5-95 gap chart
DESCRIPTION
astm 5-95TRANSCRIPT
REFINERY-WIDE OPTIMIZATION
by
XUAN LI, B.E.
A DISSERTATION
IN
CHEMICAL ENGINEERING
Submitted to the Graduate Faculty of Texas Tech University in
Partial Fulfillment of the Requirements for
the Degree of
DOCTOR OF PHILOSOPHY
Approved
May, 2000
COPYRIGHT 2000, XUAN LI
ACKNOWLEDGEMENTS
Refinery-wide optimization would become "Mission Impossible" without
innumerable help from other people. Many people have given help in many different
ways. It is imperative for me to give them my appreciation.
I want to express my sincere appreciation to Dr. James B. Riggs for providing me
with the opportunity to study at Texas Tech University, for guiding me and encouraging
me throughout the whole project, and for supporting me financially during the course of
this work. His style of clarifying every statement and developing insight of every
problem and his determination to get things done will influence me in my whole career.
I also want to extend my sincere thanks to Dr. Theodore F. Wiesner for his
guidance in my qualifying project. His fighting spirit and positive attitude show me the
correct way to deal with obstacles. I would also like to thank my other committee
members. Dr. Richard W. Tock and Dr. W. J. Bryan Oldham for their assistance in my
Ph.D. research. Appreciation is extended to the industrial members of the Texas Tech
Process Control Consortium for their financial support and invaluable inputs to my
research. A special thanks go to Scott Boyden of Aspen Technology Incorporation and
Dr. Charlie Cutler, for their input on my research.
This work would not have much industrial relevance without the plant data
obtained from the refinery considered in this work. I wish to express my appreciation to
all the engineers and technicians who helped me during their busy schedule.
I also wish to extend my thanks to Dr. Taskar and Robert Ellis for leading me
through the modeling and optimization of reformer and FCCU and for introducing the
NFL to me. Go Cowboys! I would also like to thank Joe, Marshall, and Matt, for their
technical support on all computer issues. Special thanks go to Govindhakannan for
reviewing my dissertation draft and for innumerous discussion on research and life,
which is invaluable during the last one and a half years.
Finally, I would like to thank my parents for their love and support through the
years. Lastly, I would like to dedicate this work to my dear wife, Jenny. Let us be
together and start a more exciting life!
ni
TABLE OF CONTENTS
ACKNOWLEDGEMENTS ii
ABSTRACT vii
LIST OF TABLES ix
LIST OF FIGURES xiv
CHAPTER
1. INTRODUCTION 1
2. LITERATURE REVIEW 8
2.1 Single-Unit Modeling and Feed Characterization 8
2.2 Refinery-Wide Optimization 14
3. CRUDE UNIT MODELING 16
3.1 Introduction 16
3.2 Atmospheric Tower Modeling 20
3.2.1 Feed Characterization 22
3.2.2 Side-Draw Product Calculation 28
3.2.3 Atmospheric Tower Furnace Calculation 36
3.2.4 Flash Zone Calculation 36
3.2.5 Side Stripper Calculation 42
3.2.6 Draw Tray Locations 43
3.2.7 Draw Tray Temperature Calculation 45
3.2.8 Side-Draw product temperatvire calculation 48
3.3 Vacuum Tower Modeling 50
3.3.1 Vacuum Tower Furnace Calculation 51
3.3.2 Side-Draw Product Calculation 52
3.3.3 Flash Zone Calculation 52
3.4 Separation Section Calculation 54
3.5 Comparison between the Developed Model and Tray-to-Tray ChemCad Model 55
3.6 Auxiliary Processes in the Crude Unit 63
3.6.1 Rerun Unit 64
IV
3.6.2 Residue Oil Solvent Extraction (ROSE) Unit 65
3.6.3 Debutanizer 66
3.6.4 Naphtha Splitter 66
4. FCC FEED CHARACTERIZATION AND MODEL BENCHMARKING 68
4.1 Process Overview 68
4.2 Model IV FCC Unit Modeling 71
4.3 FCC Feed Characterization 73
4.3.1 Volume and Weight of FCC Feed 73
4.3.2 FCC feed Characterization 74
4.3.3 Weight Fractions ofLumps in the FCC Feed 82
4.4 Model Benchmarking 87
4.5 FCC Gasoline Octane Model Modification 92
5. REFORMER FEED CHARACTERIZATION AND MODEL BENCHMARKING 94
5.1 Process Overview 94
5.2 Reformer Modeling 96
5.3 Reformer Feed Characterization 100
5.3.1 Naphtha Desulfurizer 100
5.3.2 Paraffins-Naphthenes-Aromatics (PNA) in the Reformer Feed 103
5.3.3 Molar Flow Rates of the Pseudo-Components in the Reformer Feed 108
5.4 Reformer Model Benchmarking 110
5.5 Average Reformer Operation 114
5.5.1 Operation Time Fraction of Low Severity 114
5.5.2 On-Stream Factor 115
5.5.3 Regeneration Cost 118
6. MODELING OF GAS PLANT, ALKYLATION UNIT AND DIESEL HYDROTREATER 120
6.1 Gas Plant 120
6.1.1 Sources of Light Gas 121
6.1.2 Fuel Gas Production 123
6.1.3 Depropanizer 124
6.2 Alkylation Unit 126
6.3 Diesel Hydrotreater 134
7. GASOLINE BLENDING MODELING 136
7.1 Process Overview 136
7.2 Properties of the Gasoline Blending Stocks 141
7.2.1 Light Straight-Run (LSR) Gasoline 141
7.2.2 Other Gasoline Blending Stocks 145
7.3 Gasoline Blending Model 148
7.3.1 Octane Model 148
7.3.2 RVP Blending Model 152
7.3.3 Percent Distilled Model 155
8. REFINERY-WIDE OPTIMIZATION 156
8.1 Formulation of the Optimization Problem 156
8.1.1 Formulation of the Obj ecti ve Function 15 6
8.1.2 Obj ective Function Evaluation 15 9
8.1.3 Price Structure 160
8.1.4 Decision Variables 162
8.1.5 Constraints 166
8.1.6 Optimization Algorithm 171
8.2 Optimization Case Studies 171
8.2.1 Base Case 171
8.2.2 Summer Mode 172
8.2.3 Winter Mode 181
8.2.4 Optimal Solution Analysis 187
8.2.5 Profitability Improvement 189
8.3 Single-Unit Optimization 190
9. CONCLUSIONS AND RECOMMENDATIONS 196
9.1 Conclusions 196
9.2 Recommendations 200
BIBLIOGRAPHY 203
APPENDIX 208
VI
ABSTRACT
A fuel-oriented refinery converts crude oil into various fuel products which are
used for transportation and heating. It also provides feedstock for petrochemical plant.
Based on a real refinery in the Gulf coast, a first-principle, nonlinear, plant-wide
model was developed by integrating several single-unit models into an overall model.
Detailed models were developed for two major units, crude unit and gasoline blending.
The crude unit model is a non-stage-by-stage, steady-state model based on material
balance and energy balance. The model calculates the yields and properties of the
products based on the feed information and product specifications. The gasoline blending
model calculates the complete set of gasoline specifications of three grades of gasoline
from the information of gasoline blending stocks.
Existing detailed models of fluidized catalytic cracking (FCC) unit and catalytic
reformer were used in this work after minor modification. Simplified first-principle
models were developed for other units in the refinery. Each single-unit model was
benchmarked against the industrial data obtained from the refinery.
In the refinery-wide model, the outputs of models of upstream units are used as
the inputs to the models of downstream units. The intermediate streams are characterized
in the overall model to provide necessary information for the models of downstream
units. Detailed composition information of feeds to FCC unit and catalytic reformer is
calculated. General properties, volume, weight, and specific gravity are calculated for
other intermediate streams.
A constrained nonlinear optimization was carried out using the developed
refinery-wide model. The objective of the optimization is to maximize the daily revenue
of the whole refinery. The decision variables are the collection of the process variables of
each unit that has significant influence on the economy of whole refinery operation. The
nonlinear and linear constraints in the optimization are the summation of constraints of
each unit. Two operation modes. Summer Mode and Winter Mode, were studied. The
optimal solutions obtained from refinery-wide optimation show that the revenue increase
vu
over the normal operating conditions is 4.5% for Summer Mode and 3.6%) for Winter
Mode. The revenue from refinery-wide optimization is about 1.6% over single-unit
optimization.
vni
LIST OF TABLES
3.1 Feedstock Makeups. 22
3.2 Gap (5-95) ASTM Temperature between Adjacent Side-Draw Products in the Atmospheric Tower. 29
3.3 A and B Variables for Equation 3.10. 33
3.4 Pressure in the Atmospheric Tower. 37
3.5 Tray Numbers of Separation Sections in Atmospheric Tower. 44
3.6 Draw Tray Location. 44
3.7 Separation Specifications of Side-Draw Products. 57
3.8 Comparison of the Gains Obtained from the Simplified Model with ChemCad model, ASTM 95% Point, Furnace Outlet Temperatiire, and VGO TBP End Point versus Product Flow Rate, Summer Mode. 60
3.9 Comparison between the Gains Obtained from the Simplified Model and ChemCad Model, Pumparound Heat Duty versus Product Flow Rate, Atmospheric Tower, Summer Mode. 61
3.10 Comparison between the Gains Obtained from the Simplified Model and ChemCad Model, Pumparound Return Temperature versus Product Flow
Rate, Vacuum Tower, Summer Mode. 62
4.1 Boiling Range ofLumps in Ten-Lump FCC Reaction Network. 73
4.2 Industrial Data of FCC Feed. 75 4.3 Comparison between the Industrial Data and Feed Characterization of the FCC
Feed. 82
4.4 Weight Fractions of Eight Lumps in an FCC Feed of API=23. 84
4.5 Weight Fractions of the Eight Lumps in the FCC feed across the Operating Range. 86
4.6 FCC Model Benchmarking: Industrial Data and Model Prediction. 88
4.7 Adjustable Variables in FCC Model Benchmarking. 89
4.8 Process Constraints of FCC Unit. 91
4.9 FCC Octane Model. 93
5.1 Octane Ratings of the Typical Gasoline Blending Stocks. 94
5.2 Chemical Components ofthe Reformer Feed. 98
5.3 Ratios in the Calculation of the Naphtha Hydrotreater. 101
IX
5.4 Industrial Data of Crude A. 104
5.5 Industrial Data of Crude B. 104
5.6 Industrial Data of Crude C. 105
5.7 Industrial Data of Crude D. 105
5.8 Comparison of Volume Percentages of PNA from Feed Characterization and
the Industrial Data. 108
5.9 Catalyst Weight in Each Reactor Bed. 110
5.10 Comparison ofthe Industrial Data and Model Prediction after Benchmarking. 111
5.11 Adjustable Prameters in the Reformer Model Benchmarking. 112
5.12 Reformer Operation Limit. 116
5.13 Coke Contents in Dfferent Regions. 116 5.14 Ratios between Average Coking Rate and Coking Rate at the Begiiming of a
Cycle. 117
6.1 Light Gas Production Rates in the Crude Unit. 122
6.2 Compositions of Light Gas from FCC Unit. 123
6.3 Losses of Heavy Hydrocarbons in the Fuel Gas. 124
6.4 Portion ofthe Propylene in the C3 Product. 124
6.5 Weight Percentage ofthe Propylene that Reacts in the Second Reaction Path
in the Alkylation Unit. 129
6.6 Octane Number ofthe Butylene Alkylate and Propylene Alkylate. 130
6.7 Weight Percentage ofthe Unreacted Isobutane in the Alkylation Unit. 132
6.8 Weight Ratios ofthe Hydrogen and the Products in the Diesel Hydrotreater. 134 6.9 Volumetric Ratios ofthe Hydrogen and the Products in the Diesel
Hydrotreater. 135
7.1 Volumes and Sources ofthe Gasoline Blending Stocks in Typical Summer Mode Operation. 137
7.2 Specification of Three Grades of Gasoline Produced in the Refinery
Considered in This Work. 138
7.3 Industrial Data ofCrude A and Crude B. 142
7.4 Industrial Data of Crude C and Crude D. 143
7.5 Properties ofGasoline Blending Stocks. 147
7.6 Universal Set ofthe Binary Interaction Parameter between Components. 150
8.1 Chemicals Purchased by the Refinery Considered in This Work. 157
8.2 Price Structure ofthe Refinery-Wide Optimization. 158
8.3 Decision Variables of Refinery-Wide Optimization. 164
8.4 Nonlinear Constraints of Refinery-wide Optimization. 167
8.5 Comparison of Model Prediction ofthe Base Case with the LP report. Summer Mode. 173
8.6 Comparison of Model Prediction ofthe Base Case with the LP Report, Winter Mode. 174
8.7 Optimum Values ofthe Decision Variables of Refinery-wide Optimization,
Summer Mode. 177
8.8 Active Constraints in Refinery-wide Optimization, Summer Mode. 178
8.9 Comparison ofthe Decision Variables of Refinery-wide Optimization with Base Case, Summer Mode. 179
8.10 Comparison ofthe Decision Variables of Refinery-wide Optimization with
Base Case, Winter Mode. 183
8.11 Active Constraints in Refinery-wide Optimization, Winter Mode. 184
8.12 Comparison ofthe Product Slates ofthe Optimum Solution with the Base
Case, Winter Mode. 185
8.13 The Mean and Variance of the Feasible Solutions. 187
8.14 Change of the Objective Function Value around Optimal Solutions. 189
8.15 Profitability Improvement of the Refinery-wide Optimization. 190
8.16 Prices of the Side-draw Products from the Crude Unit, Summer Mode. 191
8.17 Prices of the Side-draw Products from the Crude Unit, Summer Mode. 192 8.18 Comparison of Single-unit Optimization with Refinery-wide Optimization,
Summer Mode. 193 A.l Constants of Polynomial Expression for the TBP Curve ofthe Mixed Crude in
Summer Mode, Calculating the Vol.%) Given the Temperature in °F. 208
A.2 Constants of Polynomial Expression for the TBP Curve ofthe Mixed Crude in Summer Mode, Calculating the Temperature in °F Given the Vol.%. 208
A.3 Constants of Polynomial Expression for the TBP Curve ofthe Crude in Winter Mode, Calculating the Vol.% Given the Temperature in °F. 209
A.4 Constants of Polynomial Expression for the TBP Curve ofthe Crude in Winter Mode, Calculating the Temperature in °F Given the Vol.%. 209
A.5 Constants of Polynomial Expression for the API Gravity Curve ofthe Mixed Crude in Summer Mode, Calculating the API Gravity Given the Vol.%. 210
XI
A.6 Constants of Polynomial Expression for the API Gravity Curve ofthe Mixed Crude in Winter Mode, Calculating the API Gravity Given the Vol.%. 210
A.7 Constants of Polynomial Expression for the Sulfur Curve ofthe Mixed Crude in Summer Mode, Calculating the Sulfur Given the Vol.%o. 211
A. 8 Constants of Polynomial Expression for the Sulfur Curve ofthe Mixed Crude in Winter Mode, Calculating the Sulftir Given the Vol.%. 211
A.9 Constants of Polynomial Expression for Converting ASTM End Point to TBP End Point. 212
A. 10 Constants of Polynomial Expression for Converting Gap (5-95) ASTM to Gap (0-100) TBP. 212
A. 11 Constants of Polynomial Expression for Calculating the Molecular Weight of a Crude Cut Given the Mean Average Boiling Point and API gravity. Characteristic Factor from 12.1 to 12.6. 213
A. 12 Constants of Polynomial Expression for calculating the molecular weight of a Crude Cut Given the Mean Average Boiling Point and API gravity. Characteristic Factor from 11.7 to 12.0. 213
A. 13 Constants of Polynomial Expression for Calculating the Molecular Weight of a Crude Cut Given the Mean Average Boiling Point and API gravity. Characteristic Factor from 11.3 to 11.6. 214
A. 14 Constants of Polynomial Expression for Calculating the Enthalpy of a Crude Cut from Its API Gravity, Temperature and Phase. 214
A. 15 Constants of Polynomial Expression for Calculating the Enthalpy of a Crude Cut in Saturated Vapor Phase from Its API Gravity and Temperature. 219
A. 16 Constants of Polynomial Expression for Calculating the Steam-free Delta T Given the Value of the Percent Stripout of Crude Cuts. 221
A. 17 Constants of Polynomial Expression for Calculating Stripout from Steam Rate. 222
A. 18 Constants of Polynomial Expression for Calculating the Steam-free DT Minus Actual DT(F) from the Temperature (F) Difference between Feed and Stripping Steam and Percent Stripout. 223
A. 19 Constants of Polynomial Expression for Calculating the Enthalpy of Steam from Temperature. 223
A.20 Constants of Polynomial Expression for Calculating the Enthalpy of Air from Temperature. 224
A.21 Constants of Polynomial Expression for Calculating the Enthalpy of Water from Temperature. 224
xu
A.22 Constants of Polynomial Expression for Calculating the Slope of Flash Reference Line from Corresponding Distillation Reference Line. 225
A.23 Constants of Polynomial Expression for Calculating the Ratio of DT(flash)/DT(TBP) from Volumetric Percent Distillated. 225
A.24 Constants of Polynomial Expression for Calculating the Temperature Difference between the Distillation and Flash Reference Curves from the Slope of Distillation Reference Curve. 226
A.25 Constants of Polynomial Expression for Calculating the T50 of Flash Curve under Vacuum from the Pressure and T50 ofthe Flash Curve under Atmospheric Pressure. 227
xni
LIST OF FIGURES
3.1 Block Flow Diagram of a Fuel-Oriented Refinery. 17
3.2 Schematic of a Fuel Type Crude Unit. 18
3.3 Schematic of the Model of the Atmospheric Tower of the Crude Unit. 21
3.4 Crude Cuts on a TBP-Volumetric Curve. 23
3.5 TBP Curve of the Crude Used in Summer Mode. 25
3.6 API Curve of the Crude Feed Used in Summer Mode. 27
3.7 Sulfur Curve ofthe Crude Feed Used in Summer Mode. 27
3.8 Flash Zone of the Atmospheric Tower. 39
3.9 Flowchart for the Calculation of Flash Zone Pressure and Temperature. 40
3.10 Energy and Material Balance Quantities at a Side-Draw Tray. 46
3.11 Flowchart ofthe Side-Draw Tray Temperature Calculation. 49
3.12 Schematic of a ChemCad Crude Unit Model. 56
3.13 Comparison of Simplified Model and ChemCad Rigorous Model, Summer Mode. 58
3.14 Comparison of Simplified Model and ChemCad Rigorous Model, Winter
Mode. 58
3.15 Comparison of Temperature Profile in the Atmospheric Tower. 63
4.1 Schematic of a Model IV Fluidized Catalytic Cracking (FCC) Unit. 70
4.2 Ten-Lump Reaction Network. 72
5.1 Process Flow Scheme of a Semi-Regenerative Catalytic Naphtha Reformer. 97
6.1 Schematic of the Gas Plant in the Fuel-Oriented Refinery. 121
6.2 Schematicof a Hydrofluoric Acid Alkylation Unit. 127
7.1 Flowchart for the Calculation ofthe Tio%, TSQO/O, T9OO/„ ofGasoline Blends. 154
8.1 Flowchart of the Execution of the Refinery-Wide Model. 161
8.2 Comparison of Product Slates of Refinery-Wide Optimization with Base Case, Summer Mode. 180
8.3 Comparison of Product Slates of Refinery-Wide Optimization with Base Case, Winter Mode. 186
8.4 Comparison of Product Slates of Single-Unit Optimization (Crude Unit) with Refinery-Wide Optimization, Summer Mode. 195
xiv
CHAPTER 1
INTRODUCTION
Crude oil is the dominant fuel resource in the world today. Products from crude
oil are extensively used in industry and normal life. The products directly coming from
crude oil include fuel gas, liquidified petroleum gas (LPG), gasoline, jet fuel, diesel,
heating oils, lubricated oil, fuel oils, solvents, asphalt, etc. Crude oil is also the raw
material for the petrochemical and chemical industries.
To use crude oil efficiently and to make its usage environmental safe, it is
necessary to refine crude oil into various products that have different specifications that
satisfy the usage requirement and environmental regulations. The process of refining the
crude oil into various products is normally carried out in refineries. Although there is a
long history of using crude oil as burning material, it is not until 1860 that the first real
petroleum refinery was built at Titusivlle, PA, at a cost of about $15,000 (Nelson, 1958).
Since then, petroleum refining has developed into a major industry in almost every
country. In 1998, the total worldwide crude oil production was about 73 million barrels
per day. The worldwide refining capacity is about 67 million barrels per day (Beck,
1999). Population growth and continued world economic expansion produce an ever-
increasing demand for fuel. This indicates that worldwide refining capacity will keep
increasing in the future.
Although conventional fuel from petroleum faces challenges from fuels obtained
from renewable resources, it is expected to remain a dominant player at the beginning of
the 21st century (Bensabat, 1999). The worldwide oil reserves in 1999 are about I trillion
barrels. If the oil production is maintained at the level of 1998, the current oil reserve can
sustain about 39 years. With technical advancement and further exploration, the oil
reserve keeps increasing. The reasonable forecast is that crude oil will stay as the largest
fuel resource in the first half of 21st century.
Petroleum refining has developed from simple separation in the early stage to a
very complex process today. The early development of petroleum refining technology
includes applying continuous distillation, vacuum distillation, and thermal cracking, etc.
The refining industry has undergone tremendous expansion and change since World War
II. Many new processes with high efficiency have been invented. These new processes
include Fluidized Catalytic Cracking (FCC), Catalytic reforming, Alkylation, catalytic
desulfurization, delayed coking, etc. Enormous increases in the size of process units, new
catalytic processes, shifting product demands, and new sources of petroleum from tar
sands and oil shales have made present-day technology and economics of petroleum
refining a very complex and sophisticated science (Gary and Handwerk, 1984).
For decades, the large and mature U.S. transportation fiiel market has been
dominating the global petroleum supply (Bensabat, 1999). Consequently, transportation
fuels including gasoline, jet fuel and diesel, has the largest quantity among all the
products made from crude oil. By far, passenger vehicles make up the largest sector with
respect to number of vehicle and fuel consumption. Most of U. S. passenger vehicles are
fueled by gasoline in conventional combustion engines. They consume about 8.5 million
barrel per day of gasoline, or 12% of global petroleum demand (Bensabat, 1999). Most of
transportation fuel consumed in U. S. is produced domestically. Due to such massive
production, many refineries in U. S. are fuel-oriented refineries whose main fiinction is to
produce transportation fiiel. There are other types of refineries. For example, some
refineries provide aromatics and olefin for petrochemical pleints. Some refineries also
produce lubricants and asphalt while producing transportation fuel.
In the past thirty years, innovation in new products and new processing
approaches has slowed dowoi. Even the capacity expansion has slowed down in the
United States. No grass-root refinery has been buih after 1980. The emphasis ofthe
refining industry has shifted to improving economic performance ofthe existing plants.
Increasing competition in the refining business and stricter environmental and safety
pressures have forced refiners to invest more money and time on process monitoring,
process control and optimization. Recent crude oil price decrease resulted in a sharp
decline in the refining margin, and consequently, decline in revenues and profits for
North American oil and gas companies (Beck, 1999). In order to survive in such
changing market, companies needs to lower the operating cost and increase the revenue
to remain competitive. Recent industrial practices showed that advanced process control
and optimization are the way to accomplish this.
The common goal of all refiners is to provide safe, profitable, quality product
manufacturing (Pelham and Pharris, 1996). Process control and optimization have
become indispensable tools to realize this goal. Significant advancement in
instrumentation and computers has made the implementations of process control and
optimization cheaper and more reliable.
Before we go into the discussion of optimization which is the main topic of this
dissertation, we need to introduce process control first since the benefits of optimization
can not be realized without implementation of process control. Chemical Process Control
(CPC) is concerned with operating a plant such that the product quality and production
rate specifications are met in a safe and reliable manner (Riggs, 1999). Process Control
is also necessary to reach other operation objectives such as environmental protection,
equipment protection, and profit return, etc. (Marlin, 1995).
The controllers in a refinery were mostly PID Controllers 20 years ago. Two key
technical developments occurred during the late 1970s that led to dramatic acceptance
and growth in the number of advanced control systems. The two key developments were
the Distributed Control System (DCSs) and Model Predictive Control (MPC) Technology
(Pelham 1993). The DCS combines the hardware and software needed for data
acquisition and basic control fiinctions. It is based on using a number of local control
units which have their owoi microprocessors and are connected by shared communication
lines as well as connected to operator/engineer consoles, a date acquisition system, and a
general purpose computer (Riggs, 1999). The industrial standard for MPC is dynamic
matrix control (DMC) technology, which was implemented in late 1960s. Since then,
model-based process control has become a common industrial practice in refining
industry. It has been estimated about 3%o incremental profit can be realized through
implementing model-based process control (Ellis, 1998).
The basic task for a controller is to maintain a process variable at a given setpoint.
Since every refining company needs to make profit, these setpoints must be as
economically favorable as possible. This economic target can be expressed in different
form: largest production, the greatest profit, the minimum cost, and the least energy usage
(Edgar and Himmelblau, 1988).
Now the question is raised: how to choose those setpoints which make the process
most profitable while observing process constraints and meeting all product
specifications? This is an optimization problem. In some refineries, the values of those
setpoints are set by operators and process engineers based on their experience and
intuitions. Such decision making approach can not be consistent due to different
backgrounds of people making the decisions.
In a modem refinery, a more systematic approach is followed. Usually, there is
one experienced individual known as a planner/scheduler. The responsibility of this
individual is to develop an operating plan for the next several days, given current levels
of crude stocks, operating capacities, offtakes and inventory constraints (Pelham and
Pharris, 1996). He may use linear programming as a pragmatic guideline. Today, large-
scale linear programming (LP) technology is well established with respect to planing and
scheduling and no major change in that status is anticipated (Pelham, 1993). After the
solution is found using an LP, the planning engineer sends the values in the solution as
setpoints to engineers in unit operation. Usually, the planer/scheduler gives a range for
the setpoints to a process variable instead of a single value in order to enable some
flexibility. In plant operation, the engineers in a specific unit then use the information to
choose the setpoints for each respective control loop. They also need to decide the
setpoints that are not provided by the LP.
There are several obstacles in using LP for operation scheduling in a refinery: (1)
If the refinery is operated at constant conditions and LP model well represents the
refining processes at the fixed operating conditions, LP is the answer to scheduling
problem in a refinery. However, this is rarely the case. Due to continuous change ofthe
crude quality, and fluctuating market demands and prices, the operation of a refinery is
always changing to adapt to such changes. Since refining processes are inherently
nonlinear, especially those processes with reaction systems, linear models will not be able
to precisely represent those processes with linear relationship, and (2) the linear models
used by the planner/scheduler are simplified models that do not include all the details
(Jones, 1999). This simplification is done to speed up LP solution finding. The linear
models are developed by linearizing a nonlinear process at certain operating conditions.
When the operating conditions changes, the linear models become invalid. Hence, the
optimization results based on such linear models are susceptible to error, and (3) the LP
does not consider the elements of time and storage. It assumes that all activities occur
simultaneously (Hartmann, 1997), which is not the case in reality, and (4) the LP is only
suited to find an optimum at the intersections of constraints. However, it is quite possible
that the optimal solution of refinery-wide operation lies inside constraints due to the
nonlinearity ofthe optimization problems.
A possible remedy to linear programming is to update the linear models
continuously based on plant data (Cutler, 1999). However, with the increasing
complexity of refineries, it is very difficult to maintain the required data consistency
(Hartmann, 1997). In addition, the continuous updating will add tremendous workload.
On the other hand, nonlinear models have been built and single-unit optimization
has been implemented for several important refining processes, such as crude unit, FCC
unit, and gasoline blending, etc. Some software vendors have provided simulation
software packages for modeling, such as AspenPlus from Aspen Technology, PR02 from
Simulation Sciences, and HYSYS from Hyprotech, etc. Some vendors even supply
optimization packages, such as real-time optimziation (RT-Opt) from Aspen Technology,
and Romeo process optimization from Simulation Sciences. It has been estimated about
3% incremental profit can be realized through implementing real-time on-line
optimization on single unit (Ellis, 1998).
However, a single-unit optimizer covers only a subset of a large problem
(Friedman, 1995). It is hard to make the optimal solutions from single-unit optimization
to be consistent with the plant-wide operation strategy. The reason is that single-unit
optimization needs correct price information ofthe intermediate streams. However, such
price information is not always available from the market since some intermediate
streams are not sold on the market. Intermediate product price is a function of quality and
product rate. It also depends on tankage consideration, lifting schedule, the current
content of intermediate tanks and finally on future crude runs (Friedman, 1995). To solve
this problem, some engineers used the shadow prices ofthe refinery LP to evaluate the
prices of intermediate products. A shadow price is the change in the optimal value ofthe
objective fimction per unit change in an active constraint. However, the shadow prices of
LP are not valid when the operation shift to different product qualities or a different
schedule. The single-unit optimization based on imprecise price information can results
in conflict with plant-wide operation strategy.
In order to overcome the inherent disadvantages of LP and single-unit nonlinear
optimization, a new approach is applied to tackle the optimization problem in the
refinery. In this work, several nonlinear single-unit models are integrated into a refinery-
wide model and this model is used in nonlinear optimization to find the optimal operating
conditions for the entire refinery. The main advantage of such an approach is that it uses
detailed nonlinear models to represent nonlinear refining processes and it does not need
prices of intermediate streams.
The primary objectives of this work are to:
1. Develop nonlinear single-unit models for important processes in a fuel-oriented
refinery.
2. Integrate developed single-unit models into a overall refinery-wide model.
3. Carry out nonlinear optimization using the developed models to find the optimal
operating conditions for the whole refinery.
4. Evaluate the incremental profit of using such optimization approach by comparing
with results from normal operating conditions.
5. Compare refinery-wide nonlinear optimization with single-unit nonlinear,
optimization.
Modeling every unit in detail is formidable task if not impossible. However, a
detailed plant-wide model is not necessary for optimization to capture the major part of
the benefit obtained from nonlinear optimization just like LP does not need to use
detailed models to be helpfiil in guiding the planer/scheduler. To keep this work
manageable, the following approach is applied. Detailed models for four major units
were developed. Those units include crude unit, FCC unit, catalytic reformer, gasoline
blending. Simplified models are used to represent other units in the refinery.
The previous research on refinery modeling and optimization is reviewed in
Chapter 2. The single-unit model development and benchmarking are described from
Chapter 3 to Chapter 7. Those units including crude unit, FCC unit, catalytic reformer,
gasoline blending, gas plant and alkylation unit, etc. Most ofthe models were
benchmarked against the industrial data obtained from a fuel-oriented refinery. Chapter 3
discusses the development of a simplified first principle model of a crude unit and the
model benchmarking. In Chapter 4, the rigorous steady-state model of a Model IV FCC
unit is briefly discussed. The emphasis is on the modification ofthe existing model to fit
the FCC unit in the ftael-oriented refinery. Again, the model benchmarking is discussed.
In Chapter 5, the rigorous steady-state model of a catalytic reformer unit is briefly
discussed. The emphasis is on the modification of the existing model to fit the reformer
unit in the fuel-oriented refinery. Again, the model benchmarking is discussed. Chapter 6
discusses the modeling of gas plant, alkylation unit and other auxiliary units in the
refinery. Chapter 7 discusses the development a detailed model ofthe gasoline blending
process. Chapter 8 discusses the optimization results obtained from applying successive
quadratic programming (SQP) technique to the refinery-wide model. The comparisons
among nonlinear refinery-wide optimization, normal operating conditions and nonlinear
single-unit optimization are also given. The results of this work and recommendations for
future studies are presented in Chapter 9.
CHAPTER 2
LITERATURE REVIEW
This research is divided into two parts: (1) Single-unit modeling and feed
characterization; (2) Steady-state optimization. The literature review is also divided into
the same two parts.
2.1 Single-Unit Modeling and Feed Characterization
The focus ofthe present work is the modeling and optimization of a fiiel-oriented
refinery. A typical fiael-oriented refinery includes the following major units: crude unit,
FCC unit, catalytic reformer, gasoline blending, gas plant, and alkylation unit. For a
large-scale refinery processing heavy crude, a hydrocracker or a delayed coker is often
included to process the heavy end ofthe crude. The refinery that the model was
benchmarked against is a small-scale refinery processing light crude. Hence, a
hydrocracker or delayed coker was not installed. Beside these major units, there are some
auxiliary and pretreating units in the refinery: naphtha desulfurizer, diesel hydrotreater,
Debutanizer, naphtha splitter, Claus and Scott units, Merox and Amine treater, fuel gas
mix drum, and gas turbines, etc. They are also important in the refinery-wide
optimization because their capacity may become active constraints in searching the
optimal operating conditions.
Refining process modeling is a hot topic in both industry and academia. The
industry has an edge in this area because it has large amount of operation data which
accurate and detailed models can be built on. However, refining companies rarely publish
their models. Models are built in academia for control and optimization studies. The
models in the public domain usually are based on a specific set of industrial data. Hence,
they can not be used directly in this work without further benchmarking.
In U.S., 40-50 percent of gasoline comes from FCC gasoline. Therefore, FCC unit
is a critical unit when studying the economy of a fuel-oriented refinery. Ellis (1996)
developed a steady-state model of FCC unit on the basis of a Model IV FCCU Dynamic
Simulator (McFarlane et al., 1993) while using the Mobil ten-lump yield model (Jacob et
8
al., 1976) to predict product yields. The ten-lump model includes eight lumps for gas oil
components, one lump for FCC gasoline and another lump for coke and light gas. The
detailed composition modeling for FCC gasoline and light gas is not available from the
ten-lump model. Empirical correlations were used in the FCC model to calculate octane
number of FCC gasoline and the light gas composition. A detailed literature survey on
FCC modeling and optimization is included in the thesis of Ellis (1996).
Catalytic reformer produces about 20-35 percent gasoline ofthe gasoline pool.
Reformate, the gasoline blending stock produced in reformers, is the major gasoline
booster in the gasoline pool. A rigorous steady-state model of catalytic reformer was
developed by Taskar (1996). The model uses 35 components, including real chemical
species and pseudo-components, and 36 reactions to model the reaction system of a
reformer. The model provides detailed information of reformate. A detailed literature
survey on reformer modeling and optimization is included in the dissertation of Taskar
(1996).
The literature review here focuses on crude unit modeling and gasoline blending
modeling. A crude unit includes pretreating facilities, preheat train, fiimace,
atmospheric tower, vacuum tower and separation train. A detailed model, which includes
all the units, has not been presented in literature. Instead, most ofthe publishing research
focused on the tray-to-tray rigorous modeling ofthe atmospheric and vacuum towers.
Edmister (1955) proposed an integral method for making petroleum distillation
calculations. Taylor and Edmister (1971) applied the method to a gasoline rerun column.
However, this method requires a curve of K versus mole fraction, which is not easily
obtained. In addition, it uses a trial and error in root searching, which causes slow
convergence in the digital computation. Cechhetti et al. (1963) studied a 62-stage Exxon
crude tower and built a steady-state model which uses 6-method of convergence. Hess et
al. (1977) and Holland and Liapis (1983) used 2N Newton-Raphson method to solve this
steady-state model. In the above studies, the pseudo-component approach is used in vapor
liquid equilibrium (VLE) calculations. The crude is divided into 35 pseudo-components
in order to represent the true-boiling-point (TBP) curve. Cechetti et al. (1963) first
presented the physical properties ofthe pseudo-components. Hess (1977) curve-fitted
those physical properties into formulas to correlate the equilibrium K values and
enthalpies as a function of temperature only.
Since the conventional bubble-point algorithm in temperature calculation is
inherently unstable in the face of enthalpy inversion caused by the extremely wide-
boiling nature ofthe crude feed, Chung and Riggs (1995) proposed a dynamic stagewise
adiabatic flash (DSAF) algorithm which efficiently provides stable solutions for an
extensive operating range. A dynamic model was constructed based on DSAF algorithm
and was used for control studies.
All the above studies used sequential modular approach in modeling. Mizoguchi
et al. (1995) constructed a dynamic model using open-equation approach for control
study. The model is based on a crude unit in the Petro-Canada Ontario Refinery. It uses
34 pseudo-components to represent the crude feed. The complete model has 2297
differential/algebraic equations, 263 fixed external variables, 9 external manipulated
variables, and 2297 dependent variables.
The present work concentrates on the economic optimization ofthe whole
refinery instead of a single unit. Hence, it is not strictly necessary to build a tray-to-tray
rigorous model for atmospheric tower and vacuum tower. The most important
characteristic that a crude unit model must have is that it should be able to predict the
volumetric flow rate and properties ofthe products accurately. Watkins (1979) presented
a design method which can be used to build a first-principle steady-state model. This
approach uses material and energy balance to calculate the sfreams in and out of each
fractional section between adjacent side-draw trays. Empirical correlations are used to
evaluate the properties of feed and products. Since this approach serves the main purpose
of this work, the crude unit has been modeled using the approach outlined by Watkins
(1979).
Gasoline cuts coming from various upstream process units are blended into
various grades of gasoline in the gasoline blending unit. The purpose ofthe gasoline
blending model is to predict the property specifications for various grades of gasoline
10
from the properties of incoming gasoline cuts. Some specifications ofthe final product
can simply be calculated as the summation of corresponding properties ofthe blending
agents. Such specification include oxygen content, sulfur content, benzene content, etc.
Other specifications need a more complex, usually nonlinear, model in order to make
accurate predictions. Such specifications include octane number, Reid Vapor Pressure
(RVP), and volatility. The refinery considered in this work mainly produces regular
grades of gasoline. Reformulated gasoline which is used in some areas of U.S. has more
specifications. The calculations of extra specifications of reformulated gasoline are not
included in the gasoline blending model.
There are two approaches used in octane number calculation ofthe gasoline
blending model. The first approach is to convert the octane numbers of gasoline cuts into
blending octane values and calculate the octane of final gasoline on a volumetric average
basis. This approach is widely used in industry. Gary and Handwerk (1984) and
Unzelman (1996) gave the blending octane values of some gasoline cuts. However,
blending octane numbers are usually derived from regression analysis of a small data set,
e.g., those derived from gasoline of a single refinery. Muller (1992) used the concept of
"excess octane number" and formulated the equations to calculate the deviation of
blending octane number from ideal blending. The excess octane numbers are derived
directly from original refinery blending data. This approach is very similar to the
blending value approach. Zahed et al. (1993) presented ein empirical correlation of
calculating gasoline octane number using constants regressed from a specific refinery.
The disadvantage ofthe blending value approach is that blending values can only
be to calculate the blends where the blending values are derived from. The blending value
of one gasoline cut may change dramatically when blending is changed. The advantage of
the approach is that if the blending values are derived from a complete gasoline blending
study in a refinery, the blending value approach is more accurate for that specific refinery
than general methods introduced below.
A more theoretical approach is the interaction method. It is well known that the
octane numbers do not blend linearly due to nonlinear interactions among gasoline cuts.
II
If the interaction coefficients among various gasoline cuts can be calculated, the octane
number of blended gasoline can be predicted accurately. Schoen and Mrstik (1955) first
presented a graphical correlation for predicting octane numbers of binary blends from the
octane numbers and olefin content ofthe two base blending stocks. Stewart (1959) used
the same approach but made the method more self-sustained and expanded it to multi-cut
blends. The interaction method is first proposed by Morris (1986). The key to the
accuracy ofthe interaction method is having accurate interaction coefficients. Interaction
coefficients can be derived from an octane blending study wherein accurate octane data
are obtained on all components and all 50:50 blends (Morris, 1986). Morris et al. (1994)
then expanded the interaction method to multi-agents blending.
The disadvantage of Morris's approach is that interaction coefficients can only be
used for the blending ofthe specific set of gasoline cuts where the interaction coefficients
were derived from. The interaction parameters not only depend on types ofthe gasoline
cuts, but also depend on the octane levels and octane difference ofthe gasoline cuts in the
blending. The value of interaction coefficient between two gasoline cuts can vary from
large negative to large positive in different blending situations (Twu and Coon, 1996).
The ideal solution is to have an accurate generalized method to predict interaction
coefficients without the need for a blending study. Such a method is commercially
available but is not available in open literature (Morris, 1985).
The interaction method proposed by Morris only considers the interaction
coefficients among blending cuts. It does not use the information ofthe composition of
gasoline cuts. Twu and Coon (1996) proposed a component-oriented interaction approach
that is general and can be used in any gasoline blending without blending studies. This
approach only needs the octane numbers of gasoline agents and the information ofthe
concentrations of olefins, aromatics, and saturates in the gasoline cuts. A universal set of
the binary interaction parameters is given. Twu and Coon (1997) further extend this
method to be more consistent in methodology by applying the same binary interaction
parameters to components in each gasoline cut and their blends. Due to the lack of a
12
complete blending study ofthe refinery considered in this work, Twu and Coon's
approach was used in the octane number calculation in this work.
Reid vapor pressure (RVP) is widely used as a criterion to measure the volatility
of gasoline. Thus, the accurate prediction of RVP is critical in product blending and
refinery-wide optimization. A similar blending value approach is widely used in industry.
Chevron Research Company developed an empirical method which uses vapor pressure
blending indices (VPBI), which is a fiinction ofthe RVP ofthe individual blending cuts.
The table of VPBI versus RVP is given in Gary and Handwerk (1984).
Stewart (1959) formulated the first theoretical method to predict RVPs of ftiel
blends. It is based on the standard RVP test defined by the American Society for Testing
and Materials (ASTM) under the designation ASTM D323-56. It makes a VLE
calculation with respect to the Reid test conditions with the assumption of ideal gas phase
and uses the Sounders' state of equation to calculate the vapor-liquid equilibrium K
values. However, this method ignores the presence of air and water vapor in the Reid
test. It also assumes that the volatile components have the molar density of butanes and
the nonvolatile components have the thermal expansion characteristics of n-octane.
Vazquez-Esparragoza et al. (1992) followed the same approach but used the more
accurate Soave-Redlich-Kwong equation of state in VLE calculation. Stewart's approach
represents gasoline blending cuts in pseudo-components and requires the physical
properties of those pseudo-components, which can only be obtained through empirical
correlations. This adds to the inaccuracy of this approach. Since Chevron's VPBI method
is accurate enough (Gary and Handwerk, 1984) and widely used in the refining industry,
the VPBI method is used in the RVP calculation in the present work.
Empirical correlations are used in the feed characterization for the FCC unit and
the reformer unit. The most important feed information is the compositions of paraffins,
olefins, naphthenes, and aromatics (PONA). The PONA information in FCC feed can be
calculated by TOTAL method (Dhulesia, 1986) and n-d-M method (ASTM, 1985). The
reformer feed characterization uses the crude assays obtained from the refinery
considered in this work.
13
After a refinery-wide model had been developed, optimization studies were
carried out to find the optimal operating conditions for the whole refinery.
2.2 Refinery-Wide Optimization
Refinery-wide optimization involves many decision variables and constraints.
Linear Programming (LP) is the most widely used technique in refinery operation
optimization, which is called planning and scheduling in industry. The linear
programming was first proposed by Dantzig in 1947 to refer to the optimization problems
in which both the objective function and the constraints are linear (Dantzig, 1963). LP
problems exhibit the special characteristic that the optimal solution ofthe problem must
lie on some constraints or at the intersection of several constraints. Dantzig first proposed
the most popular algorithm in LP called the Simplex algorithm in 1947. Symonds (1956)
used an LP to solve a simplified gasoline refining and blending problem. The advantage
of LP is its quick convergence and ease to implement. However, it is suitable only for
linear and nearly linear process.
One method to solve a nonlinear problem by using LP is to repeatedly linearize
the objective function and constraints at some estimate ofthe solution (Edgar and
Himmelblau, 1988). This procedure is the basis of a method called successive linear
programming (SLP). According to Edgar and Himmelblau (1988), SLP has been
demonstrated to be successful on problems with a moderate degree of nonlinearity. The
disadvantage of SLP is that it may converge very slowly when the optimum lies in the
interior ofthe feasible region and when there are a large number of nonlinear variables.
Generalized Reduced Gradient (GRG) algorithm is a popular nonlinear
optimization method used in industry. Basically, GRG uses a reduced form of gradient to
find the search direction. It defines new variables that are normal to the constraints and
expresses the gradient in terms of this normal basis. GRG may sometimes have difficult
to return from an infeasible point to the feasible region during the search. Generally, the
performance ofthe GRG algorithm is comparable to that of Successive Quadratic
Programming discussed below.
14
Successive Quadratic Programming (SQP) is widely used to solve large-scale
nonlinear problems. Edgar and Himmelblau (1988) made the statement that SQP might
be the best in solving nonlinear programming problems. Riggs (1994) suggested using
SQP for large-scale problems. SQP is the method used in the leading on-line optimization
software package, RT-OPT, developed by Aspen Technology, Inc. SQP approximates
the objective function locally by a quadratic function and linearized constraints. The
search direction is decided by solving the quadratic programming subproblem. After that,
a minimization algorithm is used to calculate a step size in the search direction. Several
tries of line search are employed in deciding the appropriate step size. SQP repeatedly
applies the procedure until it successfully finds a point that satisfies the first-order Kuhn-
Tucker condition or fails after certain number of tries. Detailed mathematical proof on
SQP can be found in Gill et al. (1981). SQP usually requires less iterations and converges
much faster to interior optimum points than Successive Linear Programming. It should be
noted that SQP may go through an infeasible path while searching for the optimum. The
bounds and linear constraints are always satisfied along the search while the nonlinear
constraints will not generally be satisfied until an optimal point is reached (Gill et al.,
1986). The NPSOL 4.0 (Gill et al., 1986) software package is used as the optimization
engine in this work.
15
CHAPTER 3
CRUDE UNIT MODELING
3.1 Introduction
The crude unit separates the crude into various product cuts with different boiling
ranges. The downstream units, such as FCC unit, reformer, further process these cuts to
make final products. A block diagram of a fuel-oriented refinery is shown in Figure 3.1.
It can be seen that downstream units receive their feed from the crude unit.
The crude unit is the first major processing unit in almost all the refineries. The
products from crude oil are usually characterized by their boiling range. Many properties
of crude cuts are related to the boiling range. For example, molecular weight and sulfur
content increase with boiling range. The aromatics content also increases with boiling
range while the pEiraffin content decreases with boiling range.
Distillation is the cheapest and the easiest method to separate crude into different
cuts. Higher efficiencies and lower costs are achieved if the crude oil separation is
accomplished in two steps: first, by fractionating the total crude oil at a atmospheric
pressure; then, by feeding the high-boiling bottoms fraction from the atmospheric still
(atmospheric tower) to a second fractionator (vacuum tower) operated at a high vacuum
(Gary and Handwerk, 1984). The flow diagram ofthe atmospheric tower and the vacuum
tower is shown in Figure 3.2.
From the point of view of plant-wide economy, the cut points between adjacent
cuts in the distillation columns ofthe crude unit are among the most important operating
decision points. If the crude cuts are not specified properly, the downstream processing
units may have trouble in processing the cuts to make products within the specifications.
The quality of crude cuts may also affect the normal operation of downstream units, such
as catalyst poisoning, equipment corrosion, etc. Therefore, operating atmospheric tower
and vacuum tower properly is crucial for the entire refinery operation.
16
u
0)
-o c
17
^
Atmospheric Tower
^
29
- • Vapor Distillate
f ^ Water Liquid * • Light Naphtha
U^ 4 Steam
~:©*~fieavy Naphtha
Steam Kerosene
— •
Steam
-:0^ Diesel
Steam
— * ^ ^ — • Atmospheric Gas Oil
*• To jets
>• Light Vac. Gas Oil
*• Heavy Vac. Gas Oil
Overflash
*• Vac. Bottoms
Figure 3.2 Schematic of a Fuel Type Crude Unit.
18
The crude unit modeling in this work focuses on the atmospheric tower and the
vacuum tower, the core ofthe crude unit. The modeling procedure as described in
Watkins (1979) and Lin (1988) has been followed in most ofthe work.
In the crude unit, the crude from crude storage tanks first goes through
dewatering, desalting and then exchanges heat with the side-stream products and
pimiparounds from the atmospheric tower and the vacuum tower. The crude is further
heated in a furnace before entering the bottom ofthe atmospheric tower called flash zone.
The crude then partially vaporizes in the flash zone due to the pressure drop at the
injection point.
The atmospheric tower has only a rectifying section. The vapor formed in the
flash zone flows upward and is separated into overhead gas, light naphtha (LN), and
several side-draw products: heavy naphtha (HN), light distillate (LD), heavy distillate
(HD), and atmospheric gas oil (AGO). These side-draw products are drawn out ofthe
tower from the side-draw trays ofthe atmospheric tower. The light naphtha is further
separated into liquidified petroleum gas (LPG) and light straight-run gasoline (LSR). The
heavy naphtha is fed to the reformer to make reformate, a gasoline blending stock. The
light distillate is further treated to make jet fuel. The heavy distillate goes through the
hydrodesulfurization unit to make diesel. The AGO goes through the rerun unit to recover
some diesel components and the rest ofthe AGO is fed to the FCC unit to produce FCC
gasoline.
The nonvolatile part ofthe crude, which is called the reduced crude, exits from
the bottom ofthe atmospheric tower and is fed to the vacuum tower for ftirther
separation. The function of vacuum tower is to maximize the extraction of distillate
liquids from the raw crude feed. The reduced crude is first heated up in a vacuum tower
furnace and then enters the flash zone ofthe vacuum tower at the bottom. The reduced
crude vaporizes in the flash zone due to pressure drop. The vapor flows upward and is
separated into light vacuum gas oil (LVGO) and heavy vacuum gas oil (HVGO) and
product cuts are drawn from the tower on side-draw trays. The nonvolatile part ofthe
vacuum tower feed leaves the bottom ofthe vacuum tower and goes for further treatment.
19
The LVGO and the HVGO are combined and fed to FCC unit. The vacuum residue goes
through the residue oil solvent extraction (ROSE) unit to recover the gas oil in the
vacuum residue. The rest of residue is further treated to make No. 6 fuel oil.
The crude unit model in this work includes an atmospheric tower, a vacuum
tower, a debutanizer, a rerun unit, a ROSE unit and a naphtha splitter. The inputs to the
crude unit model are crude assays and crude feed rate, the ASTM 95% points ofthe light
naphtha, the heavy naphtha, the light distillate and the heavy distillate, the true boiling
point (TBP) cut point ofthe heavy vacuum gas oil, atmospheric tower fiamace outlet
temperature, and vacuum tower furnace outlet temperature. The model calculates the
volume and the properties of those side-draw products and the vacuum bottom stream. It
also calculates the temperature profile in the towers.
Two standard tests are defined here since they will be often referred to in the
follow text. The ASTM 95%) point is the temperature corresponding to the 95 vol.%) on
the distillation curve measured by the distillation test defined by the American Society
for Testing and Materials (ASTM) under the designation of ASTM D86. The ASTM
method D86 is an easy test that is run in an Engler-flask at atmospheric pressure with no
trays or reflux between the stillpot and the condenser. TBP curve is measured by the
ASTM D2892 test, which uses a distillation column with 14 or more theoretical stages
and reflux ratio of 5 or more. Temperature at any point on the TBP-volumetric yield
curve represents the actual boiling point ofthe hydrocarbon material present at the
volume percentage corresponding to that point. ASTM D2892 test is more expensive than
ASTM D86 test.
3. 2 Atmospheric Tower Modeling
The schematic ofthe atmospheric tower model is shown in Figure 3.3. The text
box with gray background has internal iterations inside. The text box with white
background does not have internal iterations. The indices ofthe text boxes with gray
background are referred to when discussing some calculations with iterations in the
following text.
20
Start
Side-draw Product Specifications,
ASTM 95% point TBP 100% point
Calculate the mixed crude's curves of TBP, API gravity,
and sulfur content
Operating pressure, Crude Temperature,
steam rate
Crude Assays, Crude Makeup
Calculate the properties of side-draw prodcuts
Steam rate, side stripper type
Calculate flash zone temperature, Calculate the
extra energy left in the tower after flash zone
(1)
Calculate the properties of unstripped product
witlidrawn from the draw trays (2)
Draw tray location Calculate the temperature of
the draw trays (3)
Calculate the temperature of the condenser
Calculate the temperature of side-draw products
(4)
End
Figure 3.3 Schematic ofthe Model ofthe Atmospheric Tower ofthe Crude Unit.
21
3. 2.1 Feed Characterization
The refinery that the model was benchmarked against processes four different
crude oils: Crude A, Crdue B, Crdue C, Crdue D. They are from different resources with
slight different properties. The refinery uses a Linear Programming (LP) technique for
scheduling and planning. In the present work, nonlinear optimization methodology is
used and results are compared with LP report. The normal crude unit feedstock makeups
are given in Table 3.1.
Table 3.1 Feedstock Makeups.
Crude Type
Crude A
CrudeB
CrudeC
CrudeD
API Gravity
37.4
34.2
33.7
36.1
Sulftir,
0.08
0.54
1.04
0.32
Wt. Summer Mode Vol.%
17
8
48
27
Winter Mode Vol.%
0
0
0
100
All four crudes processed in the refinery can be characterized as light crude type.
The specific gravity of these crudes range from 33.7 to 37.4. Among these crudes, only
crude C is a sour crude and has a sulfur content of 1.04 wt.%. Other crudes have very low
sulfur content, less than 0.54wt.%.. The refinery was designed to process light crude with
low sulfur content. It can not process heavy crudes or crudes with high sulfur content.
The volumetric flow rate of a side-draw product is equal to the volumetric flow
rate ofthe total crude feed multiplied by the volumetric percentage ofthe side-draw
product in the crude feed. Once the initial boiling point (IBP) and end boiling point
(EBP) ofthe side-draw product are known, the volume percentage ofthe side-draw
product can be found from the true boiling point (TBP)-volumetric yield curve ofthe
whole crude as shown in Figure 3.4.
22
1000
^ 800 £ 3
5 600 (U Q. E jfl) 4 0 0
200
LN
HN LD
HD
AGOLVGO H V G p / Resid.
10 20 30 40 50 60 volume%
70 80 90 100
Figure 3.4 Crude Cuts on a TBP-Volumetric Curve.
The volume percentage of a side-draw product is calculated using the formula
given below:
%V,=%V,,,,-%V^j,„ (3.1)
where
i - side-draw product; light naphtha (LN), heavy naphtha (HN), light distillate (LD),
heavy distillate (HD), atmospheric gas oil (AGO), light vacuum gas oil (LVGO), heavy
vacuum gas oil (HVGO),
%)Vi- volume percentage ofthe side-draw product i in the crude,
%oVijBp, %iVi,EBP- volume percentage corresponding to the IBP ofthe side-product i on
the TBP-volumetric yield curve ofthe crude, respectively.
Hence, the TBP-volumetric yield curve ofthe crude feed is essential to calculate
the volumetric flow rates ofthe side-draw products.
The crude assays of these four types of crude oils were obtained from the refinery.
The TBP-volumetric yield curve ofthe crude oil entering the crude unit is calculated
23
The crude assays of these four types of crude oils were obtained from the refinery.
The TBP-volumetric yield curve ofthe crude oil entering the crude unit is calculated
based on the TBP curves of each crude and its volumetric fractions in the feed. Ideal
mixing is assumed here because these crude oils themselves are complex mixtures where
deviations from the ideal mixing are assumed to be cancelled out. In addition, they are all
low sulfur, light crudes with similar properties. The equation to calculate the TBP curve
of mixed crude oil is given below:
v(T) = Y^v,(T)xvol,, (3.2)
where
i - crude type: A, B, C, and D,
v(T)- volumetric percent ofthe mixed crude with boiling temperature lower than T,
vol.%,
Vi(T)- volxmietric percent of crude i with boiling temperature lower than T , vol.%,
vol,- volume fraction ofthe crude i in the mixed crude.
The TBP curve ofthe mixed crude oil used in Summer Mode operation is shown
in Figure 3.5. For the convenience of digital computations, the TBP curve ofthe mixed
crude is regressed using a polynomial expression. The regression was carried out in
MathCad Plus 6.0 from Mathsoft Incorporation. The order of a polynomial expression is
selected by looking at the mean square errors ofthe predictions. In this work, a
polynomial expression usually has the order in the range of 5 to 7. For this polynomial
expression and any polynomial expression mentioned in the following text, the order and
the constants ofthe polynomial expression are listed in Appendix A. It should be noted
that different TBP curves are used in the Summer Mode and the Winter Mode due to the
fact that different crudes are used for different operation Mode.
24
" 600
10 20 30 40 50 60
volume%
70 80 90 100
Figure 3.5 TBP Curve ofthe Crude Used in Summer Mode.
Another curve required by the crude unit model is the API-volumetric yield curve.
The relation between API Gravity and specific gravity is given below:
141 5 API = ^^!±^-131.5,
SPG (3.3)
where
API- API gravity,
SPG- specific gravity.
The API-volumetric yield curve ofthe mixed crude is calculated from the API-
volumetric yield curves ofthe crude sources. The procedure of calculating the API-
volumetric yield curve ofthe mixed crude is given below:
a. Approximate the API-volumetric yield curve of a particular crude by a polynomial
expression.
25
b. Use a certain number of intermediate temperature points to represent the boiling range
ofthe crude. For each temperature, calculate the corresponding volume percentage from
the TBP-volumetric yield curve ofthe crude.
c. For each volume value, find the corresponding API value using the API-volumetric
yield curve ofthe crude.
d. Convert API to specific gravity using equation 3.3.
e. Repeat step a to step b for all four types of crudes.
f. The API ofthe mixed crude is calculated using the following equation:
SPG(v) = Y, SPG^ (v) X vo/. , (3.4)
where
SPG(v)- specific gravity of mixed crude,
SPGi(v)- specific gravity of crude i,
voli- volume percentage of crude i.
g. Convert specific gravity to API gravity.
h. Construct API-volumetric yield curve using the calculated API and approximate it by
a polynomial expression.
The API-volumetric yield curve ofthe mixed crude oil used in Summer Mode operation
is shown in Figure 3.6.
The sulfur-volumetric yield curve ofthe mixed crude is also calculated using
similar procedure. It should be noted that the sulfur concentration is expressed as weight
percentage instead of volume percentage. The sulfur-volumetric yield curve ofthe mixed
crude oil used in the Summer Mode is shown in Figure 3.7. The sulfur-volumetric yield
curve was also regressed using a polynomial expression. The polynomial expressions
developed above are then used repeatedly in the atmospheric tower model and vacuum
tower model.
The crude processed in Winter mode operation has one crude type and no mixing
calculation is required.
26
0 10 20 30 40 50 60 70 80 90 volume%
100
Figure 3.6 API Curve of the Crude Feed Used in Summer Mode.
2.5 T
Figure 3.7 Sulfur Curve ofthe Crude Feed Used in Summer Mode.
27
3.2.2 Side-Draw Product Calculation
The boiling range specification for a side-draw product is usually defined by
ASTM 95% point. Since TBP test is more expensive, TBP test is only run on crude oils
but not on product fractions. Hence, the side-draw product specification is defined using
ASTM data instead TBP data. Conversion from ASTM 95% points to TBP cut points in
the model is required in order to use the TBP-volumetric curve ofthe crude feed.
It is assumed that the atmospheric tower and the vacuum tower are able to
separate the side-draw products according to the ASTM 95%) specifications. This
assumption is made based on the fact that the values ofthe ASTM 95% points are limited
in a narrow range in the optimization studies and the crude unit has enough separation
capacity to satisfy the separation requirements in this range. This assumption is
confirmed by the operating personel in the refinery considered in this work. The
properties ofthe side-draw products can then be calculated using the ASTM 95% point
specifications without considering whether these specifications can be reached or not.
Since the volume percentages ofthe side-draw products in the crude feed can only
be obtained from the TBP ciuve ofthe crude feed, it is necessary to convert the
specifications in the form of ASTM 95% points to TBP end points using the relation
curve given by Watkins (1979). The curve has been regressed into a polynomial
expression given below:
TBP end po int = X ^' ' i^^^TM 95% po int) , (3.5) i=0
where
Ci- constants ofthe polynomial expression.
In order to decide the volume percentage of a side-draw product in the crude feed,
the initial boiling point (IBP) is also required. It should be noted that the overlap ofthe
TBP curves of two adjacent side-draw products can not be avoided since two side-draw
streams can not be completely separated in the atmospheric tower and the vacuum tower.
28
In other words, there are always some components in the boiling range ofthe lighter
stream entering the heavy streams and vice versa. Therefore, the IBP ofthe heavier
product is always lower than the end boiling point (EBP) ofthe adjacent lighter product.
The extent ofthe overlap represents the separation efficiency in the atmospheric tower.
The smaller the overlap, the better the separation. The separation is defined by Gap (5-
95) ASTM, which is the temperature difference between the ASTM 5% point ofthe
heavier product and the ASTM 95% point ofthe adjacent lighter product. The values of
Gap (5-95) ASTM in normal industrial practice (Watkins, 1979) are used here for all
pairs of adjacent side-products except the pair ofthe heavy distillate and the atmospheric
gas oil. The value from the Lin (1988) is used for the Gap (5-95) ASTM between the
heavy distillate and the atmospheric gas oil uses. The Gap (5-95) ASTM for each pair of
adjacent side-draw products is listed in Table 3.2.
Table 3.2 Gap (5-95) ASTM Temperature between Adjacent Side-Draw Products in the Atmospheric Tower.
Stream Gap Spec, (5-95) ASTM, °F
Light Naphtha- 25
Heavy Naphtha
Heavy Naphtha- 35
Light Distillate
Light Distillate- 10
Heavy Distillate
Heavy distillate- 5
Atmospheric gas oil
The Gap (0-100) TBP, which is the temperature difference between the 0% TBP
point ofthe heavier product and the 100%) TBP point ofthe adjacent lighter product, can
be calculated from corresponding Gap (5-95) ASTM temperature using the conversion
curves given by Watkins (1979). The curve has been regressed into a polynomial
expression shown below:
29
Gap {O -1 GO) TBP = X z • [Gap (5 - 95) ASTM] , (3.6) ; • = «
where
Cj- constant ofthe polynomial expression.
Once the values ofthe Gap (0-100) TBP are knowoi, the IBP of each side-draw
product is calculated from the EBP ofthe adjacent lighter product using the following
formula:
IBP of heavier product = EBP of adjacent light product - Gap (0-100) TBP, (3.7)
The IBPs of all side-draw products are calculated using equation 3.7 except the light
naphtha, the lightest stream. The IBP ofthe light naphtha is the IBP ofthe whole crude
oil.
The TBP cut point of a side-draw product is then calculated using following
equation:
TBP cut point = -(t,„„, +to„), (3.8)
where
tiooL- TBP 100% point ofthe lighter stream, ""F,
toH- TBP 0% point ofthe heavy stream, ""F.
This methodology has been established by analysis of operating data which has shown
that, for well stripped sidestreams, the volume interchange between two streams around
the TBP cut point are equal (Watkins, 1979). The TBP cut points can then be applied to
determine the volume percentage of each side-draw product in the crude feed from the
whole crude TBP-volumetric curve.
30
The volumetric flow rates of all side-draw products except the AGO can be
calculated by multiplying the volume percentage of the side-draw product by the
volumetric flow rate ofthe crude feed. For the AGO, the ASTM 95% point, which
determines the total volume of products from the atmospheric tower, is not an
independent variable once the atmospheric furnace outlet temperature is defined. This is
because that the total volume ofthe top product and the side-draw products is
approximately equal to the volume ofthe feed vaporized in the flash zone, which is
determined by the temperature and pressure in the flash zone. The total volume ofthe
product can then decide the ASTM 95% ofthe AGO. Hence, the ASTM 95% point ofthe
AGO is not an independent variable in the atmospheric tower model. Instead, it is
calculated in the crude unit model according to the conditions in the flash zone ofthe
atmospheric tower. At the beginning ofthe crude unit model calculation, an initial value
ofthe ASTM 95%) point ofthe AGO is set and the final value is calculated in the model.
The mass flow rate ofthe side-draw products can be obtained from the
corresponding volumetric flow rates and API gravity values. The API gravity for a crude
cut with narrow boiling range is calculated by applying the middle-point temperature to
the API-volumetric curve. Since the API-volumetric curve ofthe whole crude is very
close to a slight line over a narrow temperature interval, this approximation gives almost
the same value as the more accurate approach in which numerical integration is used
leading to a high computational load. However, for a wide cut, the API-volumetric curve
is not a straight line over the boiling range ofthe cut. Hence, the weight ofthe cut is first
calculated by integration along the volumetric-API curve over its boiling range. The TBP
boiling range ofthe side-draw product is divided into 10 intervals. The API gravity value
of each interval is obtained from the API-volumetric curve and is converted to specific
gravity. Simpson's rule is then used to calculate the weight ofthe side-draw product by
integrating along volumetric-API curve over its boiling range. The API gravity ofthe cut
is then determined by the volume and the weight ofthe cut. The weights of all side-draw
products are obtained from the API-volumetric curve using either ofthe two methods
introduced above.
31
The TBP curve of each side-draw product is the same as the portion ofthe whole
crude TBP curve between the IBP and EBP ofthe side-draw product. For example, the
TBP curve ofthe heavy naphtha is the same as the portion ofthe whole crude TBP curve
between the IBP and EBP ofthe heavy naphtha.
The ASTM curve of a side-draw product is an important property. The ASTM
curve can be used to calculate other properties ofthe side-draw product. The ASTM
curve of each crude cut is converted from the TBP curve using the correlation given by
Daubert (1994). Daubert gave the procedure to convert an ASTM curve to a TBP curve
explicitly. Converting a TBP curve to an ASTM curve is done by reversing the
procedure. The conversion from a TBP curve to an ASTM curve follows the steps above:
a. Calculate the TBP 50% point from ASTM 50% point using the following formula:
/ ASTM(50) = (TBP( 50 ))""'', (3.9)
0.87180
where
TBP(50)- TBP distillation temperature at 50 vol.% distilled, °F,
ASTM(50)- ASTM distillation temperature at 50 vol.% distilled, °F.
It is claimed that equation 3.9 best correlates data with ASTM 50% point temperatures
below 480 °F.
b. Calculate the temperature difference among the points on the ASTM curve from the
temperature difference among the points on the TBP curve using the formula given
below:
(AASTM)^ = -(ATBP)i B, (3.10) A
where
(A TBP)i- difference in TBP distillation temperatures between two cut points, °F,
32
(A ASTM)i- difference in ASTM D86 distillation temperatures between two cut points,
°F,
The values of A and B are given in Table 3.3 for various cut point ranges with the
maximum values of (A ASTM)i that can be applied in the correlation.
Table 3.3 A and B Variables for Equation 3.10.
Number
1
2
3
4
5
6
Cut-point range
100% to 90%
90% to 70%
70% to 50%
50% to 30%
30% to 10%
10%toO%
A
0.11798
3.0419
2.5282
3.0305
4.9004
7.4012
B
1.6606
0.75497
0.82002
0.80076
0.71644
0.60244
Maximum applicable (A ASTM)i, °F
-
100
150
250
250
100
c. The TBP temperatures are calculated using equations from 3.11 a to 3.11 f.
ASTM(O) = ASTM(50)-(AASTM), -(AASTM), -(AASTM),, (3.11a)
ASTM(10) = ASTM(50)-(AASTM), -(AASTM),, (3.11b)
ASTM(30) = ASTM(50)-(MSTM),, (3.11c)
ASTM{1Q) = ASTM {SO) + (AASTM)^, (3.1 Id)
ASTM(90) = ASTM (50) + (AASTM), + (MSTM),, (3.1 le)
ASTM(IOO) = ASTM(50) + (AASTM), + (AASTM)^ + (AASTM),. (3.1 If)
It is claimed that this method is more accurate than other methods (Daubert, 1994).
The volume average boiling point (VABP) and mean average boiling point
(MeABP) are calculated from the ASTM curve using the correlation given by Lin (1988).
The VABP is calculated from the following formula:
33
-., „„ ASTM(IO) + ASTM(30) + ASTM(50) + ASTM(70) + ASTM(90) VABP =
(3.12)
MeABP can not be calculated by its definition. It is calculated from VABP using
the following formula:
MeABP = VABP-AT, (3.13)
where
AT- temperature difference between VABP and MeABP, " C.
AT is a function ofthe VABP and the slope ofthe ASTM curve S. The slope ofthe
ASTM curve S is calculated using the following formula:
^_ASTM(90)-ASTM(10)
90-10
Where
S- Slope of ASTM cruve, °C/%.
AT is calculated using following formula:
lnAT = -1.53181-0.0128-(VABP)"''"' +3.6474-S""'\ (3.15)
The units of VABP, MeABP, and AT are all °C.
The molecular weight of each side-draw product is calculated from the MeABP
and the API gravity ofthe side-draw product using the correlation given by Riazi and
Daubert (1980) using the following formula:
34
Molecular Weight = 4.5673E - 5 • (MeABP)'"" • SPG-""'', (3.16)
where
SPG- specific gravity of the crude cut.
However, it is found that this formula predicts molecular weights of heavy cuts
lower than the industrial data obtained from the refinery considered in this work. Hence,
the molecular weights of crude cuts heavy than light naphtha are calculated from the
curves given by Maxwell (1950) which correlate the molecular weight of crude cut to its
MeABP and characterization factor. The characterization factor is calculated using the
formula given below:
Characterization Factor = , (3.17) SPG
where MeABP has units of °R.
Maxwell (1950) gave three molecular weight curves of crude cuts corresponding
to three regions ofthe characterization factors, 12.1-12.6, 11.7-12.0, and 11.3-11.6. The
three curves were regressed using polynomial expressions in this work. The polynomial
expression has the following formula:
n
Molecular Weight = J ] ^/" {MeABP) , (3.18)
where MeABP has units of °F. The molecular weight ofthe crude cut whose
characteristic factor falls in one ofthe three regions is calculated from equation 3.18
using the polynomial constants corresponding to it. For example, the characteristic factor
of a crude cut is 11.8, which is in the region of 11.7-12.0. The molecular weight of this
crude cut is calculated using the polynomial constants corresponding to the region of
35
11.7-12.0. For all the crude cuts in this work, the characteristic factors are between 11.3
and 12.6. Hence, Maxwell method can be applied to any crude cut in this work.
3.2.3 Atmospheric Tower Furnace Calculation
The temperature ofthe crude before entering the atmospheric furnace is set at 481
°F according to the operation data obtained from the refinery considered in this work.
The heat duty ofthe fiimace is calculated as the enthalpy difference between the crude
entering and exiting the furnace. The enthalpy ofthe crude cut is calculated from the
graph of enthalpy of petroleum fractions in Watkins (1979). The enthalpy is a function of
API gravity, temperature and phase. For a particular API gravity and phase, an enthalpy-
temperature curve is given. For the convenience of digital computation, polynomial
expressions were used to represent the curves in the graph. The vaporization enthalpy of
a crude cut is calculated by evaluating the difference between enthalpies ofthe crude cut
in liquid phase and in vapor phase.
3.2.4 Flash Zone Calculation
The temperature and the pressure in the flash zone are required to calculate the
volume of total distillate products. The flash zone pressure is often set at the minimum
possible level in order to maximize the crude vaporization at a certain temperature. The
pressure in the condenser ofthe atmospheric tower and pressure drops across the
condenser and associated piping, trays, and transfer line between the atmospheric furnace
and flash zone in the atmospheric tower considered here are listed in Table 3.4.
The pressure in the flash zone is then calculated as below:
P - P + AP + AP Ci 1Q"* flashzom condenser piping lower' \-'• ^ ^)
36
Table 3.4 Pressure in the Atmospheric Tower.
Condenser Pressure
Pressure drop across the
condenser and associating line
Pressure drop between the
column top and the flash zone
Pressure drop across the transfer
line
Pressure value
14.5
1.9
1.9
5
Pressure unit
Pisa
psi
psi
psi
It is assumed that in the overflash, the extra vaporization which provides reflux in
the section between the flash zone and the first side-stream product draw tray, is 2 vol.%
ofthe crude feed based on common industrial practice (Watkins, 1979). Further, it is
assumed that the bottom stream is steam stripped at a rate of 10 pound steam per barrel of
the reduced crude according to common industry practice. The overflash and steam rate
used in the refinery varies in the operation. The assumed values are in the normal
operation range ofthe refinery.
The flash zone ofthe atmospheric tower is shown in Figure 3.8.
The steps involved in the heat and material balance calculations are listed below:
a. Calculate the volume, weight and API gravity of each side-draw product except AGO
using the given ASTM 95% points and whole crude TBP-volumetric curve and API-
volumetric curve.
b. Assume an initial value for ASTM 95% cut point of AGO.
1. Calculate the volume, weight and API gravity of AGO.
2. Calculate the volume and weight of total distillate product ofthe atmospheric tower.
3. Calculate the volume and weight ofthe reduced crude.
c. Assume an initial value for the flash zone temperature. Calculate the feed flash and
bottom stripping required to yield the required volume of vapor. The curves given by
Watkins (1979) are used to calculate the volume percentage of nonvaporized crude which
37
is stripped out. The curves in Watkins (1979) were regressed using polynomial
expressions.
1. Calculate the volume percentage of crude distilled in the flash zone. Equilibrium
flash vaporization (EFV) curves are constructed from the flash data calculated in
ChemCad 4.0 software package from Chemstations, Incorporation. The constants of
the EFV curves are listed in Appendix A. Flash curves between 26 psia and 48 psia
are constructed in this work. The pressure in the flash zone is then calculated by
interpolating among these EFV curves according to the volume percentage distilled.
Calculate the total enthalpy in the flash zone.
2. Calculate the total enthalpy at the furnace outlet using the given fiimace outlet
temperature.
d. Compare the total enthalpy in the flash zone and the ftimace outlet. If they do not
match, calculate the flash zone inlet temperature using secant method (Riggs, 1994).
Return to step c.
e. Compare the flash zone pressure obtained above and the flash zone pressure
calculated from equation 3.19. If they do not match, calculate new value of ASTM
95% point of AGO using secant method. Return to step b.
The flowchart ofthe calculation ofthe flash zone pressure and temperature is
shown in Figure 3.9. The above iterative procedure is inside the gray block no.I in the
schematic ofthe atmospheric tower model shown in Figure 3.3.
38
Crude
fo
"fo
Furnace
Stripping Steam
Tray 5
Vapor
Liquid
Tray 4
Tray 1 Stripout
v.
Figure 3.8 Flash Zone ofthe Atmospheric Tower.
39
Start
Crude Assay
Initial Guess of ASTM 95% Point of
AGO
Property Calculation of Side-draw Products except
AGO
Initial Guess of Flash Zone Temperature
ASTM 95% Points of Side-draw
Products
AGO Calculation Total Product Calculation
Reduced Crude Calculation
Flash Calculation at Furnace Outlet and Flash Zone Calculate New Flash
Zone Temperature
Yes
Yes
Calculate New ASTM 95% of AGO
No
End
Figure 3.9 Flowchart for the Calculation of Flash Zone Pressure and Temperature.
40
In step c, the curves in Watkins (1979) to calculate the amount of stripout from
the steam rate are regressed using polynomial expressions given by the following
formula:
n
Stripout = ^ c, • {steam rate) (3.20) i=0
where stripout has the unit vol.% and steam rate has the units of pounds of
steam/barrels of stripped liquid.
The heat balance ofthe flash zone is then calculated using the flash zone
temperature obtained above. The vapor in the flash zone is the summation ofthe feed
vaporized in the flash zone and the steam stripout ofthe nonvaporized feed dropping
from the flash zone to the tower bottom. Thus, the enthalpy of vapor in the flash zone is
the summation ofthe enthalpies of these two streams which have been calculated above.
The temperature ofthe flash zone vapor is calculated from the enthalpy ofthe flash zone
vapor.
There are usually four trays between the flash zone and the tower bottom. The
temperature ofthe reduced crude is calculated on the basis ofthe energy balance for the
tower bottoms. The calculation is carried out using following steps:
a. Assume the temperature ofthe tower bottom is about 30°F cooler than the tower inlet
temperature. Calculate the temperature of tray 4 assuming linear temperature
distribution in the four trays between the flash zone and the tower bottom. It should
be noted that the temperature of tray 4 is different from the inlet temperature ofthe
crude feed.
b. Calculate the enthalpy ofthe stripout ofthe liquid dropping from the flash zone. The
amount ofthe stripout is calculated using equation 3.20.
c. Calculate the enthalpy ofthe overflash liquid. It is assumed that the overflash falls to
the flash zone at a temperature of 20' F cooler than the tower inlet temperature.
d. Calculate the enthalpy ofthe reduced crude by making an energy balance around the
tower bottom.
41
e. Calculate the temperature drop across the four trays between the flash zone and the
tower bottom using the enthalpy ofthe reduced crude and the specific heat ofthe
reduced crude. Calculate the tower bottom temperature.
f. Compare the calculated tower bottom temperature with the initial guess. If they do
not match, return to step a with the new value ofthe tower bottom temperature equal
to the calculated tower bottom temperature.
The above iterative procedure is inside the gray block no.2 in the schematic ofthe
atmospheric tower model shown in Figure 3.3.
It is assumed that the temperature ofthe reduced crude is equal to the tower
bottom temperature. The external energy left in the tower above the flash zone is then
calculated using the equation below:
External energy = enthalpy of crude and steam - enthalpy of the reduced crude . (3.21)
The enthalpy of steam is obtained from Moran and Shapiro (1996). The data were
regressed into polynomial expression for the convenience of digital computations. The
external energy calculated here will be used in the following side-draw temperature
calculation.
3.2.5 Side Stripper Calculation
When the side-draw product liquid is withdrawn from the tower, there are some
light components in the stream which belong to lighter side-draw products. To recover
these light components, side strippers are used to strip the light components from the
unstripped product stream. There is a side stripper for each side-draw product. Although
the strippers with reboiler are sometimes used in industry, only the side strippers with
steam are used in the refinery considered in this work. Hence, only steam side strippers
were considered in the present work. The side stripper models are used to calculate the
properties of unstripped products for later use in side-draw temperature calculation. It
42
should be noted that the volume and properties ofthe stripped product have already been
calculated in the procedure presented in section 3.2.2.
After an unstripped side-draw product stream is withdrawn from the draw tray, it
enters the side stripper at the top. The steam enters the side stripper at the bottom. Since
the partial pressure of crude components in the gas phase is low in the side-stripper, some
light components are vaporized and flow with the steam back to the column. The stripped
product exits from the bottom ofthe stripper.
The volume percentage of the unstripped side-draw products vaporized in the side
strippers is calculated using equation 3.20. For the unstripped product, its EBP is the EBP
ofthe stripped product while its IBP is obtained from the whole crude TBP-volumetric
curve using the temperature corresponding to the volume pecentage equal to the
summation ofthe volumetric flow rate ofthe stripout and the stripped product.
Volumetric Flow Rates of Unstripped Pr oduct = Stripout +
Volumetric Flow Rates of Unstripped Pr oduct
After the corresponding IBP and EBP ofthe unstripped side-draw product are
obtained, the API gravity ofthe unstripped side-draw product can then be obtained from
the whole crude API-volumetric curve. The mass flow rates ofthe unstripped side-draw
product are calculated using its volumetric flow rates and the API gravity.
3.2.6 Draw Tray Locations
The atmospheric tower model here represents the preflash tower and the
atmospheric tower ofthe refinery considered in this work together. Thus, the draw tray
locations ofthe atmospheric tower in the refinery are not applied here. In addition, since
the crude unit is not a common distillation column, normal shortcut methods to calculate
the number of theoretical trays are not applicable here. Actually, accurate draw tray
location is not vital in the present modeling work because material balances and energy
balances are carried out around separation sections instead of single trays.
43
The number of trays for a separation section between a particular pair of adjacent
side-draw products is almost the same for every atmospheric tower. The numbers
suggested by Lin (1988) are used here. The number of trays of each separation section is
listed in Table 3.5. The total number of trays in the atmospheric tower is 38.
Table 3.5 Tray Numbers of Separation Sections in Atmospheric Tower.
Separation section
Light naphtha-heavy naphtha
Heavy naphtha-light distillate
Light distillate-heavy distillate
Heavy distillate-atmospheric gas oil
Feed-atmospheric gas oil
Bottom-Feed
Tray number
7
8
7
9
3
4
The draw tray location can then be calculated from Table 3.5. It should be noted
that the numbering is from the bottom to the top. In the crude unit ofthe refinery
considered in the work, a packing section is used between AGO side-draw tray and the
flash zone. The separation capability ofthe packing section is equal to 3-4 trays. In this
study, 3 trays are assumed. The important tray locations are listed in Table 3.6.
Table 3.6 Draw Tray Location.
Separation section Tray number
Reduced Crude Bottom
Feed tray 4
AGO draw tray 7
Diesel draw tray 16
Kerosene draw tray 23
Heavy naphtha draw tray 31
Light naphtha draw tray Top
44
There are two pumparounds with this crude column. One pumparound is located
in the separation section between the heavy naphtha side-draw tray and the kerosene side-
draw tray and the other pumparound is located in the separation section between the
diesel side-draw tray and the atmospheric gas oil side-draw tray. The locations ofthe
pumparound are set according to normal industrial practice (Watkins, 1979). Usually, the
pumparound withdraws liquid from a tray above the lower draw tray, exchanges heat
with crude feed and returns the liquid to a tray ftirther up in the tower but below the upper
draw tray. In the model, the upper pumparound returns the liquid to the tray below the
corresponding upper draw tray. The lower pumparound returns the liquid to the third tray
below the corresponding draw tray.
The fimction of pumparounds is to reduce the liquid and vapor traffic at the upper
section ofthe atmospheric tower and reduce the heat load ofthe overhead condenser. The
disadvantage of using pumparounds is that several trays between the pumparound
withdrawing tray and returning tray can only be considered as only one actual tray for
fractionation purposes (Watkins, 1979). The benefit of having two pumparounds is that
two different temperature levels of heat sources are provided for crude preheating.
3.2.7 Draw Tray Temperature Calculation
The calculation procedure for draw temperature is similar for all draw trays. Only
the general methodology is described here. The sequence of draw tray calculation is from
the bottom to the top. The envelops used in the energy balance and material balance
include all the lower part ofthe tower to the bottom ofthe tray above the draw tray. A
draw tray and its envelop for energy balance and material balance are shown in Figure
3.10. The draw tray calculation follows the procedure in Watkins (1979).
The draw tray temperature is calculated using energy balance. For each energy
balance around the envelop corresponding to the draw fray, the difference between the
enthalpy ofthe vapor going up and the enthalpy ofthe liquid coming down is calculated.
This enthalpy difference is regarded as the external energy left in the tower which will be
absorbed by pumparounds above the draw tray and by overhead condenser. The external
45
energy calculated in the lower draw tray can be used directly in energy balance of
the upper draw tray. This approach eliminates the need to calculate the all the inlet
streams and outlet streams below the draw tray. The energy balance for a draw tray only
requires that the inlet and outlet streams around the draw tray are calculated. Usually, the
outlet stream in the energy balance is the side-draw product stream. The inlet stream is
the steam used in the side-stripper which then enters the tower. If a pumparound exists
between the draw tray and adjacent lower draw tray, the external energy left in the tower
is subtracted by the amount ofthe heat load ofthe pumparound. The pumparound heat
loads are fixed according to common industrial practice (Watkins, 1979).
tray
Envelop containing all the lower part o f the
tower
stripout
unstriped • product
Envelope for the energy balance of a side-draw tray
Envelope containing all the lower part o f the
tower
steam
side-draw product
Figure 3.10 Energy and Material Balance Quantities at a Side-Draw Tray.
46
The draw temperature calculation follows the steps below:
a. Make an initial guess for the draw tray temperature. Calculate the enthalpies ofthe
liquid and vapor leaving the draw tray. The volume ofthe vapor is the summation of
volumes of all the lighter products withdrawn above the separation section. The liquid
is the unstripped side-draw product that has already been calculated in side stripper
calculation.
b. Calculate the heat absorbed in passing across the draw tray by the liquid that is
vaporized in the side-draw product stripper. The temperature of tray above the draw
tray is set at I5°F higher than the draw tray temperature.
c. Calculate the reflux heat on the basis ofthe energy balance ofthe envelope
corresponding to the draw tray.
d. Calculate the volumetric flow rate ofthe reflux falling from the tray above the draw
tray according to reflux heat and the specific heat ofthe refiux. The specific heat of
the reflux is assumed to be the same as the specific heat capacity ofthe side-draw
product.
e. Calculate the molar flow rate ofthe reflux and all the lighter products. Calculate the
pressure ofthe draw tray according to the known flash zone pressure and assumed
constant pressure drop for each tray.
f. Calculate the hydrocarbon partial pressure according to the mole fraction ofthe
reflux, which has the same composition as the side-draw product, in the total vapor
leaving the draw tray. This approach is suggested by Lin (1988).
g. Calculate the TBP curve ofthe stripped product. Convert the TBP curve to the
Equilibrium Flash Vaporization curve (EFV) wdth the partial pressure using the
method given by Maxwell (1950). The initial point ofthe EFV curve is the bubble
point ofthe stripped side-draw product.
h. Compare the calculated bubble point with the draw tray temperature. If they do not
match, use the calculated bubble point as the draw tray temperature and return to step
a.
47
The flowchart for the calculation ofthe draw tray temperature is shown in Figure
3.11. The above iterations are inside the gray block no.3 in the schematic ofthe
atmospheric tower model shown in Figure 3.3.
The temperature ofthe condenser is calculated as the bubble point ofthe overhead
product, light naphtha, under the condenser pressure. Only the temperature calculation in
step g above is required for condenser temperature calculation.
3.2.8 Side-Draw product temperature calculation
In order to calculate the enthalpy ofthe side-draw product which is used in the
calculation ofthe external heat left in the tower, the temperature ofthe stripped side-draw
product is required. The temperatwe ofthe side-draw product can be calculated through
applying an energy balance arovmd the side stripper. It is assumed that each side stripper
has four trays (Watkins, 1979). The vmstripped product enters the first tray from the top
and the steam enters the fourth tray. The stripped side-draw product leaves the side
stripper at the bottom. The stripout and steam exit from the top ofthe side-stripper and
flow back to the atmospheric tower. The calculation follows the steps below:
a. Estimate the steam-free At across the side-stripper using the graph in Watkins (1979).
Polynomial expressions are used to represent the curves in the graph.
b. Estimate the temperature correction for the steam-free At using the correlation in
Watkins (1979). Calculate real At across the side-stripper. This value becomes the
initial guess for At.
c. Assume equal temperature differences across each ofthe four stripping trays
(Watkins, 1979). Calculate the temperature ofthe top tray and the bottom tray
according to the side-draw tray temperature.
d. Make an energy balance around the side-stripper using the temperature calculated
above. The enthalpy of exiting water comes from Moran and Shapiro (1996). The
enthalpy ofthe stripped side-draw product is calculated using this enthalpy balance.
The temperature ofthe stripped side-draw product is obtained from the enthalpy
value.
48
Initial Guess ofthe Temperature ofthe
Side-draw Tray
Start
Enthalpy Calculation of Vapor and Liquid Leaving
the Side-draw Tray
Reflux Heat Calculation Reflux Rate Calculation
Calculation ofthe Molar Fraction ofthe Reflux
Hydrocarbon Partial Pressure CaVs^ation
Side-draw Product Bubble Point Calculation
No
End
Figure 3.11 Flowchart ofthe Side-Draw Tray Temperature Calculation.
49
e. Compare this temperature with the bottom temperature calculated before. If they do
not match, return to step c with the new calculated bottom temperature.
The above iterations are inside the gray block no.4 in the schematic ofthe
atmospheric tower model shown in Figure 3.3.
After the temperature ofthe side-draw product is calculated, the external heat left
in the tower is then calculated by subtracting the heat output ofthe side-draw product and
adding the heat input of steam to previously calculated external heat. This value is then
used in temperature calculation of upper draw trays. In Calculating the temperature
profile in the atmospheric tower, it is assumed that temperature distribution between
adjacent draw trays is linear. Temperature profile along the trays can then be obtained on
the basis ofthe temperature and pressure of draw trays.
3.3 Vacuum Tower Modeling
Under normal pressure and temperature in the flash zone of an atmospheric tower,
the maximum amount of oils which will vaporize is described by the whole crude TBP
cut points between 700°F and 800°F. Further increasing the temperature in the flash zone
will result in thermocracking and coking in the flash zone. The reduced crude usually still
contains a large volume of distillable oil which can be recovered by vacuum process. The
distillable oil is lighter, cleaner, thus, has higher market value than the reduced crude.
The fimction ofthe vacuum tower is to recover this part of distillable oil.
The vacuum tower modeled here is a fuel-oriented type. That is, the vacuum
tower produces feed to FCC unit for gasoline production and produces vacuum residue
for the No.6 fuel oil. Since No. 6 fuel oil has a low market price, the amount of vacuum
residuum should be minimized. This is called pitch operation. If the vacuum residue is
used for asphalt, some gas oil must be left in the residuum in order to provide the proper
degree of plasticity (Watkins, 1979). Pitch operation is used in the refinery considered in
this work. Hence, the same operation mode is considered for the vacuum tower modeling.
A fliel-oriented vacuum tower normally does not require any particular degree of
fractionation between cuts. The HVGO and LVGO side-draw streams produced in a
50
vacuum tower will be combined together after the vacuum tower and become the feed to
FCC unit. Using two side-draw streams is to decrease the liquid and vapor traffic in the
upper section ofthe vacuum tower, which is a common constraint of vacuum tower
operation. A fiael-oriented vacuum tower usually uses pumpback reflux, grid type
contacting sections and chimney draw trays (Watkins, 1979). The pumpback withdraws
liquid on the product side-draw tray, cools the liquid, and return the liquid to the top of
corresponding grid type contacting section. It can be seen from the schematic ofthe
vacuum tower shown in Figure 3.2 that pumpback is used for both separation sections.
There is also an overflash stream in the vacuum tower to provide enough wetting liquid
for the section between overflash liquid draw tray and the flash zone. The overflash can
also be used to control the quality ofthe HVGO, preventing high viscosity material from
entering HVGO. The overflash is set at 2 vol.% ofthe reduced crude according to normal
industrial practice (Watkins, 1979). The overflash stream withdrawn from the vacuum
tower on the overflash draw tray returns to the vacuum crude as the feed to the vacuum
tower.
The light crude processed in the refinery considered in this work does not have a
large volume of heavy ends. Hence, the capacity ofthe vacuum tower is not an active
constraint in operation. In fact, the capacity of downstream units limits the product rates
from the vacuum tower. Therefore, for the range ofthe TBP end point ofthe HVGO,
enough separation capacity is assumed for the vacuum tower.
The modeling of a ftiel-oriented vacuum tower follows the procedure in Watkins
(1979). The basic approach is similar to that used in atmospheric tower modeling.
Therefore, only the steps which are different from the atmospheric tower model are
described here.
3.3.1 Vacuum Tower Furnace Calculation
The outlet temperature ofthe furnace ofthe vacuum tower is set at 763 °F
according to the design documents for the refinery. The reduced crude exiting the
atmospheric tower bottom first exchanges heat with the fresh crude feed and then flows
to the furnace. The inlet temperature is set at 558°F according to the process flow
51
diagram (PFD) ofthe vacuum operation. The heat duty ofthe furnace is calculated as the
enthalpy difference between the reduced crude entering and exiting the furnace.
3.3.2 Side-Draw Product Calculation
The total volume of side-draw products is calculated using the TBP-volumetric
curve ofthe whole crude using the given TBP end point ofthe HVGO. According to
industrial data, the volumetric percent ofthe LVGO in the total VGO is 61.22 vol.% in
the winter and 49.78 vol.%o in the summer. Hence, the volumes of LVGO and HVGO can
be calculated using these values. The volume ofthe overflash is also calculated according
to 2 vol.%) ofthe reduced crude.
3.3.3 Flash Zone Calculation
The flash zone temperature is assumed to be the same as the fiimace outlet
temperature, 780 °F. The flash zone hydrocarbon pressure is calculated based on the
equilibrium flash vaporization. An initial value ofthe flash zone hydrocarbon pressure is
assumed and an equilibrium flash vaporization is performed. The calculated total
vaporization is compared with the total volume ofthe side-draw products plus the
overflash. If two values are different, new value is set for the flash zone pressure. The
secant method (Riggs, 1994) is used in the flash zone pressure solving.
The equilibrium flash vaporization (EFV) curve for the flash zone pressure is
needed to calculate the volume percent ofthe reduced crude flashed. The EFV curve for
atmospheric pressure is obtained from the TBP curve ofthe reduced crude using the
method given by Maxwell (1950). The steps ofthe method are as follows:
a. Construct a distillation reference line using the formulas given below:
c/ fr^i?r TBP{70)-TBP{\0) Slope of DRL = ^f^Z^ ' (^-23)
DRL{50) = TBP{\ 0) + Slope of DRL -(50-10), (3.24)
where
52
DRL- the distillation reference line,
DRL(50)- the 50% ofthe distillation reference line.
b. Construct a flash reference line. The slope and 50%) point ofthe flash reference line
are obtained from those ofthe distillation reference line based on the graph in
Maxwell (1950). The curves in Maxwell (1950) were regressed using polynomial
expression.
c. Use intermediate points to represent TBP curve ofthe reduced crude and
corresponding distillation reference curve. The ATdri, the temperature difference
between the corresponding intermediate points on TBP curve and distillation
reference curve, is calculated using the following equation:
AT^^, = TBP(i) - DRL(i). (3.25)
d. Obtain the ratios between ATDRL and ATFRL, the temperature difference between the
corresponding intermediate points on the EFV curve and the flash reference curve
from the curves given Maxwell (1950). The curves in Maxwell (1950) were regressed
using polynomial expression. ATFRL is then calculated using the following equation:
^Tp^j = AT^^j • Ratio^^. (3.26)
e. Use intermediate points to represent the EFV curve ofthe reduced crude. These
intermediate points are calculated using the following equation:
EFV(i) = FRL(i) + ATp^,. (3.27)
The difference between EFV curves under atmospheric pressure and flash zone
pressure is calculated using the method given by Lin (1988). The steps ofthe method are
given below:
53
a. Find the EFV(50) under the low pressure from the EFV(50) of atmospheric pressure
based on the curves given by Lin (1988). The curves in Lin (1988) were regressed
using polynomial expressions.
b. Use intermediate points to represent the EFV curve under atmospheric pressure.
Usually 11, 9, 7, and 5 intermediate points are used to represent the EFV curve
depending on the requirement ofthe empirical correlations. Calculate temperature
difference among these intermediate points.
c. Assume that the temperature difference among these intermediate points do not
change with pressure. Construct the EFV curve by calculating the intermediate points
ofthe EFV curve under the flash zone pressure using the EFV(50) and temperature
difference obtained in step a and b.
It is claimed that the error of above method is no greater thein 14°C (Lin, 1988).
A constant air leak is assumed for the vacuum tower. The weight of air leak is set
at 44 pounds per hour according to Watkins (1979). The air is considered as an inert in
the calculation. The total pressure ofthe flash zone is then calculated assuming ideal
mixture. The temperature ofthe vacuum residue is assumed to be the flash zone
temperature.
An energy balance is carried out around the flash zone. The enthalpy of air is
obtained from Moran and Shapiro (1996). The external heat left in the tower, which is
enthalpy ofthe vapor going upward, is calculated. The flash zone calculation of vacuum
tower is similar to that of atmospheric tower.
3.4 Separation Section Calculation
The vacuum tower has three separation sections: Overflash section, HVGO
section and LVGO section. There is no overhead condenser. The cooling is provided
totally by the pumpbacks.
It is assumed that 3.81 millimeter Hg pressure drop for each chimney tray and
each grid separation section according to plant data. The pressure distribution is then
calculated from the assumed pressure drop and the flash zone pressure calculated above.
54
The energy and material balances calculation ofthe separation sections in the vacuum
tower are similar to those in the atmospheric tower model.
3.5 Comparison between the Developed Model and Tray-to-Tray ChemCad Model
Since the crude unit model is a simplified model, it is necessary to compare it
with a more rigorous model to find out if the developed model can satisfy the
requirement ofthe refinery-wide optimization. For this purpose, a ChemCad model was
constructed for comparison. The ChemCad model is a rigorous tray-to-tray model
constructed using software package CHEMCAD 4.0. In the ChemCad model, 45 pseudo-
components are used to represent the crude feed. The atmospheric tower and vacuum
tower in ChemCad model have exactly the same design as the developed model. Same
crude is used in the ChemCad model in the Summer Mode and the Winter Mode. The
values ofthe overhead pressure, pressure drop and steam usage used in the developed
model are also used in the ChemCad model. The side-draw tray locations and the
sidestripper type are also the same. In addition, the heat duties ofthe pumparounds and
condenser for both models are the same. The fiowsheet of ChemCad model is shown in
Figure 3.12.
The most important fimction ofthe crude unit is predicting the volumetric flow
rates ofthe side-draw products. The volumefric flow rates ofthe side-draw products are
decided by the given ASTM 95%) point or TBP 100%. The separation specifications of
side-draw products are shown in Table 3.7. The same specifications are used in the
ChemCad model except that the ASTM 90%) point is used for Heavy Vacuum Gas Oil.
The discrepancy may be due to the fact that the Crude Assays obtained from the refinery
do not have detailed information at the high boiling range which results in the inaccurate
information ofthe pseudo-components at the heavy ends ofthe distillation curve.
55
0) • 0
o 2
C D
0) •0 3 H U
•a u e tt)
u 14-1
o 0
•H 4-> IB
e o
0)
u
•H [I4
Table 3.7 Separation Specifications of Side-Draw Products.
Side-draw Product
Light Naphtha
Heavy Naphtha
Light Distillate (Kerosene)
Heavy Distillate (Diesel)
Heavy Vacuum Gas Oil
Specification, °F
257
430
530
650
1050
SpecificationType
ASTM 95%
ASTM 95%
ASTM 95%
ASTM 95%
TBP 100%
The comparisons between the side-draw product flow rates ofthe developed
model and ChemCad model in the Summer Mode and the Winter Mode are shown in
Figure 3.13 and Figure 3.14. The flow rates match well for most ofthe side-draw
products. The small mismatch will only affect the refinery-wide optimization if the
separation specifications, ASTM 95%) or TBP 100%) point, are at the specification limits,
which is rarely the case in the optimization studies. The side-draw product rates are
usually constrained by the capacities of downstream units instead ofthe specification
limits.
Another important test is the gain comparison, that is, the comparison between the
changes of product flow rate versus the changes ofthe separation specifications obtained
from two models. Having correct gain is critical in optimization studies. The optimization
routine needs the gain information between the decision variables and the optimization
objective fianction to decide the optimization search direction. The gains between the
separation specifications and the product rates significantly affect the gain between the
separation specifications and the objective function. Hence, the developed model must
have correct gains so that it can be used in the following optimization studies.
The normal operating values for the base case are listed in Table 3.7. The furnace
ouflet temperature is set at 653°F in the base case. Each ofthe ASTM 95% point, TBP
100% point, and furnace outiet temperature is perturbed by plus 5°F and minus 5°F
sequentially in the test.
57
16000
14000
12000
^ 10000 (3
2 8000 oa
® 6000
4000
2000
0
D Simplified Model
• ChemCad Model
m light heavy kerosene diesel
naphtha naphtha AGO VGO vacuum
residue
Figure 3.13 Comparison of Simplified model and ChemCad Rigorous Model, Summer Mode.
18000
16000
14000
12000
I" 10000
m 8000 CQ
6000
4000
2000
0
D Simplified Model
• ChemCad Model
M light heavy kerosene diesel AGO VGO vacuum
naphtha naphtha , „, residue volume%)
Figure 3.14 Comparison of Simplified Model and ChemCad Rigorous Model, Winter Mode.
58
The product rates after the perturbation were compared to those ofthe base case
and the gains were estimated by the formula given below.
^ ^ - ^ > . .
^^y Ti-Tj,^,, (3.28)
The average gains were calculated as the arithmetic average of gains ofthe
positive and negative perturbation.
The comparison between the Gains obtained from the developed model and the
ChemCad model are shown in Table 3.8. It is observed that only diagonal elements have
significant values. It means that the ASTM 95% or TBP 100% for one product mainly
influences the volumetric flow rates of this product and adjacent heavier product while
having little effect on the volumetric flow rates of other products. It is also observed that
although there is some mismatch between the gains ofthe developed model and the
ChemCad model, the corresponding gains are in the same order and have the same sign.
The mismatch is expected because the developed model uses simplified approaches and
empirical correlations. However, the mismatch is not expected to distort the searching
direction in optimization.
Since the volumetric fiow rates ofthe side-draw product are the most important
in the refinery wide optimization, it is also necessary to find if other operating variables
have significant influence on the product rates. Another process variable which may have
significant influence is the pumparound heat duty. The pumparound heat duties are
defined explicitly in the atmospheric tower modeling. In the vacuum tower, the
pumparound duties are defined by the returning temperature.
59
o CL,
^ o^ i n ON
s H OJ < . ^ u
-a o B
X ! rt o B u
.£3
u J5 • * — •
• ^
del
o
T3 U
s c u
.13 +-•
g o
^4—(
-o
'3 -> .£i
u x) o s ;-! u s H P
00
_o E o
-T3 O
OH IZl
3 on i-i U > +-; _g "o
T3 C2
o m CO
_c '3
J 3
t 4 - l
O
o w
'S Cu B o U
00 rn u
i
OH
m O o > -a §
r\
0)
s t i
lU
! • (L» H -t-» (U
"3 H O
c
<1
I f^"" ^
o o o
9 o o
d o ^
C50
i n
fa fa
^
T3 O
T 3
U
a U
^
O
U
a >< o O H
0 0
d f^ I o ^
>o oo J2 ^ ^ s i n
Ci3
9 9 9 i-J H J hJ PQ 03 CQ m m OQ
o B
u S u
O
oo
VO
00 ON
9 OQ DQ
(L)
o
ON ON
m
o
d 00
OS
fa fa
9 OQ OQ
'O 1) 13 S o
<u T3 O B
-o u a o
.a .s -§ fa fa fa < < <
fa fa < <
vo ON
vo
00 ON
H 2 vo 1
0 0
VO
i n
d <N
VO ON
Q
OQ CQ
p^
a
OQ OQ
-a o
-a u
a >< o
2 S
O
O
a U
t ^ i n
o 00 (N VO
fa <
fa
OQ OQ
-a o a u 15
o i-i
-a' fa <
oo d
Q
OQ CQ
O
a u a
.£5
u o" GO c3
fa <
i n 00 i n
i n
fa fa
^
OQ CQ
o
<U
Id
a o ;-! O H o, < 0 0 CO
fa <
o o r<-i
i n
i n vo
(N 00
m vo vo ON
o
ON
fa o
Q
OQ CQ
o
a T 3 Ci3 U a u 00 >
fa <1
i n od i n
fa o I' OQ CQ
-a o
• T 3 (U H-»
CS
a >< o kH
O H
< d 00 > fa <
o p
i n vo
ON
o ON
o d
00
o
ca Q
OQ OQ
-a o a u a u
CO
fa < ]
r<-i
i n
fa o
Q
CQ CQ
u -a o a -a (U H-t CO
o O , O H
<
CO
LH
fa <
60
The results ofthe corresponding sensitivity tests on pumparounds are shown in Table 3.9
and Table 3.10. It is found that the pumparound heat duties have little effect on the
product flow rates as long as the pumparounds satisfy the cooling requirements of each
tower and provide enough reflux in each separation section. Pumparounds may affect the
energy usage in the crude unit. Nevertheless, energy consumption is a secondary factor in
the refinery-wide optimization. Hence, the pumparound heat duties are fixed when
refinery-wide optimization is conducted and they are not used as decision variables. The
same approach of selecting decision variables was used in other units introduced in the
following text. Those process variables only affecting energy consumption are not
selected to be decision variables ofthe refinery-wide optimization.
Table 3.9 Comparison between the Gains Obtained from the Simplified Model and ChemCad Model, Pumparound Heat Duty versus Product Flow Rate, Atmospheric Tower, Summer Mode.
AFin, ChemCad model
AFjn, Approximated model
AFhn > ChemCad model
AFhn, Approximated model
AFid. ChemCad model
AFjd, Approximated model
AFhd: ChemCad model
-^Fhd' Approximated model
AFago, ChemCad model
AFago, Approximated model
Units
BBL/hr/MMBtu
BBL/hr/MMBtti
BBL/hr/MMBtu
BBL/hr/MMBtu
BBL/hr/MMBtu
BBL/hr/MMBtti
BBL/hr/MMBtu
BBL/hr/MMBtti
BBL/hr/MMBtu
BBL/hr/MMBtti
AF
l^Qpa^
-0.195
0
0.3749
0
-0.0993
0
0.1097
0
-0.0035
0
AF
^Qpal
0.0335
0
-0.4677
0
-0.4229
0
0.8632
0
-0.0008
0
61
Table 3.10 Comparison between the Gains Obtained from the Simplified Model and ChemCad Model, Pumparound Rettim Temperature versus Product Flow Rate, Vacuum Tower, Summer Mode.
Units
APV
^Tpal
APV
^Tpa2
AFhvgo, ChemCad model
AFhvgo, Approximated model
AFresid, ChemCad model
AFresid, Approximated model
BBL/hr/°F -0.0009
BBL/hr/°F 0
BBL/hr/T 0.0008
BBL/hr/°F 0
-0.1765
0
0.0327
0
On the other hand, pumparounds may become the decision variables of single-unit
optimization ofthe crude unit. This is because energy usage is a major factor in single-
unit optimization. The pumparounds are used to preheat the crude. While satisfying
separation requirement and heat requirement in the atmospheric tower and vacuum tower,
maximizing pumparounds may be beneficial since it provides maximal heat to preheat the
crude, which may decrease the fuel usage in the preheat furnace.
Another comparison is done on the temperature profile ofthe atmospheric tower
ofthe developed model and ofthe ChemCad Model. The comparison is on the basis of
using the same separation specifications, condenser pressure, pressure drop in the tower
and the fiimace outlet temperature. The comparison ofthe temperature distribution in the
crude tower between the developed model and the Chemcad model is shown in Figure
3.15.
62
800
700
600
500
2 400 PQ OD
300
200
100
0
— Simplified Model
- ChemCad Rigorous Model
10 15 20 25 tray number
30 35 40
Figure 3.15 Comparison of Temperature Profile in the Atmospheric Tower.
It is observed that although the crude unit model in this work is a simplified
model and linear temperature distribution is assumed in each separation section,
temperature profiles in two models are similar in shape. The biggest temperature
difference appears on tray 29 to 35, where the temperatures predicted by the simplified
model are about 20%) higher than those of ChemCad model. There are only small
differences from tray 1 to tray 28, where the average relative difference is 2.9%. The
average relative temperature difference for all trays is 5.8%. Tray temperature difference
only affect the estimation of utility cost in the atmospheric tower, which only has an
insignificant effect on plant-wide economics.
3.6 Auxiliary Processes in the Crude Unit
There are some auxiliary processes in the crude unit which prepare the side-draw
products exiting the atmospheric tower and vacuum tower for ftirther processing in the
refinery. These processes includes Rerun unit. Residue Oil Solvent Extraction (ROSE)
unit, debutanizer, Naphtha Splitter, etc. These units are not critical from plant-wide
63
operation point of view. However, they must be included in the refinery-wide model in
order to run the refinery-wide model. The models of these units are required when
calculating the amount ofthe feeds to downstream units or products for sale. In addition,
the capacities of these processes may become active constraints in refinery-wide
optimization.
Simplified models were developed for these processes. Since the operations of
these processes in the Summer Mode are quite different from the operations in the Winter
Mode, different parameters were used to account for the difference in operation.
3.6.1 Rerun Unit
The fianction ofthe Rerun Unit is to recover the diesel components from the AGO
and the LVGO. Due to incomplete separation between the diesel, the AGO and the
LVGO, the AGO stream and the LVGO stream may contain some components whose
boiling points are in the diesel boiling range. Since diesel itself has high market value, it
is more profitable to recover this part of diesel component and sell it directly instead of
using it as the feed to FCC unit to make FCC gasoline.
The core ofthe rerun unit is a distillation column. Using the AGO stream and part
ofthe LVGO stream as feed, the diesel components come out from the column overhead.
The overhead stream combines with the diesel directly produced from the atmospheric
tower and flows to hydrodesulftirizer (HDS) unit. The bottom stream is combined with
the rest ofthe LVGO stream and the HVGO stream as the feed to the FCC unit.
All AGO side-draw product from the atmospheric tower is sent to Rerun unit.
Only part ofthe LVGO stream from the vacuum tower is sent to the rerun unit. The
typical percent of LVGO used as Rerun feed is 13 vol.%) in the Summer Mode and 11
vol.% in the Winter Mode. The volumetric flow rate ofthe overhead product ofthe rerun
distillation column is about 25 vol.%) ofthe total rerun feed in the summer and 23 vol.%)
ofthe total rerun feed in the winter. These values are used directly in the model to
calculate the amount ofthe LVGO stream sent to the rerun unit and the amount ofthe
diesel component produced in the Rerun unit.
64
3.6.2 Residue Oil Solvent Extraction (ROSE) Unit
The function ofthe ROSE unit is to recover FCC cracking feed from the vacuum
residue. The vacuum residue is rich in metals, tar, and asphalt. Metals are poisonous to
the FCC catalyst. Tar and asphalt can deposit large amount of carbon on the FCC catalyst
which could severely decrease the catalyst activity and could lower the FCC unit
processing capability. Hence, the vacuum residue can not be used directly as FCC feed.
Without treatment, vacuum residue can only be sold as low-value No. 6 fuel oil.
However, the vacuum residue still contains a large amount of hydrocarbons that is
suitable for catalytic cracking. If this portion of hydrocarbons in the vacuum residue can
be recovered, it can be used to produce high-value FCC gasoline. Thus, recovering FCC
feed from residue is economically favorable.
Since the vacuum residue has very high boiling range, distillation can not be used
to separate the vacuum residue. The most efficient way to separate the vacuum residue is
solvent extraction. The solvents normally used in a refinery are propane and butane.
Paraffinic and naphthenic hydrocarbons have a large solubility in the above mentioned
solvents while metals and asphalt material have very low solubility. The extraction
operation consists of a series of countercurrent contacts between oil and solvent (Nelson,
1958). The separation between the solvent and the hydrocarbon is easily done by flash
because the solvent is much lighter than the hydrocarbon. The recovered hydrocarbon is
called deasphalted oil (DAO).
According to the refinery considered in this work, the entire vacuum residue is
sent to the ROSE unit. The typical percent of DAO product is about 60 vol.%) ofthe
vacuum residue in both the Summer Mode and the Winter Mode. This value is directly
used to calculate the DAO rate from the vacuum residue. DAO is then considered as a
crude cut and its properties are calculated as the same as those of any other crude cut.
DAO is combined with the HVGO produced directly from the vacuum tower and rerun
column bottom stream as the feed to FCC unit. The insolvable part of vacuum residue is
sold as No. 6 fuel oil.
65
3.6.3 Debutanizer
The function ofthe debutanizer in the crude unit is to separate the light
components from the light naphtha in order to stabilize the light straight-run gasoline
(LSR). Most ofthe overhead products from the debutanizer are propane and butane. This
stream becomes liquefied petroleum gas (LPG) and flows to the gas plant ofthe refinery.
The bottom product from the debutanizer flows to the naphtha splitter to make LSR and
reformer feed. The Debutanizer adds flexibility to refinery operation. When the Reid
Vapor Pressure (RVP) specification for gasoline is high in the winter, more butane is left
in the LSR. When the RVP specification is lower in the summer, most ofthe butane
enters LPG and is used as alkylation feed to produce alkylate, a lower RVP gasoline
blending stock.
In the atmospheric tower model, the vapor stream and liquid stream coming out
the overhead condenser are combined as the light naphtha stream. Before the calculation
ofthe debutanizer, the amount of light gas in the light naphtha is calculated according to
industrial data and is subtracted from the light naphtha
The LPG production rate from the debutanizer depends on the quality ofthe
crude. Since the crude slates are different in different seasons, the LPG product rates are
different. Using industrial data, the weight and volume of LPG are calculated from the
weight and volume ofthe debutanizer feed. The weight ofthe debutanizer bottom steam
is then estimated according to material balance. According to industrial data, the total
volume loss is negligible in the debutanizer, less than \%. In other words, the summation
ofthe volumetric flow rates of LPG and debutanizer bottom product is almost equal to
the volumetric flow rate ofthe debutanizer feed. Assuming that there is no volume loss,
the volumetric flow rate ofthe debutanizer bottom stream is then calculated.
3.6.4 Naphtha Splitter
The function ofthe naphtha splitter is to split the debutanizer bottom stream into
light straight-run (LSR) gasoline and heavy naphtha. The naphtha splitter also adds
flexibility in refinery operation. The operation ofthe naphtha splitter can be adjusted
66
according to downstream gasoline blending. If the octane number ofthe gasoline pool is
low, more light naphtha is used as the reformer feed to produce high-octane reformate.
When RVP specification of gasoline is low in the summer, the amount of high RVP light
straight-run gasoline (LSR) is reduced. More naphtha is used as reformer feed to produce
medium-RVP reformate.
Using industrial data, the volumetric flow rates ofthe LSR are calculated from the
volumetric flow rate ofthe feed to naphtha splitter. The LSR is then considered as a
crude cut and its properties, including weight, are calculated as any other crude cut. The
weight ofthe splitter bottom is estimated based on material balance. According to
industrial data, the total volume loss is negligible in the naphtha splitter and is less than
1%). Hence, assuming that there is no volume loss, the volumetric flow rate ofthe
debutanizer bottom stream is calculated from the volumefric flow rates ofthe feed and
the LSR.
67
CHAPTER 4
FCC FEED CHARACTERIZATION AND
MODEL BENCHMARKING
4.1 Process Overview
Fluidized Catalytic Cracking, better known as FCC, is one ofthe most important
and complicated processes in petroleum refining. For many refiners, FCC is the key to
profitability in that the successful operation ofthe unit can determine whether or not a
refiner can stay in business and remain competitive in today's market (Sadeghbeigi,
1995). In 1989, 40 vol.% of gasoline blending stocks in the U.S. were produced from the
FCCU (Lin, 1993). In the refinery considered in this work, FCC provides about 45 vol.%
blending stock ofthe gasoline pool. Since gasoline is the largest volume product from a
fuel-oriented refinery, an economic study on the refinery-wide operation must consider
the operation ofthe FCC unit.
The FCC unit converts large quantities of heavy feed into more valuable product,
called FCC gasoline. The FCC unit also produces light cycle oil (LCO), heavy cycle oil
(HCO) or slurry oil, and light gas. LCO has the same boiling range as of diesel. It is
blended with the diesel stream directly from the crude unit to produce the diesel product.
HCO from FCC is either recycled to the FCC feed or combined with the vacuum residue
from the crude unit to make No. 6 fuel oil. In the refinery considered in the study, most of
the HCO is used to make No. 6 fuel oil. Hence, the HCO is not recycled to the FCC feed
in the model. Light gas produced in the FCC unit is the main resource of light gas in the
refinery. Light gas is sent to the gas plant to produce fuel gas, liquidified petroleum gas
(LPG), propylene, and butane. Light gas also provides the feedstock to the alkylation unit
in the refinery.
From the block flow diagram ofthe refinery in Figure 3.1, it can been seen that
the FCC unit receives atmospheric gas oil (AGO), light vacuum gas oil (LVGO), heavy
vacuum gas oil (HVGO) and deasphalted oil (DAO) from the crude unit. In many
refineries, FCC feed goes through a hydrotreater before it enters the FCC unit if the FCC
68
feed has a high content of sulfur and metals. Sulfur in the feed may enter the flue gas of
the FCC unit to cause environmental pollution. Sulfiir also enters the product streams of
the FCC unit such as gasoline, LCO, and HCO that have upper limits on sulfur content in
the products. Metals in the FCC feed are catalyst poisons. Since the crude oils processed
in the refinery considered in this work belong to light, low sulfur crude type, the FCC
feed does not have high content of sulfur and metals. Therefore, a hydrotreater for the
FCC feed is not required in this particular refinery.
The FCC unit in the refinery considered in the work is a Model IV FCC unit. The
flow diagram of a Model IV FCC unit is shown in Figure 4.1. The gas oil from the crude
unit is first preheated in a fiimace and then mixed with the regenerated catalyst and enters
the reactor riser. Most ofthe cracking reaction occurs in the riser. The residence time of
the gas oil in the riser is about 2 seconds (Sadeghbeigi, 1995). Short contacting time is
required to prevent secondary cracking that cracks high-value products such as gasoline
into light gas. The catalyst is separated from the product vapor in the disengaging zone on
the top ofthe reactor and flows to the regenerator. The coke deposited on the catalyst is
bumed in the regenerator. After regeneration, catalyst regains its activity and flows to the
riser again. The product vapor exits the reactor from the top and enters the main
fractionator. In the main fractionator, the mixed product stream is separated into several
products. The liquid stream withdrawn from the overhead condenser is FCC gasoline.
The vapor steam from the overhead condenser flows to the wet gas compressor and on to
the gas plant for ftirther treatment. LCO is withdrawn from a side-draw tray ofthe tower.
After steam stripping, LCO flows to the diesel storage tank. HCO or slurry is withdrawn
from the bottom ofthe main fractionator and the bottom ofthe sidestripper. Most of HCO
or slurry is used as the feedstock for No. 6 fuel oil.
69
G o 00
c o c« ; -< u o
*H—»
13 13 O T3 <U N
•-3
fa >
O
O o
a o
C/3
3 GO
70
The distinguishing feature of a model IV FCCU is that it does not have a slide
valve on the catalyst circulation line like most of modem FCCU does. Hence, there is no
direct control of catalyst circulation rate between the reactor and regenerator. Only
indirect control is possible by varying the driving force for catalyst flow (Huq and
Morari, 1995). The model IV FCCU also has low metallurgical limit, about 1000 °F for
the riser temperature, while modem FCCUs may have the riser temperatures as high as
1050 °F(Arbeletal., 1995).
4.2 Model IV FCC Unit Modeling
McFarlane et al. (1993) developed a dynamic Model IV FCC unit model. The
model uses a continuous stirred-tank reactor (CSTR) for the riser and does not include a
yield model. Ellis(1996) developed a steady-state model based on McFarlane's dynamic
simulator. Plug-Flow Reactor (PFR) model is used to describe the flow and reaction in
the reactor. The ten-lump yield model proposed by Jacob et al.(I976) is used in the
steady-state model to calculate the product distribution. This model characterizes the gas
oil feed on the basis ofthe molecular stmcture and the molecular weights. The ten-lump
reaction network is shown in Figure 4.2. The boiling ranges ofthe individual lumps are
shown in Table 4.1.
Among the ten lumps, the lump of aromatic substituent molecules represents the
branches ofthe aromatic rings. Since they have completely different cracking
characteristics from the aromatic rings that they attach to, they form separate lumps. The
aromatic substituent group can be paraffin chains or naphthenic groups (Jacob et al.,
1976).
The FCC feed components with boiling points (I) greater than 650°F and (2)
between 430 °F and 650 °F are each grouped in four lumps: paraffins, naphthenics,
aromatics, and aromatic substituent groups. All eight lumps are cracked into gasoline,
and light gas in the riser. Four lumps with boiling point greater than 650°F compose
HCO. Four lumps with boiling point between 430°F and 650°F compose LCO. Gasoline
contains Cs's up to a boiling point of 430°F. The "C" lump contains coke and light gases
71
(C1-C4). Gasoline lump and "C" lump are the products ofthe cracking reactions. The
kinetic models used for this reaction network were taken from Jacob et al. (1976) and
Arbel et al. (1995) and assume first-order reactions with Arrhenius rate constants
for each ofthe 20 reactions between the ten lumps as shown in Figure 4.2.
I - PI
Ph - "
NI
Nh -I
As! -1
I-Ash
Arl —,
Asl
Figure 4.2 Ten-Lump Reaction Network.
72
Table 4.1 Boiling Range of Lumps in Ten-Lump FCC Reaction Network.
Lump Description Boiling Range
Ph Wt. % paraffinic molecules
Nh Wt. % napththenic molecules
Ash Wt. % aromatic substituent molecules
Arh Wt. % carbon atoms among aromatic rings
Pi Wt. % paraffinic molecules
Ni Wt. % napththenic molecules
Asi Wt. % aromatic substituent molecules
Ari Wt. % carbon atoms among aromatic rings
G Wt. % gasoline
C Wt. % coke and
1. Butane 4. Propene
2. Isobutane 5. Propane
3. Butane 6. Gases<C2
T>650°F
T>650°F
T>650°F
T>650°F
430°F<T<650°F
430°F<T<650°F
430°F<T<650°F
430°F<T<650°F
C5's-430°F
(C1-C4, and coke)
4.3 FCC Feed Characterization
Feed information is an input to the FCC model. Feed information required by the
FCC model includes volumetric flow rate, API gravity, and the weight fractions ofthe
eight lumps in the feed.
4.3.1 Volume and Weight of FCC Feed
FCC feed is composed of AGO from the rerun unit bottom, LVGO, HVGO, and
DAO. The volume and weight of these four streams have been calculated in the cmde
unit model described in Chapter 3. The volume and weight ofthe FCC feed is the
summation of these of four streams:
^FCC ~ ^.-iGO "'" ^LVGO "*• ''^HVGO + ''^DAO ' (4.1)
73
^fCC = ^AGO + WUGO + KvGO + ^OAO > (4-2)
where
Vpcc, VAGO, VLVGO, VHVGO, VDAQ- volumetric flow rates of FCC feed, AGO, LVGO,
HVGO, and DAO, respectively, BBL/D,
WFCC. WAGO, WLVGO, WHVGO, WDAO- weight flow rates of FCC feed, AGO, LVGO,
HVGO, and DAO, respectively, MLB/D.
The API gravity ofthe FCC feed is calculated using the weight and the volume of
the FCC feed using the formula below:
141 5 APIpcc= • 13^-5, (4.3)
(Wp,.,. • 0.454)/(Vp^c-0.159)
where
APIFCC- API gravity ofthe FCC feed,
WFCC- weight flow rate ofthe FCC feed, MLB/D,
Vpcc- volumetric flow rate ofthe FCC feed, BBL/D.
4.3.2 FCC feed Characterization
To use the ten-lump yield model, the weight percentages ofthe lumps in the FCC
feed must be known. Only the first eight lumps are in the feed: Ph, Nh, Ash, Arh, Pi,N|„
Asi,and Ari Lump gasoline and lump "C" are not in the feed.
Some industrial data of FCC feed obtained from the refinery considered in this
work are listed in Table 4.2. Although the given data can not be directly used to calculate
the weight percentage ofthe eight lumps in the FCC feed, at least they provide the
general composition ofthe FCC feed which can be used to validate the correlations used
in the feed characterization.
74
Tabel 4.2 Industrial Data of FCC Feed.
Feed Property Plant Data, Typical FCCU Combined Fresh Feed Analysis, May through August, 1998
Aniline point, °F 197-224
Viscosity, centipoise 6.4
Refractive Index, 60°F 1.5067-1.5082
API 22.0-23.3
Light Components, <650°F 10% TBP, 606-622°F
20% TBP, 678-694°F
Paraffins 56.9-59.1 (vol.%)
Naphthenes 22.2-24.8 (vol.%)
Aromatics 18.1-18.7 (vol.%)
To make the comparison between the feed characterization and the limited
industrial data, the operating conditions in the cmde unit must be the same for the model
and as for the industrial data because the operation of cmde unit significantly affects the
properties ofthe FCC feed. Since the given data for the FCC feed is for the Summer
Mode Operation, typical operating conditions in the summer are used. They are the same
as the normal values ofthe operating variables listed in Table 3.7 in Chapter 3.
The weight percent ofthe four light lumps in the FCC feed can be determined
from the boiling point ofthe light lumps. The light lumps all have a boiling point less
than 650°F. Among the four streams of FCC feed, AGO is the lightest and DAO is the
heaviest. The portion ofthe FCC feed on the whole cmde TBP curve begins with the IBP
of AGO and ends with EBP of DAO. From the whole cmde TBP-volumetric curve, the
cmde volume with the boiling point between the IBP of AGO and 650 °F is obtained.
The volume fraction of light lumps in the total FCC feed is then calculated as below:
VT _ "^lighilump
'^ light lump jrj. ' y.^-^)
y i fcc
75
where
Viight lump- volume fraction ofthe light lumps in the FCC feed,
VTiight lump- volumetric flow rate ofthe four light lumps in the FCC feed, BBL/D,
VTFCC- volumetric flow rate ofthe FCC feed, BBL/D.
The API gravity ofthe light lumps is estimated from the API-volumetric curve ofthe
whole cmde. Then, the specific gravity of light lumps is calculated. The weight fraction
ofthe light lumps in the FCC feed is calculated as blow:
^ _SPG„^,-Vi,i,ii.np-01S9
""""""" WTp,,-0.4536 ' ^ ^
where
SPGiight lump- specific gravity ofthe light lumps in the FCC feed,
Wiightiump- weight fraction ofthe light lumps in the FCC feed,
WTFCC- weight flow rate ofthe FCC feed, MLB/D.
To calculate the weight percentages ofthe eight lumps in the feed, the weight
percentages of paraffins, naphthenes, and aromatics (PNA) in the FCC feed must be
known. The TOTAL method (Dhulesia, 1986) and n-d-M method (ASTM, 1985) are
used here.
TOTAL method is used to calculate the refractive index (RI) at 20°C and 60°C.
TOTAL method is also used to calculate the fraction of carbon in the aromatic ring in the
FCC feed. It is claimed that TOTAL method is more accurate than other methods in
predicting refractive index (Dhulesia, 1986) and aromatic carbon content (Sadeghbeigi,
1995). The correlations in the TOTAL method were developed from multiple linear
regression of 33 feedstocks (Dhulesia, 1986).
RI is an important index of FCC feed. It shows how refractive or aromatic a FCC
feed is. The higher the RI, the less crackable the FCC feed is (Sadeghbeigi, 1995). The
correlations of TOTAL method are given below:
76
RI(20) = 1 + 0.8447-SPG""'' -(VABP^ + 273.16)'"'"'' -MW'"""'', (4.6)
RI(60) = 1 + 0.8156 • SPG"''' -(VABP^ + 273.16)-""''' -MW'""""', (4.7)
where
RI (20)- refractive index at 20°C,
RI (60)- refractive index at 60°C,
SPG- specific gravity of FCC feed,
VABP°c- volumetric average boiling point, °C,
MW- molecular weight of FCC feed.
For consistence reason, the molecular weight ofthe FCC feed used in the
equations 4.6 and 4.7 are also calculated from the correlation in the TOTAL method as
below:
MW = 7.8312 -10-' - SPG'""''' - VABPc'"'' • AP"'"', (4.8)
where
AP- Aniline point, °C.
VABP is calculated from the ASTM boiling curve ofthe FCC feed. The ASTM
boiling curve is converted from TBP curve of FCC feed. Please refer to Chapter 3 for the
details ofthe calculations. The aniline point ofthe FCC feed uses the average value ofthe
aniline point ofthe FCC feed in industrial data, 99.2°C. The moles ofthe FCC feed is the
summation ofthe moles of AGO, LVGO, HVGO, and DAO. The molecular weight of
FCC feed is then calculated using the moles and the weight ofthe FCC feed.
The RI (20) and RI (60) calculated in equations (4.6) and (4.7). Only RI (60) of
the FCC feed is measured in the refinery considered in the work. The RI (60) predicted
by the TOTAL method, 1.49982, is just a little smaller than the normal range of FCC
feed 1.5067-1.5082. However, the n-d-M method which is used to calculate the
77
naphthene content and the paraffin content ofthe FCC feed is very sensitive to the value
of refractive index (RI). Small differences in RI may cause a large error in PNA
prediction when using the n-d-M method. It was reported that a 35%) drop in the aromatic
content was observed when the RI (20) dropped from 1.5105 to 1.5000 (Sadeghbeigi,
1995). Hence, accurate values of RI(20) and RI(60) are required. Here, a bias is used to
adjust the calculated RI(60). The bias between the average value of RI (60) ofthe FCC
feed in the industrial data, 1.50745, and the calculated RI (60) of normal operating
condition is calculated as below:
dRI(60) = 1.50745 - RI(60)„„^,^,, (4.9)
where
RI(60)normai- Calculated RI(60) under normal operating conditions using TOTAL method,
dRI (60)- the bias of RI(60).
The dRI (60) calculated here is then used in adjusting the calculated RI (60)
thereafter as shown below:
RI(60)^,j^,^, = RI(60),o,^ +dRI (60), (4.10)
where
RI(60)adjusted- adjusted value of RI(60),
RI (60)TOTAL- refractive index at 60°C calculated by TOTAL method.
The TOTAL method and the n-d-M method use the value of RI (20) in
composition calculations instead of RI (60). Now the question is how to estimate the
difference between RI (20) and RI (60). It is assumed that TOTAL method is consistent
in predicting RI (60) and RI (20) for one feed. Hence, the RI (60) and RI (20) calculated
from the TOTAL method is used to estimate the difference between RI (60) and RI (20).
The final value of RI (20) is calculated using the following formula:
78
RI(20)^,,..,,,, = RI (60),,j^,,, - (RI(60),orAL - Rl(20)rorAL ) , (4-11)
where
RI(20)adjusted- adjusted value ofthe RI(20),
RI (20)TOTAL- refractive index at 20°C calculated by TOTAL method.
The correlation in TOTAL method is used to calculate the aromatic carbon
content in the FCC feed. The correlation has the formula as below:
%C, = -814.136 + 635.192 • RI(20)^,j^^,^, -129.266 - SPG + 0.013 • MW
-0.340- SUL + 0.757 In(VIS)
where
%)CA- aromatic carbon content ofthe total carbons in the FCC feed, wt.%),
SPG- specific gravity ofthe FCC feed,
SUL- sulftir content ofthe FCC feed, wt.%,
VIS- viscosity of FCC feed, centistoke.
The viscosity in the industrial data has the units of centipoise. The conversion between
centistoke and centipoise uses the following formula:
, centipoise centistokes = . (4.13)
density
The n-d-M method is an ASTM (D-3238-85) method used to estimate the carbon
distribution in aromatic ring stmcture, naphthenic ring stmcture, and paraffin chains.
Only the correlation of refractive ring, which includes aromatic ring and naphthenic ring,
is used here. The formulas are as follows:
V = 2.5-[RI(20)-1.4750\-(SPG,O^^ -0.8510), (4.14)
79
m = (SPG,o,c -0.8510)-1.11-[RI(20)-I.4750] , (4.15)
where
V, Tu- n-d-M method parameters,
SPG20''c- specific gravity at 20°C.
If T!j is negative: %C« =820-m-3-SUL + ^ ^ ^ , (4.16) MW
If tu is positive: %C« = 1440-m-3-SUL + ^^^ , (4.17) MW
where
% C R - weight percentage ofthe carbons of refractive ring stmcture in the total carbons in
the FCC feed, wt.%.
Using the aromatic carbon content calculated in equation 4.12, the naphthenic
carbon content c£in be calculated as below:
%C^=%C^-%C^ (4.18)
%Cp=100-%C^ (4.19)
where
% C N - weight percentage ofthe carbons of naphthenic rings in the total carbons in the
FCC feed, wt.%,
%)Cp- weight percentage ofthe carbons of paraffin chains in the total carbons in the FCC
feed, wt.%.
CA, CN, and C? calculated above are the weight percentage ofthe carbon atoms in
a certain molecular structure in the total weight of carbon atoms. The weight percentage
of aromatic rings, naphthenic rings, and paraffin chains are calculated next. For aromatic
ring, the carbon/hydrogen ratio is 1:1 while the carbon/hydrogen ratio in naphthenic ring
is 1:2. Considering the fact that the paraffin molecules in the FCC feed are big molecules
80
with long paraffin chains, most of carbon atoms are in the middle of paraffin chains.
Hence, carbon/hydrogen ratio in the paraffin chains is also set at 1:2. The weight fraction
of paraffins, naphthenic rings, and aromatic rings are then calculated as below:
WT, = %C, - "^^^'•'•"-'-^^^^•^^^ny-o^'"' (4.20) AW,^,,,„-RATIO,
where
I-paraffin, naphthene ring, and aromatic ring,
C]- weight percentage of carbons of molecule type I in the total carbons in the FCC feed,
wt.%),
RATIOi- the carbon/hydrogen ratio in the molecule type I,
WTi- weight fractions ofthe molecule type I in the FCC feed,
AWcarbon- atomic weight of carbon, 12,
AWhydrogen- atomic weight of hydrogen, 1.
The comparison between the industrial data and the weight percentages ofthe
paraffin, naphthene rings, and aromatic rings in the FCC feed calculated by equation 4.20
are listed in Table 4.3.
The industrial data do not include the volume percentage ofthe light lumps.
However, it can be estimated that the volume percentage of light lumps is between 10
vol.%> and 20 vol.%) since 650°F, the cut point for light lumps, is greater than the highest
possible point for 10 vol.%, 622°F, while less than the lowest possible point for 20 vol.%,
678°F. It can be seen from Table 4.3 that the calculated value, 12.8 vol.%, is in this
range. Since the specific gravity values of paraffin, naphthene, and aromatics in the FCC
feed are unknown, the volumetric percentage of these components can not be calculated
in the feed characterization. However, the calculated weight percentages of paraffin,
naphthene and aromatics are close to the volume percentages of corresponding
components in the industrial data. The wt.% of aromatics is 1.3% higher than its vol.%.
81
which is expected because aromatics have heavier density than other species. The
opposite is true for paraffins.
Table 4.3 Comparison between the Industrial Data and Feed Characterization ofthe FCC Feed.
Feed Property Plant Data, Typical FCCU Model prediction Combined Fresh Feed Analysis, May through August, 1998
Light Components, <650 °F 10% TBP, 606-622 °F 13.4 (vol.%)
20% TBP, 678-694 °F 12.5 (wt.%)
Paraffins 56.9-59.1 (vol.%) 56.1 (wt.%)
Naphthenes 22.2-24.8 (vol.%) 23.8 (wt.%)
Aromatics I8.I-I8.7 (vol.%) 20.0 (wt.%)
The weight percentage ofthe aromatics from the feed characterization, 20.0 wt.%,
is higher than the values ofthe volume percentage ofthe aromatics in industrial data,
18.1-18.7 Vol.%). This is expected because aromatics have higher specific gravity than
those of paraffins and naphthenes with the same boiling point. Hence, the weight
percentage ofthe aromatics in the FCC feed is greater than its volume percentage from
the absolute value. The comparison made here is not rigorous due to the lack of detailed
FCC analysis from industrial data. General speaking, the calculated values from feed
characterization are in good agreement with the industrial data considering that empirical
correlations are used in the calculations.
4.3.3 Weight Fractions ofLumps in the FCC Feed
According to the 10-lump model, the weight percentage of aromatic rings is the
summation ofthe weight percentage of Arh and Ari. Arh and Ari have the same molecular
stmcture. The only difference between the two is their boiling range. This is also tme for
each pair of Ph and Pi, Nh and Ni, and Ash and Asi. An assumption is made that the weight
percentage ofthe heavier lump in the total heavy lumps is equal to the weight percentage
of lighter lump in the same pair in the total light lumps. The assumption is based on the
82
fact that the composition of FCC feed does not change dramatically with respect to its
boiling range. The relative small amount of light lumps, which is about 15 vol.%) ofthe
FCC feed, causes an insignificant error due to this assumption. The weight percentage of
Arh and Ari are calculated using following formula:
WT W,= --!-!- , (4.18)
WT^+WT^+WT/ W,=W„^,,,,^^-W,, (4.19)
^ / . = ^ / - ^ v (4.20)
where
I - aromatic rings, naphthene rings, and peiraffin,
Wi- weight fraction ofthe molecular type I in the FCC feed,
Wii- weight fraction ofthe light lump ofthe molecular type I in the FCC feed,
Wih- weight fraction ofthe heavy lump ofthe molecular type I in the FCC feed,
Wiightiump- weight fraction of total light lumps in the FCC feed.
The weight fractions of lump Arh and lump Ari are equal to WArh and WAFI,
respectively:
%Ar,=W,,^, (4.21)
%An=W,^^, (4.22)
where
%Arh, %)Ari- weight fractions of lump Arh and lump Ari in the FCC feed, respectively.
Only the weight fractions of lump Ath and lump Ari can be directly obtained from
equation 4.18 to 4.22. Since the lump Ash and Itimp Asi can be the paraffin chains or the
naphthenic group, the remaining six lumps belong to either naphthenic molecules or
83
paraffin molecules. No correlation is given in literature to calculate the weight
percentage of aromatic substituent lumps, Ash and Asi.
Ellis (1998) provided the composition of an FCC feed with an API gravity of 23,
which is close to the API gravity ofthe FCC feed, 22.0 to 23.3, studied here. The weight
fractions of eight lumps in the FCC feed of API=23 given by Ellis (1998) are listed in
Table 4.4.
Table 4.4 Weight Fractions of Eight Lumps in an FCC Feed of API=23.
Lump Name Ph Nh Ash Arh Pi Ni Asi
Weight Fraction 0.36 0.15 0.25 0.14 0.04 0.02 0.02
Ari
0.02
Assuming the same ratios of Ash to Arh and Asi to Ari in the feed given in Table
4.4 and the FCC feed studied here, the weight fractions of Ash and Asi are calculated as
below:
o/oAs,=o/oAr,-y'''^'-", (4.23)
o/oAs,=%Arr2.,'^'-'\ (4.24)
where
%Ash, %)Asi- weight fractions of lump Ash and lump Asi in the FCC feed, respectively,
%Arh,APi=23, %)Ari, APi=23, %Ash,APi=23, %Asi,APi=23 -Weight fractions of lumps Arh, Ari,
Arh, and Ari in the FCC feed of API=23, respectively.
The weight fractions of lumps Ph, Pi, Nh, and Ni are calculated using the following
formula:
W %n=(I-fl'„„.,.., -%As, -%ArO-- <^^*^ . (4.25)
paraffin .heavy naphlhene .heavy
84
w %N„ =(1-W„^,,„,^ -o/oAs, -%ArJ-- ".^mUf!^ , (4.26)
paraffin .heavy naphlhene .heavy
W %P, = (W,^„ ,„„, - %As, - %Ar,) - '-^^rsff^ , (4.27)
paraffin,light naphlhene .light
w %N, = (W„^,„„^ - o/oAs, - o/oAr,) - "-^^!^ , (4.28)
paraffin.lighl naphlhene .lighl
where
%Ph, %)Pi ,%Nh, %Ni -weight fractions of lump Ph, lump Pi, lump Nh, lump Ni in the FCC
feed, respectively,
Wparaffm.heavy- Weight fraction of the hcavy paraffin in the FCC feed,
Wparaffinjight- weight fraction ofthe light paraffin in the FCC feed,
Wnaphthene.heavy- Weight fraction of heavy naphthene in the FCC feed,
Wnaphthenejight- weight fraction of light naphthene in the FCC feed.
In order to determine if the feed characterization procedure gives reasonable
results in the operating ranges which are used in the optimization studies, the feed
characterizations were carried out in the operating ranges and the results were compared
with the base case calculated above. The two main operating variables that affect the
composition of FCC feed are the heavy distillate ASTM 95% point and the HVGO TBP
100%) point. The heavy distillate is the adjacent lighter side-draw product ofthe FCC
feed in the atmospheric tower. The heavy distillate ASTM 95% point determines the
amount ofthe light components in the FCC feed. HVGO is the heaviest side-streams
among the four streams composing ofthe FCC feed. The HVGO TBP 100% will affect
the composition of heavy lumps in the FCC feed. The weight fractions of eight lumps and
other properties of FCC feed under different operating conditions are listed in Table 4.5.
85
Table 4.5 Weight Fractions ofthe Eight Lumps in the FCC feed across the Operating Range.
Base Case Case 1 Case 2 Case 3 Case4 Industrial data
Operating conditions
HD ASTM 650
95%, °F
HVGO TBP 1050
100%, °F
630
1050
670
1050
650
1010
650
1090
650
1050
Feed properties
Light lump, vol.% 13.4
Aromatics, wl.%o 20.0
Naphthenes, wt.% 23.9
Paraffins, wt.% 56.1
18.9
19.3
23.0
57.7
7.4
20.7
25.0
54.3
13.7
19.8
25.2
55.0
13.0
20.4
22.5
57.1
10%-20%
18.I-I8.7
(vol.%)
22.2-24.8
(vol.%)
56.9-59.1.
(vol.%)
Weight fraction of lump
Ph 0.272
Nh 0.115
Ash 0.313
Arh 0.175
P, 0.053
Ni 0.022
Asi 0.025
An 0.025
0.272
0.108
0.284
0.159
0.078
0.031
0.034
0.034
0.269
0.124
0.345
0.193
0.028
0.013
0.014
0.014
0.268
0.123
0.308
0.172
0.054
0.025
0.026
0.026
0.272
0.107
0.320
0.179
0.051
0.020
0.025
0.025
In Case 1 and Case 2, the heavy distillate ASTM 95% point is perttirbed by 20°F.
It is observed from Table 4.5 that light lump content increases with the decrease ofthe
heavy distillate ASTM 95%o point and vice versa. It is expected because if the heavy
86
distillate ASTM 95% point drops, more light components are left in the FCC feed. The
opposite is tme when the heavy distillate ASTM 95% point increases.
In Case 3 and Case 4, the HVGO TBP 100% point is perturbed by 40°F. It is
observed from Table 4.5 that the light lump content decreases with the increase ofthe
HVGO TBP 100%) point and vice versa. The change is small compared to the light
components change caused by perturbing the heavy distillate 95% point. This is expected
because the HVGO have a small fraction in the heavy lumps.
4.4 Model Benchmarking
The steady-state model developed by Ellis et al. (1998) is used in the present
study. The steady-state model has been quantitatively benchmarked against published
results. These results show a good representation ofthe available data. However, the FCC
unit in the refinery considered in this work is slightly different from what was described
in the model. The biggest difference is that the FCC unit studied here has only about 70%
of throughput ofthe original model. The feed qualities are also different. The feed quality
significantly affects the product distribution predicted from the FCC model. The FCC
model using the FCC feed characterized above show significant difference in product
distribution than the industrial data. This may be explained by the different catalysts
used. The type of catalyst in the original reaction system that the model is based on is
unknown. Since there are many types of FCC catalysts, it is very likely that catalyst used
in the FCC unit considered in this work is different from catalyst in the unit described by
the original model. In order to represent the FCC unit accurately, ftirther benchmarking
ofthe original model is required.
The original model was benchmarked against the industrial data obtained from the
refinery considered in the work. Only the industrial operating data for one day of
operation is available. The industrial data are listed in Table 4.6.
87
Table 4.6 FCC Model Benchmarking: Industrial Data and Model Prediction.
Process variable
FCC Feed, MLB/D
Reactor temperature, °F
Regenerator temperature, °F
O2 in flue gas, mol%)
FCC gasoline, MLB/D
LCO, MLB/D
HCO, MLB/D
Light Gas, MLB/D
Industrial Data
6378
987
1284
0.011
3433
1096
450
1201
Model Prediction
-
-
3434
1097
454
1206
Relative error (%)
-
-
-
0.03
0.09
0.89
0.42
The most important fiinction ofthe FCC model is to correctly predict the product
distribution. The values ofthe reaction parameters directly affect the product distribution.
Only the activation energy ofthe reactions are used in parameterization because product
distribution is more sensitive to the activation energy than to the frequency factor. Since
only bulk volumes of four products from industrial data are available for the
parameterization, three adjustable factors are used to adjust the values of activation
energy ofthe cracking reactions. Each adjustable parameter corresponds to the formation
of one ofthe three products: FCC gasoline, LCO, and light gas. It can been seen from
Figure 4.2 that HCO is not formed in the FCC cracking. Hence, there is no adjustable
parameter for HCO. Each original value of activation energy is adjusted by one of three
parameters based on the observed products ofthe reaction. For example, the activation
energy ofthe reactions from lump Ph, an component of HCO, to Pi, an component of
LCO, is multiplied by the adjustable parameter of LCO formation and becomes the new
value ofthe activation energy. The adjustable parameters that are used in the
parameterization and their values are listed in Table 4.7.
88
Table 4.7 Adjustable Variables in FCC Model Benchmarking.
Adjustable parameter Description Parameter value
^^gasoline Adj ustable parameter for activation 0.8576
energy of gasoline formation reactions
^LCO Adjustable parameter for activation 0.8507
energy of LCO formation reactions
•Alightgas Adj ustable parameter for activation 1.0198
energy of light gas formation reactions
ki,(lboil)(s)/lb
catalyst
Kinetic frequency factor for the 8.626699E5
formation of gasoline from gas oil
kc,
0.4
Frequency factor for the formation of 0.48
(lb coke/lb catalyst) coke
A3, Btti/lb Ratio of heat transfer coefficient of
preheat box versus gas oil specific
heat
14.33
Hcrack, BTU/(lb oil) Heat for cracking reaction -326.86
Oa.ref, reference O2, in estimating coke on
Mole oxygen/mole air regenerated catalyst
0.01197
ko2, (lbcoke)(s)/ft^ Frequency factor in the rate ofthe
depletion of oxygen
2.55644E9
89
The process constraints are also parameterized to prevent the violations of process
constraints. For this purpose, some process adjustable parameters are also included in the
parameterization. These adjustable parameters have been used to develop the original
model. Their values are also listed in Table 4.7. The values ofthe process constraints are
from the design documents ofthe refinery and the original model. Personnel from the
refinery agreed on the values of these process constraints. The values of process
constraints are listed in Table 4.8. For the details of FCC model, adjustable parameters,
and process constraints, the reader can refer to Ellis (1996).
FCC model parameterization is an optimization problem. The least square type
objective function is used in the parameterization. The objective function is shown
below:
Objfparameterization ~ LI'^I V^model,I ~^base,I) ' (4-29) /
where
Objfparamterization- objective ftuiction of the FCC modcl parameterization,
Wi- weighting factor,
Wmodeij- weight flow rates of product I predicted by the FCC model, MLB/D,
Wbase,!- weight flow rates of product I in industrial data, MLB/D.
NPSOL optimization package (Gill et al., 1986) was used in the parameterization.
Table 4.6 shows the comparison ofthe industrial data and model predictions. For this set
of data, the model predictions fit the industrial data very well. The largest relative error is
less than 1%.
90
Table 4.8 Process Constraints of FCC Unit.
Constraint Lower number Bound
Process Description Variable
Upper Bound
1 500.0
0.0
14.7
Temperature of fresh feed exiting 700
preheat furnace, °F
Flow rate of fuel to preheat 39.5
furnace, SCM
Reactor pressure, psig 30
14.7 Regenerator pressure, psig 30
5100
0.0
-5.0
0.0
sa
r'sucn.lift"
1" surge,! ift
P6-P4
t sp
Actual speed of lift air blower, 6100
RPM
Difference between suction flow None
and surge flow for lift air blower,
ICFM
Reactor-regenerator pressure 2.0
difference, psi
Level of catalyst in standpipe, ft 20.0
10
35000 Fsucn.comb Combustion air blowcr inlet 42000
suction flow, SCFM
0.0 Fwg Flow rate ofwet gas to wet gas 0.67
compressor,
(lbmol)/s
91
4.5 FCC Gasoline Octane Model Modification
Since the FCC gasoline has about 45 vol.% ofthe gasoline pool, the changes of
the properties ofthe FCC gasoline have great effect on gasoline blending. It is highly
desirable to predict the properties of FCC gasoline accurately. In the original model, Ellis
(1996) developed an empirical correlation to related the motor octane number to the riser
temperature and conversion as below:
MON = MON,^, + a, (T, - 7;„,,; + a,(Com - Conv,^,^), (4.30)
where
a i - constants, MON/°F,
a i - constants, MON/conversion
Conv- weight conversion, lb (gasoline+light gas)/ lb gas oil,
Convbase- base weight conversion, lb (gasoline+light gas)/ lb gas oil,
MON- motor octane number ofthe FCC gasoline,
MONbase- motor octane number ofthe base FCC gasoline,
Tf- riser temperature, °F
Tr, base- basc riscr temperature, °F.
The values of base case and constants are listed in Table 4.9.
It is found that MONs calculated from this correlation were about 2 octane
number higher than the corresponding industrial data. In order for this correlation to fit
the industrial data, modification is necessary. One set of data from the industrial data was
selected as the base case. The value ofthe constant al is also changed based on the
industrial data. The new values ofthe base case and constants are listed in Table 4.9.
92
Table 4.9 FCC Octane Model.
Parameter Unit Original Model Current Model
Convbase (gasoline+light gas)/ lb gas oil 0.55 0.80
MONbase, None 72.5 80.7
Tr.base °F 900 995
ai MON/°F 0.05 0.022
a2 MON/conversion 0.17 0.17
93
CHAPTER 5
REFORMER FEED CHARACTERIZATION
AND MODEL BENCHMARKING
5.1 Process Overview
Catalytic reformer is an important unit in gasoline production. The reformate, the
product from the catalytic reformer, is a major blending stock in gasoline pool. Catalytic
reformate furnishes approximately 40% ofthe United States gasoline requirements (Gary
and Handwerk, 1984). The reformer in the refinery considered in this work provides
about 30 vol.% blending stock for the gasoline pool.
To appreciate the importance of catalytic reforming in gasoline production, the
octane number must be understood. Octane number is an index used to measure the
antiknock quality ofthe gasoline. Higher the octane number, the better the antiknock
quality. Octane number is the most important specification for gasoline. Gasoline with
higher octane number normally sells at higher price in the market. Two values of octane
number, research octane number (RON) and motor octane number (MON), are used.
Octane rating is the arithmetic average of MON and RON. Octane rating is used to refer
the general antiknock quality ofthe gasoline in the United States.
The octane ratings ofthe typical gasoline blending stocks in the refinery are
shown in Table 5.1.
Table 5.1 Octane Ratings ofthe Typical Gasoline Blending Stocks.
Gasoline blending stock Octane rating, (R0N+M0N)/2
Low severity reformate 92.3
High severity reformate 94.5
FCC gasoline 87.1
Alkylate 91.1
Light sfraight-run (LSR) gasoline 69.0
Butane 91.5
94
Low severity reformate is the reformate produced when the severity ofthe
catalytic reformer is low and high severity reformate is the reformate produced when the
severity ofthe catalytic reformer is high. The inlet bed temperatures usually indicate the
severity of the operation. The higher the inlet bed temperature, the more severe the
operation. It can been seen from Table 5.1 that the reformate has the highest octane
number among all the blending stock. Considering the large quantity ofthe reformate, it
is clear that the reformate is the major octane booster in gasoline blending.
It is not economical to process the Light straight-mn (LSR) gasoline (C5-180°F)
as feed to the reformer as this fraction is largely composed of low-molecular-weight
paraffins that tend to crack to low-value butane and light fractions (Gary and Handwerk,
1984). The typical feedstock to the catalytic refomer is the heavy naphtha with a boiling
range from 180 to 375°F.
The heavy naphtha produced from the cmde unit has an octane rating around 70,
which is too low for gasoline blending. The reformer converts the heavy naphtha to the
high-octane reformate. Reforming Process are classified as continuous, cyclic, or semi-
regenerative depending upon the frequency of catalyst regeneration. In a continuous
process, the catalyst is removed and replaced during normal operation. In a semi-
regenerative process, the unit is shut down periodically to regenerate the catalyst. The
normal intervals is 3 to 24 months. The cyclic process is a compromise between these
extremes and is characterized by having a swing reactor in addition to those on-stream in
which the catalyst can be regenerated without shutting the unit down (Gary and
Handwerk, 1984). The catalytic reformer in the refinery considered in this work is a
semi-regenerative type. The semi-regenerative type reformer has the advantage of
minimal capital costs compared to continuous and cyclic type reformers (Gary and
Handwerk, 1984). The disadvantage ofthe semi-regenerative type reformer is that the
reformer needs to be taken off-stream periodically to regenerate the reforming catalyst
that looses its activity during the operation.
The reformer plays a key role in the hydrogen usage in the refinery. The reformer
is the main provider ofthe hydrogen that is used in several hydrotreaters in the refinery to
95
treat feeds and final products. Without a reformer, a refinery has to rely on either a steam
reforming unit or purchased hydrogen from resources outside the refinery.
The flow diagram ofthe reformer in the refinery is shown in Figure 5.1. The
reaction system consists of three fixed-bed reactors in sequence. The heavy naphtha from
the cmde unit goes through a naphtha desulfurizer to eliminate the sulfiir and other
impurities in the feed. Then the heavy naphtha is mixed with the hydrogen stream and
exchanges heat with the product stream from the third reactor. The heavy naphtha is
further heated in the preheat fiimace to reach the reaction temperature, about 890 to 980
°F. Since the main reactions taking place in the reactor are endothermic, the temperature
drops sharply in the reactor. The temperature drops to such a level that no reforming
reaction can take place near the exit ofthe reactor. The effluent stream is withdrawn from
the reactor and is heated in an intermediate heater to the reaction temperature. The hot
stream flows to the next reactor.
The product stream exits the third reactor and exchanges heat with incoming
naphtha. The product stream then flows to a separator, which is a flash dmm, to separate
the hydrogen in the product stream. The major part of hydrogen is returned to the reaction
system. The extra hydrogen is used in other units in the refinery. After the separator, the
product stream flows to a stabilizer to separate the C4 and lighter from the heavier
components. C4 and lighter components are fed to the gas plant for ftirther treatment. The
bottom stream ofthe stabilizer is the main product, reformate, which goes to the storage
tank for gasoline blending.
5.2 Reformer Modeling
The reformer modeling has been studied by many researchers. Taskar (1996) gave
an exhaustive literature survey on reformer modeling. The steady-state model developed
by Taskar (1996) was used in this work. The core of Taskar's model is a detailed kinetic
scheme involving 35 pseudo-components connected by a network of 36 reactions in C5-
Cio range using Hougen-Watson-Langmuir-Hinshelwood-type reaction rate expressions.
The compositions of pseudo-components in a typical reformer feed (Lin, 1988; are
listed in Table 5.2.
96
u CO nj QL 0)
• a 0) 0)
>»!: O <!. (U C D) <U C 3 TO £ -S 0 y o <" (J CD (U
a:
,o <+3
ca J5 H-» J3
O H
ca
z o -*-> >^ ca . ^ j
ca U (U
> ca kH 1) c 1) 00 (U
fti • l-H
a (U C/2 ca
l+H
o <D
a <u JS CJ
C/2
^ o
fa c/3 1/3 (U o o kH
fa '—' i n
kH 3
Fig
97
Table 5.2 Chemical Components ofthe Reformer Feed.
Chemical components
Hydrogen
CI
C2
C3
C4
C5-
n-Pentane
Iso-Pentane
Multi-branched hexanes
Single-branched hexanes
n-Hexane
Five-carbon ring C6
naphthenes
Benzene
Multi-branched heptanes
Single-branched heptanes
n-Heptane
Five-carbon ring C7
naphthenes
Six-carbon ring C7
naphthenes
Toluene
Volume fraction of chemical component in a typical reformer feed
0
0
0
0
0
0
0.011533793
0.005766896
0
0.034154728
0.068309457
0.03902341
0.005057543
0.02882307
0.02882307
0.093747762
0.028779382
0.052533793
0.023964035
Molar specific volume. cm^/mole
31.0
52.
68.
84.
101.4
116.1
116.1
117.4
I3I.6
132.9
131.6
113.1
89.4
147.5
147.5
147.5
128.8
128.3
106.8
Molar fraction of chemical component in a typical reformer feed
0
0
0
0
0
0
0.0136791382
0.00676383225
0
0.0353870741
0.071473286
0.0553827156
0.0076970243
0.026907108
0.026907108
0.0875160473
0.0358655286
0.0657239621
0.0305287825
98
Table 5.2 Continued.
Chemical components
n-Octane
Single-branched octanes
Multi-branched octanes
Five-carbon ring C8
naphthenes
Six-carbon ring C8
naphthenes
C8 Eiromatics
n-nonane
Single-branched nonanes
Multi-branched nonanes
Six-carbon C9 naphthenes
C9 aromatics
n-decane
Single-branched decanes
Multi-branched decanes
Six-carbon ring CIO
naphthenes
C10 aromatics
Volume fraction of chemical component in a typical reformer feed
0.123659717
0.030914929
0.030914929
0.028199327
0.056398654
0.050215496
0.055560406
0.013890101
0.013890101
0.080274821
0.03693356
0.01043014
0.00521507
0.00521507
0.024351882
0.013418857
Molar specific volume. cm^/mole
163.5
162
164.4
144.7
143
123.1
179.6
178.2
170.4
159.8
139.6
196
194.5
191.5
176.3
156.8
Molar fraction of chemical component in a typical reformer feed
0.104142811
0.0262767739
0.0258931714
0.0312810878
0.0633059217
0.0555009602
0.0425968892
0.0107328858
0.0112241799
0.0806332576
0.0359961908
0.00732745014
0.00369198002
0.00374981783
0.0221713355
0.0116436799
It is worth noting that some pseudo-components are real chemical species, such as
n-pentane and iso-pentane. The important difference between pseudo-components in the
reformer and lumps used in the FCC model is that all the chemical species in a pseudo-
component ofthe reformer model has the same carbon number while the chemical
99
species of a lump in the FCC model do not have the same carbon number. One reason for
this modeling difference is that the FCC feed is much heavier than the reformer feed. The
FCC feed is so complicated that it is very difficult to use the same approach as the
reformer model. Using detailed pseudo-components in the reformer provides much
required information for the gasoline blending while the feed characterization of reformer
becomes more difficult.
Deactivation ofthe catalyst in the reformer was also modeled. The model was
parameterized by benchmarking against the industrial data obtained from Phillips
Petroleum Company. The reader can refer to Taskar (1996) for detailed discussion ofthe
kinetics ofthe reforming reactions.
5.3 Reformer Feed Characterization
5.3.1 Naphtha Desulfiirizer
In the refinery considered in this work, the reformer feed is composed of heavy
naphtha, a side-draw stream ofthe atmospheric tower and the bottom stream ofthe
naphtha splitter. The reformer feed flows through a naphtha desulfurizer before entering
the reformer. The metals, hydrogen sulfide, ammonia, organic nitrogen and sulfur
compounds will deactivate the catalyst in the reformer and must be removed before the
feed enters the reformer (Gary and Handwerk, 1984). Hydrotreating is used to remove
these impurities from the feed.
The feed to the naphtha desulfurizer includes reformer feed from the cmde unit
and the hydrogen stream from the reformer. The products ofthe naphtha desulfiirizer
consist ofthe stripper overhead gas, desulfurizer separator overhead gas, liquidified
petroleum gas (LPG), and sweetened naphtha. Stripper overhead gas and separator
overhead gas flow to the gas plant for further treatment. The LPG flows to the
debutanizer in the cmde unit.
A detailed model was not built for the naphtha desulfurizer. Instead, material
balance and fixed ratios are used to calculate the amotmts ofthe hydrogen, the stripper
overhead gas, the separator overhead gas, LPG, and the sweetened reformer feed. The
100
ratios of each operation mode. Summer Mode or Winter Mode, are based on the
industrial data of a typical day in each operation mode. The ratios are listed in Table 5.3.
Table 5.3 Ratios in the Calculation ofthe Naphtha Hydrotreater.
Rates Summer Mode Winter Mode
H2/Feed, MLB H2/MLB feed
H2/Feed, BBL H2/BBL feed
Reformer feed/total feed,
MLB reformer feed/MLB total feed
Reformer feed/total feed,
BBL reformer feed/BBL total feed
Overhead gas/total feed,
MLB overhead gas/MLB total feed
Overhead gas/total feed,
BBL overhead gas/BBL total feed
LPG/total feed,
MLB LPG/MLB total feed
LPG/total feed,
BBL LPG/BBL total feed
0.06997
0.07756
0.9108
0.06366
0.07553
0.8920
0.9020
0.0029
0.0024
0.0509
0.0623
0.9035
0.0017
0.0016
0.0183
0.0255
The amount ofthe hydrogen required by the naphtha desulfurizer is calculated
using the formulas given below:
^H2 = ^reformerfeed ' ^TIO^j21 reformer feed, W '
^Hl = Reformer feed " ^^10^^21 reformer feed, V '
(5.1)
(5.2)
where
WH2- mass flow rate ofthe hydrogen used in the naphtha desulfurizer, MLB/D,
VH2- volumetric flow rate ofthe hydrogen used in the naphtha desulfurizer, BBL/D,
101
Wreformer feed" niass flow rate of the reformer feed from the crude unit, BBL/D,
Vreformer feed- volumctric flow rate of the reformer feed from the cmde unit, BBL/D,
RatioH2/reformer feed, w" niass ratio between the hydrogen and the reformer feed from the
crude unit,
RatioH2/reformer feed, v" volumctric ratio between the hydrogen and the reformer feed from
the crude unit.
The volume and the weight ofthe total feed are the summations ofthe volumes
and the weights of incoming streams, reformer feed and hydrogen. For each product
stream, the volume and the weight are calculated by the formulas given below:
W,=W,,,,-RATIO,„, (5.3)
V,=V^^^,-RATIO^,., (5.4)
where
i- stripper overhead gas, separator overhead gas, LPG, sweetened reformer feed,
RATIOi,v- volumetric ratio between the product i and the total feed to the naphtha
desulfiirizer,
RATIOi,w- mass ratio between the product i and the total feed to the naphtha
desulfurizer,
Vi- volumetric flow rate ofthe product stream i, BBL/D,
Vfeed- volumetric flow rate of total feed to the naphtha desulfurizer, BBL/D,
Wi- mass flow rate of product stream i, MLB/D,
Wfeed- mass flow rate of total feed to the naphtha desulfurizer, MLB/D.
The volumefric flow rate and mass flow rate ofthe sweetened reformer feed
calculated above are then used as the inputs to the reformer model.
102
5.3.2 Paraffins-Naphthenes-Aromatics (PNA) in the Reformer Feed
The molar flow rates ofthe pseudo-components in the feed are required by the
reformer model. The feed characterization is to calculate the molar flow rates of these
pseudo-components in the feed.
It can been seen from the FCC feed characterization that feed characterization is a
complicated process. For a much more complicated reformer pseudo-component system,
no simple empirical correlation is available to calculate the molar flow rates. Taskar
(1996) divided the reformer feed into several cuts based on boiling range and used
empirical correlations (Daubert, 1994) to calculate the physical properties of each cut.
Using the boiling point and the physical properties ofthe pseudo-components in the feed,
the molar percentages ofthe pseudo-components are calculated by using Nelder-Mead
optimization algorithm (Riggs, 1994) to find the composition which will give the same
physical properties of each cut ofthe feed.
One disadvantage of this approach is that the boiling point and the physical
properties of each cut are not accurate because ofthe inaccuracy ofthe empirical
correlations. Another disadvantage is that there may be several combinations of pseudo-
components which can give the same physical properties. In addition, local minimums
exist in the optimization searching which means that the unique solution is not
guaranteed in every optimization searching. If this approach were used in the refinery-
wide model, it will cause erroneous results in the reformer model when the optimization
routine gives unreasonable reformer feed compositions. Although the paraffin-
naphthenes-aromatics (PNA) calculated from the pseudo-component molar flow rates are
close to the industrial data, the molar flow rates of pseudo-components calculated using
the approach above can not be verified due to the lack of industrial data.
The attempts to use the above approach failed in this work. The optimization
algorithm did not give a reasonable solution. This may be due to the inaccuracy ofthe
empirical correlations used to calculate the physical properties. However, some data from
laboratory analysis ofthe portion ofthe cmde oil in the reformer feed boiling range are
103
available in the industrial data. It is more accurate to calculate the reformer feed
information from these laboratory analysis than from empirical correlations. The
industrial data are listed in Table 5.4 through Table 5.7.
Table 5.4 Industrial Data of Crude A.
Property
Yield
API Gravity
Aromatics
Naphthenes+2x
Aromatics (vol.%))
RON
MON
Sulfiir
Unit
Vol.%
API
Vol.%
Vol.%
None
None
wt.%)
Light naphtha I50-200°F
2.5
67.69
1.515
37.93
71.832
67.992
0.007
Heavy naphtha 200-360°F
16.0
55.597
5.692
54.195
49.193
48.730
0.009
Extra heavy naphtha 360-385°F
3.1
46.688
9.474
74.459
33.907
34.407
0.014
Table 5.5 Industrial Data of Cmde B.
Property
Yield
API Gravity
Aromatics
Naphthenes+2x
Aromatics, (vol.%))
RON
MON
Sulftir
Unit
Vol.%
API
Vol.%
Vol.%
None
None
Wt.%
Light naphtha 150-200°F
1.9
73.961
8.556
40.037
71.832
67.992
0.013
Heavy naphtha 200-360°F
9.7
55.534
18.169
60.471
42.657
41.863
0.028
Extra heavy naphtha 360-385°F
2.8
44.077
23.499
78.858
34.648
34.408
0.037
104
Table 5.6 Industrial Data of Crude C.
Property
Yield
API Gravity
Paraffins
Naphthenes
Aromatics
RON
MON
Sulfiir
Unit
Vol.%
API
Vol.%
Vol.%
Vol.%
None
None
Wt.%
Light naphtha 155-265°F
10.33
63.1
62.3
31.9
5.8
-
-
<.02
Heavy naphtha 265-350°F
9.35
51.7
55.4
28.5
16.1
-
-
0.03
Extra heavy naphtha 350-400°F
4.86
45.8
42.4
42.6
15.0
-
0.05
5.7 Industrial Data of Cmde D.
Property
Yield
API Gravity
Paraffins
Naphthenes
Aromatics
RON
MON
Sulftir
Unit
Vol.%
API
Vol.%
Vol.%
Vol.%
None
None
Wt.%
Light naphtha 155-265°F
9.18
60.9
57.4
34.3
8.3
-
-
0.02
Heavy naphtha 265-350°F
9.78
51.7
55.8
27.6
16.7
-
0.02
Extra heavy naphtha 350-400°F
5.44
46.1
41.8
45.8
12.4
-
-
0.02
The naphtha desulfurizer that treats the reformer feed mainly removes the sulfur
from it. Hydrocarbon in the reformer feed does not react in the naphtha desulfurizer.
Hence, the PNA ofthe sweetened reformer feed after the naphtha desulfurizer is
considered to be the same as the reformer feed entering the naphtha desulfurizer. The
105
normal boiling range ofthe reformer feed is 180 to 375°F. It can be seen from Table 5.4
through Table 5.7 that all or part of light naphtha, heavy naphtha, and extra heavy
naphtha is in the boiling range ofthe reformer feed. Therefore, the PNA ofthe reformer
feed can be calculated from the PNAs of these cmde cuts.
The cmde cut ofthe light naphtha is the source for both the light straight-mn
(LSR) gasoline and the reformer feed. The portion of light naphtha in the reformer feed is
calculated from the volume corresponding to the EBP ofthe light naphtha and the volume
corresponding to the cut point ofthe LSR, which has been calculated in the naphtha
splitter model in Chapter 3. The calculation uses the follow formula:
V -V n .,. '^ EBP.In ' cul poinl. LSR / c ^N
Portion^^j„„„^^j^^j,„= —^ , (5.5)
where
PortioUreformer feed. In- volumetric portion of the light naphtha in the reformer feed,
VEBP.III- volume percentage corresponding to the EBP ofthe light naphtha on the whole
crude TBP- volumetric curve, vol.%,
Vcut point, LSR- volumc percentage corresponding to the cut point ofthe light naphtha on
the whole crude TBP- volumetric curve, vol.%),
Vin- volume percentage the light naphtha in the total cmde oil, vol.%.
It is to be noted that the EBP ofthe light naphtha ofthe cmde A and cmde B is 200°F
while the EBP of cmde C and cmde D is 265°F.
It can been seen from Table 5.4 and Table 5.5 that the industrial data ofthe cmde
A and cmde B have the volumetric percentages ofthe aromatics and
naphthenes+2x Aromatics. The volumetric percentages ofthe naphthenes and paraffins
can be calculated by the following equations:
%naphthene = %N2A - %aromatics, (5.6)
%paraffin = 100 - %aromatics - %naphthene, (5.7)
106
where
%)paraffin, %naphthene, %aromatics- the volume percentages of paraffin, naphthene,
aromatics, respectively, vol.%,
%)N2A- the volume percentages of naphthene+2xaromatics, vol.%).
Now we have the PNA information of all the cmde types in the cmde feed. The
PNA ofthe reformer can be calculated as the summation ofthe PNAs ofthe cmde cuts in
the reformer feed:
(Portion -V -V +\ 0/ ^ V^O/T/ ^^^^ 'reformer feed.ln.i '^ In.i '^X.ln.i^ A)Ji = / /OV: -
^-' V -V +V -V I V hn.i ' X.hn.i^ ' ehn.i " X.ehn.i
(5.7)
where
i- cmde type A, B, C, and D,
X- molecule type, paraffin, naphthene, and aromatics,
%)X- volume percentage of molecule type X, paraffin, naphthene, aromatics, vol.%,
%)Vi- volume percentage of cmde type i in the total cmde, vol. %,
Vin,i- volume fraction ofthe light naphtha in the cmde type i,
Vx,in,i- volume fraction ofthe molecule type X in the light naphtha in the cmde type i,
Vhn,i- volume fraction ofthe heavy naphtha in the cmde type i,
Vx,hn,i- volume fraction ofthe molecule type X in the heavy naphtha in the cmde type i,
Vehn,i- volume fraction ofthe extra heavy naphtha in the cmde type i,
Vx,ehn,i- volumc fraction ofthe molecule type X in the extra heavy naphtha in the cmde
type i.
The typical PNA ofthe reformer feed is available in the industrial data. The
comparison between the PNA calculated above and the typical industrial data of the
refinery considered in this work are shown in Table 5.8. The operating variables ofthe
cmde unit use the normal values in Table 3.7 in Chapter 3. It can be seen from Table 5.5
107
53
33
14
_
0.1
1.9
1.8
1.3
that the average absolute difference between the results ofthe feed characterization and
typical industrial data is 1.3 vol.%. The biggest difference exists in naphthene with 1.9%.
Table 5.8 Comparison of Volume Percentages of PNA from Feed Characterization and the Industrial Data.
Molecule Type Feed Characterization Industrial Data Difference, vol.%
Paraffin, vol.% 5Z9
Naphthene, vol.% 34.9
Aromatics, vol.% 12.2
Average difference -
5.3.3 Molar Flow Rates ofthe Pseudo-Components in the Reformer Feed
The calculation ofthe molar flow rates ofthe thirty-five pseudo-components in
the reformer feed follows the steps given below:
a. Constmct a typical reformer feed. The volume fractions ofthe pseudo-components in
the typical reformer feed are obtained from literature (Lin, 1988; Turpin 1994;
Taskar, 1996).
b. Assume the ratios ofthe pseudo-components in each molecule type, the paraffins, the
naphthenes, and the aromatics, do not change. Calculate the volume fractions ofthe
pseudo-components in the reformer feed using the formula given below:
p ^ . . = F , . . ^ ^ ^ ^ , (5.8)
where
j - paraffins, naphthenes, and aromatics,
Vi j - volume fraction of a pseudo-component i which is a molecule type j ,
Vij,typicai- volume fraction of a pseudo-component I which is a molecule type j in the
typical reformer feed.
108
Vj- volume fraction of a molecule type j ,
Vj.typicai- volume fraction of a molecule type j in the typical reformer feed,
c. Calculate the molar flow rate of each pseudocomponent using the molar specific
volume ofthe pseudo-component and its volumetric flow rate using the formula given
below:
/ w o / , . = — ^ , (5.9) " Vmol^j
where
molij- molar fraction of a pseudo-component i which is a molecule type j ,
Vmolij- molar specific volume of a pseudo-component i which is a molecule type j .
The volume fractions ofthe pseudo-components are listed in Table 5.2. The molar
specific volumes of pseudo-components are also listed in Table 5.2. The volume fractions
and molar fractions of pseudocomponents calculated in equations 5.8 and 5.9 are
normalized after all the pseudo-components have been calculated.
The total molar flow rate ofthe reformer feed is calculated following the steps as
given below:
a. Calculate the mass flow rate of each cmde cut using the API gravity ofthe cmde cut,
the volume percentage with respect to the particular cmde, and the volumetric flow
rate ofthe particular cmde.
b. Calculate the molecular weight ofthe each cmde cut using the approach described in
the Chapter 3 of this dissertation.
c. Calculate the molar flow rate of each cmde cut using the molecular weight and the
mass flow rate ofthe cmde cut.
d. Calculate the molar flow rate of reformer feed by summing the molar flow rates of
the individual crude cuts.
109
The molar flow rate of a pseudo-component in the feed is then calculated as
below:
Fmol,j=mol,j-V. -Fmol^^^,, (5.10)
where
Fmolij- molar flow rate of a pseudo-component i which is a molecule type j , mol/s,
Fmolfeed- total molar flow rate ofthe reformer feed, mol/s.
The calculated molar flow rates ofthe pseudo-components in the reformer feed
then become the input to the reformer model.
5.4 Reformer Model Benchmarking
The reformer model developed by Taskar (1996) is based on a reformer in
Phillips Petroleum Company. The reformer in the refinery considered in this work is
slightly different from the reformer in Phillips Petroleum Company. For example, the
throughput and catalyst load ofthe reformer considered in this work are significantly less
than those in the Phillips's unit. Some minor modifications in the model have been made
to enable the reformer model to represent the reformer considered in this work.
The amounts of catalyst in each bed ofthe reformer unit considered in this work
have been obtained from the industrial data. They are shown in Table 5.9.
Table 5.9 Catalyst Weight in Each Reactor Bed.
Bed number
1
2
3
4
Total
Catalyst weight original Model,
25000
25000
25000
50000
125000
in lb.
the Industrial Data, lb,
10781
21965
44652
0
77398
110
It can be seen from Table 5.9 that the reformer in the refinery considered in this
work loads has about 40% less catalyst in the reactor than the unit described by the
Taskar (1996). In addition, there are four beds in the Phillip's unit while only three beds
are used in the reformer considered in this work.
The reaction system also needs to be modified to fit the industrial data. The
reformer operation in the refinery considered here has two operation modes: low severity
and high severity. The typical operation data ofthe low-severity operation mode and the
high-severity operation mode were used in the model benchmarking. The industrial data
are listed in Table 5.10.
Table 5.10 Comparison ofthe Industrial Data and Model Prediction after Benchmarking.
Operation
Mode
Low severity Conversion
Research Octane number
Aromatics in the reformate.
vol.%
Hydrogen Production,
SCF H2/BBL feed
Industrial
data
0.802
95.
59
1176.54
Model
prediction
0.797
94.9
55
1011.74
Relative
error, %
0.62
0.11
6.78
14.01
High severity
Benzene in the reformate, wt. % 2.7
Conversion 0.757
Research Octane number 99.7
Aromatics in the reformate,
vol.% 63
Hydrogen Production,
SCF H2/BBL feed 1260
Benzene in the reformate, wt. % 3.5
Average Error
1011.74
2.5
0.756
100.
62.4
1150.43
3.45
-
14.01
7.41
0.13
0.30
0.95
8.70
1.43
4.04
111
The activation energy and frequency factor ofthe thirty-six reactions were
considered as adjustable parameters to benchmark the model against industrial data. All
these thirty-six reactions belong to four reaction types: hydrocracking, ring closure,
dehydrogenation, and isomerization. The reader can refer to Taskar (1996) for detailed
description ofthe reaction system. It is found that the model predictions are not sensitive
to the activation energy and frequency factor ofthe isomerization reactions. Therefore,
only the activation energy and the frequency factor ofthe reactions of hydrocracking,
ring closure, dehydrogenation are adjusted in the benchmarking. In order to simplify the
parameterization, two adjustable parameters are used to adjust the activation energy and
frequency factor of all the reactions belong to one reaction type. Therefore, there are total
six adjustable parameters were used in the benchmarking. These parameters adjust the
activation energy and the frequency factor ofthe reactions by multiplying the original
values to form the new values. The values ofthe adjustable parameters that were obtained
in the benchmarking are listed in Table 5.11.
Table 5.11 Adjustable Parameters in the Reformer Model Benchmarking.
Adjustable parameter Description Parameter value
^hydrocracking
r hydrocracking
Aring closure
F ring closure
Adehydrogenation
F dehydrogenation
Adjustable parameter for the activation
energy of hydrocracking reactions
Adjustable parameter for the frequency
factor of hydrocracking reactions
Adjustable parameter for the activation
energy of ring closure reactions
Adjustable parameter for the frequency
factor of ring closure reactions
Adjustable parameter for the activation
energy of dehydrogenation reactions
Adjustable parameter for the frequency
factor of dehydrogenation reactions
1.7996
1.000248EI7
0.6993
5.004975E-6
l.OOI
1.000261
112
The parameterization is an optimization problem. The optimization routines try to
find a set of values ofthe adjustable variables to minimize the difference between the
model predictions and the corresponding industrial data. Like the FCC model
benchmarking, a least square error type function is used as the objective function for the
optimization problem. The formula is given below:
^bjJparamelerizalion ~ 2-1 '^ "'"del.! ~-^hase.I y ' ( • ' • i ^ J
/
where
I - operation variables, such as conversion, RON, etc.,
Objfparamterization- objcctive functiou valuc of the reformer model parameterization,
Wj- weighting factor,
Xmodei,!- valuc of the operation variable I predicted by the reformer model,
Xbase,!- value ofthe operation variable I in industrial data.
NPSOL optimization package (Gill et al., 1986) was used in the parameterization.
Table 5.10 shows the comparison ofthe industrial data and model predictions. The
average relative error ofthe model predictions compared to the industrial data is 4.04%.
The largest error exists in hydrogen production with 14.01% in Summer Mode and 8.70%
in Winter Mode. Since the refinery produces more hydrogen than what it needs, the extra
hydrogen is sent to the fuel gas. Therefore, the error on the prediction of the hydrogen
production has insignificant effect on the economy ofthe refinery-wide operation. The
difficulty in the reformer model benchmarking is that one set of adjustable parameters
was used to benchmark against the data of both operation modes. Using two sets of
adjustable variables for two operation modes may make the model predictions agree with
the industrial data better. However, it is incorrect theoretically to use two sets of
adjustable parameters. The reason is that the basic kinetics are the same for both the high
severity mode and the low severity mode. Hence, the same reaction constants, activation
energy and frequency factor, should be used for both modes. To conclude, the model
113
predictions reasonably agree with the industrial data after benchmarking using one set of
adjustable parameters.
5.5 Average Reformer Operation
The reformer is operated at low severity mode and high severity mode
altematively. Low severity mode uses lower inlet bed temperature which produces
reformate with research octane number (RON) around 95. High severity mode uses
higher inlet bed temperature which produces reformate with RON around 100. Which
severity the reformer is operated is decided by gasoline blending situation. When the
average octane number ofthe whole gasoline pool is high, the reformer is operated at low
severity. On the contrary, the reformer is operated at high severity when the average
octane number ofthe whole gasoline pool is low.
The reformer is also not operated continuously. The reformer is shut down
periodically to regenerate the catalyst which loses it activity during the operation. The
regeneration time is about 2 weeks. The regeneration costs about $450,000 each time.
The reformer is not operated at the same operation mode and is not operated
continuously. However, the refinery-wide model of which the reformer is one unit is a
steady-state model. Hence, the operation shifting and periodical shut down ofthe
reformer must be accounted.
5.5.1 Operation Time Fraction of Low Severity
In order to represent the fact that the reformer altemates operation mode during
each reformer operation cycle, two executions ofthe reformer are carried out in one
execution ofthe refinery-wide model. One execution is for the high severity mode and
the other execution is for the low severity mode. In order to represent the fact that
different mode is used for different time, the product rates from each execution of
reformer model are multiplied by the time fraction discussed above. According to the
engineers from the refinery, the reformer is operated at low severity about 70%) ofthe
total operation time. This operation time fraction of low severity becomes a handle for
adjusting reformer operation. Hence, this fraction is a decision variable for the refinery-
114
wide optimization. The upper limit ofthe operation time fraction of low severity is set at
0.8 and the lower limit is set at zero according to engineers in the refinery.
5.5.2 On-Stream Factor
The On-stream factor is the fraction of time that the reformer is at operation in the
total time. The cycle length ofthe reformer can be approximately calculated using the
formula given below:
C rn _ coke .final /c i '^\ ^cycle - - ^ ' P - i ^ ;
coke .average
where
Tcycie- cycle length, hr,
Ccoke,fmai- cokc contcut at the end of a cycle, kg coke/kg catalyst,
ACcoke,average- the average coking rate in a cycle.
A cycle usually ends when the octane number ofthe reformate can not be
maintained at a certain level even using the highest inlet bed temperature and maximum
H2 recycle ratio. Since octane requirement ofthe reformate for the low severity mode is
different from that ofthe high severity mode, the corresponding coke contents are
different. In this study. Lower limit of octane requirement for each operation mode, the
highest inlet bed temperature and maximum H2/Hydrocarbon recycle ratio are listed in
Table 5.12.
115
Table 5.12 Reformer Operation Limit.
Low severity High severity
Research octane number (RON) 95
Highest Inlet bed temperature, °F 980
Maximum H2/Hydrocarbon recycle
ratio, mol H2/mol Hydrocarbon 7.5
99.5
980
7.5
Ccoke.finai is the cokc contcut at the end of a cycle defined by these operating
conditions. It is known the coke contents on catalysts are not the same across the three
reactor. There are three reactors in the reformer unit. Each reactor is divided into several
regions according to coke content and the coke content in each region is assumed to be
the same (Taskar, 1996). An example ofthe coke contents in different regions is shown
in Table 5.13. The coke contents shown in Table 5.13 are the coke contents at the end of
the cycle ofthe low severity mode.
Table 5.13 Coke Contents in Dfferent Regions.
Section number Low severity, coke content, kg High severity, coke content,
coke/kg catalyst kg coke/kg catalyst
la 3.31 E-2
lb 8.02 E-3
Ic 3.90 E-3
2a 6.36 E-2
2b 8.42 E-2
3a 9.07 E-2
3b 1.02 E-1
(la+2a+2b+3a+3b)/5 7.47 E-2
2.41 E-2
5.76 E-3
2.91 E-3
4.58 E-2
6.21 E-2
6.71 E-2
7.55 E-2
5.49 E-2
116
The first digit ofthe section number indicate which reactor the section belongs to.
The second digit indicates the location of section in the reactor. For example, section 1 b
is the section behind section la and section Ic is the section behind section lb, all in
reactor 1. It can be observed from Table 5.13 that coke contents in section la, 2a, 2b, 3a,
3b have the same order and are much higher than the coke contents in section lb and Ic.
The phenomena have been explained in detail in Taskar (1996). To simplify the
calculation, an average coke content is calculated using the coke contents in section 1 a,
2a, 2b, 3a, and 3b. This average coke content is used as the Ccoke.fmai.
The steady-state model ofthe reformer used in the refinery-wide model has zero
coke content on the catalyst, which represents the catalyst condition at the beginning of
each cycle. The coking rate, with an unit of kg coke/kg catalyst/hr, under this condition is
calculated in the model. However, equation 5.12 requires the value of average coking rate
in one cycle. The relations between the coking rates at zero coke content and the average
coking rates in one cycle was also obtained from the above tests and are listed in Table
5.14. Again, the average coking rate of section la, 2a, 2b, 3a, 3b is used for both coking
rates. The average coking rate in one cycle is calculated as the product of the coking rate
at zero coke content and coking rate ratio for either the low severity mode or the high
severity mode.
Table 5.14 Ratios between Aerage Coking Rate and Coking Rate at the Beginning of a Cycle.
Section number
la
2a
2b
3a
3b
(la+2a+2b+3a+3b)/5
Coking rate ratio
Low severity
4.19
5.19
12.2
4.85
8.25
6.94
Coking rate ratio
High severity
1.92
2.10
3.22
2.01
2.61
2.37
117
After the cycle length is calculated, the on-stream factor is calculated using the
formula given below:
F , = ^-^^ , (5.13) on-slream T T
cycle regeneralion
where
Tregeneration- reformer shut down time or catalyst regeneration time, hr.
According to engineers in the refinery considered in this work, the regeneration
time is about two weeks. Hence, the total hours in two weeks, 336, is used for Tregeneration
for both low severity mode and high severity mode.
In order to account for periodically shut down ofthe reformer, the product rates
from the reformer model are discounted by multiplying each product rate by the on-
stream factor and operation time fraction using the equation given below:
V =v - F T rs M"* product produci .model on-slream operalion lime fraclion ' v*- '-*- ' /
where
Vproduct- volumetric flow rate of a product from the reformer as an output to other units,
bbl/day,
Vproduct, model- volumctric flow rate of a product calculated from the reformer model,
bbl/day,
Toperation time fraction" Operation time fraction of either the low severity mode or the high
severity mode.
5.5.3 Regeneration Cost
The cost of each regeneration ofthe reformer catalyst is normally $450,000.
Refinery-wide optimization requires the regeneration cost for each day. For each
118
operation mode, low severity and high severity, the regeneration cost for each day is
calculated by dividing the total cost of each regeneration by the cycle length calculated in
equation 5.12. The regeneration cost for each day for the reformer as a whole is then
calculated using the equation given below:
regeneralion.reformer regeneralion Jow severily low severity ,^ ^ C\
regeneralion.high .severity \ low severily ^
where
L-OStregeneration,reformer, C^OStregeneration,low severity, C^OStregeneration,high severity— rCgncrat lOn COStS 01
the reformer as a whole, low severity, high severity, respectively,
Tiow severity- Operation time fraction ofthe low severity mode.
119
CHAPTER 6
MODELING OF GAS PLANT,
ALKYLATION UNIT AND
DIESEL HYDROTREATER
Besides the models ofthe four main processing units presented in the previous
chapters, there are other processing units in the refinery, whose models are required to
carry out plant-wide optimization studies. The models ofthe gas plant, the alkylation
unit, and the diesel hydrotreater are discussed here.
6.1 Gas Plant
The gas plant processes all the light gases generated by various units in the
refinery. The main sources of light gases are the cmde unit, the FCC unit, and the
reformer. The naphtha desulfurizer and diesel hydrotreater also produce some light gases
as by-products. All the light gases are combined together and sent to the gas plant. The
product streams coming out ofthe gas plant include fiiel gas, propylene, and alkylation
feed. The fuel gas is used as fiiel in the furnaces in the refinery. Propylene is sold directly
in the market. The alkylation feed stream flows to the alkylation unit to make alkylate, a
gasoline blending stock.
Simplified models were built for the units in the gas plant based on material
balances. The gas plant is very complicated in the sense that the light gas streams come
from multiple sources and the streams are mixed in several places. It is very difficult to
calculate the flow rates ofthe light gas components in all the streams in the gas plant. To
simplify the modeling, the gas plant is divided into two blocks, fuel gas production and
depropanizer. Only the compositions ofthe streams around these two blocks are
calculated.
All the light gas streams from other units in the refinery enter the fuel gas
production block. The fuel gas and the heavier hydrocarbon are two streams exiting the
fuel gas production block. The heavier hydrocarbon stream goes to the depropanizer.
Propylene, known as C3 product in the refinery considered in this work, exits from the
120
top ofthe depropanizer and is sold as a final product. The stream that exits from the
bottom ofthe depropanizer becomes the feed stream to the alkylation unit. The
schematic ofthe gas plant is shown in Figure 6.1.
6.1.1 Sources of Light Gas
Light gas mainly comes from the cmde unit, the FCC unit, and the reformer unit.
The light gas produced in the cmde unit was calculated based on the LP reports obtained
from the refinery considered in this work. The light gases from the cmde unit are listed in
Table 6.1. The same product rates are used to calculate the amounts of light gases from
the cmde unit in the model disregarding the operating conditions in the cmde unit.
Light gas from crude unit
Light gas from FCC
Light gas from reformer
i
Fuel gas
Fuel gas production block
C3/C4 stream
C3 product
Depropanizer
Alicylation feed
•
Figure 6.1 Schematic ofthe Gas Plant in the Fuel-Oriented Refinery.
121
Table 6.1 Light Gas Production Rates in the Cmde Unit.
Light gas Summer Mode Winter Mode Difference
Ethane, lb/lb cmde feed 0
Propane, lb/lb crude feed 0.00175
Isobutane, lb/lb cmde feed 0.00202
Normal Butane,
Lb/lb crude feed 0.00593
0
0.00054
0.00217
0
0.00121
0.00015
0.00625 0.00032
The largest amount of light gas produced in a refinery comes from the FCC unit.
In the original FCC model of Ellis (1996), the light gases are calculated by the empirical
correlations based on the API gravity ofthe FCC feed and the conversion ofthe FCC
feed. However, the predictions from the empirical correlations are not consistent with the
industrial data obtained from the refinery considered in this work. Hence, the industrial
data for light gas production rates in the FCC unit are used here. The percentages ofthe
light gas components in the total light gas ofthe FCC unit are shown in Table 6.2.
It can be seen from Table 6.2 that both modes have similar light gas production
exception propane. This is because the operation ofthe FCC unit in the Winter Mode is
almost the same as that ofthe Summer Mode. The average values listed in Table 6.2 are
used to represent the components of light gases modeled for both operation modes. The
FCC model can only predict the total amount ofthe light gas, but not individual
components present in the light gas. Assuming the same weight percentages ofthe light
gas components in the total light gas as those in the industrial data, the weights ofthe
light gas components from the FCC unit are calculated using the formula given below:
^ti = ^ttotal -^i^ (6.1)
Where
wti- the weight ofthe light gas component i,
v^totai- the total weight ofthe light gas.
122
Xi- the weight fraction of light gas component i in the total light gas.
Table 6.2 Compositions of Light Gas from FCC Unit.
Light gas
Gases < C2
Propane
Propylene
Isobutane
Butylene
Normal Butane
Summer Mode
wt.%)
9.3
29.3
17.1
21.7
9.0
13.6
Winter Mode
wt.%
9.5
29.2
16.9
21.6
9.1
13.6
Average
wt.%)
9.4
29.2
17.0
21.6
9.1
13.6
The light gas components from the reformer have already been calculated by the
detailed reformer model discussed in Chapter 5 of this dissertation. However, the
reformer model only predicts the total amount of butane, which includes both normal
butane and isobutane. Therefore, it is assumed that isobutane is about 42 wt.% ofthe
total butane according to the industrial data obtained from the refinery considered in this
work.
6.1.2 Fuel Gas Production
The fuel gas mainly contains the hydrocarbons with carbon number less than three
and hydrogen. The light gas stream from the reformer contains some hydrogen. The fuel
gas also contains a small amount of heavy hydrocarbons with carbon number equal to or
greater than three. These heavier hydrocarbon components enter the fuel gas due to
incomplete separation in the gas plant. Since we do not have rigorous models for each
separation units in the gas plant, the average industrial data ofthe loss of heavier
hydrocarbons in the fuel gas shown in Table 6.3 are used in the model. These losses are
assumed to be constants for each operation mode and do not change with operating
conditions. The total amotmt ofthe fuel gas is the sum ofthe light hydrocarbons and the
losses of heavy hydrocarbons in the fuel gas.
123
Table 6.3 Losses of Heavy Hydrocarbons in the Fuel Gas.
Light gas
Propane, wt.%
Propylene, wt.%o
Isobutane, wt.%
Butylene, wt.%)
Normal Butane, wt.%)
Summer Mode
33.4
26.3
16.9
6.2
11.4
Winter Mode
32.0
26.1
16.8
6.2
8.2
6.1.3 Depropanizer
The depropanizer separates the heavier hydrocarbon stream into C3 product and
alkylation feed. The C3 product, the overhead stream, contains propylene and propane.
The alkylation feed, the bottom stream, contains mainly normal butane and isobutane. It
also contains some propylene and propane due to incomplete separation in the
depropanizer. Propylene can also react in the alkylation unit to form alkylate. From the
industrial data obtained from the refinery considered in this work, the C3 product stream
does not contain any C4 hydrocarbons while the alkylation feed stream contains some C3
hydrocarbons. If the distributions ofthe C3 hydrocarbons in the two streams are known,
the compositions ofthe two streams can be calculated. The industrial data for propylene
entering the C3 product stream shown which are in Table 6.4 are used in the model.
These values do not change with respect to operating conditions.
Table 6.4 Portion ofthe Propylene in the C3 Product.
Mode
Portion of total propylene entering C3 product, wrt.%
Summer
Mode
60.7
Winter Mode
79.4
The mass flow rate ofthe propylene in the C3 product can then be calculated from
the total mass flow rate ofthe propylene in the depropanizer feed and the values in Table
6.4. There is no industrial data about the portion of propane entering the C3 product
124
stream available. However, it is known from the industrial data that the propane is about
40 vol.%) and the propylene is about 60 vol.%) in the C3 product. This value is valid for
both operation modes. Hence, the amount of propane entering the C3 product stream can
be calculated from the amount ofthe propylene entering the C3 product stream using the
formula given below:
V - ^PC TT/ _ propane ' propane ^ g 2^)
propane y _ ^ n ^ y ^p^ ' \ • ) propane propane propylene propylene
W = I-W (6 3\ propylene propane'' v"--"/
where
Wpropane, Wpropyiene- weight percentages of propane, propylene in the C3 product,
respectively,
Vpropane, Vpropyiene- volumc percentages of propane, propylene in the C3 product,
respectively.
The densities of propane and propylene are from literature (Edmister and Lee,
1984).
The mass flow rate ofthe propylene can be calculated from its weight percentage
and the mass rate ofthe propane using the formula given below:
W WT =WT ._p^£py!f!!L (f.A) ' propylene propane rry ' v " ' ^ ^
propane
where
WTpropane, WTpropyiene- mass flow ratcs of the propanc and propylene in the C3 product,
respectively.
The mass flow rates of propane and propylene in the alkylation feed are obtained
by subtracting the propane and propylene in the C3 product from the total propane and
propylene in the feed to the depropanizer.
125
6.2 Alkylation Unit
In the alkylation unit, the propylene and butylene react with isobutane to form
isoheptane and isooctane, respectively. Isoheptane and isooctane are two major
components ofthe alkylate. Isooctane and isoheptane are excellent gasoline blending
stocks with high octane number and low Reid Vapor Pressure (RVP). The normal
paraffins, such as propane and normal butane, do not react in the alkylation unit. They are
separated from the alkylate in the separation train ofthe alkylation unit.
Although the alkylation reactions can take place at high temperature and pressures
without catalysts, the only processes of commercial importance involve low-temperature
alkylation conducted in the presence of either sulfuric or hydrofluoric acid (Gary and
Handwerk, 1984). The refinery considered in this work uses hydrofluoric acid as the
catalyst in the alkylation unit.
In the alkylation unit, high isoparaffin/olefin ratios (4:1 to 15:1) are used to
minimize polymerization and to increase product octane (Gary and Handwerk, 1984).
The isobutane produced inside the refinery is not enough to satisfy the requirement ofthe
alkylation reactions. Isobutane purchased in the market is added to alkylation feed before
it enters the alkylation unit.
A simplified schematic ofthe alkylation unit is shown in Figure 6.2. In the
reaction part ofthe alkylation unit, the alkylation feed is mixed with hydrofluoric acid in
an acid settler. Since the acid phase is heavier than the hydrocarbon phase, the reaction
mixture settles into two liquid layers in the settler. The acid is withdrawn from the bottom
ofthe settler and then recycled and mixed with more fresh feed. The hydrocarbon layer is
a mixture of propane, isobutane, normal butane, and alkylate. The hydrocarbon layer is
withdrawn from the top ofthe settler and flows to a depropanizer.
126
O 3
0)
3 K . 5 .N a> C
X)
Q
td p t-(u F y ^ o «
OH I>
0) c CO a. o
C N
i> S
c >~ 5
•O O 'o U
<
o x> <u o
<u f )
>.
Rec
c m ^
sob
^ 1/3 • f
<u H-» 3
XI O
c .2 •5 "^
CO ai
^ fa
<
X3 T3
£ <
c
c o
'C o
o
ca
o
ca a (U
J3 o
1)
S)
127
The propane is withdrawn from the top ofthe depropanizer and becomes a final
product. The bottom stream withdrawn from the depropanizer goes to a deisobutanizer.
The isobutane exits the deisobutanizer from the top and is recycled to react with more
fresh feed. Normal butane and alkylate exits the deisobutanizer from the bottom and
flows to debutanizer. The butane is withdrawn from the top ofthe debutanizer and goes
to the gasoline blending unit as a blending stock. The alkylate is withdrawn from the
bottom ofthe debutanizer and goes to the gasoline blending unit as a blending stock.
A simplified model was developed for the alkylation unit. The model is based on
the stoichiometric relations ofthe alkylation reactions. Three major alkylation reactions
are given below:
CH3 CH3 CH3 CH3
CH3-C=CH2 + CH3-CH-CH3 -^ CH3-C-CH2-CH-CH3
CH3
Isobutylene Isobutane 2,2,4-trimethylpentane (Isooctane)
CH3 CH3 CH3
CH3-CH2-C=CH2 + CH3-CH-CH3 -» CH3-CH2-C-CH2-CH-CH3
butylene Isobutane 2,4-dimethylhexane
: H 3 CH3
CH3=C-CH3 + CH3-CH-CH3 ^ CH3-C-CH2-CH2-CH3
Propylene Isobutane 2,2-dimethylpentane (Isoheptane)
The first two reactions can be considered as one reaction in the model since the
properties ofthe reactants and the product of both reactions are very similar since they
are isomers. The model does not distinguish between the normal butylene and the
isobutylene in the alkylation feed.
128
Another path in propylene alkylation is the combination of propylene with
isobutane to form propane plus isobutylene. The isobutylene then reacts with more
isobutane to form isooctane (Gary and Handwerk, 1984). The reaction of propylene with
isobutane is shown below:
CH3 CH3
CH3=C-CH3 + CH3-CH-CH3 -^ CH3-CH2-CH3 + CH3-C=CH2
Propylene Isobutane Propane Isobutylene
It is assumed that all the propylene and the butylene in the alkylation feed react
with isobutane to form the alkylate. Since there is only one reaction path for the butylene,
the amount of isobutane required and the amount of isooctane produced from butylene
can be calculated from the stoichoimetric relation ofthe butylene alkylation.
There are two reaction paths for the propylene to form alkylate. The amount of
propylene for each reaction path needs to be determined. The weight percentage of
propylene that reacts in the second reaction path based on industrial data is listed in the
Table 6.5. The difference between two modes is 0.0029 wt.%. hence, the average value
listed in Table 6.5 is used for both operation modes in the model. It is assumed that this
average weight percentage is constant and does not change with operating conditions.
Table 6.5 Weight Percentage ofthe Propylene that Reacts in the Second Reaction Path in the Alkylation Unit.
Propylene going though the second reaction path, wt.%o
Summer Mode 0.0458
Winter Mode 0.0429
Difference 0.0029
Average 0.0444
The amount ofthe isooctane and the isoheptane in the alkylate can be calculated
from the stoichoimetric relations. Since all the stoichoimetric constants in all the
129
alkylation reactions are equal to unity, the stoichoimetric constants do not appear in the
equations of calculating the isooctane and the isoheptane in the alkylate. The calculations
use the following formula:
W • MW W -X . .-MW j-fT _ "butylene ^'-"'isooctane , "propylene ^'^ sec ond path •* i.sooctane (f,A\
isooctane ~ H/flJ/ A/fW ' V • / ^ ' ^ butylene ^ ' ^ propylene
„•• propylene \ second path) ^^ isoheptane rr r\ isoheplane i rwrr ' v * /
MW I propylene
where
Wisooctane, Wisoheptane" Hiass flow ratcs of isooctauc and isoheptane, respectively, MLB/D,
Wbutyiene, Wpropyiene- mass flow ratcs of butylcnc and propylene in the alkylation feed,
respectively, MLB/D,
MWisooctane, MWisoheptane, MWbutylene, MWpropylene" m o l c C u l a r WCightS o f isOOCtaUC,
isoheptane, butylene, and propylene, respectively, g/mole,
Xsecond path- Weight fraction of propylene that reacts in the second reaction path.
The alkylate formed by butylene is called butylene alkylate and the alkylate
formed by propylene is called propylene alkylate. The octane number ofthe butylene
alkylate and the propylene alkylate are listed in Table 6.6. These numbers come from
Gary and Handwerk (1984).
Table 6.6 Octane Number ofthe Butylene AH
Type RON
Butylene alkylate 96
Propylene alkylate 93
kylate and Propylene Alkylate.
MON
94
91
The octane number ofthe alkylate is calculated as the volumetric average value
using the formula given below:
130
alkylate butylene alkylate propylene alkylate
propylene alkylate propylene alkylate ' V • /
where
Ouaikyiate- MON Or RON ofthe alkylate product,
Oubutyiene alkylate, Oupropyiene alkylate" MON Or RON of the butylcne alkylate and the
propylene alkylate, respectively,
Vbutyiene alkylate, Vpropyiene alkylate- volumctric fractions of the butylcnc alkylate and the
propylene alkylate in the alkylate product stream, respectively.
The volumetric fractions ofthe butylene alkylate and the propylene alkylate are
calculated from corresponding mass flow rates and the densities ofthe two components.
The amount ofthe isobutane required in the reactions can be calculated using the
formula given below:
W - HfW J-.-, _ butylene isobutane
isobutane, reaction A/fJJ/ ^ ' ^ butylene
propylene ( J ' - ^ second path / - ' - ' ' ' isobutane . , _ .
propylene
where
Wisobutane, reaction- mass flow rate of the isobutane required in the alkylation reactions,
MLB/D,
MWisobutane- molccular weight of isobutane, g/mole.
It should be noted that one mole of propylene that reacts in the second path
consumes two moles of isobutane. Hence, one mole of propylene in the alkylation feed
consumes (1+Xsecondpath) moles of isobutane.
131
The isobutane can not be completely separated from the normal butane in the
deisobutanizer. Some isobutane enters the mixed butane product stream. The weight
percentage of the unreacted isobutane in the total isobutane in both operation modes is
obtained from the industrial data from the refinery considered in the work. These values
are listed in Table 6.7. It can been seen from Table 6.7 that the difference between two
modes is only 0.0003 wft.%. Hence, the average value is used for both operation modes in
the model and it is assumed that the weight percentage does not change with the
operating conditions ofthe alkylation unit.
Table 6.7 Weight Percentage ofthe Unreacted Isobutane in the Alkylation Unit.
Unreacted isobutane, wt.%)
Summer Mode
Winter Mode
Difference
Average
0.0211
0.0208
0.0003
0.0209
The total amount ofthe isobutane required by the alkylation unit is calculated
using the formula given below:
W - J . isobutane. reaction ,-. Q , " isobutane. total ~ ~, 7^ ' t ^ - O j
isobutane, unreacted
where
Wisobutane, total- total mass flow rate of isobutane required by the alkylation unit, MLB/D,
Xisobutane,unreacted- weight fraction of the unrcacted isobutane in the total isobutane required
by the alkylation unit.
The volumetric flow rate ofthe isobutane is calculated from the mass flow rate
and the density of isobutane. The amount ofthe isobutane purchased on the market is the
132
calculated as the difference between the total isobutane required by the alkylation unit
and the available isobutane in the alkylation feed:
W =W -W (6.9) isobutane. purchased isobutane. total isobutane. feed ^ ^ ^
where
Wisobutane, purchased- mass flow rate of isobutauc purchased in the market, MLB/D,
Wisobutane, feed- mass flow rate of isobutanc in the alkylation feed, MLB/D.
The total amount ofthe mixed butane is the sum ofthe butane in the alkylation
feed and the unreacted isobutane calculated using the following formula:
W =W -^W. - X ('6 10) bulane.lolal normal butane isobutane .total isobutane .unreacted ' V • /
where
Wbutane, total" mass flow rate of mixed butane, MLB/D.
The volumetric flow rate ofthe mixed butane is calculated from the mass flow rate and
the density of butane.
The amount ofthe propane formed in the second path of propylene can be
calculated using the formula given below:
W - X -MW „ . propylene second path propane /"/: 1 1 \
propane. reaction h/fJJf/ ' V ^ * ^ ^ / IVI rr p^QpyigfjQ
where
Wpropane, reaction- mass flow rate of propauc formed in the alkylation unit, MLB/D,
M Wpropane- molccular weight of propane, g/mole.
133
The total mass flow rate ofthe propane is the sum ofthe propane in the alkylation feed
and the propane formed in the alkylation unit. The volumetric flow rate ofthe propane is
calculated from the mass flow rate and the density of propane.
6.3 Diesel Hydrotreater
The purpose ofthe diesel hydrotreater is to eliminate most ofthe sulfiir in the
diesel to satisfy the diesel product specification. The diesel produced in the refinery
considered in this work is composed of three diesel streams. These streams are the side-
draw diesel from the atmospheric tower in the cmde unit, the overhead diesel from the
remn colunm in the cmde unit, the light cycle oil (LCO) from the main fractionator of the
FCC unit. The mass flow rates and volumetric flow rates of these three streams have been
calculated in the cmde unit model and FCC model, respectively. The diesel hydrotreater
model calculates the hydrogen required and the quantities ofthe product streams.
There are three product streams coming out ofthe diesel hydrotreater: diesel,
naphtha, and sour gas. The diesel goes to the storage tank. The naphtha goes to the cmde
unit and is combined with the light naphtha stream. The sour gas goes to Amine treater
for further treatment. The average values ofthe industrial data from the refinery
considered in this work shown in Table 6.8 and Table 6.9 are used in the model. It can
been seen from Table 6.8 and Table 6.9 that the values for the summer mode and the
winter mode are close, with the largest difference of 0.003 in weight ratios and 0.005 in
volumetric ratios. Hence, the average values are used for both operation modes in the
model. These ratios are constants and do not change with the operating conditions.
Table 6.8 Weight Ratios ofthe Hydrogen and the Products in the Diesel Hydrotreater.
Summer Mode Winter Mode Difference Average
Hydrogen/Charge 0.018 ~"
Diesel/Charge 0.970
Naphtha/Charge 0.024
Sour Gas/Charge 0.025
0.018
0.973
0.022
0.022
0.0
0.003
0.002
0.003
0.018
0.971
0.023
0.024
134
0.026
0.983
0.029
0.015
0.001
0.005
0.001
0.003
0.027
0.981
0.030
0.016
Table 6.9 Volumetric Ratios ofthe Hydrogen and the Products in the Diesel Hydrofreater.
Summer Mode Winter Mode Difference Average
Hydrogen/Charge 0.027 ~
Diesel/Charge 0.978
Naphtha/Charge 0.030
Sour Gas/Charge 0.018
Since the diesel hydrotreater has a capacity of 13,000 barrels charge per day, not
all diesel streams go through the diesel hydrotreater. Part of diesel withdrawn from the
atmospheric tower in the cmde unit directly goes to the storage tanks without the
treatment. About 20 vol.% in the Winter Mode and 24 vol.% in the Summer Mode of
diesel from the atmospheric tower do not go through the treatment in the diesel
hydrotreater. These values are used as constants in the model directly.
The refinery considered in this work produces two diesel products: low sulfur
diesel fuel and No. 2 diesel fiiel. There is not a strict limit on the quantities of no. 2 diesel
and low sulfur diesel. In the winter, the market demand for low sulfur diesel is high.
Thus, maximum low sulftir diesel is produced while reaching the upper limit of diesel
hydrotreater capacity. The rest ofthe diesel which is not processed by the diesel
hydrotreater is sold as No. 2 diesel fuel. In the summer, however, the low sulfiir diesel
demand is down and there is no need to reach the HDS limit to produce the maximum
amount ofthe low sulfiir diesel. The low sulfur diesel is about 76 vol.%) ofthe entire
diesel produced in the refinery. The rest ofthe diesel, whether processed by the diesel
hydrotreater or not, is sold as No. 2 diesel fiiel. That portion of low sulfur diesel in the
summer, 76 vol. %, is assumed as a constant in the model.
135
CHAPTER 7
GASOLINE BLENDING MODELING
7.1 Process Overview
Gasoline is the primary product in a fuel-oriented refinery. In the refinery
considered in this work, the volume ofthe gasoline is about half of the total volume of all
products produced in the refinery. From the point of view of economics, about 60-70%) of
a typical refinery's total revenue comes from the gasoline sale (Singh et al., 2000).
Gasoline blending is the final step in making gasoline product. The gasoline blending
operation often determines the operating conditions ofthe upperstream units. Due to the
importance ofthe gasoline blending, a gasoline blending model must be included in the
refinery-wide model.
Gasoline blending is the process of blending several gasoline blending stocks that
are produced in upperstream units or purchased from the market to make several grades
of gasoline according to the specifications. The objective ofthe gasoline blending is to
allocate the available gasoline blending components in such a way as to meet product
demands and specifications at the least cost and to produce products which maximize the
overall profit. Different gasoline blending stocks have different properties. Different
grades of gasoline also have different specifications. The core of a gasoline blending
model is the prediction of gasoline properties from the properties ofthe blending stocks.
Table 7.1 lists the volumes and sources ofthe gasoline blending stocks in the
typical Summer Mode operation. In Table 7.1, the first seven blending stocks are
produced and blended in the refinery while the ethanol is off-site. The high solvability of
the water in ethanol makes it inconvenient to blend ethanol in the refinery and transport
the gasoline with ethanol in it. Water may enter the gasoline during the transportation.
It can be seen from Table 7.1 that the refinery itself makes more than 97 vol.%
percent of gasoline blending stocks. Therefore, adjusting the operating conditions of
upstream units according to the gasoline blending is essential to make the refinery
operation profitable.
136
Table 7.1 Volumes and Sources ofthe Gasoline Blending Stocks in Typical Summer Mode Operation.
Gasoline blending stock Volume, BBL/D Vol. Vo Source
FCC gasoline 10915
Low-severity reformate 5934
Alkylate 4000
Light straight-run gasoline 1783
High-severity reformate 1244
Butane 288
Toluene
Ethanol
200
379
44.11349
23.98254
16.16619
7.206078
5.027685
1.163966
0.808309
1.531746
FCC unit
Reformer
Alkylation unit
Cmde unit
Reformer
Gas plant and/or
market purchase
Market purchase
Market purchase
The refinery makes three grades of regular gasoline: super unleaded (SNL),
unleaded (NL), and sub. octane with ethanol. The super unleaded gasoline has an octane
rating of 93 and the unleaded gasoline has an octane rating of 87. The sub. octane with
ethanol gasoline has an octane rating of 84 inside the refinery and its octane rating is
increased to 87 when ethanol is blended into the gasoline at the pump. The refinery does
not make any reformulated gasoline (RFG) at present. The specifications of these three
grades of gasoline are listed in Table 7.2.
The octane number of a fuel is defined as the percentage of iso-octane (assigned
an octane number of 100) in a blend with n-heptane (assigned an octane number of 0) that
exhibits the same resistance to knocking as the test fuel under standard conditions in a
standard engine (Palmer and Smith, 1985). Two standard test procedures are used to
characterize the antiknock properties of fuels for spark engine: (I) the ASTM D908 test
gives the research octane number (RON) and (2) the ASTM D357 gives the motor octane
number (MON). The RON represents the antiknock properties under the condition of low
137
ork.
^ c/n
15 H . f i
1)
der
tzi fi O
u
nery
i n
n th
e R
e uc
ed i
- o o V-i
olin
e
CO
•fga
c/3
;e G
rad
.5 H Ct-I o fi o
ta o
«
eci
o , in
ble
7.2
ca H
I-I
H-»
c
I - I
c 3
<U fi ca
H-» CJ
o
Sub.
ad
ed
nle
:D
T3
lea(
fi ^
I - I
<u OH 3
C/3
Oct
ane
Sub.
ad
ed
u
Unl
le
aded
fi
I - I
D OH
fi _o
ecif
icat
OH
pper
P
Low
er
Upp
er
I—.
owei
H J
pper
P I - I
Low
l U
pper
ow
er
hJ
Upp
er
Low
er
pper
P ^ (L>
o H-l
"a
limit
limit
H-» . .—1
a *
1
.H-»
lim
limit
mit
• i-H
limit
-t-»
lim
1
s—»
1
fi o :z;
CN 00
c o
7.
CN 00
fi o 7.
m fi o 7.
Non
e
CN 00
fi O
"7,
(N 00
Non
e
u fi o 2
MO
N
fi o
2
t ^ oo
fi o
7
00
fi o
2
r ' l ON
Non
e
t ^ 00
fi O
"7,
oo
Non
e
m as
)/2
2 O
ON
+M
6
»n r<i
6.4
U-) CN
6.4
m CN
^ vd
oo od
6.4
oo t~-
6.4
7.8
'^ so
p, p
sia
^
00
0.7
0.72
00
0.7
CN t ^ O
00
0.7
CN t ^
d>
0.78
4 0.
72
oo
0.7
CN t ^ O
0.78
4
CN t ^ O
H-»
> ca I-I
ecif
ic g
OH
1200
N
one
o o CN
u fi o 7
o o CN
u fi o 7
1250
N
one
o i n CN
one
7
1250
<u fi o 7
X
T3 fi
•^ H-»
ivea
bili
I-I
Q
^ m
Non
e
^ m
(U fi o
Z
,_, m
u fi o 2
158
Non
e
oo i n
one
7:
oo m
u fi o
:^
•*-J
w
Q ^ o^ O ^
•lat
ility
.
>
i n m CN
170
i n
O
'"—'
i n m CN
o t ^
250
170
o i n (N
o
250
o t ^ '"'
H-* C«
b O i n
ilatil
ity,
>
>n
rn
Non
e
i n so m
<a c o
7
i n
U fi O
:^
374
Non
e
^
one
2
374
OJ fi o 2 • 4 - * C/3
• i - H
Q ^ o^ O as
ilatil
ity.
>
O ^
3.7
r^ (N
U C o
z t ^ CN
u fi o
z
4.0
3.7
t~-CN
one
7
2.7
u c o 2
: wt.%
CN
o
^.^
cd
Non
e
o
u c o
2
o
u c o 7.
0.1
Non
e ^ o
one
7
O.I
u C o 2
^
Iftir
, w
t
3 or)
ON 'S-'
Non
e
as ^
<\i fi o
7
as ^
u fi o 7,
4.9
Non
e
as •^
one
2
4.9
u fi o 2
^
i
nzen
e.
CQ
138
speed and frequent accelerations while the MON represents the engine performance
under more severe high speed conditions (Singh et al., 2000). In RON test, the engine
runs at 600 r/min and with 125 °F intake air temperatiire. In MON test, the engine mns at
900 r/min and with 300 °F intake air temperatiire (McKetta, 1992). The arithmetic mean
ofthe RON and MON, (R0N+M0N)/2, is called octane rating and is posted at gasoline
stations. Both the octane rating and MON are used in the gasoline specification.
Automobile engine performance is affected by gasoline volatility. A vapor
pressure that is too high for the given ambient temperature will result in vapor locking
and motor stalling, while a vapor pressure which is too low will lead to difficulties in
engine start-up (Palmer and Smith, 1985). Reid vapor pressure (RVP) is widely used as a
criterion to measure the volatility of gasoline. RVP test is defined by the American
Society for Testing and Materials (ASTM) under the designation ASTM D323-56. The
American Petroleum Institute (API) describes the RVP test procedure in detail, including
the apparatus (API, 1955).
The boiling range also affects the engine during start-up and driving, and is
particularly important for good performance during quick acceleration and high speed
operations (Singh et al., 2000). The 10, 50, and 90 vol.% distilled are used in the gasoline
specifications. The 10 vol.% distilled affects the start-up and vapor blocking. The 50
vol.%) distilled affects the acceleration and smoothness. The 90 vol.% represents the
completeness of gasoline combustion (Lin, 1988). ASTM D86 standard test is used to
measure the 10, 50, and 90 vol.% distilled ofthe gasoline.
The volatility of fuels is varied for seasonal climatic changes and conformance to
U.S. EPA volatility regulations by providing six vapor pressure/distillation classes and
six vapor lock protection classes for fuel (ASTM, 1997). The volatility of fuels is also
varied for different geographical regions. According to the market locations ofthe
refinery considered in this work, the volatility specifications of gasoline grades SI and S4
are applied for RVP and 10, 50, and 90 vol.% in the summer and the winter, respectively.
It is to be noted that the upper limit of RVP ofthe gasoline sub. octane with ethanol is I
psi higher than other two grades of gasoline. The additional 1 psi RVP is an incentive to
139
use the replaceable ethanol in the gasoline. The ethanol content in the gasoline sub.
octane with ethanol is about 10. vol.%.
The auto and oil industry accepts a driveability index (DI) based on the ASTM
D86 distillation curve to protect against the performance problems related to volatility. It
is suggested that the DI should not exceed 1,200 to assure satisfactory performance
(Unzelman, 1996). The Driveability index is calculated using the following formula:
DI = I.5-Tj„+3-T,„+T,o, (7.1)
where
DI- driveability index,
Tio, T50, T90- temperature at 10, 50, and 90 vol.%) distilled, respectively, °F.
The specification ofthe O2 wt.% is to control the oxygen content in the gasoline.
The super unleaded gasoline and the unleaded gasoline are conventional gasoline. The
gasoline sub. octane with ethanol is an oxygenated gasoline. The lower limit of oxygen
wt. % for the oxygenated gasoline is defined in 1990 Amendment to the clear air act. The
upper limit of O2 wt.% is set to control the Nox emission from the gasoline. It is noted that
the upper limit of O2 wt.% for the super unleaded gasoline and the unleaded gasoline is
lower than that of gasoline sub. octane with ethanol.
The specific gravity specification is to control the density ofthe gasoline. The
lower limit of specific gravity can prevent the gasoline from getting too light. A heavy
gasoline usually yields better mileage than a lighter gasoline. In gasoline blending, the
lower limit is mainly used to limit the content of alkylate, a light high-octane gasoline
blending stock, in the super unleaded gasoline. However, a gasoline that is too heavy
usually has low volatility, which may cause ignition problem. A upper limit of specific
gravity is also set in the gasoline specifications.
The sulfur specification is to limit the emission of SOx from the gasoline. SOx is
harmful to the environment and considered to be responsible for acid rain. The upper
limit of benzene wt. %> in the gasoline specifications is to limit the emission of CO from
140
the gasoline. Since the hydrogen/carbon ratio is low in benzene, gasoline with high
content of benzene tends to have high emission of CO.
The benzene content, O2 content, and sulfur content can be easily calculated by
summing the corresponding contents in the gasoline blending stocks. The specific gravity
ofthe gasoline can be easily calculated from the volume and the weight ofthe gasoline. It
is assumed that there is no volume loss in gasoline blending. Hence, the volume of one
grade of gasoline is the summation ofthe volumes of gasoline blending stocks blended
into the gasoline. The weight ofthe gasoline is calculated by summing the weights of
gasoline blending stocks blended into the gasoline.
The calculations of octane number, RVP, and percent distilled are more
complicated and will be described in detail in the following text. After the 10, 50, and
90%) distilled are calculated, the driveablity index can be calculated using equation 7.1.
7.2 Properties ofthe Gasoline Blending Stocks
In order to calculate the properties ofthe gasoline blends, the properties ofthe
gasoline blending stocks must be known. Some properties ofthe gasoline blending stocks
are calculated in the unit models. Average industrial data are used for other properties.
The properties ofthe blending stocks required by the gasoline blending model includes
twelve items: volumetric flow rate, mass flow rate, MON, RON, saturates
(paraffin+naphthene) vol.%), aromatics vol.%, olefins vol.%), RVP, ASTM D86 curve,
benzene w^.%, sulfur wt.%, and O2 wt.%). There are all together eight blending stocks:
light straight-run (LSR) gasoline, FCC gasoline, low-severity reformate, high-severity
reformate, alkylate, butane, toluene, and ethanol. The eight blending stocks will be
introduced in the following text.
7.2.1 Light Straight-Run (LSR) Gasoline
Recall that in the cmde unit the overhead product of naphtha splitter becomes the
LSR. The LSR flows direct from the crude unit to the gasoline storage tank for blending
without going through any treatment in between. Therefore, the properties ofthe LSR can
be calculated directly from the information ofthe portion ofthe cmde feed in the LSR
141
boiling range, which are listed in Table 7.3 and Table 7.4. The LSR consists the whole
gasoline cut and part of light naphtha cut in the cmde feed. The rest of light naphtha cut
becomes reformer feed. The portion of light naphtha cut entering the LSR is calculated
using the formula given below:
Portion,,^,„ = 1 - Portion ^j„^,^^j^^,,„, (7.2)
where
PortiouLSR, In- volumetric fraction ofthe light naphtha in the LSR,
Portionreformer feed. In" volumctric fraction of the light naphtha in the reformer feed.
Recall that the Portionreformer feed, In is calculated in the reformer model.
Table 7.3 Industrial Data of Cmde A and Cmde B.
Property Unit Cmde A CmdeB
Item Gasoline
C5-150°F
Light naphtha 150-200°F
Gasoline
C5-150°F
Light naphtha I50-200°F
Yield
API Gravity
Aromatics
Naphthenes+2x
Aromatics
RON
MON
Benzene
Sulfiir
Vol.%
API
vol.%
vol.%
None
None
wt.%
wt.%
2.5
88.687
0.223
6.447
72.479
71.684
O.I
0.001
2.5
67.69
1.515
37.93
71.832
67.992
4.2
0.007
3.1
91.041
1.151
5.705
70.325
69.318
O.I
0.003
1.9
73.961
8.556
40.037
71.832
67.992
4.2
0.013
142
Table 7.4 Indusfrial Data of Cmde C and Cmde D.
Property
Item
Yield
API Gravity
Paraffins
Naphthenes
Aromatics
RON
MON
Aromatics
Sulfiir
Unit
Vol.%
API
vol.%
vol.%
vol.%
None
wt.%
wt.%
Wt. %
CmdeC
Gasoline
68-155 °F
3.98
86.5
92.8
6.2
0.1
69.5
68.3
1.0
<.02
Light
naphtha
155-265 °F
10.33
63.1
62.3
31.9
4.2
-
-
5.8
<.02
CmdeD
Gasoline
C5-150°F
2.88
83.6
90.8
6.6
O.I
71.4
69.8
2.7
0.02
Light
naphtha
150-200 °F
9.18
60.9
57.4
34.3
4.2
-
-
8.3
0.02
The volumetric flow rate and the mass flow rate ofthe LSR are calculated in the
naphtha splitter part ofthe cmde unit model. The MON and RON ofthe LSR can be
calculated using the formula given below:
2^/oVi - \ygasoline,i ' ^gasoline,! "^ "^^^^^^LSR.In.i ' '^ln,i ' ^ln,i )
Y _i ~ E ^ ^ / • (^gasoline,i + Portion^^j,,,,, - V,„, ) '
(7.3)
where
i- cmde types of A, B, C, and D,
Y- MON or RON,
%Vi- volume percentage of crude type i in the total cmde, vol. %,
Vgasoiine,i- volumc fraction ofthe gasoline cut in the cmde type i,
143
Vin,i- volume fraction ofthe light naphtha in the crude type i,
Ygasoiine,i- MON Or RON of the gasoline cut in the cmde type i,
Y|n,i- MON or RON ofthe light naphtha in the cmde type i.
It can be seen from Table 7.4 that the MON and RON ofthe light naphtha cut in
the cmde C and cmde D are not available in the industrial data. It was assumed that the
MON and RON ofthe light naphtha cut in these two cmde types are the same as the
MON and RON ofthe gasoline cut in the same cmde type, respectively.
The saturates include paraffins and naphthenes. The volume percentages ofthe
saturates in the gasoline cut and light naphtha cut ofthe cmde A and cmde B can be
calculated by summing corresponding volume percentages ofthe paraffins and
naphthenes. The volumetric percentage ofthe olefins in the gasoline cut and the light
naphtha cut are set to zero based on industrial data. The volume percentages ofthe
saturates, the aromatics, and the olefins in the LSR can be calculated from the volume
percentage of these components in the gasoline cut and light naphtha cut of four cmde
oils using the formula given below:
X ^ ^ ' • (^gasoline.i " ^x.gasoline.i + Portion,^„j„j - V,„j - V^j„)
"^oX = ^ v^TTTT-rr ^T-TT 7^ ' (7-4) Y^oV. • (V^asoiinci + Portion,SR,,„„ • V,J
where
i- cmde type A, B, C, and D,
X - component type, saturates, aromatics and olefins,
%X- volume percentage of component type X, saturates, aromatics and olefins, vol.%,
Vx,gasoiine,i- volumc fraction ofthe molecule type X in the gasoline cut ofthe cmde type i,
Vx,in,i- volume fraction ofthe molecule type X in the light naphtha ofthe cmde type i.
The benzene content and the sulfur content in the LSR can also be calculated from
the corresponding contents of gasoline cut and light naphtha cut of four cmde oils using
the formula given below:
144
Y:/ow, *^gasoline.! ''^"^gasoline.i '" Z.gasoline.i ''^ "0rtl0n[^n,^j
V^SPG,„rW.. %Z = - ^ - ^ '^—^-^ -_ ^ '-, (7.5)
^Vgasoline.i ' ^PGga^oline.i + PortiOn,^^,„, "^
KVln.i-SPG,„^,
where
i- cmde types of A, B, C, and D,
Z - benzene or sulfur,
%)Wi- weight percentage of cmde type i in the total crude, wt. %,
%Z- benzene content or sulfur content, wt.%,
Wz,gasoiine,i- weight fraction ofthe benzene or sulfiir in the gasoline cut ofthe cmde type
i,
Wz,in,i- volume fraction ofthe benzene or sulfiir in the light naphtha ofthe cmde type i.
The RVP, and the O2 wt.% in the LSR are assumed to be constants in the model.
They are set at the average industrial data. According to the operation personnel in the
refinery considered in this work, the ASTM D86 curves of gasoline blending stocks
change little during the operation. Hence, the average industrial values are also used for
the ASTM D86 curve ofthe light naphtha. These constants are listed in Table 7.5.
7.2.2 Other Gasoline Blending Stocks
The mass flow rate, MON, and RON ofthe FCC gasoline are calculated in the
FCC model introduced in the Chapter 4 of this dissertation. However, the FCC model
does not predict the volumetric flow rate ofthe FCC gasoline. The API gravity ofthe
FCC gasoline is assumed to be a constant and is set at the average API gravity ofthe FCC
gasoline in the industrial data. The FCC model can not predict the volumetric percentages
ofthe chemical components in the FCC gasoline, either. The industrial average values are
used for the volumetric percentages ofthe saturates, the aromatics, and the olefins in the
FCC gasoline. The RVP, ASTM D86 curve, benzene wt.%, the sulftir wt.%, and the O2
145
wt.% in the FCC gasoline also can not be predicted from the FCC model. Their values are
set at the average industrial data.
For both the low-severity operation and high-severity operation ofthe reformer,
the detailed reformer model provides the volumetric flow rate and the mass flow rate of
the reformate. The reformer model also provides the contents ofthe saturates, the
aromatics, and the olefins in the reformate. In addition, the model provides the benzene
content in the reformate. The RVP, ASTM D86 curve, sulfur wt.%), and the O2 wt.% in
the reformate can not be predicted from the refomate model. Their values are set at the
average industrial values.
The alkylation model provides the volumetric flow rate, mass flow rate, MON and
RON ofthe alkylate. Other properties are not calculated in the model and they are set as
the average industrial values.
The volumetric flow rate and the mass flow rate ofthe butane are calculated in the
gas plant model. Other properties ofthe butane are obtained from literature (Edmister and
Lee, 1984; Gary and Handwerk, 1984) and from industrial data.
While all the gasoline blending stocks introduced above are produced inside the
refinery, the toluene and the ethanol are only purchased from the market. The refinery
also purchases some butane from the market in the winter. The butane purchased is
assumed to have the same properties ofthe butane produced in the refinery. The
volumetric flow rate and mass flow rate ofthe toluene and the ethanol are assumed to be
at the upper limit ofthe purchase at the beginning ofthe calculations. The real amount of
the toluene and the ethanol used are calculated in the gasoline blending model. Other
properties ofthe toluene and ethanol are obtained from literature (Edmister and Lee,
1984; Unzelman, 1996) and from industrial data.
146
CO
M o o
H-»
c
fi
s _fi
CO
ca O o CO
<L)
u OH O
i n t~~ u
u C <L>
_3 "o
(U fi
3 CQ
j=l 00
CO K
h-1
i n CN CN i n ^ vd >^ OO CN ON 00 T t ^ ON O O O —I
t ^ o i n ^ O r n o ^ o CN o o n m - ^ ^ o T^ o o
ON
d ^ - ^ o >—I r<i o o r s , -—1 ON ON ^ ^ O O i n
o ?: <u u C^
S S S as o od C3N
m ^ vo • ^ t ^ ON 0 0 c ^
^ g : 9 t< ^ o d ^ . " : ^ . vo m i n ON —I
O O vo ON ^ —I ^
s s s s s s
i n CN i > i n S2
vq ' ^ CN m r~
t; OH O I-I
( IH
2; o
"o >
ta
•^ Pii S c/3 ^ o
"o >
fi
u
ca ^ • ^ o
PH H > 03
O
H 00 m 00
O i n
H 00 <
o as i n
ON
H 00
<
CN
t ^ — o
9 9 - ^ o m
m m o o I CN - ^ O O
^ p p p m o o d
O N - ^ v o c N O o o m o o N m i n t ^ - r t o m m c N v o v o
S t ^ c J N ^ ^ ' c N ' ^ i ^ v d - ^ ' ^ ' ^ ' ^ ' ^ ^ ' ^ • 0 ^ t ^ ( ^ t ~ - 0 ' * t ^ C N
- ^ m r O C N V O O N V O V D C N ^ ' ~ ~ : ' ~ i ^ ^ ^ ' ^ ° ® ^ f ^ L 2 — ' i n — i v d i n d r < S r n i n
' ^ ' ^ ' ^ ' ^ ' ^ ' ^ . — ' c o ' ^ i n t ^ o - ^ t ^ o l ^ i ^ P « S l ^ i « ! 5 ( < t ^ ' - H r - l , — I . — I , — l ( N | ( N l C N m I
o o o d d
ss
O t ^ ON 00 o ON vq i n ON ON
t~~ t~~ ON t~-^ CN t ^ m ON
CN CN CN r n m
v O ' ^ v o t ^ v o o N C j N O N i n t ^ v q p c N i n r n o ^ ^ O - * S ^ ' c N r < - i v d v d c N v d v d t ~ ~ ^
. o i n t - ~ c N v o o - * t ^ ^ \ 0 , - H ^ H ^ H ( v ) r N l r < ^ m r < - ) H ^
• ^ o o t ^ O N i n ' ^ t ^ o T t ' v t ^ ^ m o o ^ H ^ H O v i n ^ ^ ^ " ^ r n ' ^ ' d ' S ^ d o d — J c N
^ CN ON vo o CN
o p o o CN d d
o o o d d
00 oo o o d) d> <d'
tin O
ON. O o _ ^ CL, 5 00 r^ fi I i | s
O 1) CQ CQ 00 O
ID -o o
a -o
-a <L> I-I O H
CO
_3 ta > CO
- f i .22
"o,
H-t o c
_3 l a > CO
.a ca
J3
<u
ca J3
I i — CN
147
All these industrial data and literature data for the properties ofthe gasoline
blending stocks mentioned above are listed in Table 7.5. After all the properties ofthe
gasoline blending stocks are known, they are used as inputs to the gasoline blending
model.
7.3 Gasoline Blending Model
7.3.1 Octane Model
The Octane Model is to calculate the MON and RON of three grades of gasoline
from the MONs and RONs ofthe gasoline blending stocks. It is well known that the
octane blending is not linear. In other words, the octane number ofthe gasoline is not
equal to the volumetric average ofthe octane numbers ofthe blending stocks. One way to
account for the nonlinearity is using interaction parameters. Morris et al. (1975) proposed
an equation for binary blending as below:
a = Xjaj +x^a,+b,2X,X2, (7.6)
where
a- RON or MON of a blend,
ai, a2- RON or MON ofthe blending component 1 and 2, respectively,
bi2- binary interaction parameter between component 1 and 2,
xi, X2- the volume fractions ofthe blending component 1 and 2, respectively.
The disadvantage of Morris' approach is that the interaction parameter between
each pair of gasoline cut has to be known for the blending. These interaction parameters
can only be obtained from the regression of industrial blending data. For example, in the
refinery considered in this work, blending of seven stocks needs twenty-one binary
parameters for MON and RON calculation each. Twu and Coon (1996) proposed a
method which only needs three interaction parameters for RON and MON each for
blending any number of gasoline blending stocks. Twu and Coon's approach uses the
concept of interaction parameter and also the compositions of gasoline cuts. It was
148
claimed that the correlation describes blending behavior accurately throughout the entire
composition and it works not only for binaries, but also for multicomponent systems
(Twu and Coon, 1996). The average absolute deviation ofthe model predictions is 1.00%
for RON and 1.19% for MON in a gasoline blending test on a total of 161 blends from
157 gasoline cuts (Twu and Coon, 1996). Because of its simplicity and accuracy, the
approach of Twu and Coon (1996) has been used in this work.
In Twu and Coon's method, each gasoline blending stock consists three
components: aromatics, olefins, and saturates. The saturates include paraffins and
naphthenes. The approach uses several assumptions stated below:
Each ofthe three components in a blending stock has the same octane number as
the blending stock. For example, if the RON ofthe FCC gasoline is 87, then the RONs of
the olefins, the aromatics and the saturates in the FCC gasoline are all assumed to be 87.
a. The values ofthe interaction parameters between components inside the same
gasoline blending stock are zero.
b. If i and j are both the same hydrocarbon type (e.g., aromatics in cut A and aromatics
in cut B), the binary interaction parameter between i and j is assumed to be zero.
c. Gasoline blending is symmetric blending. That is, the binary interaction parameters
between two gasoline components i and j , kij and kji, have the same value.
d. The binary interaction parameters in the pairs with same components are the same.
For example, the binary interaction parameter between aromatics in cut A and olefins
in cut B is equal to that between the olefins in cut A and the aromatics in cut B.
The total number ofthe binary interaction parameters for blending two 3-
component gasoline cuts is only three (i.e., binary interaction parameters between
aromatics A and olefins in B, aromatics in A and saturates in B, olefins in A and saturates
in B). The binary interaction parameters between components are derived from
regressing blending data of gasoline blending stocks. The universal set of binary
interaction parameters between components were given by Twu and Coon (1996). They
were obtained from the regression of 161 blends from 157 gasoline cuts. The values of
the universal set of binary interaction parameters are listed in Table 7.6. Subscripts O, A,
149
and S represent olefins, aromatics and saturates, respectively. For example, KQA
represents the binary interaction parameters between olefins and aromatics.
Table 7.6 Universal Set ofthe Binary Interaction Parameter between Components.
Interaction Parameter
K Q A
K Q S
K-AS
Twn and Coon
RON
0.0670
-0.1021
-0.0232
MON
0.0354
-0.0800
0.0271
Model Regression
RON
-0.00274
-0.1439
0.1841
MON
0.07014
-0.1385
0.1066
The binary interaction parameters between two gasoline blending stocks can be
calculated from the binary interaction parameters between components in these two
gasoline blending stocks. Assume that gasoline blending stock X is a mixture of three
components with compositions xi, X2, and X3, and the gasoline blending stock Y is a
mixture ofthe same three components with compositions yi, y2, and y^. For two three-
component gasoline blending stocks, the binary interaction parameter between gasoline
blending stocks X and Y is calculated using the following formula:
3 3
YJl(^>yj+^jy'^ K^ -1 I J
-^(^i+^j) (1-k,)
(a^+aj) (7.7)
where
ai- RON or MON ofthe component i,
kij- binary interaction parameter between the component i and j ,
KxY- binary interaction parameter between the gasoline blending stocks X and Y,
Xi, yi- volume fraction ofthe component i in gasoline blending stocks X and Y,
respectively.
150
After generating the binary interaction parameters between gasoline blending
stocks, the octane numbers ofthe a gasoline blend with several gasoline blending stocks
are calculated using the formula given below:
where
Zi,Zj- volume fractions of gasoline blending stocks i and j , respectively,
a- RON or MON ofthe gasoline,
aij- the blending parameter.
The aij is calculated using the formula below:
ci,j=^(a,+aj)(l-k,j), (7.9)
where
ai, aj- RON or MON ofthe gasoline blending stocks i and j , respectively,
kij- binary interaction parameter between the gasoline blending stocks i and j .
Ethanol is very different from the other gasoline blending stocks. The ethanol
does not belong to any ofthe three molecule types of saturates, aromatics, and olefins.
Hence, the ethanol is treated different from the other seven gasoline blending stocks in
the octane model. Instead of using the real octane numbers ofthe ethanol, the blending
octane numbers are used for the ethanol. The ethanol only exists in the gasoline grade of
sub. octane with ethanol. The ethanol in this grade is fixed at IO.vol% according to
practice in the refinery considered in this work. After obtaining the octane number ofthe
gasoline blend of other seven blending stocks, the final octane number ofthe sub. octane
with ethanol gasoline is calculated as the volume average ofthe blending octane number
ofthe ethanol and the octane number ofthe gasoline blends of other seven gasoline
blending stocks.
151
In order for the octane model to match the blending in the refinery considered in
this work, the three interaction parameters for RON and the three interaction parameters
for MON are regressed using the industrial blending data. The industrial blending data
used in the regression include 11 super unleaded blends, 67 unleaded blends, and 17 sub.
octane with ethanol blends. The properties of gasoline blending stocks in each blend are
unknown. Therefore, the average properties ofthe industrial data are used for the
gasoline blending stocks in each blend. These blends include both the blends in the
summer and in the winter. The regression is an optimization problem. A least square error
type fimction is used as the objective function for the optimization problem given below:
^W parameterization ~ 2^^ ^model.i ~ ^base.i ) ' ^base.i I ' ( ' • ' ^ j
where
Objfparamterization- objcctivc fiinction of the octane model parameterization,
Omodei.i- MON or RON predicted by the octane model,
Obase,i- MON or RON in the industrial data.
NPSOL optimization package (Gill et al., 1986) was used in the parameterization.
The average relative difference between industrial data and model prediction is 0.67% for
RON and it is 0.72% for MON. The maximum relative difference is 2.3% for RON and it
is 2.7% for MON. Considering the use of average properties for gasoline blending stocks
in each blend and different blends in the regression, the gasoline blending correlations
predict MON and RON accurately using only three interaction parameters each. The
interaction parameters obtained in the regression are listed in Table 7.6.
7.3.2 RVP Blending Model
Reid vapor pressure (RVP) is widely used as a criterion to measure the volatility
of gasoline. Theoretical approaches (Stewart, 1959; Vazquez-Esparragoza et al., 1992)
152
use pseudo-components to represent the gasoline blending stocks. Empirical correlations
are used in the theoretical approaches to calculate the properties of pseudo-components,
which deteriorate the accuracy ofthe theoretical approaches. In addition, the
computations required in the theoretical methods are complex in comparison to those
required in other approaches (Singh et al., 2000). Chevron's vapor pressure blending
indices (VPBI) method uses empirical correlations. VPBI method has a simple form and
has been proved to have reasonable accuracy (Gary and Handwerk, 1984). It has been
widely used in industry (Singh et al., 2000). In the present work. VPBI method is used in
the RVP calculation.
The VPBI method converts the RVP of each gasoline blending stock to the
VPBI. The VPBI ofthe gasoline blend is calculated as the volumetric average ofthe
VPBIs of all the gasoline blending stocks. The RVP ofthe gasoline blend is obtained by
converting the calculated VPBI ofthe gasoline blend to RVP.
The relation between the VPBI and the RVP of a gasoline cut is given below:
VPBI = RVP"\ (7.11)
where
VPBI- vapor pressure blending index,
RVP- Reid vapor pressure.
Equation 7.11 can be modified to calculate RVP from VPBI.
153
Start
Boiling points of butane, toluene, and
ethanol
Initial Guesses ofthe
T
Calculate the volume distilled for the mixed
blending stocks using Cubic Spline method
ASTM D86 curves of mixed blending
stocks
Calculate the total volume distilled from the mixed
blend stocks
Add the volume of pure component i to the total volume distilled, i=i+l
Calculate the new temperature using the
Secant method
J^Q_
Figure 7.1 Flowchart for the Calculation ofthe Tio»/„, T5o»/o, T9oo/„ of Gasoline Blends.
154
7.3.3 Percent Distilled Model
The percent distilled model is to calculate the temperatures at 10%), 30%) and 90%
distilled ofthe gasoline based on the ASTM D86 curves ofthe gasoline blending stocks.
It is assumed that the gasoline is an ideal mixture in this work. The temperatures at 10%,
30% and 90%o distilled are represented by Tio%, or T5o%, or T9o%, respectively. The flow
chart for the above iterative procedure is shown in Figure 7.1.
The calculation of Tio%, or T5o%, or Tgoo/, follows the steps given below:
a. Make an initial guess of Tio%, or T5o%, or T9oo/„. Use the estimated value and the
ASTM D86 curves ofthe mixed blending stocks to calculate the volume percentages
ofthe mixed blending stocks distilled. Nine intermediate points are used to represent
each ASTM D86 curve. Cubic spline method (Riggs, 1994) is used to interpolate
between the known points on the ASTM D86 curves.
b. Calculate the total volume distilled by summing the volume distilled of each mixed
blending stocks.
c. Compare the temperature with the boiling points of three pure blending components:
butane, toluene, and ethanol. If the temperature is higher than one ofthe boiling
points, the corresponding component is assumed completely distilled. Then the
volume of this component is added to the total volume distilled. Repeat the procedure
until all three boiling points have been compared with.
d. Calculate the volume percentage that is vaporized. Compare the volume percentage
with 10 vol.%, 50 vol.%, or 90 vol.%). If they do not match, calculate the new
temperature using Secant method (Riggs, 1994) and return to step a.
155
CHAPTER 8
REFINERY-WIDE OPTIMIZATION
In the previous chapters, the single-unit models in the overall refinery-wide model
and the feed characterizations that connect the single-unit models are presented. Till now,
the overall refinery-wide model has been built. This chapter discusses the optimization
analysis that was conducted on this refinery-wide model.
8.1 Formulation ofthe Optimization Problem
8.1.1 Formulation of the Obj ective Function
Although optimization can be stated in many different ways, the common
optimization to an industrial process is to maximize the profitability ofthe process. In
this work, the whole refinery is considered to be one process. This process uses the given
cmde slate to produce various petroleum products to achieve economic objectives.
The objective of optimization in hand is to reach the maximal profitability given
the cmde slate and refining facilities. No major hardware change in the current facilities
is considered in the optimization. The optimization tries to find the optimal operating
conditions that maximize the overall profit ofthe whole refinery while observing all the
process constraints.
In the objective fimction, only the feed cost, operating cost and revenue from
product sales are taken into account. The fixed costs, such as the capital cost and salaries,
are excluded from the objective fimction, since we can not influence these costs by
optimizing the operating variables.
The feed cost includes the cost of cmde oil feeds and the cost of other chemicals
purchased from the market. Four types of cmde oils are used in the refinery. These four
types of crude oils have been discussed in Chapter 3. It is assumed that each type of
crude oil has a fixed fraction for each operation mode. The compositions ofthe cmde
feed are shown in Table 3.1 of Chapter 3. The composition ofthe cmde oil for each
operation mode is fixed. Hence, a single cmde price is calculated from the prices of four
156
types of crude oils and this single crude price is used as constant in each operation mode.
The chemicals purchased from the market and their usage are listed in Table 8.1.
Table 8.1 Chemicals Purchased by the Refinery Considered in This Work.
Chemicals
Butane
Isobutane
Toluene
Ethanol
Usage
Gasoline blending stock
Alkylation feed
Gasoline blending stock
Gasoline blending stock
The operating cost includes the costs in four categories: catalyst and additives,
natural gas, power, and cooling water. To calculate the exact usage of each category in
every unit in the refinery requires very detailed models for each unit, which is not in the
scope ofthe present work. Hence, all four categories are combined into one category
called utility. Since the operating costs are much smaller compared to other items in the
objective function, this simplification has an insignificant effect on the optimization
results. The utility cost is calculated based on the data in the linear programming (LP)
reports obtained from the refinery considered in this work. The utility costs are calculated
using the formula given below:
Utility i = — ^ • Utility i^Lp, (8.1) ^i,LP
where
Utilityi- utility cost for unit i,
Utilityi„LP- utility cost for unit i from LP report,
Vi- throughput of unit i,
Vi Lp- throughput of unit i from LP report.
157
The income from the product sale includes the sales of all products produced in
the refinery. These products are listed in Table 8.2.
Table 8.2 Price Stmcture ofthe Refinery-Wide Optimization.
Name
Cmde
Butane (purchase)
Isobutane
Toluene
Ethanol
Propane
C3 product
Butane (sale)
Light Naphtha
Gasoline, Sub. Octane with ethanol, rack
Gasoline, Sub. Octane with ethanol, spot
Gasoline, unleaded, rack
Gasoline, unleaded, spot
Gasoline, super unleaded, rack
Gasoline, super unleaded.
Jet Fuel A
Jet Fuel 5
Low sulftir diesel
No. 2 diesel
No. 6 fuel oil
spot
Summer Mode
13.97
12.38
12.65
24.92
43.75
10.47
13.02
9.16
13.93
20.07
19.65
18.62
18.2
20.98
19.72
17.32
17.44
17.21
16.35
9.22
Winter Mode
14.54
14.55
11.33
23.47
43.29
11.85
13.96
11.33
14.60
18.90
18.48
17.17
16.75
18.30
17.04
17.01
17.28
16.65
16.04
13.2
158
The objective function has the formula given below:
objf = Y Vproduct I • Ppruduct^ i
- 2_, VcrudCj - PcrudCj J
- 2 J Vchemical/^ - Pchemical,^ k
- Utility
- reformer regeneration cost (8.2)
where
Vproducti, VcmdCj, Vchemicalk- volumetric flow rate of product i, cmde type j , and
chemical k, respectively, BBL/Day,
Pproducti, Pcmdcj, Pchemicalk- price of product i, cmde type j , and chemical k,
respectively, $/BBL,
Utility- utility cost, $/Day,
Reformer regeneration cost- regeneration cost of reformer catalyst, $/Day,
It is clear that the objective function will be computed as $/day.
8.1.2 Objective Function Evaluation
Each objective function evaluation needs one execution ofthe re finery-wide
model to calculate the volumetric flow rates ofthe products. The utility cost and reformer
regeneration cost can only be evaluated through the execution ofthe refinery-wide
model.
The refinery-wide model essentially is a straight-through model. The sequence of
model executions follows the material flow in the refinery. During the refinery-wide
model execution, the cmde unit model is solved first. After that, the FCC model is
solved. Then the reformer model is solved twice, one execution for the low severity
159
mode, the other execution for the high severity mode. The FCC model and the reformer
model can be solved simultaneously. The gas plant are executed after the executions of
the FCC model and the reformer model because the light gas products from the FCC unit
and reformer becomes the feed to the gas plant. The alkylation unit is then executed after
the execution ofthe gas plant model. Last, the gasoline blending model is executed. The
execution sequence ofthe refinery-wide model is shown in Figure 8.1. Although there are
recycle streams inside the single unit models, such as the hydrogen recycle in the
reformer unit, there is no recycle stream among the major processing units in the
refinery-wide model. In the refinery considered in this work, there are only a few streams
flowing in the direction opposite the execution sequence. However, these recycle streams
only have small quantities and are considered to have an insignificant effect on the
accuracy ofthe refinery-wide model.
8.1.3 Price Structure
The operation ofthe refinery considered in this work varies with seasons. This is
also tme for most of refineries in U.S. The operation changes are necessary to cope with
the market demands and price changes with respect to seasons. The price stmcture is
greatly affected by the market demand. For example, the gasoline has a higher price in
the summer when a lot of gasoline is consumed by travelers. The diesel has a higher price
in the winter because the demand of heating oil is high and diesel can be used to make the
heating oil.
The two major operation modes are Summer Mode and Winter Mode. Each
operation mode has its own price stmcture. The prices ofthe cmde oil, chemicals, and
products for two operation modes are listed in Table 8.2. It can be observed from Table
8.2 that the price gap between the gasoline and the diesel is large in the summer while the
same is small in the winter. This difference has great effect on refinery operation. Since
the price stmcture directly affects the value ofthe objective function of refinery-wide
optimization, optimization study is conducted for each mode separately.
160
FCC unit Decision Variables
LSR
' '
Optimizer
Decision Variables 1 r Gasoline Blending
Refinery-wide Model
Interface
Decision Variables
Reformer Decision Variables
Crude Unit Decision Variables
Crude Uni t Model
FCC Model
FCC Gasoline
Lit 3ht Gas
Butane
1—•
' ' '
Crude Assays,
Crude Makeup
"
Reformer Model
(Low Severity, H igh Severity)
'
Gas Plant Model
' '
Alkylat ion Unit Model
' Alkylate
Gasoline Blending Model
Light Gas
Low Reformate, High Reformate
Figure 8.1 Flowchart ofthe Execution ofthe Refinery-Wide Model.
161
8.1.4 Decision Variables
In this study, the decision variables are those variables whose values have
significant effect on the overall economics ofthe fuel-oriented refinery. These variables
are normally the handles that an operator can adjust to change the operation ofthe
refinery. For the point of view of modeling, the decision variables are the inputs to the
model whose optimal values are sought by the optimization algorithm. The number ofthe
decision variables is equal to the number of freedom degrees ofthe model used in the
optimization study. The decision variables for the refinery-wide optimization are the
collection ofthe decision variables of each unit in the refinery.
Based on the characteristics ofthe decision variables, the decision variables can
be divided into two groups: the decision variables ofthe processing units and the decision
variables ofthe gasoline blending. The decision variables ofthe processing units include
those process Vciriables, such as cut points, temperatures, recycle ratios, etc.
The gasoline blending is different from other units ofthe refinery in the sense that
it is only a mixing process. There is no chemical reaction or separation involved in the
gasoline blending. The decision variables for the gasoline blending are the fractions of
each gasoline blending stocks blended into three grades of gasoline products. Among the
seven blending stocks in the gasoline blending, five geisoline blending stocks, FCC
gasoline. Low severity reformate, high severity reformate, alkylate. Light straight-run
(LSR) gasoline, come from the processing units inside the refinery. In other words, there
is no purchase for these blending stocks from outside resources. Hence, the sum ofthe
fractions of each gasoline blending stock in three grades of gasoline is equal to unity. In
order to eliminate this equality constraint, only two fractions, instead of three, for each
blending stocks among these five blending stocks are used as the decision variables for
the refinery-wide optimization. The two fractions for each blending stock are the
fractions ofthe blending stock in the Super Unleaded Gasoline and Unleaded Gasoline.
The fraction ofthe blending stock in the Sub. Octane Gasoline, which now becomes a
dependent variable, is calculated using the formula given below:
162
•^Sub.0ct.i •' ^ Super Unleaded.! -^Unleaded.!' \^-^)
where
Xsuper unieaded.i, Xunieaded.i, Xsub.Oct.i- fractions of blending stock i in Supct Unleaded,
Unleaded, and Sub. Octane Gasoline, respectively.
The refinery usually purchases some butane from outside resources in the winter
and sells some butane on the spot market in the summer. This is because of different
evaporation specifications for gasoline in different seasons. Gasoline in the winter has a
higher RVP upper limit so that the refinery can purchase butane and blend it into the
gasoline. Since butane has a lower market price than gasoline, it is beneficial to blend as
much as butane into the gasoline. The refinery sells part of butane produced inside the
refinery because the RVP upper limit is low and the amount of butane used in the
gasoline blending is low.
It is assumed initially that the total amount of butane used in the blending in the
summer is equal to what is produced within the refinery. It is assumed initially that the
total amount of butane used in the blending in the winter is equal to the sum ofthe butane
produced plus five thousand barrels per day purchase of butane. The five thousand barrels
per day purchase of butane is the maximum amount of butane that refinery usually
purchases from outside resources. In both modes, the tme amount of butane used in the
gasoline blending may not be equal to amount assumed initially. The assumed amount is
used as the base to calculate the amount of butane in barrel per day used in three grades
of gasoline from the fractions, which are the decision variables for the refinery-wide
optimization. Since the total amount of butane used in the gasoline blending is unknown
beforehand in the refinery-wide model, the sum ofthe fractions of butane in three grades
of gasoline is not necessarily to be unity. Hence, three fractions must be used for butane.
The toluene used in the blending comes only from outside resources since the
refinery does not produce toluene. The maximum amount of toluene used in the blending
is set at 200 barrels as per the operation department in the refinery. The 200 barrels per
day is used to calculate the amount of toluene in barrel per day from the fractions in the
163
decision variable set. Three fractions for toluene are also used since the real amount of
toluene used in the blending is unknown beforehand.
One special decision variable in the gasoline blending is the fraction of Light
Straight-run (LSR) gasoline for sale. The refinery considered in this work sells part of
LSR on the spot market. LSR can be either sold or blended into the gasoline.
The decision variables and their lower and upper bounds are listed in Table 8.3.
The lower and upper bounds ofthe decision variables came from the normal operating
range used in the refinery considered in this work. The engineers from the refinery have
agreed upon the values of these lower and upper bounds.
Table 8.3 Decision Variables of Refinery-Wide Optimization.
Decision variable
No. Decision variable name
3
4
5
Cmde Unit: 1 Feed to cmde unit
2 Atmospheric Tower furnace outlet
temperature, °F
Light naphtha ASTM 95% point, °F
Heavy naphtha ASTM 95% point, °F
Jet Fuel ASTM 95% point, °F
6 Diesel ASTM 95% point, °F
7 Heavy Vacuum Gas Oil TBP end point, °F
FCC Unit: 8 Regenerator temperature, °F
9 Reactor temperature, °F
10 02% in the flue gas, mol%
Reformer: 11 First bed inlet temperature, K, low severity
12 H2/Hydrocarbon recycle ratio, mol H2/mol
Hydrocarbon, low severity
13 First bed inlet temperature, K, high severity
Lower Bound
Upper Bound
35,000
650
230
360
460
620
950
1,250
980.00
0.01
900.00
5.00
50,000
665
260
385
525
670
1,100
1,340
1,005
0.02
980.00
7.50
900.0 980.0
164
Table 8.3 Continued.
Decision variable
No. Decision variable name Lower Bound
Upper Bound
Reformer: 14 H2/Hydrocarbon recycle ratio, mol Hi/mol 5.0
Hydrocarbon, high severity
15 Low severity operation time/total operation 0.1
time
16 Fractionof FCC gasoline in Super Unleaded 0.0
17 Fraction of low reformate in Super Unleaded 0.0
Blending:
Super
Unleaded
Unleaded
Sub. Oct.
Light
Naphtha:
18 Fraction of alkylate in Super Unleaded
19 Fraction of LSR in Super Unleaded
20 Fraction of high reformate in Super Unleaded 0.0
21 Fraction of butane in Super Unleaded
22 Fraction of toluene in Super Unleaded
23 Fraction of FCC gasoline in Unleaded
24 Fraction of low reformate in Unleaded
25 Fraction of alkylate in Unleaded
26 Fraction of LSR in Unleaded
27 Fraction of high reformate in Unleaded
28 Fraction of butane in Unleaded
29 Fractionof toluene in Unleaded
30 Fraction of butane in Sub. Oct.
31 Fraction of Toluene in Sub. Oct.
32 Fraction of Light naphtha sold directly
7.5
0.80
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
1.0
1.0
1.0
1.0
1.0
1.0
I.O
I.O
1.0
I.O
1.0
I.O
I.O
1.0
1.0
1.0
1.0
165
8.1.5 Constraints
There are many constraints in the operation of a fuel-oriented refinery. The
optimization problem in this study includes 63 nonlinear constraints and 7 linear
constraints. In this study, the nonlinear constraints are not expressed explicitly. Instead,
all the nonlinear constraints are evaluated using the nonlinear refinery-wide model.
These constraints can be divided into several categories: capacity limits, process variable
constraints, and product specifications.
A capacity limit is the maximum throughput of a unit. The capacity limits ofthe
refinery-wide optimization were obtained from the Linear Programming (LP) reports
obtained from the refinery considered in this work.
Process variable constraints are the maximum or minimum value of a process
variable. If the value of a process variable is outside the interval defined by its maximum
and minimum values, violation of safety regulation or damage to the facility may happen.
For instance, a temperature can not surpass the metallurgical limit ofthe contacting
materials.
The product specifications in this study are mainly the specifications for the three
grades of gasoline products: Super Unleaded Gasoline, Unleaded Gasoline, and Sub.
Octane Gasoline with Ethanol. The gasoline specifications from Designation D4814-96
of ASTM standards are used in this study. It includes eleven specifications for each grade
of gasoline. It should be noted that there are different product specifications for gasoline
vaporization in different seasons.
The nonlinear constraints are listed in Table 8.4. Engineers in the refinery
considered in this work have agreed upon the values ofthe upper limits and the lower
limits of these nonlinear constraints.
166
Table 8.4 Nonlinear Constraints of Refinery-wide Optimization.
Nonlinear Constraint
Crude Unit:
FCC Unit:
No
I
2
3
4
5
6
7
8
9
10
11
12
13
. Constraint name
Diesel flow rate in
Atmospheric tower
Summer Mode
Lower Bound
0
Reduced cmde to vacuum 4,000
tower
Gasoline splitter bottom
flow rate
Crude debutanizer
Rose unit capacity
Rerun unit capacity
Naphtha hydrotreater
capacity
Jet fuel tank capacity
Heater outlet temperature,
op
Fuel flow to furnace.
SCM
Reactor pressure, psig
Regenerator pressure,
psig
Actual speed of lift air
blower (sa), RPM
0
0
0
0
0
0
500
0
14.7
14.7
5,100
Upper Bound
12,000
23,000
1,000
10,000
6,500
5,000
10,000
5,800
700
39.5
30
30
6,100
Winter Mode
Lower Bound
0
4000
0
0
0
0
0
0
500
0
14.7
14.7
5,100
Upper Bound
12,000
23,000
1,000
10,000
6,500
5,000
10,000
5,800
700
39.5
30
30
6,100
167
Table 8.4 Continued.
Nonlinear No. Constraint name Constraint
Summer Mode Winter Mode
Lower Bound
Upper Bound
Lower Bound
Upper Bound
14
15
16
17
18
19
20
21
22
Difference between lift-aii
blower inlet suction flow
and surge flow, ICFM
Regenerator reactor
pressure difference (P6-
P4), psia
Level of catalyst in the
standpipe. foot
Combustion air blower
inlet suction flow, SCFM
Wet gas compressor inlet
suction flow, Ibmol/s
Combustion air blower
flow rate (Ib/s)
FCCU charge
Reformer Charge
Research Octane of Low
•0
-5
0
35,000
0
30
0
6,000
94
Infinite
2
20
42,000
0.67
65
20,000
10,000
96.5
0
-5
0
35,000
0
30
0
6,000
94
Infinite
2
20
42,000
0.67
65
20,000
10,000
96.5
reformate
23 Research Octane of High 98.5
reformate
Other 24 Distillate HDS 0
constraints: 25 Alkylation unit charge 0
26 Alkylate product 0
100.5 98.5 100.5
14,500
8,000
4,000
0
0
0
14,500
8,000
4,000
168
Table 8.4 Continued.
Nonlinear Constraint
Blending:
Super
Unleaded
Gasoline
Unleaded
Gasoline
No.
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
Constraint name
C3 splitter
MON
(R+M)/2
RVP
Specific gravity
Sulftu-
Drivability index
10%) vaporized
50%) vaporized
90% vaporized
02 wt.%
Benzene, vft.Vo
MON
(R+M)/2
RVP
Specific gravity
Sulftir
Drivability index
10% vaporized
50%o vaporized
90% vaporized
02 wt.%
Summer Mode
Lower Bound
0
83
93.1
6.4
0.72
0
0
0
170
0
0
0
82.1
87.1
6.4
0.72
0
0
0
170
0
0
Upper Bound
15,000
Infinite
94
7.8
0.784
O.I
1250
158
250
374
4
4.9
84
88.1
7.8
0.784
0.1
1250
158
250
374
4
Winter Mode
Lower Bound
0
83
93.1
6.4
0.72
0
0
0
170
0
0
0
82.1
87.1
6.4
0.72
0
0
0
170
0
0
Upper Bound
15,000
infinite
94
12.5
0.784
O.I
1200
131
235
365
4
4.9
84
88.1
12.5
0.784
O.I
1200
131
235
365
4
169
Table 8.4 Continued.
Nonlinear Constraint
Sub. Oct.
No.
49
50
with ethanol 51
Gas
Contract
52
53
54
55
56
57
58
59
60
61
62
63
Constraint name
Benzene, wt.%
MON
(R+M)/2
RVP
Specific gravity
Sulfiir
Drivability index
10%) vaporized
50% vaporized
90% vaporized
02 wt.%
Benzene, wt.%
Super Unleaded Rack
Unleaded Rack
Sub. Oct. with Ethanol
Summer Mode
Lower Bound
0
82.1
87.1
6.4
0.72
0
0
0
170
0
3.7
0
3300
16000
3700
Upper Bound
4.9
84
88
8.8
0.784
0.1
1250
158
250
374
4
4.9
3300
16000
3700
Winter Mode
Lower Bound
0
82.1
87.1
6.4
0.72
0
0
0
170
0
3.7
0
3300
16000
3700
Upper Bound
4.9
84
88
13.5
0.784
0.1
1200
131
235
365
4
4.9
3300
16000
3700
Linear constraints are only for the fractions ofthe gasoline blending stocks in the
three grades of gasoline blending. They have a common formula given below:
l>Y^j,>0, (8.4)
where
j - Gasoline blending stocks.
170
i - grades of gasoline,
Xji- fraction of gasoline blending stock j in the gasoline of grade i.
Since there are seven gasoline blending stocks, there are seven linear constraints.
It should be noted that for the linear constraints corresponding to those five blending
stocks only produced inside the refinery, only two fractions are used in each linear
constraint.
8.1.6 Optimization Algorithm
Each objective fimction evaluation requires solving the refinery-wide model once.
Solving the refinery-wide model is time consuming since the refinery-wide model is
fairly large. Therefore, it is necessary to find an optimization algorithm that has fast
convergence that requires less objective function evaluations. It is reported that
successive quadratic programming (SQP) converges fast and it may be the best method in
solving nonlinear problem (Edgar and Himmelblau, 1988). A software package using
SQP algorithm, known commercially as NPSOL was used as the optimization engine.
The documentation of NPSOL software was given by Gill et al. (1986).
From a random starting point, NPSOL can not guarantee the convergence to the
global optimum, nor does any other software package. The best that the NPSOL can do is
to converge to a point that satisfies the first-order Kuhn-Tuck conditions, which is the
necessary conditions for a local minimum. To increase the possibility of finding the
global optimum, several random starting points are used for each case discussed below.
The solution with the best value ofthe objective function is regarded as the global
optimum and is used in the following analysis.
8.2 Optimization Case Studies
8.2.1 Base Case
In the refinery considered in this work, unit operations follow the schedule
determined by the linear programming (LP). However, the decision variables used by LP
are not exactly the same as those used in nonlinear optimization studies. It is necessary to
171
know the normal operating values ofthe decision variables used in optimization studies
in order for fair comparison.
The normal values of some decision variables are obtained from normal operating
conditions in the refinery. The normal values ofthe rest ofthe decision variables are
found by trial and error to make the model prediction consistent with the product
information in the LP reports. The complete sets ofthe normal values ofthe decision
variables for both modes are listed in Table 8.6 and Table 8.9. Engineers from the
refinery have agreed upon these normal values. These two sets of normal values are
regarded as the base cases in this work.
It is desirable to mn the refinery-wide model using these normal operation
conditions and compare the product slate and purchase plan with the LP reports. The
results are listed in Table 8.5 and Table 8.6.
It can be seen from Table 8.5 and Table 8.6 that the average relative errors ofthe
volumetric flow rates of products and purchases ofthe LP reports and model predictions
under normal operating conditions are 1.36% for Summer Mode and 1.31% for Winter
Mode. There are relative big difference for the quantitis of propane, ethanol, butane and
C3 product in both modes or one mode. However, those streams have relative small
amount. Therefore, the mismatch have an insignificant effect on the overall revenue of
the refinery.
8.2.2 Summer Mode
The refinery-wide optimization was conducted for Summer Mode. The optimum
values ofthe decision variables of refinery-wide optimization are listed in Table 8.7. The
active constraints in the optimum solution are listed in Table 8.8. The comparison ofthe
product slates between the optimum solution and base case is shown in Table 8.9 and
Figure 8.2.
172
Table 8.5 Comparison of Model Prediction ofthe Base Case with the LP report. Summer Mode.
Item
Purchase, bbl/day
Cmde
Toluene
Ethanol
Isobutane
Sale, bbl/day
Light naphtha
Butane
SNL spot
NL spot
Sub.Oct. spot
SNL rack
NL rack
Sub. Oct. rack
Propane
C3 product
Jet fiiel A
Jet fiiel 5
Low sulfur diesel
No. 2 diesel
No.6 fiiel oil
Average error
Purchase, $/day
Sale, $/day
Utility, $/day
Revenue, $/day
LP report
50000
200
379
1377
1017
950
0
1742
0
3300
16000
3700
371
1254
800
5000
10813
3419
2440
-
737529
881742
24671
119543
Model Prediction
50000
198
370
1,407.4
1009.5
927.17
0
1744.5
0
3300
16000
3700
311
1264
800
5000
10812
3418
2460
-
737470
881121
23897
119754
Relative Deviation, %
0.00
-1.00
-2.37
2.21
-0.74
-2.40
0.00
0.14
0.00
0.00
0.00
0.00
-16.10
0.80
0.00
0.00
-0.01
-0.03
0.82
1.36
-0.01
-0.07
-3.14
0.18
173
Table 8.6 Comparison of Model Prediction ofthe Base Case with the LP Report, Winter Mode.
Item
Purchase, bbl/day
Cmde
Toluene
Ethanol
Butane
Isobutane
Sale, bbl/day
Light naphtha
SNL spot
NL spot
Sub.Oct. spot
SNL rack
NL rack
Sub. Oct. rack
Propane
C3 product
Jet fuel A
Jet fiiel 5
Low sulfur diesel
No. 2 diesel
No.6 fiiel oil
Average Error
Purchase, $/day
Sale, $/day
Utility, $/day
Revenue, $/day
LP report
50000.000
18.000
362.000
1044.300
945.000
595.000
0.000
3853.290
0.000
3300.000
16000.000
3700.000
129.000
1700.000
800.000
5000.000
13024.000
1339.000
1823.000
-
771508.105
865929.018
22843.928
71576.984
Model Prediction
50000.000
18.000
370.000
1041.630
945.440
600.450
0.000
3839.810
0.000
3300.000
16000.000
3700.000
108.020
1754.770
800.210
5000.000
13000.000
1321.680
1819.520
771821.816
865578.997
21932.920
71824.261
Relative Deviation, %
0.00
0.00
2.21
-0.26
0.05
0.92
0.00
-0.35
0.00
0.00
0.00
0.00
-16.26
3.22
0.03
0.00
-0.18
-1.29
-0.19
1.31
0.04
-0.04
-3.99
0.35
174
It should be noted that the revenue in the optimum solution shown in Table 8.8
does not include the reformer regeneration cost. The reason is that the reformer operation
is not stable in the refinery considered in this work and the cycle length varies in the real
operation. It is difficult to estimate the regeneration cost per day for the base case. Hence,
the reformer regeneration cost is not included in the revenue ofthe base case.
To make a fair comparison, the reformer regeneration cost is also not included in
the revenue of the optimum solution. Same approach is used in the case of Winter Mode
and the case of single unit optimization.
It should be noted that the cmde throughput in both the optimum solution and the
base case reached the upper limit ofthe cmde unit capacity. Hence, no benefit is obtained
from incremental throughput in the optimum solution. The only thing the optimizer can
do to improve the profitability ofthe refinery is to adjust the product slate and make more
high-value products and less low-value products.
It can be seen from Figure 8.2 that the significant differences between base case
and the optimum solution are the gasoline production and diesel production. It should be
noted that the refinery has contracts with retailing companies for all three grades of
gasoline. These contracts become the lower limits for gasoline production. In other
words, the refinery must produce certain amount for each grade of gasoline. Any amount
of gasoline above the amount in the contract is sold on the spot market. In the optimum
solution, more spot Unleaded Gasoline is produced them the base case. This is
understandable because the Unleaded Gasoline has a relative high price in Summer Mode
The question may be raised that why the optimizer does not try to make more spot
Super Unleaded Gasoline which has a higher price than the Unleaded Gasoline. The
answer is that to make more Super Unleaded Gasoline, either the reformer will be
operated longer at high severity mode, or the FCC unit will be operated at higher
severity, or more toluene is purchased. None ofthe three options is favorable from the
economic point of view. Operating the reformer longer at high severity mode decreases
the conversion from the feed to reformate and produces more low-value light gas. It also
shortens the cycle length ofthe reformer, which results in decrease of on-stream factor
175
and increase ofthe regeneration cost. For FCC operation, since the FCC operation has
already reached the upper limit ofthe wet gas compressor constraint in the optimum
solution, decreasing throughput is necessary when the severity of FCC unit is increased.
Increasing the severity of FCC unit also increases the production of low-value light gas.
Purchasing more toluene is also not a good option because the toluene price is higher
than that of gasoline. Therefore, it is a better choice to make more spot Unleaded
Gasoline instead of spot Super Unleaded Gasoline.
The extra spot Unleaded Gasoline produced in the optimum solution mainly
comes from cutting more diesel components into the gasoil and blending all light naphtha
in the gasoline. It is observed from Figure 8.2 that less diesel is produced in the optimum
solution. The light naphtha has a relative low market price and it should be all blended
into gasoline.
The optimum solution also shows that low severity operation time/total operation
time reaches the upper limit. It means the low severity operation should be longer than
normal operation. This can be explained by the fact that high severity operation produces
more low-value light gas and less high-value reformate, though the octane number ofthe
reformate is higher. High severity operation also results in more coke deposited on the
catalyst and consequently, shorter cycle length. The gain of operating reformer at high
severity longer is that more Super Unleaded Gasoline can be produced. However, it
seems that the cost of producing more Super Unleaded Gasoline is too high. The
information sent by the optimum solution is that as long as there are enough octane
number in the gasoline, the reformer should be operated at low severity in Summer
Mode.
176
Table 8.7 Optimum Values ofthe Decision Variables of Refinery-wide optimization. Summer Mode.
Decision
variables
No. Decision variable name Base Case
Summer
Optimum
Summer
Crude Unit: I
2
3
4
5
6
7
Feed to crude unit
AT furnace outlet temperature, °F
Light naphtha ASTM 95% point, °F
Heavy naphtha ASTM 95% point, °F
Jet Fuel ASTM 95% point, °F
Diesel ASTM 95% point, °F
Heavy Vacuum Gas Oil TBP end point, °F
FCC Unit: 8 Regenerator temperature, °F
9 Reactor temperature, °F
10 02%) in the flue gas, mol%
Reformer: 11 First bed inlet temperature, K, low severity
12 H2/Hydrocarbon recycle ratio, mol/mol, low
severity
13 First bed inlet temperature, K, high severity
14 H2/Hydrocarbon recycle ratio, mol/mol, high
severity
15 Low severity operation time/total operation
time
16 Fraction of FCC gasoline in Super Unleaded
17 Fraction of low reformate in Super Unleaded
Blending
Super
Unleaded
50000.000
665.000
260.000
370.000
475.350
622.000
1091.000
1270.000
1000.000
0.013
900.000
6.000
912.000
5.500
50000.000
664.999
230.228
384.824
489.021
634.983
1099.460
1250.013
1002.251
0.020
900.000
7.486
911.736
7.309
0.750
18 Fraction of alkylate in Super Unleaded
19 Fraction of LSR in Super Unleaded
20 Fraction of high reformate in Super Unleaded 0.750
21 Fraction of butane in Super Unleaded
0.800
0.000
0.001
0.565
0.000
0.750
0.054
0.079
0.000
0.573
0.000
0.040
0.047
177
Table 8.7 Continued.
Decision
variable
No. Decision variable name Base Case
Summer
0.990
0.991
0.705
0.176
0.321
O.I 00
0.187
0.000
0.034
0.000
0.363
Optimum
Summer
1.000
0.910
0.710
0.236
0.600
0.922
0.106
0.000
0.035
0.000
0.000
Unleaded
Sub. Oct.
Light
naphtha
22 Fraction of toluene in Super Unleaded
23 Fraction of FCC gasoline in Unleaded
24 Fraction of low reformate in Unleaded
25 Fraction of alkylate in Unleaded
26 Fraction of LSR in Unleaded
27 Fraction of high reformate in Unleaded
28 Fraction of butane in Unleaded
29 Fractionof toluene in Unleaded
30 Fraction of butane in Sub. Oct.
31 Fraction of Toluene in Sub. Oct.
32 Fraction of Light naphtha sold directly
Table 8.8 Active Constraints in Refinery-wide Optimization, Summer Mode.
Active Constraint Name Limit type
Jet Fuel Tank Capacity Upper
Wet gas compressor inlet suction flow Upper
FCC throughput Upper
Super Unleaded Gasoline, (R+M)/2 Lower
Super Unleaded Gasoline, RVP Upper
Super Unleaded Gasoline, Specify Gravity Lower
Unleaded Gasoline, MON Lower
Unleaded Gasoline, RVP Upper
Sub. Oct. Gasoline, (R+M)/2 Lower
Sub. Oct. Gasoline, RVP Upper
Sub. Oct. Gasoline volumetric flow rate Lower
178
Table 8.9 Comparison ofthe Decision Variables of Refinery-wide Optimization with Base Case, Summer Mode.
Item
Purchase, bbl/day
Crude
Toluene
Ethanol
Isobutane
Sale, bbl/day
Light naphtha
Butane
SNL spot
NL spot
Sub.Oct. spot
SNL rack
NL rack
Sub. Oct. rack
Propane
C3 product
Jet ftiel A
Jet ftiel 5
Low sulftir diesel
No. 2 diesel
No.6 fuel oil
Summary
Purchase, $/day
Sale, $/day
Utility, $/day
Revenue, $/day
Model Prediction
50000
198
370
1,407.4
1009.5
927.17
0
1744.5
0
3300
16000
3700
311
1264
800
5000
I08I2
3418
2460
737470
881121
23897.00
119754
Optimum Solution
50000
200
370
1314
0
976
72
3009
0
3300
16000
3700
306
1192
800
5000
10539
3332
2464
736338.6
884888.93
23467.00
125083.33
Relative Deviation, %
0.00
I.Ol
0.00
-6.64
-100.00
5.27
-
72.48
-
0.00
0.00
0.00
-1.69
-5.70
0.00
0.00
-2.52
-2.51
0.17
-0.15
0.43
-1.80
4.45
179
<L> xn CO
U
OS OQ
fe rt N
• ^H
s 4-» & I
o <u T3
^
i u
d)
c<
( N
o Z
% o
<n (L>
u
dies
U l
e. 3 l/J
_
(U
•o
o
ner
M
3 m VI cS
U ID
<u <s 1 .
<u • ~ >
u c es n. o I-I
u.
•^ o o
X3 3 on
O O . w
J
z o o. U5
J
z !/3
4) c BS
-^.» 3 X I
igh
—
-» o npo
I - I D .
t ' O o.
« J=
a. ta c
OQ
c o
N
O
ID
c
o CO
o 3 o
c o Vi
. I—I
53 O H £ o O (N 00 (U l i
3
O O O
O O o o
o o o 00
o o o so
o o o o o o
o/iee
180
Some spot Super Unleaded Gasoline is produced in the optimum solution of
Summer Mode. This increase in Super Unleaded Gasoline is realized by blending
gasoline closer to its lower limit of octane specifications. In the gasoline blending process
ofthe refinery considered in this work, the octane number ofthe gasoline is usually
blended to about 0.1 to 0.4 octane number higher than its specifications. This is a
common industrial practice to avoid disqualification and reblending. Since the refinery
considered in this work has not implemented advanced control system, which is a
necessity for tight gasoline blending, the octane giveaway can not be decreased. In the
optimum solution, either one ofthe two octane specifications, (R0N+M0N)/2 or MON,
reaches the lower limit. By blending the gasoline closer to the octane specification, more
Super Unleaded Gasoline can be produced.
It is found that gasoline blending agrees with the common industrial practice by
hitting the lower limits of octane number specification and the upper limits of RVPs. The
upper limits of RVPs are always active because by doing so, more low-value butane can
be blended into high-value gasoline.
8.2.3 Winter Mode
The refinery-wide optimization was conducted for the Winter Mode. The
optimum operating conditions that maximize the revenue were found. The optimum
values ofthe decision variables of refinery-wide optimization are listed in Table 8.10.
The active constraints in the optimum solution of Winter Mode are listed in Table 8.11.
The comparison ofthe product slates ofthe optimum solution with the base case is shown
in Table 8.12 and Figure 8.3.
It can be seen from Figure 8.3 that the significant differences between base case
and optimum solution are also the gasoline production and diesel production.
In the optimum solution, the refinery produces significant amount of spot Super
Unleaded Gasoline where there is no spot Super Unleaded Gasoline in the base case. The
optimizer adjusts the operating conditions ofthe processing units and the gasoline
181
blending to maximize the Super Unleaded Gasoline, which has the highest market value
among all the grades of gasoline.
More cmde has been cut into diesel in the optimum solution than in the base case.
This can be explained by the fact that diesel has high market value in the Winter Mode.
Although diesel price is still lower than the gasoline price in the winter in this case, there
is more volumetric lost in gasoline production route. The loss mainly happens in the FCC
unit and the reformer where large amount of low-value light gases is produced.
Considering both the price gap and the volumetric loss, it is more beneficial to produce
more diesel in Winter Mode.
However, there is also a limit of increasing diesel production. Increasing diesel
product means less feed for the FCC unit. Further decrease in FCC feed may result in
overcracking in FCC unit if the riser temperature remains high. Overcracking produces
more light gases, which result in more volumetric loss for gasoline production. If the riser
temperature is lowered to avoid overcracking, the octane number ofthe FCC gasoline
will drop, which results in less Super Unleaded Gasoline that can be produced since the
octane number of FCC gasoline drops. The optimizer makes a compromise between
diesel production and gasoline production and chooses an appropriate value for the
ASTM 95% point ofthe diesel that maximizes the profitability ofthe refinery.
Again, the light naphtha has a relative low market price. The optimum solution
shows that it should be all blended into gasoline. The optimum solution also shows that
low severity operation time/total operation time reaches the upper limit. It means the low
severity operation should be longer than normal operation in the Winter Mode. This can
be explained by the factor that the gasoline pool has a higher average octane number
because more high-octane butane has been blended into the gasoline pool. Hence, there is
less demand on the high octane reformate produced by high severity reformer operation.
182
Table 8.10 Comparison ofthe Decision Variables of Refinery-wide Optimization with Base Case, Winter Mode.
Decision
variable
No. Decision variable name Base Case
Winter
Optimum
Winter
Feed to cmde unit
AT fiimace outlet temperature, °F
Light naphtha ASTM 95% point, °F
Heavy naphtha ASTM 95% point, °F
Jet Fuel ASTM 95% point, °F
Diesel ASTM 95% point, °F
Heavy Vacuum Gas Oil TBP end point, °F
8 Regenerator temperature, °F
9 Reactor temperature, °F
10 02% in the flue gas, mol%
11 First bed inlet temperature, K, low severity
12 H2/Hydrocarbon recycle ratio, mol/mol, low
severity
13 First bed inlet temperature, K, high severity
14 H2/Hydrocarbon recycle ratio, mol/mol, high
severity
15 Low severity operation time/total operation
time
16 Fraction of FCC gasoline in Super Unleaded
17 Fraction of low reformate in Super Unleaded
18 Fraction of alkylate in Super Unleaded
19 Fraction of LSR in Super Unleaded
20 Fraction of high reformate in Super Unleaded
21 Fraction of butane in Super Unleaded
Crude Unit:
FCC Unit:
Reformer:
Blending
Super
Unleaded
50000.000
665.000
250.000
370.000
465.280
593.000
1055.000
1270.000
1000.000
0.013
900.000
6.000
910.101
5.500
50000.000
650.000
246.669
364.123
460.000
620.000
1070.000
1300.531
1004.997
0.012
900.000
7.500
910.000
7.500
0.750 0.800
0.000
0.000
0.485
0.000
0.846
0.053
0.000
0.112
0.823
0.000
1.000
0.077
183
Table 8.10 Continued.
Decision
variables
No. Decision variable name Base Case
1. Winter
0.090
0.900
0.999
0.267
0.526
0.025
0.209
0.000
0.045
0.000
0.224
Optimum
2. Winter
0.000
0.862
0.888
0.000
0.694
0.000
0.186
0.000
0.042
0.000
0.000
22 Fraction of toluene in Super Unleaded
23 Fraction of FCC gasoline in Unleaded
24 Fraction of low reformate in Unleaded
25 Fraction of alkylate in Unleaded
26 Fraction of LSR in Unleaded
27 Fraction of high reformate in Unleaded
28 Fraction of butane in Unleaded
29 Fraction of toluene in Unleaded
30 Fraction of butane in Sub. Oct.
31 Fraction of Toluene in Sub. Oct.
32 Fraction of Light naphtha sold directly
Table 8.11 Active Constraints in Refinery-wide Optimization, Winter Mode.
Active Constraint Name Limit type
Jet Fuel Tank Capacity Upper
Wet gas compressor inlet suction flow Upper
Super Unleaded Gasoline, (R+M)/2 Lower
Super Unleaded Gasoline, RVP Upper
Super Unleaded Gasoline, Specify Gravity Lower
Unleaded Gasoline, MON Lower
Unleaded Gasoline, RVP Upper
Sub. Oct. Gasoline, MON Lower
Sub. Oct. Gasoline, RVP Upper
Sub. Oct. Gasoline volumetric flow rate Lower
184
Table 8.12 Comparison ofthe Product Slates ofthe Optimum Solution with the Base Case, Winter Mode.
Base Case Optimum Solution Relative Deviation, %>
Purchase, bbl/day
Crude
Toluene
Ethanol
Butane
Isobutane
Sale, bbl/day
Light naphtha
SNL spot
NL spot
Sub.Oct. spot
SNL rack
NL rack
Sub. Oct. rack
Propane
C3 product
Jet ftiel A
Jet ftiel 5
Low sulfiir diesel
No. 2 diesel
No.6 fuel oil
Summary
Purchase, $/day
Sale, $/day
Utility, $/day
Revenue, $/day
50000.000 50000
18.000 0
370.000 370
1041.630 1047.78
945.44 818.68
600.450
0.000
3839.810
0.000
3300.000
16000.000
3700.000
108.020
1754.770
800.210
5000.000
13000.000
1321.680
1819.520
0
1495.56
1656.24
0
3300.000
16000.000
3700.000
113.98
1579.99
800
5000
13000
2755.94
1806.76
771821.816 769691.3814
865578.997 866186.1954
21932.920 22097.12
71824.261 74397.694
0.00
-100.00
0.00
0.59
-13.41
0.92
-56.87
0.00
0.00
0.00
0.00
5.52
-9.96
-0.03
0.00
0.00
108.52
-0.70
-0.28
0.07
0.75
3.58
185
(U Vi Ti U Vi
ca PQ D
3 O
efl N
s +.J O H
O (U
T 3
^ ^ U 3
"-! (^ •
<u cH vo o Z
CN
O Z
k« - 1
I * -
3 !/) » o —' m m
u , 3
o
" M V\ u
T3
_ M
0> r/1 W
TD
cS
U o
CS a. o u a.
^ CJ o
X 3 tn
O a. « J Z . -o D .
(/) J
z CA
.^.J
igh
~ ™
\ ' o o. CO
ca J3
Q. C3 C
O
I-I u 3
Vi ca U 0) c/3 ca
PQ
3 O
• ^
N
O,
O -a
3
c/5
;§ ^ - » o 3
T3 O
O 3 O Vi
'C ca o, B o
O rn 00
u
op
o o o 'a-
o o o cs
o o o o
o o o 00
o o o
o o o
o o o CN
o/iaa 186
It is found from Table 8.11 that gasoline blending also agrees with the common
industrial practice by hitting the lower limits of octane number specification and the
upper limits of RVPs. The upper limit ofthe wet gas compressor is also an active
constraint. This means that even in the Winter Mode, the highest severity ofthe FCC unit
is favorable. This can be explained by the fact that the higher the FCC riser temperature
is, the higher the octane number ofthe FCC gasoline, and the more super unleaded
gasoline can be produced.
The wet gas flow rate is a function of FCC feed rate and riser temperature. The
optimizer suggests keeping the riser temperature at the upper limit ofthe riser
temperature while adjusting feed rate to keep the wet gas flow stay at the upper limit of
the wet gas compressor flow. This is different from the Summer Mode, in which the FCC
throughput reaches the upper limit while adjusting the riser temperature to keep the wet
gas flow stay at the upper limit ofthe wet gas compressor flow.
8.2.4 Optimal Solution Analysis
The optimization problem at hand is a complexed problem with 32 decision
variables and 70 constraints. The optimizer can not find a feasible solution each time. It
was estimated that the optimizer failed to give a feasible solution in about 40 percent of
all trials. Even the a feasible solution was found, it is not necessarily the optimal solution.
The mean and standard deviation of feasible solutions from random starting points are
listed in Table 8.13.
Table 8.13 The Mean and Variance ofthe Feasible Solutions.
Operation Mode No. of feasible Tme
solutions Solution
Mean Standard
Deviation
Summer Mode 100
Winter Mode 100
-1.24169'
-0.73421'
-1.16709
-0.69699
0.053868
0.035588
1: The the objective function used in the optimization is equal to dividing the revenue per day by-10'
187
The magnitude ofthe standard deviation indicates that the solutions found by the
optimizer scatter around the real optimum in a fairly large area. In this work, about 200
sets starting points were used as starting points for each operation mode and the best
solution was regarded as the tme solution.
In order to have some confidence in the optimal solutions found in previous
studies, a depth test was condcuted to find out the shape of objective fiinction surface
around the optimal solutions. Tests were done to the optimal solutions of both Summer
Mode and Winter Mode. Tests were carried out by choosing some random points around
the optimal solution and calculating the objective fiinction value of each random point. A
random point was selected by perturbing the values of decision variables using the
formula given below:
X, = x^p,j + (0.5 - rand) - factor - {bu, - bl,), (8.5)
where
Xi- the i" decision variable ofthe random point,
Xopt,i- the i"' decision variables ofthe optimal solution,
rand- random number between 0 and 1,
factor- the factor, 0.04.
bui- the upper limit of i" decision variable,
bli- the upper limit of i* decision variable.
Since only the nearby surface was studied here, the change of a decision variable
was limited in the interval of ±2% of normal operating range. Since the optimization
problem at hand is a constrained problem, it is obvious that some random points
generated by equation 8.5 will have constraint violations. These random points were
discarded in the study. The Euclidean distance between each random point and the
optimal solution and the relative change in objective function value were calculated. The
decision variables were nomalized in calculating euclidean distances. It is found in the
tests that there is no random point that has a better objective fimction value than the
188
corresponding optimal solution. This indicated that the optimal solutions found in
previous studies are at least the local optimums. The average euclidean distances and
average relative changes of objective fiinction are listed in Table 8.14.
Table 8.14 Change ofthe Objective Function Value around Optimal Solutions.
Operation Mode Average Euclidean Average relative change
distance of objective function
Summer Mode 0.049355 -0.00086
Winter Model 0.049834 -0.00418
It can be seen that the change ofthe objective fiinction values around the optimal
solution of Winter Mode is one magnitude larger than that of Summer Mode. Considering
the stopping criteria of l.E-6 for the optimizer, the changes of objective fimction values
for both modes are significant. Hence, it is concluded that the surface around each
optimal solution has enough steepness which will not cause the optimizer to stop before it
reach the vinicity ofthe solution. Why the optimizer stops in the vinicity of a tme
optimum remains unknown and ftirther is needed on this issue.
8.2.5 Profitability Improvement
The profitability improvements ofthe two operation modes are shown in Table
8.15. The average profitability improvement is 4.1%. The total revenue increase is about
1.3 million dollars per year. The refinery considered in this work is a small refinery with
a capacity of 50,000 barrels cmde oil per day. Considering the fact that some big
refineries have a capacity of 400,000 barrels cmde oil per day, the absolute profitability
improvement can be more significant. This profitability improvement is also comparable
to the profit of implementing advanced process control, 3-5% (Ellis, 1998), and that of
implementing real-time optimization, 3-5% (Ellis, 1998).
For the refinery considered in this work, the throughput ofthe FCC unit has an
upper limit of 20,000 barrels per day determined by environment regulations. Hence, no
189
Summer Mode
Winter Model
Average
+4.5%
+3.6%
+4.1%
benefit ofthe throughput increase, which is common to other optimization projects, can
be obtained from the refinery-wide optimization.
Table 8.15 Profitability Improvement ofthe Refinery-wide Optimization.
Operation Mode NLP vs. Base Case Incremental Revenue
"~~~~~ $973,000/six months
$468,000/six months
$72I,000/sixmonths
Annual Incremental Revenue $1,341,000/year
8.3 Single-Unit Optimization
Currently, on-line optimizer has been implemented in several units in the
refineries to push the operation to against the most profitable constraints. The units which
already have the on-line optimizer implemented include crude unit, FCC unit, reformer,
etc. However, the optimizer implemented on a single unit only considers the economics
of that single unit instead ofthe economics ofthe whole refinery. It is very difficult for a
single-unit optimizer to generate an optimum solution which is consistent with those
given by a refinery-wide optimizer. This is because it is difficult to obtain appropriate
prices for the intermediate streams ofthe single umt.
Cmde Unit is used as the example to show the difference between the solutions of
single-unit optimization and refinery-wide optimization. Cmde unit is the first major
processing unit in the refinery. It produces several side-draw products that are the feeds
to the downstream processing units in a refinery. Usually the side-draw products are not
final products sold in the market. The prices ofthe side-draw products from the cmde
unit ofthe Summer Mode are estimated from Linear Programming (LP) shadow prices
and market prices. LP shadow prices and market prices are two main resources for
estimating the prices of intermediate streams. For a side-draw product, market price is
used whenever it is available. Otherwise, LP shadow price from the LP reports is used.
The prices ofthe side-draw products are listed in Table 8.16.
190
Table 8.16 Prices ofthe Side-draw Products from the Cmde Unit, Summer Mode.
Side-draw Product Price, $/bbl Source
Cmde
LSR
Heavy Naphtha
Jet Fuel
Diesel
Gas Oil
Residue
13.97
13.93
17.45
17.44
16.35
11.95
9.22
Market Price
Market Price
LP shadow price
Market Price of Jet-5
Market Price of No. 2 diesel fuel
LP shadow price
Market Price of No. 6 fuel oil
Using the same objective fimction form as that of refinery-wide optimization
shown in equation 8.2, optimization was conducted using the same cmde unit model
which has been used in the refinery-wide model. The subset of decision variables and
constraints ofthe refinery-wide optimization corresponding to the cmde unit were also
used for the single-unit optimization ofthe cmde unit. The values of decision variables in
the optimum solution ofthe single-unit optimization ofthe cmde unit are shown in Table
8.17.
It can be observed from Table 8.17 that the optimization solution ofthe single-
unit optimization ofthe cmde unit always reaches the upper limit ofthe ASTM end point
of those high-value side-draw products such as heavy naphtha, jet ftiel and diesel. This
means the optimum solution simply maximizes the side-draw product with higher value.
It can be explained by the fact that side-draw product production has the dominating role
in revenue calculation. The utility usage in the cmde unit has secondary importance in the
optimization (Xu, 1998). In addition, the utility usage does have big change even when
the ASTM 95%o points vary. Hence, the maximizing the size-draw product with higher
value is the obvious solution ofthe single-unit optimization.
191
Table 8.17 Prices ofthe Side-draw Products from the Cmde Unit, Summer Mode.
Side-draw Product Price of side-draw product, $/bbl
Optimum value of Active Limit single-unit optimization
Feed to crude unit
AT fiimace outlet temperature,
op
Light naphtha ASTM 95%
point, °F
Heavy naphtha ASTM 95%
point, °F
Jet Fuel ASTM 95% point, °F
Diesel ASTM 95%) point, °F
Heavy Vacuum Gas Oil TBP
end point, °F
13.97
13.93
17.45
17.44
16.35
11.95
50,000.000
650.000
230.000
385.000
525.000
670.000
1,080.000
Upper Limit
Lower Limit
Lower Limit
Upper Limit
Upper Limit
Upper Limit
Upper Limit
In order to make a fair comparison between single-unit optimization and refinery-
wide optimization, the optimization is conducted for the rest ofthe refinery while fixing
the decision variables ofthe cmde unit at the optimum value given by the single-unit
optimization. In other words, the subsets of decision variables and constraints
corresponding to the cmde unit are not included in the decision variable set and constraint
set of this optimization study. The solution of this optimization is compared to that of
refinery-wide optimization shown in Table 8.18 and Figure 8.4.
It can be observed from Table 8.18 and Figure 8.4 that the single-unit
optimization produces more diesel and less gasoline than the refinery-wide optimization.
This product slate change makes the revenue ofthe solution ofthe single-unit
optimization about 1.6% less than the revenue ofthe refinery-wide optimization. This
case shows that single-unit optimization may result in suboptimal solution for the point of
view of refinery-wide economics. Single-unit optimizer normally will maximize the
192
volume of the product with highest estimated price, which may not be the optimum for
the plant-wide operation.
In order to make the single-unit optimization generates solution consistent with
refinery-wide economics, appropriate prices for the intermediate streams are necessary.
Sometimes, external constraints from refinery-wide economics are also needed (Jones,
1999). This also shows that refinery-wide optimization is superior to single-unit
optimization in the sense refinery-wide optimization eliminates the need to evaluate the
prices of intermediate streams. The refinery-wide optimization only uses market prices.
In order to realize the full potential ofthe refinery. It is beneficial to carry out refinery-
wide optimization.
Table 8.18 Comparison of Single-unit Optimization with Refinery-wide Optimization, Summer Mode.
Purchase, bbl/day
Cmde
Toluene
Ethanol
Isobutane
Sale, bbl/day
light naphtha
Butane
SNL spot
NL spot
Sub.Oct. spot
SNL rack
NL rack
Sub. Oct. rack
Propane
Refinery-wide Opt.
50000
200
370
1314
0
976
72
3009
0
3300
16000
3700
306
Single-unit Opt.
50000
200
371
901
0
943
151
1564
12
3300
16000
3700
341
Relative Deviation, %
0.00
0.00
0.33
-31.44
0.00
-3.40
109.04
-48.02
0.00
0.00
0.00
0.00
11.48
193
Table 8.18 Continued.
C3 product
jet fiiel A
Jet ftiel 5
low sulfur diesel
No. 2 diesel
No.6 fiiel oil
Summary
Purchase, $/day
Sale, $/day
Utility, $/day
Revenue, $/day
Refinery-wide Opt.
1192
800
5000
10539
3332
2464
736338.6
884888.93
23467.000
125083.33
Single-unit Opt.
976
800
5000
11409
3607
2452
731165.183
876975.827
22740.000
123070.644
Relative Deviation, %
-18.13
0.00
0.00
8.25
8.24
-0.47
-0.70
-0.89
-3.10
-1.61
194
(U
o
ion
ca N 'B
O H
U • 3
P <u bO 3
m D
C ' 0
zati
B O H
0 -O
1
b u 3
efi
&i
•
vo 0 Z
(N
0
z
0
13 C/1 (1> •a
o o -g & crt
O
a. c«
z o o. on
- I
z 3 ca 3
X
ca * - X -^ : c .2? -o. — ca
3
13
I-I u 3 m
3 D u
s 3 o N
O H
O
is •B y (/5 3 o S cn c/:i
^ § •4—> C O
O N
II e:o o 3 o CO O H
s o
u 00
I-I 3 CiO
o o o fN
o o o o
o o o 00
o o o so
o o o
o o o <N
o/iaa 195
CHAPTER 9
CONCLUSIONS AND
RECOMMENDATIONS
The two main objectives of this work were:
1. Develop an overall nonlinear refinery-wide model for a ftiel-oriented refinery that
represents the refinery operation.
2. Carry out optimization studies to the overall refinery-wide model to find optimal
operating conditions that are inline with the actual practice.
This chapter is essentially a final commentary on how far successfiil we were in
meeting these objectives, and what is left for ftiture work.
9.1 Conclusions
Among the two major steps listed above, the first step required the maximum
amount of investment of time and effort. A first-principle, nonlinear steady-state refinery-
wide model was developed by building individual models for all processes in a fuel-
oriented refinery and integrating them into a refinery-wide model.
A simplified model was developed for the cmde unit. The atmospheric tower and
the vacuum tower in the cmde unit were modeled based on material balance, energy
balance and empirical correlations. The main function ofthe cmde unit model is to
predict the yields and properties ofthe side-draw products given the feed information and
cut points. Predictions ofthe simplified model agree with a rigorous ChemCad cmde unit
model on the volumetric flow rates ofthe side-draw products and the gains between
volumetric flow rates and cut points. It is concluded that the gasoline blending model
represents the actual process reasonably well.
A complete set of gasoline properties with eleven properties is calculated in the
gasoline blending model. In the octane blending model, interaction coefficient method
using chemical compositions ofthe blending stocks are applied. The interaction
196
coefficients in the octane blending model were regressed from the blending data ofthe
refinery considered in the work. The Chevron's VPBI method is used in the RVP
blending model. Chevron's VPBI method is a proven method with reasonable accuracy.
The vaporization properties are calculated from the ASTM curves ofthe blending stocks
assuming ideal mixing. Other properties of gasoline are also calculated by summing the
corresponding properties ofthe blending stocks. Basic on the proven methods used in the
gasoline blending, it is concluded that the gasoline blending model represents the actual
process reasonably well.
The modeling ofthe FCC unit and reformer unit uses the existing models. Minor
modifications to both models to accommodate the specific features of the processes in the
concerned refinery. The kinetic parameters are used as the adjustable parameters to
benchmark the models against indusfrial data. We could not find data points spread over
the operating region since both processes are operated close to steady state. The FCC
model was benchmarked at a base case operating point. Since the FCC model had already
been proven to predict reasonably well in the full operating region in earlier studies
(Ellis, 1996; Taskar, 1996) and only minor changes were made in this work. It is
concluded that the FCC model can represent the FCC unit of this particular refinery
reasonably well in the full operating range. The reformer model was benchmarked at two
base case operating points corresponding to the low severity operation and the high
severity operation. By benchmarking the reformer model against two operating
conditions in different operating regions, it is expected that the reformer model should
give reasonable predictions in the fliU operating range ofthe unit.
The alkylation unit model was developed based on stoichiometric relations and
data from linear programming (LP) reports. Simplified models are developed for other
units in the refinery including rerun unit, ROSE unit, hydrotreater, gas plant, and diesel
hydrotreater, based on the data from the LP reports. The data from the LP reports include
the data of both operation modes. Summer Mode and Winter Mode. These units do not
change much in each operation mode. Hence the models are considered adquate by using
only normal operating data.
197
TOTAL method and n-d-M method are used to calculate paraffins, naphthenes,
and aromatics (PNA) data ofthe FCC feed. These methods have been proven the most
accurate methods available in the literature. The feed characterization ofthe reformer unit
is based on the cmde assays, cut point ofthe reformer feed, and typical feed
compositions. The prediction ofthe PNA information ofthe reformer feed should be
more accurate than the prediction from empirical correlations. The cmde assays should
be used whenever possible. There may be some model mismatch on the composition of
individual chemical components in the reformer feed. The mismatch is deemed to have
an insignificant effect on refinery-wide optimization because volumetric fiow rates
instead of stream compositions have the utmost importance in refinery-wide
optimization.
The predictions from the overall refinery-wide model are close to the data in the
LP reports for both operation modes. Because the refinery operation follows the LP
reports, the model predictions are also close to the plant data. It is concluded that the
refinery-wide model represents the fuel-oriented refinery reasonable well.
The computational load is a big concern in refinery-wide optimization. The size
ofthe refinery-wide model is huge, with more than 22,000 lines of Fortran code, among
which 25%) are comment lines. Each objective function evaluation or each derivative
evaluation requires the solving ofthe entire model. The computational efficiency is
cmcial in obtaining a solution in reasonable time.
With the computational issue in mind, the refinery-wide model is coded in a
straight-through form to ease computational load. This simplification enables the model
to be solved in the range of 3-5 seconds on a 400 MHz Pentium II machine.
The optimization study of Summer Mode showed a profit improvement of 4.5%
over the normal operating conditions. This is realized mainly by producing more gasoline
and less diesel, and blending all light naphtha into gasoline. In addition, the FCC feed
reaches the upper limit while the wet compressor flow upper limit is active.
The optimization study of Winter Mode showed a profit improvement of 3.6%
over the normal operating conditions. Contrary to the Summer Mode, the profit
198
improvement is realized mainly by producing more diesel and less gasoline. In the
optimum solution of Winter Mode, all light naphtha is blended into gasoline. Although
the total amount of gasoline decreases, the amount of Super Unleaded Gasoline increased
in the optimum solution. The FCC unit is operated at the highest severity while the wet
compressor flow limit is active.
The most important characteristic of an optimization study is to that the optimum
solutions make sense. By discussing with engineers from the refinery considered in this
work, it is found that these results are consistent with the operation strategy ofthe
refinery for both operation modes. One example is that the FCC unit in the refinery is
usually operated against the wet gas compressor constraint. We concluded that the
optimizer is able to find the optimum operating conditions that make the refinery
operation more profitable. The average profit improvement is 4.1%, which is equal to
about 1.4 million dollars per year for the particular refinery. This revenue improvement is
consistent with 3-5%) expected profit improvement (Hendon, 2000). It can be argued that
real profit improvement may not be exactly the same number showed here because the
model is not perfect. Nevertheless, this work proves that refinery-wide optimization can
generate reasonable optimum solutions that agree with the industrial practice and that is
in a quantitative form with plant-wide consistence instead of vague feelings about the
profit increase.
The single-unit optimization on the cmde unit shows that the solution of a single-
unit optimization may not be consistent with the optimum operation strategy for the
entire refinery. The case study shows that single-unit optimization on the cmde unit
resuhs in suboptimal operating conditions with 1.6% profit loss compared to the refinery-
wide optimization. The conclusion is that refinery-wide optimization is necessary in order
to realize the maximum profit from the plant-wide perspective. The success of a single-
unit optimization depends on the appropriate prices ofthe intermediate streams and
appropriate extemal constraints.
In conclusion, we would like to say that we completed the two major steps
outlined at the beginning of this chapter.
199
9.2 Recommendations
Considering that current work is the first attempt to study the refinery-wide
optimization, following recommendations are made:
1. The effectiveness of process optimization is highly dependent on the quality ofthe
models used to simulate the process. Considering the complexity of refining
processes, to build detailed models for all the units in a refinery is out the scope of
this work. The cmde unit model can be improved by using strict tray-to-tray models
for the atmospheric tower and vacuum tower. The preheat train, preflash tower and
heat exchanger can also be modeled to develop a complete cmde unit model.
Although this kind of cmde unit model has not been seen in academia, this has been
accomplished in industry using commercial simulation software package.
2. The FCC ten-lump yield model should be expanded in order to predict the FCC
gasoline composition. The FCC gasoline is regarded as one lump in the ten-lump
yield model. Hence, it is impossible to calculate the PNA information ofthe FCC
gasoline, which is required by gasoline blending model.
3. A rigorous model needs to be developed for the main fractionator ofthe FCC unit in
order to have a complete FCC model. The cut points ofthe side-draw products ofthe
main fractionator are important decision variables, which are often adjusted by
engineers to change the refinery operation. The flooding constraint in the main
fractionator can be active sometimes during the operation. After the rigorous model
ofthe main fractionator is developed, the cut points may be included in the decision
variable set and the flooding constraint may be included in the constraint set.
4. Only simplified models were developed for units other than the four major units.
Hence, the next logical step in this work would be to develop detailed nonlinear
models for these units. The main benefit of doing this is that the constraints for these
units can be accurately modeled. These constraints may become active in refinery-
wide optimization. In the current model, only capacity constraints are considered in
200
these units. Other constraints may be included in the optimization study after detailed
models of these units are available.
5. All the models in current refinery-wide model are in sequential-modular form.
Sequential-modular models have an inherent disadvantage of no accurate information
ofthe derivatives due to nested convergence loops. The noisy derivatives cause the
optimizer "wandering" during the search (AspenTech, 1998). It has been already
observed that the SQP algorithm based NPSOL software package (Gill et al., 1986)
often quitted searching without finding a solution in the present work. It is suspected
that the difficulty is caused by inaccurate derivatives. Open form approach has
become the standard in industry. One ofthe major advantages of open form approach
is that accurate derivatives can be calculated since each process is modeled by
equations without nested convergence loops.
6. More detailed refinery-wide model using sequential modular approach may not be
computational efficient due to the iterations required in model solving because ofthe
existence of recycle streams. The open form approach can solve the entire refinery-
wide model altogether without iterations. In addition, computational improvement
techniques, like algorithm for sparse matrices, can be applied for large open equation
model.
7. Building a detailed refinery-wide model with open form may include as many as one
million equations (Hendon, 2000). It is recommended to build open form single-unit
model as the first attempt. Using existing models in the process model libraries
(PML) of commercial software packages, such as AspenPlus, may be the easiest way
to constmct an open form refinery-wide model.
8. It is beneficial to include the specifications of all products produced in a ftiel-oriented
refinery. Only the product specifications of gasoline are considered in this work. The
specifications of other products, such as jet fiiel, diesel, No. 6 fliel oil, are not
included. The constraints associated with these specifications, such as the freezing
point of jet fuel, and the flash point and the pour point of diesel, are indirectly
represented by cut points in the current model. Including these specifications in the
201
model should increase the accuracy of estimating the violations of product
specifications.
9. Sulftir balance becomes important as the specifications of sulfur content in all
products become more stringent. The capacity ofthe sulfiir recovering unit may
become an active constraint in refinery operation. Hence, effort should be spent on
modeling the sulfur fiow and distribution in a refinery.
10. Selecting appropriate cmde type according to market change can be important to
improve the profitability of a refinery. Hence, it is beneficial to include cmde
selection in the decision variable set ofthe refinery-wide optimization.
11. The comparison between nonlinear refinery-wide optimization and linear
programming (LP) will be critical in convincing industrial people that nonlinear
refinery-wide optimization is a better tool than LP in plainning and scheduling. To
fiirther current work, effort should be spent on developing a linear model
corresponding to the nonlinear model developed in this work.
202
BIBLIOGRAPHY
American Petroleum Institute. Measuring , Sampling, and Testing Cmde Oil. Bulletin 2500, API, New York City, New York, January 1955.
Arbel, A., Huang, Z, Rinard, I. H., and Shinnar R. Dynamic and Control of Fluidized Catalytic Cracker. I. Modeling ofthe Current Generation of FCC s. Ind. Eng. Chem. Res., Vol. 34, No. 4, 1995, 1228-1243.
Aspen Technology Inc. Real-Time Optimization System Training Course Notes, Houston, TX, 1998.
ASTM. Standard Specification for Automotive Spark-ignition Engine Fuel. ASTM Standard D4814-96, 1997.
ASTM. Carbon Distribution and Stmctural Group Analysis of Petroleum Oils by the n-d-M Method. ASTM Standard D3238-85, 1985.
Beck, R. J. Resurgent Oil Demand, OPEC Cohesion Set Stage for Optimistic Outlook for Oil Industry at the Turn ofthe Centiiry. Oil & Gas Journal, 1999, 97(42), 49-62.
Beck, R. J. Earnings Plunges along with Oil Price in 1998. Oil & Gas Journal, 1999, 97(18), 56-58.
Bensabat, L. E. U.S. Fuels Mix to Change in the Next 2 Decades. Oil & Gas Journal, 1999, 97(28), 46-48,
Hess, F. H., C. D. Holland, R. McDaniel, and N. J. Tetiow. Solve More Distillation Problems: 7. Absorber-Type Pipestills. Hydroc. Proc, 56(5), 1977, 241-249.
Cutier, C. Personal Communication, Lubbock, TX, 1999.
Chung, C. and J. B. Riggs. Dynamic Simulation and Nonlinear-Model-Based Product Quality Control ofa Cmde Tower. AIChE Journal, 41(1), 1995, 122-133.
Dantzig, G. B. Linear programming and Extensions. Princeton University Press, Princeton, N J, 1963.
Daubert, T. E. Petroleum Fraction Distillation Interconversions. Hydroc. Proc, 73(9), 1994,75-78.
Dhulesia, H. New Correlations Predict FCC Feed Characterization Parameters. Oil & Gas Journal, Jan. 13, \9S6, 51-54.
203
Edgar, T. F., and D. M. Himmelblau. Optimization of Chemical Processes. McGraw-Hill, Inc., New York City, NY, 1988.
Edmister, W. C. Improved Integral Technique for Petroleum Distillation Calculations. Ind andEngr. Chem., 47(9), 1955, 1685-1690.
Edmister, W. C , Lee B. I. Applied Hydrocarbon Thermodynamics. Gulf Publishing Company, Houston, TX, 1984.
Ellis, R. Supervisory Optimization ofa Fluidized Catalytic Cracking Unit. Master's thesis, Texas Tech University, Lubbock, TX, 1996.
Ellis, R. Personal Communication, Houston, TX, 1998.
Ellis R. C , X. Li, and J. B. Riggs. Modeling and Optimization ofa Model IV Fluidized Catalytic Cracking Unit. AIChE J., 44(9), 1998, 2068-2079.
Friedman, Y. Z. What's Wrong with Unit Closed Loop Optimization? Hydroc. Proc, 74(10), 1995, 107-116.
Hendon, S. Personal Communication, Lubbock, TX 2000.
Holland, C. D. Fundamentals of Multicomponent Distillation. McGraw-Hill, New York, 1981.
Gary, J. H., and G. E. Handwerk. Petroleum Refining: Technology and Economics. Marcel Dekker, Inc., New York City, NY, 1984.
Gill, P. E., W. Murray, and M. H. Wright. Practical Optimization, Academic Press, New York, 1981.
Gill, P. E., W. Murray, M. H. Wright and M. H. Wright. User's Guide for NPSOL (Version 4.0): A Fortran Package for Nonlinear Programming. Technical Report SOL 86-2, Department of Operations Research, Stanford University, CA, 1986.
Hartmann, J. C. M. Decision-making and Modeling in Petroleum Refining. Hydroc. Proc, 76(11), 1997,77-81.
Hess, F. H. Ph.D. Dissertation, Texas A&M University, College Station, TX, 1977.
Hess, F. H., C. D. Holland, R. McDaniel, and N. J. Tetiow. Solve More Distillation Problems: 7. Absorber-Type Pipestills. Hydroc. Proc, 56(5), 241.
204
Holland, C. D. and Liapis A. I. Computer Methods for Solving Dynamic Separation Problems. McGraw-Hill Book Company, New York City, NY, 1983.
Huq, I. and M. Morari. Modifications to Model IV Fluid Catalytic Cracking Units to Improve Dynamic Performance. AIChE Journal, Vol. 41, No. 6, 1481-1499.
Jacob, S. M., B. Gross, S. E.Voltz, and V. W. Weekman Jr. A Lumping and Reaction Scheme for Catalytic Cracking. AIChE Journal, Vol. 22, No. 4, 701-713.
Jones, J. Personal Communication, Lubbock, TX 1999.
Lin, S. Petroleum Refining Engineering. Petroleum Industry Press, Beijing, P. R. China, 1988.
Lin, T.D. V. FCC Advanced Control and Optimization. Hydroc Proc, 56(5), 107, 1993.
Maxwell, J. B. Data Book on Hydrocarbons. D. Van Norstrand Company, Princeton, NJ, 1950.
Marlin, T. E. Process Control: Designing Processes and Control Systems for Dynamic Performance. McGraw-Hill, Inc., New York City, NY, 1995.
McFarlane, R. C , R. C. Reinemann, J. F. Bartere, and C. Georgakis. Dynamic Simulator for a Model IV Fluid Catalytic Cracking Unit. Comput. Chem. Eng., 17, 275-299, 1993.
McKetta, J. J. Petroleum Processing Handbook. Marcel Dekker, Inc., New York City, NY, 1992.
Mizoguchi, A., T. E. Marlin, and A. N. Hrymak. Operations Optimization and Control Design for a Petroleum Distillation Process. The Canadian Journal of Chemical Engineering. 73(6), 1995, 896-907.
Morris, W. E. Optimum Blending Gives Best Pool Octane. Oil & Gas Journal, Jan. 20 1986,63-66.
Morris, W. E. Gasoline Compositions in no-lead era. Oil & Gas Journal, Mar. 18 1985, 99-106.
Morris, W. E., W. E. Smith, and D. D. Snee. Interaction blending equations enhance reformulated gasoline profitability. Oil & Gas Journal, 92(3), 1994, 54-58.
Moran, M. J. and H. N. Shapiro. Fundamentals of Engineering Thermodynamics. John Wiley & Sons, Inc., New York City, NY, 1996.
205
Nelson, W. L. Petroleum Refinery Engineering. McGraw-Hill, Inc., New York City, NY, 1958.
Palmer, F. H., A. M. Smith. The performance and specification of gasoline. E.G. Hancock (Ed.) Technology ofGasoline, Blackwell Scientific, London, 1985.
Pelham, R. and C. Pharris. Refinery Operations and Control: a Future Vision. Hydroc. Proc, 75(7), 1996, S9-94.
Pelham, R. Process optimization in the HPI. Hydroc Proc, July 1996, 89-94.
Riazi, M. R. and T. E. Daubert, Simplify Property Predictions. Hydroc Proc, 59(3), 1980,115-116.
Riggs, J. B. An Introduction to Numerical Methods for Chemical Engineers. Second Edition, Texas Tech University Press, Lubbock, TX, 1994.
Riggs, J. B. Chemical Process Control. Ferret Publishing, Lubbock, TX, 1999.
Sadeghbeigi, R. Fluid Catalytic Cracking Handbook. Gulf Publishing Company, Houston, TX, 1995.
Schoen, W. F. and A. V. Mrstik. Calculating gasoline blend octane ratings. Ind. and Engr. Chem., 47(9), 1955, 1740-1742.
Singh, A, J. F. Forbes, P. J. Vermeer, and S. S. Woo. Model-based real-time optimization of automotive gasoline blending operations. Journal of Process Control, 10, 2000, 43-58.
Symonds, G. H. Linear Programming Solves Gasoline Refining and Blending Problems. Ind andEngr Chem.,4%{3), 394-401, 1956.
Stewart, W. E. Predict RVP of Blends Accurately. Petroleum Refiner, 38(6), 1959, 231-234.
Stewart, W. E. Predict Octanes for Gasoline Blends. Petroleum Refiner, 38(12), 1959, 135-139.
Taskar, U. Modeling and Optimization ofa Catalytic Naphtha Reformer. Ph.D. dissertation, Texas Tech University, 1996.
Taskar, U. and J. B. Riggs. Modeling and Optimization ofa Semiregenerative Catalytic Naphtha Reformer, ^/C/z^Jowma/, 43(3), 1997, 740-753.
206
Taylor, D. L., and W. C. Edmister. Solutions for Distillation Processes Treating ?etroleum Fractions. AIChE Journal, 17(6), 1971, I324-I329.
Turpin, L. E. Modeling of commercial reformers. Chemical Industries, 61, 1994, 437-480.
Twu, C. H. and J. E. Coon. Predict Octane Numbers Using a Generalized Interaction Method. Hydroc Proc, 75(2), 1996, 51-56.
Twoi, C. H. and J. E. Coon. Estimate Octane Numbers Using an Enhanced Method. Hydroc Proc, 76(3), 1997, 65-68.
Xu, J. S., personal communication, Houston, TX, 1998.
Watkins, R. N. Petroleum Refinery Distillation. Second Edition, Gulf Publishing Company, Book Division, Houston, TX, 1979.
Unzelman, G. H. Gasoline Volatility-Environmental Interactions With Blending and Processing. Fuel Technology & Management, May/June 1996, 39-44.
Vazquez-Esparragoza, J. J., Iglesia-Silva G. A., Hlavinka M., and Bullin, J. A. How to Estimate Rvp of Blends, Hydroc Proc, 71(8), 1992, 135-138.
Zahed, A. H., S. A. Mullah, and M. D. Bashir. Predict Octane Number for Gasoline Blends. Hydroc Proc, 72(5), 1993, 85-87.
207
APPENDIX
CONSTANTS OF POLYNOMIAL EXPRESSON
Table A.l Constants of Polynomial Expression for the TBP Curve ofthe Mixed Cmde in Summer Mode, Calculating the Vol.% Given the Temperature in °F.
Order ofthe Polynomial Expression 5
Polynomial Constant Name Polynomial Constant Value
< 2
0.8957974413351622
-0.01372701021955436
0.0002952782429384016
-2.I49703441506334e-007
-3.854442121853092e-011
5.81465I604877458e-014
Table A.2 Constants of Polynomial Expression for the TBP Curve ofthe Mixed Cmde in Summer Mode, Calculating the Temperature in °F Given the Vol.%.
Order of the Polynomial Expression 5
Polynomial Constant Name Polynomial Constant Value
29.84428824723
25.62933368742233
-0.7922673091416073
0.01622221295752979
-0.0001531047698293264
5.656848165464662e-007
208
Table A.3 Constants of Polynomial Expression for the TBP Curve ofthe Cmde in Winter Mode, Calculating the Vol.% Given the Temperature in °F.
Order of the Polynomial Expression 5
Polynomial Constant Name Polynomial Constant Value
2.193431671505095
-0.0401I70I235776854
0.0003645596718904187
-1.94657827079503e-007
-I.66907967834I765e-0I0
1.25674218569036Ie-013
Table A.4 Constants of Polynomial Expression for the TBP Curve ofthe Cmde in Winter Mode, Calculating the Temperature in °F Given the Vol.%.
Order ofthe Polynomial Expression
Polynomial Constant Name Polynomial Constant Value
53.61544856365072
24.49778891041933
-0.8004076310844539
0.0170606968280822
-0.0001706104416978604
6.7218355I2472907e-007
209
Table A.5 Constants of Polynomial Expression for the API Gravity Curve ofthe Mixed Crude in Summer Mode, Calculating the API Gravity Given the Vol.%.
Order ofthe Polynomial Expression 7
Polynomial Constant Name Polynomial Constant Value
< 2
119.7573532466777
-13.51427730842261
1.155003649306309
-0.05227710492067672
0.00129277077527945
-1.763855119596425e-005
1.24550423408025e-007
-3.55I0902548I0048e-0I0
Table A.6 Constants of Polynomial Expression for the API Gravity Curve ofthe Mixed Cmde in Winter Mode, Calculating the API Gravity Given the Vol.%.
Order ofthe Polynomial Expression 7
Polynomial Constant Name Polynomial Constant Value
115.3176682424892
-13.66362146845495
1.199745867594174
-0.05421826016288378
0.001324183745545327
-1.77568580195242e-005
1.2296309055042I6e-007
-3.43531951355496Ie-0I0
210
Table A.7 Constants of Polynomial Expression for the Sulfiir Curve ofthe Mixed Cmde in Summer Mode, Calculating the Sulftir Given the Vol.%.'
Order of the Polynomial Expression 6
Polynomial Constant Name Polynomial Constant Value
-5.210428584234705e-005
7.080959174388113e-005
-3.525307004426104e-006
-I.0I7925910437589e-007
9.0797230957798I3e-009
-I.487796790868834e-010
7.430537669733543e-013
'The sulfur content is in wt.%).
Table A. 8 Constants of Polynomial Expression for the Sulfur Curve ofthe Mixed Cmde in Winter Mode, Calculating the Sulftir Given the Vol.%.'
Order ofthe Polynomial Expression 6
Polynomial Constant Name Polynomial Constant Value
~c 0.00030I4440208000I24
Cj -9.6I6598620532102e-005
c 1.523461 lI5826574e-005
c -8.795002067220337e-007
^ 2.239074678304I39e-008 ' ' 4
^ -2.49238838646I639e-010
r I.0I0096349284079e-012
The sulftu- content is in wt.%).
211
Table A.9 Constants of Polynomial Expression for Converting ASTM End Point to TBP End Point. 1,2
Polynomial Constant Name Polynomial Constant Value
-89.99134827
2.74798583
-0.01068849
3.17354667D-5
-4.87231402D-8
3.72814245D-11
-1.11569087D-14
' The ranges that the polynomial expression applies to are: ASTM 95% point, 230-800°F; TBP end point, 250-850°F.
^The polynomial expression is regressed from the curves in Figure 2.15 of Watkins (1979).
Table A. 10 Constants of Polynomial Expression for Converting Gap (5-95) ASTM to Gap (0-100) TBP. 1,2
Polynomial Constant Polynomial Constant Polynomial Constant Polynomial Constant Name Value, hd-AGO Value, Id-hd Value, Ln-hn, hn-ld
'0
C4
^5
84.0867095
-1.16113063
-5.0455864D-3
1.2298931 lD-4
1.45938226D-7
-6.04374077D-8
116.80769309
-3.34042753
0.18403357
-8.27984365D-3
1.6959039 lD-4
-1.33260699D-6
171.90777015
-5.6022091
0.16631373
-3.16940048D-3
1.97107064D-5
0
' The polynomial expression correlates data when Gap (0-100) TBP is between 0°F and 100°F, and Gap (5-95 ) ASTM is between -20°F and 60°F.
^The polynomial expression is regressed from the curves in Figure 2.16 of Watkins (1979).
212
Table A. 11 Constants of Polynomial Expression for Calculating the Molecular Weight of a Cmde Cut Given the Mean Average Boiling Point and API gravity, Characteristic Factor from 12.1 to 12.6.'"^
Order ofthe Polynomial Expression 5
Polynomial Constant Name Polynomial Constant Value
^ 2
-85.12057662010193
1.381740973796696
-0.003432246121519711
4.653977835999967e-006
-2.013575892828579e-009
2.29628573710579e-0I4
' The range of MeABP is from 200°F to 900°F.
^The polynomial expression is regressed from the curves in the figure on page 22 of
Maxwell (1950).
Table A. 12 Constants of Polynomial Expression for calculating the molecular weight of a Cmde Cut Given the Mean Average Boiling Point and API gravity. Characteristic Factor from 11.7 to 12.0.''^
Order ofthe Polynomial Expression
Polynomial Constant Name Polynomial Constant Value
-64.81157898902893
1.187273759860545
-0.002771734283669503
3.45I7646I6357878e-006
-9.924137883005058e-0I0
-2.998295810238759e-0I3
' The range of MeABP is from 200°F to 900°F.
^The polynomial expression is regressed from the curves in the figure on page 22 of Maxwell (1950).
213
Table A. 13 Constants of Polynomial Expression for Calculating the Molecular Weight of a Cmde Cut Given the Mean Average Boiling Point and API gravity. Characteristic Factor from 11.3 to 11.6. 1,2
Order of the Polynomial Expression
Polynomial Constant Name Polynomial Constant Value
^2
C-,
-52.26850727200508
1.051935468334705
-0.002316809990588808
2.74I261329930467e-006
-5.501099888116645e-010
-3.779033623234492e-013
' The range of MeABP is from 200°F to 900°F.
^The polynomial expression is regressed from the curves in the figure on page 22 of Maxwell (1950).
Table A. 14 Constants of Polynomial Expression for Calculating the Enthalpy ofa Cmde Cut from Its API Gravity, Temperature and Phase. 1,2
Phase Liquid Liquid
API
Order ofthe Polynomial
Expression
Polynomial Constant Name
0
5
Polynomial Constant Value
58.86276904234546
0.350323675930667
3.154147293926712E-4
-2.422868286555691E-7
2.670881203784828E-10
-9.569643092979734E-14
10
5
Polynomial Constant Value
63.49363322873251
0.374694305181038
2.455940050722916E-4
1.791162849984485E-8
-6.447463588954254E-I2
-2.499495895424942E-15
214
Table A. 14 Continued.
Phase Liquid Liquid
API
Order ofthe Polynomial
Expression
Polynomial Constant Name
20
5
Polynomial Constant Value
67.71119381915196
0.407442995888232
1.365238904540433E-4
3.40132834300566IE-7
-3.63881982759582E-10
1.372545297683309E-13
Phase
API
Order ofthe Polynomial
Expression
Polynomial Constant Name
^0
^1
C2
Ci
C^
n
Liquid
40
5
Polynomial Constant Vali
73.53287428134354
0.487630852423536
-4.193622I423I0005E-4
2.815101644570994E-6
-4.582843798569582E-9
2.585350426563243E-12
30
5
Polynomial Constant Value
70.58775514527224
0.445968287169308
-6.67553647062391lE-5
1.094693139080949E-6
-1.397221644590038E-9
6.189472668211555E-13
Liquid
50
6
7.852145754557569E+I
0.2332I2633516814E
3.992606531028287E-3
-2.54298013011578E-5
8.03349052350I33E-8
-1.174861450157626E-10
6.44705865814373E-14
215
Table A. 14 Continued.
Phase Liquid Liquid
API
Order ofthe Polynomial
Expression
Polynomial Constant Name
60
6
70
3
Polynomial Constant Value Polynomial Constant Value
81.30727554178702 79.85688170857611
0.361148813299224
1.71I36428I39107E-3
-9.349315025275473E-6
3.02I385889742656E-8
-4.69397245I398978E-11
2.835893371433551E-14
0.460461386308936
2.079979360163353E-4
4.238046095624004E-7
Phase
API
Order ofthe Polynomial
Expression
Polynomial Constant Name
Co
C^
Ci
Ci
^4
^5
Ce
r
Liquid
80
7
Polynomial Constant Valu
84.04810924726189
0.290888623800129
8.617715997388586E-3
-I.55I777304769075E-4
1.423607972839136E-6
-6.656375318991881E-9
1.532741903504612E-11
-1.376303736593522E-I4
Vapor
10
5
185.5089059815509
0.3I06I1488926043
2.78I889429286366D-4
3.2I0699037192732D-8
-8.566150055727608D-1I
3.49229007131788D-14
216
Table A. 14 Continued.
Phase
API
Order ofthe
Expression
Polynomial
Polynomial Constant Name
Co
c,
C2
Ci
c,
^5
Ce
Phase
API
Order ofthe Polynomial
Vapor
20
5
Polynomial Constant Value
206.0313633410842
0.263410338567155
5.549406963822889E-4
-6.I89165425302123E-7
6.05569I909972712E-10
-2.3688445303691 lE-13
-
Vapor
40
5
Vapor
30
6
Polynomial Constant Value
219.8464761304203
0.343124140861619
-1.409107793506337E-4
I.841500715915601E-6
-3.701660586125782E-9
3.545420054274547E-I2
-I.354470866I46257E-I5
Vapor
50
5
Expression
Polynomial Constant Name Polynomial Constant Value Polynomial Constant Value
^5
Ce
C-,
233.9651934063586
0.292765165784658
3.028I93795628908E-4
1.581314566512226E-7
-3.90264I490I37124E-10
2.14459I979966532E-13
244.699721654586
0.301180828185352
2.330962857755026E-4
3.511663374533569E-7
-6.03663435I300283E-I0
2.9780138812054I6E-13
217
Table A. 14 Continued.
Phase Vapor Vapor
API
Order of the Expression
Polynomial Constant Name
60
5
Polynomial Constant Value
251.8660791841976
0.306490229043447
2.448736920257488E-4
1.660I99941688756E-7
-2.37550436I9027IE-I0
9.481283506091226E-14
70
5
Polynomial Constant Value
261.3058801981097
0.302175899050781
2.705655244454874E-4
1.169952889013004E-7
-2.028500223696939E-10
8.5602828I6441859E-14
Phase
API
Order ofthe Expression Polynomial Constant Name
Vapor
80
5
Polynomial Constant Value
275.80426423467
0.299026886717911
3.42965375921267E-4
-I.56378III4599649E-7
I.8I222870965312E-I0
-9.784376469162197E-14
Vapor
90
5
Polynomial Constant Value
299.5719446004368
0.30523I3I3810509
2.9157460I0856424E-4
2.7813798764I5357E-8
-8.450989602939215E-11
3.1I26I7977833836E-14
'The polynomial expressions correlate the data in the temperature range of 0°F to I200°F. The basis is liquid at -200°F. The unit of enthalpy is Btu/lb.
^The polynomial expression is regressed from the curves in Graph 1 in Appendix 2 of Watkins (1979).
218
Table A. 15 Constants of Polynomial Expression for Calculating the Enthalpy ofa Cmde Cut in Saturated Vapor Phase from Its API Gravity and Temperature. '•
Phase Vapor Vapor
API
Order of the Expression
Polynomial Constant Name
40
6
Polynomial Constant Value
233.4883816781221
50
6
Polynomial Constant Value
243.9527180031873
c,
C2
Ci
CA
^5
Ce
Phase
API
Order ofthe Expression
Polynomial
r.
Constant Name
0.3285I8587I42753
-I.808624958812288E-4
2.632468991237147E-6
-6.258886072613157E-9
6.897744067547659E-12
-3.070086345068816E-15
Liquid
60
6
Polynomial ConstEuit Value
251.8582088107942
0.403622100740904
-1.852045328405438E-3
1.56524139338643E-5
-5.057826952398203E-8
7.465319567696318E-11
-4.157782127940892E-I4
Liquid
70
7
Polynomial Constant Value
262.0953662637621
0.338170381568489
-9.376740388233884E-4
1.340003429817216E-5
-6.I00737995340388E-8
1.2145I1902912453E-10
-8.96174754299945IE-14
0.217428358620964
2.309650226379745E-3
-2.73092370264294E-5
2.319865486377637E-7
-1.094177708507726E-9
2.487165117448216E-12
-2.138475785738538E-15
219
Table A. 15 Continued.
Phase Vapor Vapor
"API 80 '- ~
Order of the Expression 7
Polynomial Constant Name Polynomial Constant Value Polynomial Constant Value
Co 276.035738839535
c, 0.279704957385547
c, 2.320989198778989E-3
C3 -6.030673040413603E-5
c 7.08380111014I508E-7
c, -4.129376077188773E-9 ^ 5
c 1.I56369803785671E-11
c -I.246418830627948E-14
'The polynomial expressions correlate the data in the temperature range of 0°F to 1200°F. The basis is liquid at -200°F. The unh of enthalpy is Btu/lb.
^The polynomial expression is regressed from the curves in Graph 1 in Appendix 2 of Watkins (1979).
220
Table A. 16 Constants of Polynomial Expression for Calculating the Steam-free Delta T Given the Value ofthe Percent Stripout of Cmde Cuts. ''^
Constant Light Naphtha, Heavy Naphtha
Light Distillate Heavy Distillate, AGO, VGO
Order
0.04486391365208
1.876074961444829
0.070454820517625
6.651550673268503E-3
3.945026107317062E-4
1.150802438765197E-5
1.367411382319217E-7
0.047805972615606
1.472885452181799
0.047401526691829
-5.117099429298833E-3
3.396233130956716E-4
-1.063810533707965E-5
1.270384760859722E-7
-0.05627695162184
1.388574574821178
-0.0155528174116392
2.29546409696013 lE-3
-9.220926313879829E-5
1.397773963740523E-6
The polynomial expressions correlate the data in the percent stripout range of 10% to 30%. The unit of steam-free Delta T is °F.
^The polynomial expression is regressed from the curves in Figure 2.20 of Watkins (1979).
221
Table A. 17 Constants of Polynomial Expression for Calculating Stripout from Steam Rate 1,2
Constant Light Naphtha, Heavy Naphtha
Light Distillate
c^ 26.14695580938133 ''0 c^ -142.7715174853802
c^ 983.3651920557022 ^ 2
^3 -4.343146883010864E3 c, 1.035283389568329E4 ''4
c^ -1.197023190975189E4
c 5.10I304268360138E3
Constant Heavy Distillate, AGO, VGO
c^ 42.21995899023023 ^0 c, -182.3823599107564 ' ' I
c^ 914.8338382840157
c^ -3.330140159606934E3
c 7.333609569549561E3 c -8.285482303619385E3 ''5 r 3.52004I457176209E3
32.79539154772647
-140.9899756833911
673.3640444278717
-2.076813027858734E3
3.610560988426209E3
-3.2039297351837I6E3
1.1I5012320518494E3
Reduced Cmde
36.73691331990995
-276.1504411064088
1.645269501239061E3
-6.296468217134476E3
1.382960I08709335E4
-1.533274863910675E4
6.3985597968I0I5E3
' The range of steam rate that the correlations apply is from 0 to 60 pounds of steam/barrel of stripped liquid. The unit of stripout is vol.%.
^The polynomial expression is regressed from the curves in Figure 2.13 Watkins (1979).
222
Table A. 18 Constants of Polynomial Expression for Calculating the Steam-free DT Minus Actual DT(F) from the Temperature (F) Difference between Feed and Stripping Steam and Percent Stripout. ''
Percent 10% Stripout
20% 30%
Order 0.033722314836866
-1.065916488052709E-3
1.403707231880618E-6
-3.384139080733591E-8
-6.43010240533469E-12
1.4733666746051lE-13
4.334365325077462E-3
-7.77647455743441E-3
-7.249742002063996E-7
-2.896324521711872E-8
5.159958720330243E-12
-1.587679606245378E-15
0.025291736127647
-0.056599241882988
-3.732436294355723E-6
-9.642573628642823E-8
2.897515281416171E-11
-9.097404143844305E-14
'The polynomial expressions correlate the data in the range of-400°F to 400°F for the difference between feed temperature and stripping steam temperature. The unit of Steam-free DT is °F.
The polynomial expression is regressed from the curves in Figure 2.21 of Watkins (1979).
Table A. 19 Constants of Polynomial Expression for Calculating the Enthalpy of Steam from Temperature. 1,2
Order ofthe Polynomial Expression
Polynomial Constant Name Polynomial Constant Value
4.070E+0
-0.616E-3
I.281E-6
-0.508E-9
0.0769E-I2
'The polynomial expressions correlate the data in the temperature range of 540°R to 1800°R. The unit of enthalpy is Btu/lb.
^The polynomial expression constants are from Table A-21E of Moran and Shapiro (1996).
223
Table A.20 Constants of Polynomial Expression for Calculating the Enthalpy of Air from Temperature. 1,2
Order of the Polynomial Expression
Polynomial Constant Name Polynomial Constant Value
-1.705163359642029
0.248415488749743
-I.90835I259771735D-5
1.393I9320524578D-8
5.89I37245932I254D-13
-1.166776I32260656D-15
'The polynomial expressions correlate the data in the temperature range of 500°R to 1320°R. The unit of enthalpy is Btu/lb.
^The polynomial expression is regressed from the data in Table A-22E of Moran and Shapiro (1996).
Table A.21 Constants of Polynomial Expression for Calculating the Enthalpy of Water from Temperature. 1,2
Order ofthe Polynomial Expression
Polynomial Constant Name Polynomial Constant Value
-31.808
0.9984
'The polynomial expressions correlate the data in the temperature of I IO°F to 150°F. The unit of enthalpy is Btu/lb.
^The polynomial expression is regressed from the data in Table A-2E of Moran and Shapiro (1996).
224
Table A.22 Constants of Polynomial Expression for Calculating the Slope of Flash Reference Line from Corresponding Distillation Reference Line.'
Order ofthe Polynomial Expression 5
Polynomial Constant Name Polynomial Constant Value
C2
Ci
c,
1.281878608097031 E-3
0.143415450581415
0.085872677521365
-2.583246I7I070641E-3
-2.19426I556383026E-4
1.534796214297529E-5
'The polynomial expression is regressed from the curve in the top fiugre on page 228 of Moran and Shapiro (1996).
Table A.23 Constants of Polynomial Expression for Calculating the Ratio of DT(flash)/DT(TBP) from Volumetric Percent Distillated. 1,2
Order ofthe Polynomial Expression
Polynomial Constant Name Polynomial Constant Value
0.206045789459949
0.098090319869243
-0.021268318498414
2.577717061399198E-3
-1.856466131933132E-4
7.70299445962408E-6
-I.685715965774048E-7
1.50049388647662E-9
'The volumetric percent distillated is in the range of 0% to 100%. The unit of temperature is°F.
^The polynomial expression is regressed from the curve in the top figure on page 229 of Maxwell (1950).
225
Table A.24 Constants of Polynomial Expression for Calculating the Temperature Difference between the Distillation and Flash Reference Curves from the Slope of Distillation Reference Curve.''^
Case T50 (Distillation Reference T50 (Distillation Reference Curve) < 300 °F Curve) > 300 °F
Order ofthe Polynomial Expression
10 10
Polynomial Constant Name Polynomial Constant Value Polynomial Constant Value
Co
'10
0.046535948757082
-4.02163065969944
12.65744310617447
-12.82720935344696
5.004324972629547
-0.43010459802977
-0.194861501455307
0.060458100866526
-7.29041948216036E-3
4.I90444597043097E-4
-9.5I13I7557553412E-6
7.510650799842551
5.426912218332291
-19.98555105924606
26.42150974273682
-18.22189289331436
7.298733577132225
-1.746701300144196
0.253036742098629
-0.021768757607788
1.024311339278938E-3
-2.0320097I7822803E-5
'The slope is in the range of 1 to 12. The unit of temperature is °F.
^The polynomial expression is regressed from the curve in the middle figure on page 228 of Maxwell (1950).
226
Table A.25 Constants of Polynomial Expression for Calculating the T50 of Flash Curve under Vacuum from the Pressure and T50 ofthe Flash Curve under Atmospheric Pressure. 1,2
Pressure 5 mmHg lOmmHg
Order ofthe Expression
Polynomial Constant Ntime
Pressure
Order ofthe Expression
Polynomial Constant 'Name
Polynomial Constant Value
-1422.40771484375
47.98374176025391
-0.601449653506279
0.00387019373010844
-1.40265651680238Ie-005
2.897231166087977e-008
-3.1809I3909009431e-01I
1.440629782690204e-0I4
25 mmHg
7
Polynomial Constant Value
-39.01131057739258
-1.205847382545471
0.03788168542087078
-0.0003145772279822268
1.342447035312944e-006
-3.07732755944201 le-009
3.6129467202283I3e-012
-1.705918532418649e-015
Polynomial Constant Value
993.2503967285156
-26.02331018447876
0.2790574356913567
-0.001563787373015657
5.065428638317826e-006
-9.480977780640387e-009
9.524453057838112e-0I2
-3.975758540364488e-015
50 mmHg
5
Polynomial Constant Value
-147.5445291846991
2.970037258812226
-0.0I730I7894876466
6.152585774188424e-005
-9.930441327229977e-008
6.058668425965486e-011
227
Table A.25 Continued
Pressure 100 mmHg 200 mmHg
Order ofthe Expression
Polynomial Constant Name
"-1
c,
c.
Pressure
Order ofthe Expression
Polynomial Constant Name
Polynomial Constant Value
-39.28636805340648
1.059575435821898
-0.002797604639908968
l.I7I939102029285e-005
-I.94886755414I254e-008
209850868949959e-0Il
300 mmHg
5
Polynomial Constant Value
-46.21546807419509
1.752744925528532
-0.00828888484647905
3.486289179832625e-005
-6.448200663179693e-008
4.374364872I323I4e-0Il
Polynomial Constant Value
-38.44197991769761
1.383079235354671
-0.005222666145073163
2.I29300323439054e-005
-3.68I46I7920I2786e-008
2.341769013081753e-011
400 m m H g
5
Polynomial Constant Value
-29.64459969289601
1.454257622928708
-0.005063669466494503
2.092687345367494e-005
-3.797053613197043e-008
2.536417829122271e-01I
228
Table A.25 Continued
Pressure 500 mmHg 600 mmHg
Order of the Expression 5 5
Polynomial Constant Name Polynomial Constant Value Polynomial Constant Value
"1
C-,
Ci
c,
-15.75690979277715
1.200692083148169
-0.002377156030320293
-19.58084477111697
1.597486192593351
-0.008274154270111467
9.557379709423941e-006 4.942406480523687e-005
-1.668142990407517e-008
1.0758158373411Ie-011
-I.46668I26477234e-007
2.125436487237804e-010
-1.201000766633333e-0I3
' The range of T50 that the correlations apply is from 50 to 500°C.
^The polynomial expression is regressed from the curves in Figure II-1-24 in Lin (1988).
229