,3
23,4
35
16
3,1
08,2
15
2,0
90,3
67
1,0
17,8
47
167,5
95
850,2
53
-12,4
73,1
82
115
Tab
le A
27 A
nnual
cas
h f
low
and n
et p
rese
nt
val
ue
for
case
4 o
f O
PT
pla
nt
wit
h p
ress
ure
sw
ing a
dso
rpti
on t
echnolo
gy
(co
nti
nues
)
Yea
rs
Rev
enu
e
Exp
end
itu
re
Inco
me
bef
ore
tax
T
ax
In
com
e aft
er t
ax
C
ash
flo
w
17
3,1
39,2
97
2,1
00,6
60
1,0
38,6
37
171,7
52
866,8
84
-11,6
06,2
98
18
3,1
70,6
90
2,1
11,0
56
1,0
59,6
34
175,9
52
883,6
82
-10,7
22,6
16
19
3,2
02,3
97
2,1
21,5
56
1,0
80,8
41
180,1
93
900,6
48
-9,8
21,9
69
20
3,2
34,4
21
2,1
32,1
61
1,1
02,2
60
184,4
77
8,7
13,1
00
-1,1
08,8
69
NP
V
-1
3,0
46,7
71
116
Tab
le A
28 A
nnual
cas
h f
low
and n
et p
rese
nt
val
ue
for
EF
B p
lant
wit
h p
erv
apora
tion t
echnolo
gy
Y
ears
R
even
ue
Exp
end
itu
re
Inco
me
bef
ore
tax
T
ax
In
com
e aft
er t
ax
C
ash
flo
w
0
2
9,5
68,8
37
-2
9,5
68,8
37
1
3,2
62,2
30
2,2
47,6
35
1,0
14,5
95
158,8
92
855,7
04
-28,7
13,1
34
2
3,2
94,8
52
2,2
57,5
18
1,0
37,3
34
163,4
39
873,8
94
-27,8
39,2
39
3
3,3
27,8
01
2,2
67,5
01
1,0
60,3
00
168,0
32
892,2
67
-26,9
46,9
72
4
3,3
61,0
79
2,2
77,5
83
1,0
83,4
95
172,6
72
910,8
24
-26,0
36,1
48
5
3,3
94,6
89
2,2
98,6
28
1,0
96,0
61
175,1
85
920,8
76
-25,1
15,2
72
6
3,4
28,6
36
2,2
98,0
52
1,1
30,5
85
182,0
89
948,4
95
-24,1
66,7
77
7
3,4
62,9
23
2,3
08,4
40
1,1
54,4
83
186,8
69
967,6
14
-23,1
99,1
63
8
3,4
97,5
52
2,3
18,9
31
1,1
78,6
20
191,6
97
986,9
24
-22,2
12,2
39
9
3,5
32,5
27
2,3
29,5
28
1,2
02,9
99
196,5
72
1,0
06,4
27
-21,2
05,8
12
10
3,5
67,8
53
2,3
51,0
93
1,2
16,7
60
199,3
25
1,0
17,4
36
-20,1
88,3
77
11
3,6
03,5
31
2,3
51,0
41
1,2
52,4
91
206,4
71
1,0
46,0
20
-19,1
42,3
57
12
3,6
39,5
67
2,3
61,9
58
1,2
77,6
08
211,4
94
1,0
66,1
14
-18,0
76,2
43
13
3,6
75,9
62
2,3
72,9
85
1,3
02,9
77
216,5
68
1,0
86,4
09
-16,9
89,8
34
14
3,7
12,7
22
2,3
84,1
23
1,3
28,5
99
221,6
92
1,1
06,9
07
-15,8
82,9
27
15
3,7
49,8
49
2,4
06,2
33
1,3
43,6
16
224,6
96
1,1
18,9
20
-14,7
64,0
07
16
3,7
87,3
48
2,4
06,7
32
1,3
80,6
15
232,0
96
1,1
48,5
20
-13,6
15,4
87
117
Tab
le A
29 A
nnual
cas
h f
low
and n
et p
rese
nt
val
ue
for
EF
B p
lant
wit
h p
erv
apora
tion t
echnolo
gy (
conti
nues
)
Yea
rs
Rev
enu
e
Exp
end
itu
re
Inco
me
bef
ore
tax
T
ax
In
com
e aft
er t
ax
C
ash
flo
w
17
3,8
25,2
21
2,4
18,2
07
1,4
07,0
14
237,3
75
1,1
69,6
38
-12,4
45,8
49
18
3,8
63,4
73
2,4
29,7
97
1,4
33,6
77
242,7
08
1,1
90,9
69
-11,2
54,8
80
19
3,9
02,1
08
2,4
41,5
02
1,4
60,6
06
248,0
94
1,2
12,5
12
-10,0
42,3
68
20
3,9
41,1
29
2,4
53,3
25
1,4
87,8
05
253,5
33
10,7
74,4
92
732,1
24
NP
V
-1
4,7
62,1
53
118
Tab
le A
30 A
nnual
cas
h f
low
and n
et p
rese
nt
val
ue
for
100:0
of
EF
B:O
PT
plan
t
Yea
rs
Rev
enu
e
Exp
end
itu
re
Inco
me
bef
ore
tax
T
ax
In
com
e aft
er t
ax
C
ash
flo
w
0
2
9,8
37,4
77
-2
9,8
37,4
77
1
3,2
63,5
82
2,2
53,4
24
1,0
10,1
57
157,6
04
852,5
53
-28,9
84,9
24
2
3,2
96,2
17
2,2
63,3
08
1,0
32,9
09
162,1
54
870,7
55
-28,1
14,1
69
3
3,3
29,1
79
2,2
73,2
91
1,0
55,8
89
166,7
50
889,1
39
-27,2
25,0
31
4
3,3
62,4
71
2,2
83,3
73
1,0
79,0
98
171,3
92
907,7
06
-26,3
17,3
25
5
3,3
96,0
96
2,3
04,4
11
1,0
91,6
85
173,9
09
917,7
75
-25,3
99,5
49
6
3,4
30,0
57
2,3
03,8
41
1,1
26,2
16
180,8
16
945,4
00
-24,4
54,1
50
7
3,4
64,3
58
2,3
14,2
29
1,1
50,1
28
185,5
98
964,5
30
-23,4
89,6
20
8
3,4
99,0
01
2,3
24,7
21
1,1
74,2
80
190,4
29
983,8
51
-22,5
05,7
68
9
3,5
33,9
91
2,3
35,3
18
1,1
98,6
73
195,3
07
1,0
03,3
66
-21,5
02,4
02
10
3,5
69,3
31
2,3
56,8
76
1,2
12,4
56
198,0
64
1,0
14,3
92
-20,4
88,0
10
11
3,6
05,0
24
2,3
56,8
30
1,2
48,1
94
205,2
11
1,0
42,9
83
-19,4
45,0
28
12
3,6
41,0
75
2,3
67,7
48
1,2
73,3
26
210,2
38
1,0
63,0
89
-18,3
81,9
39
13
3,6
77,4
85
2,3
78,7
75
1,2
98,7
10
215,3
15
1,0
83,3
96
-17,2
98,5
43
14
3,7
14,2
60
2,3
89,9
12
1,3
24,3
48
220,4
42
1,1
03,9
06
-16,1
94,6
38
15
3,7
51,4
03
2,4
12,0
16
1,3
39,3
87
223,4
50
1,1
15,9
37
-15,0
78,7
01
16
3,7
88,9
17
2,4
12,5
22
1,3
76,3
95
230,8
51
1,1
45,5
43
-13,9
33,1
58
119
Tab
le A
31 A
nnual
cas
h f
low
and n
et p
rese
nt
val
ue
for
100:0
of
EF
B:O
PT
plan
t (c
onti
nues
)
Yea
rs
Rev
enu
e
Exp
end
itu
re
Inco
me
bef
ore
tax
T
ax
In
com
e aft
er t
ax
C
ash
flo
w
17
3,8
26,8
06
2,4
23,9
97
1,4
02,8
09
236,1
34
1,1
66,6
75
-12,7
66,4
83
18
3,8
65,0
74
2,4
35,5
86
1,4
29,4
88
241,4
70
1,1
88,0
18
-11,5
78,4
65
19
3,9
03,7
25
2,4
47,2
92
1,4
56,4
33
246,8
59
1,2
09,5
74
-10,3
68,8
91
20
3,9
42,7
62
2,4
59,1
14
1,4
83,6
48
252,3
02
10,8
58,2
42
489,3
50
NP
V
-1
5,0
38,3
65
120
Tab
le A
32 A
nnual
cas
h f
low
and n
et p
rese
nt
val
ue
for
80:2
0 o
f E
FB
:OP
T pl
ant
Yea
rs
Rev
enu
e
Exp
end
itu
re
Inco
me
bef
ore
tax
T
ax
In
com
e aft
er t
ax
C
ash
flo
w
0
2
9,8
33,6
85
-2
9,8
33,6
85
1
3,1
44,1
03
2,2
36,3
70
907,7
33
137,1
25
770,6
09
-29,0
63,0
77
2
3,1
75,5
44
2,2
46,2
35
929,3
10
141,4
40
787,8
70
-28,2
75,2
07
3
3,2
07,3
00
2,2
56,1
98
951,1
02
145,7
99
805,3
03
-27,4
69,9
04
4
3,2
39,3
73
2,2
66,2
61
973,1
11
150,2
00
822,9
11
-26,6
46,9
93
5
3,2
71,7
67
2,2
86,5
21
985,2
45
152,6
27
832,6
18
-25,8
14,3
75
6
3,3
04,4
84
2,2
86,6
91
1,0
17,7
94
159,1
37
858,6
57
-24,9
55,7
18
7
3,3
37,5
29
2,2
97,0
59
1,0
40,4
70
163,6
72
876,7
98
-24,0
78,9
20
8
3,3
70,9
04
2,3
07,5
30
1,0
63,3
74
168,2
53
895,1
21
-23,1
83,7
99
9
3,4
04,6
13
2,3
18,1
07
1,0
86,5
06
172,8
79
913,6
27
-22,2
70,1
72
10
3,4
38,6
60
2,3
38,8
85
1,0
99,7
74
175,5
33
924,2
41
-21,3
45,9
31
11
3,4
73,0
46
2,3
39,5
78
1,1
33,4
68
182,2
72
951,1
96
-20,3
94,7
35
12
3,5
07,7
77
2,3
50,4
75
1,1
57,3
01
187,0
38
970,2
63
-19,4
24,4
72
13
3,5
42,8
54
2,3
61,4
81
1,1
81,3
73
191,8
53
989,5
20
-18,4
34,9
52
14
3,5
78,2
83
2,3
72,5
97
1,2
05,6
86
196,7
15
1,0
08,9
70
-17,4
25,9
81
15
3,6
14,0
66
2,3
93,9
20
1,2
20,1
45
199,6
07
1,0
20,5
38
-16,4
05,4
43
16
3,6
50,2
06
2,3
95,1
64
1,2
55,0
43
206,5
87
1,0
48,4
56
-15,3
56,9
87
121
Tab
le A
33 A
nnual
cas
h f
low
and n
et p
rese
nt
val
ue
for
80:2
0 o
f E
FB
:OP
T pl
ant
(conti
nues
)
Yea
rs
Rev
enu
e
Exp
end
itu
re
Inco
me
bef
ore
tax
T
ax
In
com
e aft
er t
ax
C
ash
flo
w
17
3,6
86,7
08
2,4
06,6
16
1,2
80,0
92
211,5
97
1,0
68,4
95
-14,2
88,4
92
18
3,7
23,5
76
2,4
18,1
84
1,3
05,3
92
216,6
57
1,0
88,7
35
-13,1
99,7
56
19
3,7
60,8
11
2,4
29,8
67
1,3
30,9
45
221,7
67
1,1
09,1
77
-12,0
90,5
79
20
3,7
98,4
19
2,4
41,6
67
1,3
56,7
53
226,9
29
10,7
55,4
96
-1,3
35,0
83
NP
V
-1
6,0
80,2
37
122
Tab
le A
34 A
nnual
cas
h f
low
and n
et p
rese
nt
val
ue
for
50:5
0 o
f E
FB
:OP
T pl
ant
Yea
rs
Rev
enu
e
Exp
end
itu
re
Inco
me
bef
ore
tax
T
ax
In
com
e aft
er t
ax
C
ash
flo
w
0
2
9,7
47,7
37
-2
9,7
47,7
37
1
2,9
79,6
66
2,1
75,7
37
803,9
29
116,4
92
687,4
37
-29,0
60,3
00
2
3,0
09,4
63
2,1
85,2
55
824,2
08
120,5
48
703,6
60
-28,3
56,6
40
3
3,0
39,5
57
2,1
94,8
68
844,6
89
124,6
44
720,0
45
-27,6
36,5
95
4
3,0
69,9
53
2,2
04,5
77
865,3
76
128,7
81
736,5
94
-26,9
00,0
01
5
3,1
00,6
52
2,2
24,0
34
876,6
18
131,0
30
745,5
88
-26,1
54,4
13
6
3,1
31,6
59
2,2
24,2
88
907,3
71
137,1
80
770,1
91
-25,3
84,2
22
7
3,1
62,9
75
2,2
34,2
91
928,6
84
141,4
43
787,2
41
-24,5
96,9
81
8
3,1
94,6
05
2,2
44,3
95
950,2
11
145,7
48
804,4
62
-23,7
92,5
19
9
3,2
26,5
51
2,2
54,5
99
971,9
52
150,0
97
821,8
56
-22,9
70,6
63
10
3,2
58,8
17
2,2
74,5
56
984,2
60
152,5
58
831,7
02
-22,1
38,9
61
11
3,2
91,4
05
2,2
75,3
15
1,0
16,0
90
158,9
24
857,1
66
-21,2
81,7
95
12
3,3
24,3
19
2,2
85,8
29
1,0
38,4
90
163,4
04
875,0
86
-20,4
06,7
09
13
3,3
57,5
62
2,2
96,4
47
1,0
61,1
15
167,9
29
893,1
86
-19,5
13,5
24
14
3,3
91,1
38
2,3
07,1
72
1,0
83,9
65
172,4
99
911,4
66
-18,6
02,0
58
15
3,4
25,0
49
2,3
27,6
56
1,0
97,3
94
175,1
85
922,2
09
-17,6
79,8
49
16
3,4
59,3
00
2,3
28,9
45
1,1
30,3
54
181,7
77
948,5
77
-16,7
31,2
72
123
Tab
le A
35 A
nnual
cas
h f
low
and n
et p
rese
nt
val
ue
for
50:5
0 o
f E
FB
:OP
T pl
ant
(conti
nues
)
Yea
rs
Rev
enu
e
Exp
end
itu
re
Inco
me
bef
ore
tax
T
ax
In
com
e aft
er t
ax
C
ash
flo
w
17
3,4
93,8
93
2,3
39,9
95
1,1
53,8
97
186,4
86
967,4
12
-15,7
63,8
60
18
3,5
28,8
31
2,3
51,1
56
1,1
77,6
76
191,2
41
986,4
35
-14,7
77,4
26
19
3,5
64,1
20
2,3
62,4
28
1,2
01,6
92
196,0
45
1,0
05,6
48
-13,7
71,7
78
20
3,5
99,7
61
2,3
73,8
12
1,2
25,9
49
200,8
96
10,6
22,9
94
-3,1
48,7
84
NP
V
-1
7,0
72,5
50
124
Tab
le A
36 A
nnual
cas
h f
low
and n
et p
rese
nt
val
ue
for
20:8
0 o
f E
FB
:OP
T pl
ant
Yea
rs
Rev
enu
e
Exp
end
itu
re
Inco
me
bef
ore
tax
T
ax
In
com
e aft
er t
ax
C
ash
flo
w
0
2
9,8
14,1
85
-2
9,8
14,1
85
1
2,8
01,2
82
2,1
15,6
39
685,6
43
92,7
36
592,9
07
-29,2
21,2
77
2
2,8
29,2
95
2,1
24,7
95
704,5
00
96,5
07
607,9
92
-28,6
13,2
85
3
2,8
57,5
88
2,1
34,0
44
723,5
44
100,3
16
623,2
28
-27,9
90,0
57
4
2,8
86,1
64
2,1
43,3
84
742,7
80
104,1
63
638,6
16
-27,3
51,4
40
5
2,9
15,0
25
2,1
61,9
70
753,0
55
106,2
18
646,8
37
-26,7
04,6
03
6
2,9
44,1
76
2,1
62,3
47
781,8
29
111,9
73
669,8
56
-26,0
34,7
47
7
2,9
73,6
17
2,1
71,9
70
801,6
47
115,9
37
685,7
10
-25,3
49,0
37
8
3,0
03,3
54
2,1
81,6
90
821,6
63
119,9
40
701,7
23
-24,6
47,3
14
9
3,0
33,3
87
2,1
91,5
07
841,8
80
123,9
83
717,8
97
-23,9
29,4
17
10
3,0
63,7
21
2,2
10,5
75
853,1
46
126,2
37
726,9
10
-23,2
02,5
07
11
3,0
94,3
58
2,2
11,4
37
882,9
21
132,1
91
750,7
30
-22,4
51,7
77
12
3,1
25,3
02
2,2
21,5
52
903,7
50
136,3
57
767,3
93
-21,6
84,3
85
13
3,1
56,5
55
2,2
31,7
67
924,7
87
140,5
65
784,2
23
-20,9
00,1
62
14
3,1
88,1
20
2,2
42,0
85
946,0
35
144,8
14
801,2
21
-20,0
98,9
41
15
3,2
20,0
02
2,2
61,6
58
958,3
43
147,2
76
811,0
67
-19,2
87,8
74
16
3,2
52,2
02
2,2
63,0
32
989,1
70
153,4
41
835,7
29
-18,4
52,1
45
125
Tab
le A
37 A
nnual
cas
h f
low
and n
et p
rese
nt
val
ue
for
20:8
0 o
f E
FB
:OP
T pl
ant
(conti
nues
)
Yea
rs
Rev
enu
e
Exp
end
itu
re
Inco
me
bef
ore
tax
T
ax
In
com
e aft
er t
ax
C
ash
flo
w
17
3,2
84,7
24
2,2
73,6
62
1,0
11,0
61
157,8
20
853,2
42
-17,5
98,9
03
18
3,3
17,5
71
2,2
84,3
99
1,0
33,1
72
162,2
42
870,9
30
-16,7
27,9
73
19
3,3
50,7
46
2,2
95,2
43
1,0
55,5
03
166,7
08
888,7
95
-15,8
39,1
77
20
3,3
84,2
54
2,3
06,1
96
1,0
78,0
58
171,2
19
10,5
26,2
20
-5,3
12,9
57
NP
V
-1
8,3
43,3
49
126
Tab
le A
38 A
nnual
cas
h f
low
and n
et p
rese
nt
val
ue
for
0:1
00
of
EF
B:O
PT
plan
t
Yea
rs
Rev
enu
e
Exp
end
itu
re
Inco
me
bef
ore
tax
T
ax
In
com
e aft
er t
ax
C
ash
flo
w
0
2
9,8
50,5
66
-2
9,8
50,5
66
1
2,6
78,6
72
2,0
59,0
35
619,6
37
79,4
80
540,1
57
-29,3
10,4
10
2
2,7
05,4
59
2,0
67,7
94
637,6
65
83,0
86
554,5
79
-28,7
55,8
31
3
2,7
32,5
13
2,0
76,6
41
655,8
73
86,7
28
569,1
45
-28,1
86,6
86
4
2,7
59,8
38
2,0
85,5
75
674,2
63
90,4
06
583,8
57
-27,6
02,8
29
5
2,7
87,4
37
2,1
03,0
71
684,3
66
92,4
26
591,9
40
-27,0
10,8
89
6
2,8
15,3
11
2,1
03,7
14
711,5
97
97,8
72
613,7
24
-26,3
97,1
64
7
2,8
43,4
64
2,1
12,9
20
730,5
44
101,6
62
628,8
82
-25,7
68,2
82
8
2,8
71,8
99
2,1
22,2
18
749,6
81
105,4
89
644,1
92
-25,1
24,0
90
9
2,9
00,6
18
2,1
31,6
09
769,0
09
109,3
55
659,6
54
-24,4
64,4
36
10
2,9
29,6
24
2,1
49,5
64
780,0
60
111,5
65
668,4
95
-23,7
95,9
41
11
2,9
58,9
20
2,1
50,6
73
808,2
48
117,2
03
691,0
45
-23,1
04,8
96
12
2,9
88,5
10
2,1
60,3
48
828,1
62
121,1
85
706,9
76
-22,3
97,9
20
13
3,0
18,3
95
2,1
70,1
20
848,2
75
125,2
08
723,0
67
-21,6
74,8
53
14
3,0
48,5
79
2,1
79,9
90
868,5
89
129,2
71
739,3
18
-20,9
35,5
35
15
3,0
79,0
64
2,1
98,4
30
880,6
35
131,6
80
748,9
55
-20,1
86,5
80
16
3,1
09,8
55
2,2
00,0
27
909,8
29
137,5
19
772,3
10
-19,4
14,2
71
127
Tab
le A
39 A
nnual
cas
h f
low
and n
et p
rese
nt
val
ue
for
0:1
00 o
f E
FB
:OP
T pl
ant
(conti
nues
)
Yea
rs
Rev
enu
e
Exp
end
itu
re
Inco
me
bef
ore
tax
T
ax
In
com
e aft
er t
ax
C
ash
flo
w
17
3,1
40,9
54
2,2
10,1
95
930,7
58
141,7
05
789,0
54
-18,6
25,2
17
18
3,1
72,3
63
2,2
20,4
66
951,8
97
145,9
33
805,9
65
-17,8
19,2
52
19
3,2
04,0
87
2,2
30,8
39
973,2
48
150,2
03
823,0
45
-16,9
96,2
07
20
3,2
36,1
28
2,2
41,3
16
994,8
12
154,5
15
10,4
71,4
15
-6,5
24,7
92
NP
V
-1
9,0
53,9
29
128
Tab
le A
40 A
nnual
cas
h f
low
and n
et p
rese
nt
val
ue
for
dis
till
atio
n 4
wt%
to 8
0 w
t%
Yea
rs
Rev
enu
e
Exp
end
itu
re
Inco
me
bef
ore
tax
T
ax
In
com
e aft
er t
ax
C
ash
flo
w
0
2
2,7
52,0
93
2
2,7
52,0
93
1
3,5
15,3
16
1,8
96,6
64
1,6
18,6
53
289,8
53
1,3
28,8
00
-21,4
23,2
94
2
3,5
50,4
69
1,9
05,3
26
1,6
45,1
43
295,1
51
1,3
49,9
92
-20,0
73,3
02
3
3,5
85,9
74
1,9
14,0
76
1,6
71,8
99
300,5
02
1,3
71,3
96
-18,7
01,9
05
4
3,6
21,8
34
1,9
22,9
12
1,6
98,9
22
305,9
07
1,3
93,0
15
-17,3
08,8
91
5
3,6
58,0
52
1,9
55,8
16
1,7
02,2
36
306,5
70
1,3
95,6
66
-15,9
13,2
25
6
3,6
94,6
33
1,9
40,8
52
1,7
53,7
81
316,8
79
1,4
36,9
02
-14,4
76,3
22
7
3,7
31,5
79
1,9
49,9
56
1,7
81,6
23
322,4
47
1,4
59,1
76
-13,0
17,1
47
8
3,7
68,8
95
1,9
59,1
52
1,8
09,7
43
328,0
71
1,4
81,6
72
-11,5
35,4
75
9
3,8
06,5
84
1,9
68,4
39
1,8
38,1
44
333,7
51
1,5
04,3
93
-10,0
31,0
82
10
3,8
44,6
50
2,0
01,7
99
1,8
42,8
51
334,6
93
1,5
08,1
58
-8,5
22,9
23
11
3,8
83,0
96
1,9
87,2
94
1,8
95,8
02
345,2
83
1,5
50,5
19
-6,9
72,4
04
12
3,9
21,9
27
1,9
96,8
63
1,9
25,0
64
351,1
35
1,5
73,9
29
-5,3
98,4
75
13
3,9
61,1
46
2,0
06,5
28
1,9
54,6
19
357,0
46
1,5
97,5
73
-3,8
00,9
03
14
4,0
00,7
58
2,0
16,2
89
1,9
84,4
69
363,0
16
1,6
21,4
53
-2,1
79,4
50
15
4,0
40,7
65
2,0
50,1
26
1,9
90,6
39
364,2
50
1,6
26,3
89
-553,0
61
16
4,0
81,1
73
2,0
36,1
05
2,0
45,0
68
375,1
36
1,6
69,9
32
1,1
16,8
70
129
Tab
le A
41 A
nnual
cas
h f
low
and n
et p
rese
nt
val
ue
for
dis
till
atio
n 4
wt%
to 8
0 w
t% (
conti
nues
)
Yea
rs
Rev
enu
e
Exp
end
itu
re
Inco
me
bef
ore
tax
T
ax
In
com
e aft
er t
ax
C
ash
flo
w
17
4,1
21,9
85
2,0
46,1
62
2,0
75,8
23
381,2
87
1,6
94,5
36
2,8
11,4
06
18
4,1
63,2
05
2,0
56,3
20
2,1
06,8
85
387,5
00
1,7
19,3
85
4,5
30,7
91
19
4,2
04,8
37
2,0
66,5
79
2,1
38,2
58
393,7
74
1,7
44,4
84
6,2
75,2
75
20
4,2
46,8
85
2,0
76,9
41
2,1
69,9
44
400,1
11
9,1
10,6
69
15,3
85,9
44
NP
V
-2
,868,4
46
130
Tab
le A
42 A
nnual
cas
h f
low
and n
et p
rese
nt
val
ue
for
dis
till
atio
n 6
wt%
to 8
0 w
t%
Yea
rs
Rev
enu
e
Exp
end
itu
re
Inco
me
bef
ore
tax
T
ax
In
com
e aft
er t
ax
C
ash
flo
w
0
2
2,8
45,1
94
2
2,8
45,1
94
1
5,2
71,3
88
1,9
10,3
11
3,3
61,0
77
638,1
99
2,7
22,8
78
-20,1
22,3
16
2
5,3
24,1
02
1,9
19,0
90
3,4
05,0
12
646,9
86
2,7
58,0
26
-17,3
64,2
91
3
5,3
77,3
43
1,9
27,9
56
3,4
49,3
86
655,8
61
2,7
93,5
25
-14,5
70,7
65
4
5,4
31,1
16
1,9
36,9
12
3,4
94,2
05
664,8
25
2,8
29,3
80
-11,7
41,3
86
5
5,4
85,4
27
1,9
81,8
27
3,5
03,6
00
666,7
04
2,8
36,8
96
-8,9
04,4
90
6
5,5
40,2
82
1,9
55,0
92
3,5
85,1
90
683,0
22
2,9
02,1
68
-6,0
02,3
22
7
5,5
95,6
84
1,9
64,3
18
3,6
31,3
66
692,2
57
2,9
39,1
09
-3,0
63,2
13
8
5,6
51,6
41
1,9
73,6
37
3,6
78,0
04
701,5
85
2,9
76,4
19
-86,7
93
9
5,7
08,1
58
1,9
83,0
49
3,7
25,1
08
711,0
06
3,0
14,1
03
2,9
27,3
09
10
5,7
65,2
39
2,0
28,4
27
3,7
36,8
13
713,3
46
3,0
23,4
66
5,9
50,7
76
11
5,8
22,8
92
2,0
02,1
57
3,8
20,7
35
730,1
31
3,0
90,6
04
9,0
41,3
80
12
5,8
81,1
21
2,0
11,8
54
3,8
69,2
66
739,8
37
3,1
29,4
29
12,1
70,8
09
13
5,9
39,9
32
2,0
21,6
48
3,9
18,2
83
749,6
41
3,1
68,6
43
15,3
39,4
52
14
5,9
99,3
31
2,0
31,5
41
3,9
67,7
91
759,5
42
3,2
08,2
49
18,5
47,7
00
15
6,0
59,3
24
2,0
77,4
03
3,9
81,9
22
762,3
68
3,2
19,5
53
21,7
67,2
54
16
6,1
19,9
18
2,0
51,6
23
4,0
68,2
95
779,6
43
3,2
88,6
52
25,0
55,9
06
131
Tab
le A
43 A
nnual
cas
h f
low
and n
et p
rese
nt
val
ue
for
dis
till
atio
n 6
wt%
to 8
0%
(co
nti
nues
)
Yea
rs
Rev
enu
e
Exp
end
itu
re
Inco
me
bef
ore
tax
T
ax
In
com
e aft
er t
ax
C
ash
flo
w
17
6,1
81,1
17
2,0
61,8
15
4,1
19,3
02
789,8
44
3,3
29,4
58
28,3
85,3
63
18
6,2
42,9
28
2,0
72,1
09
4,1
70,8
19
800,1
48
3,3
70,6
72
31,7
56,0
35
19
6,3
05,3
57
2,0
82,5
05
4,2
22,8
52
810,5
54
3,4
12,2
98
35,1
68,3
33
20
6,3
68,4
11
2,0
93,0
06
4,2
75,4
05
821,0
65
10,8
25,2
14
45,9
93,5
47
NP
V
1
4,6
38,3
50
132
Tab
le A
44 A
nnual
cas
h f
low
and n
et p
rese
nt
val
ue
for
dis
till
atio
n 8
wt%
to 8
0 w
t%
Yea
rs
Rev
enu
e
Exp
end
itu
re
Inco
me
bef
ore
tax
T
ax
In
com
e aft
er t
ax
C
ash
flo
w
0
2
2,8
66,7
73
2
2,8
66,7
73
1
7,0
00,9
51
1,9
19,7
87
5,0
81,1
64
982,1
09
4,0
99,0
55
-18,8
18,7
27
2
7,0
70,9
60
1,9
28,6
45
5,1
42,3
15
994,3
39
4,1
47,9
76
-14,6
70,7
51
3
7,1
41,6
70
1,9
37,5
91
5,2
04,0
79
1,0
06,6
92
4,1
97,3
87
-10,4
73,3
64
4
7,2
13,0
86
1,9
46,6
27
5,2
66,4
59
1,0
19,1
68
4,2
47,2
92
-6,2
26,0
72
5
7,2
85,2
17
2,0
04,2
40
5,2
80,9
78
1,0
22,0
71
4,2
58,9
06
-1,9
67,1
66
6
7,3
58,0
69
1,9
64,9
71
5,3
93,0
99
1,0
44,4
96
4,3
48,6
03
2,3
81,4
37
7
7,4
31,6
50
1,9
74,2
80
5,4
57,3
70
1,0
57,3
50
4,4
00,0
20
6,7
81,4
57
8
7,5
05,9
67
1,9
83,6
83
5,5
22,2
84
1,0
70,3
33
4,4
51,9
51
11,2
33,4
08
9
7,5
81,0
26
1,9
93,1
80
5,5
87,8
47
1,0
83,4
45
4,5
04,4
01
15,7
37,8
10
10
7,6
56,8
37
2,0
51,2
58
5,6
05,5
79
1,0
86,9
92
4,5
18,5
87
20,2
56,3
97
11
7,7
33,4
05
2,0
12,4
59
5,7
20,9
46
1,1
10,0
65
4,6
10,8
81
24,8
67,2
78
12
7,8
10,7
39
2,0
22,2
44
5,7
88,4
95
1,1
23,5
75
4,6
64,9
21
29,5
32,1
98
13
7,8
88,8
46
2,0
32,1
26
5,8
56,7
20
1,1
37,2
20
4,7
19,5
01
34,2
51,6
99
14
7,9
67,7
35
2,0
42,1
07
5,9
25,6
28
1,1
51,0
01
4,7
74,6
26
39,0
26,3
25
15
8,0
47,4
12
2,1
00,6
75
5,9
46,7
38
1,1
55,2
23
4,7
91,5
14
43,8
17,8
39
16
8,1
27,8
86
2,0
62,3
70
6,0
65,5
16
1,1
78,9
79
4,8
86,5
37
48,7
04,3
77
133
Tab
le A
45 A
nnual
cas
h f
low
and n
et p
rese
nt
val
ue
for
dis
till
atio
n 8
wt%
to 8
0 w
t% (
conti
nues
)
Yea
rs
Rev
enu
e
Exp
end
itu
re
Inco
me
bef
ore
tax
T
ax
In
com
e aft
er t
ax
C
ash
flo
w
17
8,2
09,1
65
2,0
72,6
53
6,1
36,5
12
1,1
93,1
78
4,9
43,3
34
53,6
47,7
10
18
8,2
91,2
57
2,0
83,0
40
6,2
08,2
17
1,2
07,5
19
5,0
00,6
98
58,6
48,4
08
19
8,3
74,1
69
2,0
93,5
30
6,2
80,6
39
1,2
22,0
04
5,0
58,6
35
63,7
07,0
43
20
8,4
57,9
11
2,1
04,1
25
6,3
53,7
86
1,2
36,6
33
12,5
11,4
47
76,2
18,4
91
NP
V
3
1,9
36,1
17
134
Tab
le A
46 A
nnual
cas
h f
low
and n
et p
rese
nt
val
ue
for
dis
till
atio
n 4
wt%
to 8
5 w
t%
Yea
rs
Rev
enu
e
Exp
end
itu
re
Inco
me
bef
ore
tax
T
ax
In
com
e aft
er t
ax
C
ash
flo
w
0
2
2,7
45,0
34
-
22,7
45,0
34
1
3,5
17,1
47
1,8
96,5
59
1,6
20,5
88
290,2
51
1,3
30,3
37
-21,4
14,6
97
2
3,5
52,3
19
1,9
05,2
22
1,6
47,0
96
295,5
52
1,3
51,5
44
-20,0
63,1
53
3
3,5
87,8
42
1,9
13,9
72
1,6
73,8
70
300,9
07
1,3
72,9
63
-18,6
90,1
90
4
3,6
23,7
20
1,9
22,8
09
1,7
00,9
11
306,3
15
1,3
94,5
96
-17,2
95,5
94
5
3,6
59,9
57
1,9
43,3
93
1,7
16,5
65
309,4
46
1,4
07,1
19
-15,8
88,4
75
6
3,6
96,5
57
1,9
40,7
50
1,7
55,8
07
317,2
95
1,4
38,5
13
-14,4
49,9
63
7
3,7
33,5
23
1,9
49,8
55
1,7
83,6
68
322,8
67
1,4
60,8
01
-12,9
89,1
61
8
3,7
70,8
58
1,9
59,0
51
1,8
11,8
07
328,4
94
1,4
83,3
13
-11,5
05,8
49
9
3,8
08,5
66
1,9
68,3
39
1,8
40,2
28
334,1
79
1,5
06,0
49
-9,9
99,8
00
10
3,8
46,6
52
1,9
89,3
78
1,8
57,2
74
337,5
88
1,5
19,6
86
-8,4
80,1
13
11
3,8
85,1
19
1,9
87,1
94
1,8
97,9
24
345,7
18
1,5
52,2
06
-6,9
27,9
07
12
3,9
23,9
70
1,9
96,7
64
1,9
27,2
06
351,5
74
1,5
75,6
32
-5,3
52,2
76
13
3,9
63,2
09
2,0
06,4
29
1,9
56,7
80
357,4
89
1,5
99,2
91
-3,7
52,9
84
14
4,0
02,8
41
2,0
16,1
91
1,9
86,6
51
363,4
63
1,6
23,1
87
-2,1
29,7
97
15
4,0
42,8
70
2,0
37,7
08
2,0
05,1
62
367,1
65
1,6
37,9
96
-491,8
01
16
4,0
83,2
99
2,0
36,0
08
2,0
47,2
90
375,5
91
1,6
71,6
99
1,1
79,8
98
135
Tab
le A
47 A
nnual
cas
h f
low
and n
et p
rese
nt
val
ue
for
dis
till
atio
n 4
wt%
to 8
5 w
t% (
conti
nues
)
Yea
rs
Rev
enu
e
Exp
end
itu
re
Inco
me
bef
ore
tax
T
ax
In
com
e aft
er t
ax
C
ash
flo
w
17
4,1
24,1
32
2,0
46,0
66
2,0
78,0
66
381,7
46
1,6
96,3
19
2,8
76,2
18
18
4,1
65,3
73
2,0
56,2
24
2,1
09,1
49
387,9
63
1,7
21,1
86
4,5
97,4
04
19
4,2
07,0
27
2,0
66,4
84
2,1
40,5
43
394,2
42
1,7
46,3
01
6,3
43,7
05
20
4,2
49,0
97
2,0
76,8
46
2,1
72,2
51
400,5
83
9,1
10,2
26
15,4
53,9
31
NP
V
-2
,825,5
01
136
Tab
le A
48 A
nnual
cas
h f
low
and n
et p
rese
nt
val
ue
for
dis
till
atio
n 6
wt%
to 8
5 w
t%
Yea
rs
Rev
enu
e
Exp
end
itu
re
Inco
me
bef
ore
tax
T
ax
In
com
e aft
er t
ax
C
ash
flo
w
0
2
2,7
34,1
35
-
22,7
34,1
35
1
5,2
77,4
82
1,9
18,9
75
3,3
58,5
07
637,8
51
2,7
20,6
57
-20,0
13,4
79
2
5,3
30,2
57
1,9
27,8
64
3,4
02,3
93
646,6
28
2,7
55,7
65
-17,2
57,7
14
3
5,3
83,5
59
1,9
36,8
43
3,4
46,7
17
655,4
93
2,7
91,2
24
-14,4
66,4
90
4
5,4
37,3
95
1,9
45,9
11
3,4
91,4
84
664,4
46
2,8
27,0
38
-11,6
39,4
52
5
5,4
91,7
69
1,9
72,5
35
3,5
19,2
34
669,9
96
2,8
49,2
38
-8,7
90,2
15
6
5,5
46,6
87
1,9
64,3
21
3,5
82,3
66
682,6
22
2,8
99,7
43
-5,8
90,4
71
7
5,6
02,1
53
1,9
73,6
64
3,6
28,4
90
691,8
47
2,9
36,6
42
-2,9
53,8
29
8
5,6
58,1
75
1,9
83,1
00
3,6
75,0
75
701,1
64
2,9
73,9
10
20,0
81
9
5,7
14,7
57
1,9
92,6
31
3,7
22,1
25
710,5
74
3,0
11,5
51
3,0
31,6
32
10
5,7
71,9
04
2,0
19,7
23
3,7
52,1
82
716,5
86
3,0
35,5
96
6,0
67,2
28
11
5,8
29,6
23
2,0
11,9
80
3,8
17,6
43
729,6
78
3,0
87,9
65
9,1
55,1
94
12
5,8
87,9
20
2,0
21,8
00
3,8
66,1
20
739,3
73
3,1
26,7
47
12,2
81,9
40
13
5,9
46,7
99
2,0
31,7
18
3,9
15,0
81
749,1
66
3,1
65,9
16
15,4
47,8
56
14
6,0
06,2
67
2,0
41,7
35
3,9
64,5
32
759,0
56
3,2
05,4
76
18,6
53,3
32
15
6,0
66,3
29
2,0
69,3
17
3,9
97,0
12
765,5
52
3,2
31,4
61
21,8
84,7
93
16
6,1
26,9
93
2,0
62,0
70
4,0
64,9
22
779,1
34
3,2
85,7
89
25,1
70,5
81
137
Tab
le A
49 A
nnual
cas
h f
low
and n
et p
rese
nt
val
ue
for
dis
till
atio
n 6
wt%
to 8
5 w
t% (
conti
nues
)
Yea
rs
Rev
enu
e
Exp
end
itu
re
Inco
me
bef
ore
tax
T
ax
In
com
e aft
er t
ax
C
ash
flo
w
17
6,1
88,2
63
2,0
72,3
91
4,1
15,8
72
789,3
24
3,3
26,5
48
28,4
97,1
29
18
6,2
50,1
45
2,0
82,8
15
4,1
67,3
30
799,6
15
3,3
67,7
15
31,8
64,8
44
19
6,3
12,6
47
2,0
93,3
43
4,2
19,3
04
810,0
10
3,4
09,2
94
35,2
74,1
38
20
6,3
75,7
73
2,1
03,9
76
4,2
71,7
97
820,5
09
10,7
86,3
30
46,0
60,4
69
NP
V
1
4,7
33,8
90
138
Tab
le A
50 A
nnual
cas
h f
low
and n
et p
rese
nt
val
ue
for
dis
till
atio
n 8
wt%
to 8
5 w
t%
Yea
rs
Rev
enu
e
Exp
end
itu
re
Inco
me
bef
ore
tax
T
ax
In
com
e aft
er t
ax
C
ash
flo
w
0
2
2,7
79,6
71
-2
2,7
79,6
71
1
7,0
36,9
63
1,9
41,9
66
5,0
94,9
97
985,0
81
4,1
09,9
16
-18,6
69,7
55
2
7,1
07,3
33
1,9
51,0
76
5,1
56,2
57
997,3
33
4,1
58,9
24
-14,5
10,8
31
3
7,1
78,4
06
1,9
60,2
77
5,2
18,1
29
1,0
09,7
07
4,2
08,4
22
-10,3
02,4
09
4
7,2
50,1
90
1,9
69,5
70
5,2
80,6
21
1,0
22,2
06
4,2
58,4
15
-6,0
43,9
94
5
7,3
22,6
92
2,0
01,6
33
5,3
21,0
59
1,0
30,2
93
4,2
90,7
66
-1,7
53,2
28
6
7,3
95,9
19
1,9
88,4
35
5,4
07,4
84
1,0
47,5
78
4,3
59,9
06
2,6
06,6
78
7
7,4
69,8
78
1,9
98,0
09
5,4
71,8
69
1,0
60,4
55
4,4
11,4
14
7,0
18,0
92
8
7,5
44,5
77
2,0
07,6
79
5,5
36,8
98
1,0
73,4
61
4,4
63,4
37
11,4
81,5
29
9
7,6
20,0
23
2,0
17,4
46
5,6
02,5
77
1,0
86,5
97
4,5
15,9
80
15,9
97,5
09
10
7,6
96,2
23
2,0
49,9
89
5,6
46,2
35
1,0
95,3
28
4,5
50,9
06
20,5
48,4
15
11
7,7
73,1
85
2,0
37,2
74
5,7
35,9
12
1,1
13,2
64
4,6
22,6
48
25,1
71,0
63
12
7,8
50,9
17
2,0
47,3
36
5,8
03,5
81
1,1
26,7
98
4,6
76,7
83
29,8
47,8
47
13
7,9
29,4
26
2,0
57,5
00
5,8
71,9
27
1,1
40,4
67
4,7
31,4
60
34,5
79,3
07
14
8,0
08,7
21
2,0
67,7
65
5,9
40,9
56
1,1
54,2
73
4,7
86,6
83
39,3
65,9
90
15
8,0
88,8
08
2,1
00,8
10
5,9
87,9
98
1,1
63,6
81
4,8
24,3
17
44,1
90,3
07
16
8,1
69,6
96
2,0
88,6
04
6,0
81,0
92
1,1
82,3
00
4,8
98,7
92
49,0
89,0
99
139
Tab
le A
51 A
nnual
cas
h f
low
and n
et p
rese
nt
val
ue
for
dis
till
atio
n 8
wt%
to 8
5 w
t% (
conti
nues
)
Yea
rs
Rev
enu
e
Exp
end
itu
re
Inco
me
bef
ore
tax
T
ax
In
com
e aft
er t
ax
C
ash
flo
w
17
8,2
51,3
93
2,0
99,1
80
6,1
52,2
13
1,1
96,5
24
4,9
55,6
89
54,0
44,7
88
18
8,3
33,9
07
2,1
09,8
61
6,2
24,0
45
1,2
10,8
91
5,0
13,1
55
59,0
57,9
43
19
8,4
17,2
46
2,1
20,6
50
6,2
96,5
96
1,2
25,4
01
5,0
71,1
95
64,1
29,1
38
20
8,5
01,4
18
2,1
31,5
47
6,3
69,8
72
1,2
40,0
56
12,4
79,5
50
76,6
08,6
88
NP
V
3
2,2
31,0
73
140
Tab
le A
52 A
nnual
cas
h f
low
and n
et p
rese
nt
val
ue
for
dis
till
atio
n 4
wt%
to 9
0 w
t%
Yea
rs
Rev
enu
e
Exp
end
itu
re
Inco
me
bef
ore
tax
T
ax
In
com
e aft
er t
ax
C
ash
flo
w
0
2
2,7
66,3
23
-
22,7
66,3
23
1
3,5
13,6
31
1,9
03,0
03
1,6
10,6
28
288,2
27
1,3
22,4
01
-21,4
43,9
22
2
3,5
48,7
67
1,9
11,7
26
1,6
37,0
41
293,5
10
1,3
43,5
31
-20,1
00,3
91
3
3,5
84,2
55
1,9
20,5
37
1,6
63,7
18
298,8
45
1,3
64,8
73
-18,7
35,5
17
4
3,6
20,0
97
1,9
29,4
35
1,6
90,6
63
304,2
34
1,3
86,4
29
-17,3
49,0
89
5
3,6
56,2
98
1,9
48,3
73
1,7
07,9
25
307,6
86
1,4
00,2
39
-15,9
48,8
50
6
3,6
92,8
61
1,9
47,4
99
1,7
45,3
62
315,1
74
1,4
30,1
88
-14,5
18,6
61
7
3,7
29,7
90
1,9
56,6
67
1,7
73,1
23
320,7
26
1,4
52,3
97
-13,0
66,2
64
8
3,7
67,0
88
1,9
65,9
27
1,8
01,1
61
326,3
34
1,4
74,8
28
-11,5
91,4
37
9
3,8
04,7
59
1,9
75,2
79
1,8
29,4
80
331,9
97
1,4
97,4
83
-10,0
93,9
54
10
3,8
42,8
06
1,9
94,6
76
1,8
48,1
31
335,7
27
1,5
12,4
03
-8,5
81,5
51
11
3,8
81,2
34
1,9
94,2
65
1,8
86,9
70
343,4
95
1,5
43,4
75
-7,0
38,0
76
12
3,9
20,0
47
2,0
03,9
00
1,9
16,1
47
349,3
31
1,5
66,8
16
-5,4
71,2
60
13
3,9
59,2
47
2,0
13,6
32
1,9
45,6
15
355,2
24
1,5
90,3
91
-3,8
80,8
69
14
3,9
98,8
40
2,0
23,4
61
1,9
75,3
79
361,1
77
1,6
14,2
01
-2,2
66,6
68
15
4,0
38,8
28
2,0
43,3
40
1,9
95,4
88
365,1
99
1,6
30,2
89
-636,3
79
16
4,0
79,2
16
2,0
43,4
16
2,0
35,8
01
373,2
62
1,6
62,5
39
1,0
26,1
60
141
Tab
le A
53 A
nnual
cas
h f
low
and n
et p
rese
nt
val
ue
for
dis
till
atio
n 4
wt%
to 9
0 w
t% (
conti
nues
)
Yea
rs
Rev
enu
e
Exp
end
itu
re
Inco
me
bef
ore
tax
T
ax
In
com
e aft
er t
ax
C
ash
flo
w
17
4,1
20,0
09
2,0
53,5
43
2,0
66,4
66
379,3
95
1,6
87,0
71
2,7
13,2
32
18
4,1
61,2
09
2,0
63,7
71
2,0
97,4
38
385,5
89
1,7
11,8
49
4,4
25,0
81
19
4,2
02,8
21
2,0
74,1
02
2,1
28,7
19
391,8
45
1,7
36,8
74
6,1
61,9
55
20
4,2
44,8
49
2,0
84,5
35
2,1
60,3
14
398,1
64
9,1
07,5
77
15,2
69,5
31
NP
V
-2
,942,0
67
142
Tab
le A
54 A
nnual
cas
h f
low
and n
et p
rese
nt
val
ue
for
dis
till
atio
n 6
wt%
to 9
0 w
t%
Yea
rs
Rev
enu
e
Exp
end
itu
re
Inco
me
bef
ore
tax
T
ax
In
com
e aft
er t
ax
C
ash
flo
w
0
2
2,7
89,7
72
-
22,7
89,7
72
1
5,2
78,2
01
1,9
21,1
93
3,3
57,0
08
637,4
68
2,7
19,5
40
-20,0
70,2
32
2
5,3
30,9
83
1,9
30,0
92
3,4
00,8
90
646,2
44
2,7
54,6
46
-17,3
15,5
86
3
5,3
84,2
92
1,9
39,0
81
3,4
45,2
11
655,1
09
2,7
90,1
03
-14,5
25,4
84
4
5,4
38,1
35
1,9
48,1
60
3,4
89,9
76
664,0
62
2,8
25,9
14
-11,6
99,5
70
5
5,4
92,5
17
1,9
72,0
27
3,5
20,4
89
670,1
64
2,8
50,3
25
-8,8
49,2
45
6
5,5
47,4
42
1,9
66,5
90
3,5
80,8
52
682,2
37
2,8
98,6
15
-5,9
50,6
30
7
5,6
02,9
16
1,9
75,9
44
3,6
26,9
72
691,4
61
2,9
35,5
11
-3,0
15,1
19
8
5,6
58,9
45
1,9
85,3
91
3,6
73,5
54
700,7
77
2,9
72,7
77
-42,3
42
9
5,7
15,5
35
1,9
94,9
33
3,7
20,6
02
710,1
87
3,0
10,4
15
2,9
68,0
74
10
5,7
72,6
90
2,0
19,2
68
3,7
53,4
22
716,7
51
3,0
36,6
71
6,0
04,7
45
11
5,8
30,4
17
2,0
14,3
03
3,8
16,1
14
729,2
89
3,0
86,8
24
9,0
91,5
69
12
5,8
88,7
21
2,0
24,1
34
3,8
64,5
87
738,9
84
3,1
25,6
03
12,2
17,1
73
13
5,9
47,6
09
2,0
34,0
63
3,9
13,5
45
748,7
75
3,1
64,7
70
15,3
81,9
42
14
6,0
07,0
85
2,0
44,0
92
3,9
62,9
93
758,6
65
3,2
04,3
28
18,5
86,2
70
15
6,0
67,1
55
2,0
68,9
19
3,9
98,2
37
765,7
14
3,2
32,5
23
21,8
18,7
93
16
6,1
27,8
27
2,0
64,4
51
4,0
63,3
76
778,7
42
3,2
84,6
35
25,1
03,4
28
143
Tab
le A
55 A
nnual
cas
h f
low
and n
et p
rese
nt
val
ue
for
dis
till
atio
n 6
wt%
to 9
0 w
t% (
conti
nues
)
Yea
rs
Rev
enu
e
Exp
end
itu
re
Inco
me
bef
ore
tax
T
ax
In
com
e aft
er t
ax
C
ash
flo
w
17
6,1
89,1
05
2,0
74,7
83
4,1
14,3
22
788,9
31
3,3
25,3
91
28,4
28,8
19
18
6,2
50,9
96
2,0
85,2
18
4,1
65,7
78
799,2
22
3,3
66,5
56
31,7
95,3
75
19
6,3
13,5
06
2,0
95,7
58
4,2
17,7
48
809,6
16
3,4
08,1
32
35,2
03,5
07
20
6,3
76,6
41
2,1
06,4
04
4,2
70,2
38
820,1
14
10,8
03,1
17
46,0
06,6
23
NP
V
1
4,6
74,7
17
144
Tab
le A
56 A
nnual
cas
h f
low
and n
et p
rese
nt
val
ue
for
dis
till
atio
n 8
wt%
to 9
0 w
t%
Yea
rs
Rev
enu
e
Exp
end
itu
re
Inco
me
bef
ore
tax
T
ax
In
com
e aft
er t
ax
C
ash
flo
w
0
2
2,7
98,0
42
-
22,7
98,0
42
1
7,0
31,1
00
1,9
35,5
89
5,0
95,5
11
985,1
56
4,1
10,3
55
-18,6
87,6
87
2
7,1
01,4
11
1,9
44,6
31
5,1
56,7
80
997,4
10
4,1
59,3
70
-14,5
28,3
17
3
7,1
72,4
25
1,9
53,7
64
5,2
18,6
62
1,0
09,7
86
4,2
08,8
75
-10,3
19,4
42
4
7,2
44,1
50
1,9
62,9
87
5,2
81,1
62
1,0
22,2
87
4,2
58,8
76
-6,0
60,5
66
5
7,3
16,5
91
1,9
92,4
60
5,3
24,1
31
1,0
30,8
80
4,2
93,2
51
-1,7
67,3
15
6
7,3
89,7
57
1,9
81,7
12
5,4
08,0
45
1,0
47,6
63
4,3
60,3
82
2,5
93,0
66
7
7,4
63,6
55
1,9
91,2
15
5,4
72,4
39
1,0
60,5
42
4,4
11,8
97
7,0
04,9
64
8
7,5
38,2
91
2,0
00,8
13
5,5
37,4
78
1,0
73,5
50
4,4
63,9
28
11,4
68,8
92
9
7,6
13,6
74
2,0
10,5
08
5,6
03,1
67
1,0
86,6
87
4,5
16,4
79
15,9
85,3
71
10
7,6
89,8
11
2,0
40,4
55
5,6
49,3
55
1,0
95,9
25
4,5
53,4
30
20,5
38,8
01
11
7,7
66,7
09
2,0
30,1
88
5,7
36,5
21
1,1
13,3
58
4,6
23,1
63
25,1
61,9
64
12
7,8
44,3
76
2,0
40,1
75
5,8
04,2
01
1,1
26,8
94
4,6
77,3
06
29,8
39,2
71
13
7,9
22,8
20
2,0
50,2
63
5,8
72,5
57
1,1
40,5
65
4,7
31,9
91
34,5
71,2
62
14
8,0
02,0
48
2,0
60,4
52
5,9
41,5
96
1,1
54,3
73
4,7
87,2
23
39,3
58,4
85
15
8,0
82,0
68
2,0
90,8
99
5,9
91,1
69
1,1
64,2
88
4,8
26,8
81
44,1
85,3
66
16
8,1
62,8
89
2,0
81,1
36
6,0
81,7
53
1,1
82,4
05
4,8
99,3
49
49,0
84,7
14
145
Tab
le A
57 A
nnual
cas
h f
low
and n
et p
rese
nt
val
ue
for
dis
till
atio
n 8
wt%
to 9
0 w
t% (
conti
nues
)
Yea
rs
Rev
enu
e
Exp
end
itu
re
Inco
me
bef
ore
tax
T
ax
In
com
e aft
er t
ax
C
ash
flo
w
17
8,2
44,5
18
2,0
91,6
33
6,1
52,8
85
1,1
96,6
31
4,9
56,2
54
54,0
40,9
68
18
8,3
26,9
63
2,1
02,2
35
6,2
24,7
28
1,2
11,0
00
5,0
13,7
28
59,0
54,6
96
19
8,4
10,2
33
2,1
12,9
44
6,2
97,2
89
1,2
25,5
12
5,0
71,7
77
64,1
26,4
74
20
8,4
94,3
35
2,1
23,7
59
6,3
70,5
76
1,2
40,1
69
12,4
86,0
68
76,6
12,5
42
NP
V
3
2,2
23,9
88
CURRICULU M VITAE
CURRICULUM VITAE
NAME Monsikan Vilaipan
DATE OF BIRTH 18 July 1996
BIRTH PLACE Mahasarakam province
ADDRESS 64 Village no. 6, Udon-Nongkai Road, Kudsa Sub-district,
Mueang District, Udonthani
EDUCATION 2018 B.Eng (Industrial engineering-Logistic), Kasetsart
University Kamphaeng Saen, Nakhon pathom
SCHOLARSHIP Faculty of Engineering, Kasetsart University
Thailand Advanced Institute of Science and
Technology and Tokyo Institute of
Technology (TAIST TokyoTech)
National Science and Technology
Development Agency (NSTDA)
Top Related