MODELING OF VACUUM GAS OIL HYDROCRACKING...

MODELING OF VACUUM GAS OIL HYDROCRACKING PROCESS

USING LUMPING APPROACH

SEPEHR SADIGHI

A thesis submitted in fulfilment of the

requirements for the award of the degree of

Doctor of Philosophy (Chemical Engineering)

Faculty of Chemical Engineering

Universiti Teknologi Malaysia

SEPTEMBER 2012

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Dedicated to:

My father and mother For their love and concerns.

To my supervisor,

Professor Dr. Arshad Ahmad, Thank you for your kindness and your helpful guidelines.

For all my kind-hearted friends, Thank you for being so much kind and helpful,

Allah will bless all of u.


iv

ACKNOWLEDGEMENTS

First and foremost, in a humble way I would like to give all the Praise to

Allah, the Almighty God for with his mercy has given me the strengths and time to

complete my research.

I am deeply indebted to my supervisor, Prof. Dr. Arshad Ahmad for his great

guidance and endless supports throughout completing this project. His guidance,

helps and patience are actually encouraging me more in finishing and pursuing

further in this field.

Thanks also to Dr. Akbar Irandoukht from Research Institute of Petroleum

Industry (RIPI) and Dr. Mansoor Shirvani from Iran University of Science and

Technology (IUST) in helping and consulting me to conduct my PhD project.

Additionally, I acknowledge Dr. Mehdi Rashidzadeh who is the head of Catalysis

Research Center of Research Institute of Petroleum Industry (RIPI) because of his

supports. Not forgotten, I also acknowledge Mr. Reza Mohaddecy and Mr. Kamal

Masoudian for helping me to gather the required data to do this project.


v

ABSTRACT

Conversion of heavy feedstock into lighter products in a hydrocracking

process has a strong influence on the profitability of petroleum refining. To facilitate

initiatives toward improving the process efficiency, a good process model is

required. This thesis discusses the development and application of lump modeling

strategies in a pilot plant and industrial scale hydrocracking processes. For the pilot

plant, two different four-lump models are considered, i.e., combined bed and dual

bed models. Unlike the simpler combined bed model, the dual bed model includes

hydrogen consumption and hydrotreating reactions, and the reactor is subdivided into

two different layers so that the effect of hydrotreating reactions can be integrated. To

extend the application to a commercial refinery, a full-lump model is configured to

predict the product yields of a commercial hydrocracking unit known as Isomax.

Using bed temperatures, flow rate of fresh vacuum gas oil (VGO), recycle rate and

catalyst life as process variables, the model is proven capable of predicting the yield

of all products. The model also provides improvement to previous works by

considering liquefied petroleum gas (LPG), light gases, fresh VGO and recycle feed

as separate lumps. This model is then used in optimizing the reactor operation where

the bed temperature, flow rate of fresh VGO and combined feed ratio are adjusted to

increase the plant profitability whilst maintaining all process limitations and

operating constraints. To extend the model for dynamic applications, a modified

space-time conservation element and solution element method (CE-SE) is

introduced. The results obtained from simulation of the pilot plant based on four-

lump model using CE-SE method are comparable to those obtained using finite

difference method, thus providing opportunities for further works involving dynamic

behavior of the process. Although the study is focussed on hydrocracking, this thesis

has proved the practicality of using lumped modeling technique to address model

development requirement for complex industrial processes.


vi

ABSTRAK

Penukaran bahan mentah berat kepada produk yang lebih ringan dalam proses

pemecahan-hidro mempunyai pengaruh yang kuat ke atas keuntungan proses

penapisan petroleum. Untuk memudahkan inisiatif ke arah meningkatkan kecekapan

proses, satu model yang baik diperlukan. Tesis ini membincangkan pembangunan

dan aplikasi strategi permodelan tergumpal dalam proses pemecahan-hidro berskala

loji pandu dan industri. Untuk proses berskala loji pandu, dua model empat-gumpal

dipertimbangkan, iaitu model lapisan-tergabung dan model dwi-lapisan. Berbeza

dengan model lapisan-tergabung yang lebih mudah, model dwi-lapisan mengambil-

kira penggunaan hidrogen dan tindakbalas hidrorawat, dan reaktor dibahagikan

kepada dua bahagian yang berbeza supaya kesan daripada tindakbalas hidrorawat

boleh disepadukan. Bagi membolehkan penggunaan untuk penapisan komersil,

model gumpal-penuh telah dibentuk untuk meramalkan hasil produk unit

pemecahan-hidro yang dipanggil Isomax. Dengan menggunakan suhu lapisan, kadar

aliran minyak gas vakum (VGO) segar, kadar kitar semula dan hayat pemangkin

sebagai input kepada model, peramalan yang baik untuk semua produk telah

diperolehi. Model ini juga menyediakan penambahbaikan kepada kerja-kerja

sebelumnya dengan mempertimbangkan VGO, LPG, gas ringan, suapan segar dan

kitar semula sebagai gumpalan berasingan. Seterusnya, ia digunakan dalam

mengoptimumkan operasi reaktor di mana suhu lapisan, kadar alir VGO segar dan

nisbah suapan tergabung diselaraskan untuk meningkatkan keuntungan loji dengan

mengambilkira semua batasan proses dan kekangan operasi. Untuk menambah

penggunaan model gumpalan kepada aplikasi dinamik, teknik permodelan ruang-

masa elemen keabadian elemen penyelesaian (CE-SE) diperkenalkan. Keputusan

yang diperolehi dari simulasi loji pandu berasaskan empat-gumpal adalah setanding

dengan apa yang diperolehi dengan menggunakan kaedah beza terhingga, sekaligus

membuka peluang kepada kajian lanjut yang melibatkan tingkah laku dinamik.

Walaupun kajian ini memfokus kepada proses pemecahan-hidro, tesis ini telah

membuktikan kesesuaian penggunaan teknik permodelan tergumpal bagi memenuhi

keperluan permodelan bagi proses-proses yang kompleks di industri.


vii

TABLE OF CONTENTS

CHAPTER TITLE PAGE

DECLARATION

DEDICATION

ACKNOWLEDGEMENTS

ABSTRACT

ABSTRAK

TABLE OF CONTENTS

LIST OF TABLES

LIST OF FIGURES

LIST OF ABBREVIATIONS

LIST OF SYMBOLS

LIST OF APPENDICES

ii

iii

iv

v

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xi

xiii

xvi

xviii

xxii

1 INTRODUCTION

1.1 Background of Research

1.2 Problem Statement

1.3 Research Objectives

1.4 Scope of Study

1.5 Significant of the Study

1.6 Layout of the Thesis

1

1

3

4

4

7

8


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2 LITERATURE REVIEW

2.1 Introduction

2.2 Hydrocracking process

2.2.1 Process Description

2.2.2 Feedstock and Products

2.2.3 Hydrocracking Reactor

2.2.4 Hydrocracking Catalysis

2.2.5 Process Variables

2.2.6 Chemical Reactions

2.3 Steady State Modeling Methodology

2.3.1 Continuous Lumping Models

2.3.2 Discrete Lumping Models

2.4 Dynamic Modeling

2.4.1 Application of the Dynamic Model

2.5 Process Optimization

2.6 Summary

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10

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31

33

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3 RESEARCH METHODOLOGY

3.1 Introduction

3.2 Data Gathering from Pilot Scale Plant

3.2.1 Pilot Scale Hydrocracking Plant

3.2.2 Pilot Scale Catalyst

3.2.3 Feed and Product Characterization

3.2.4 Test Conditions

3.3 Data Gathering from Industrial Scale Unit

3.4 Modeling Methodology

3.4.1 Steady State Model

3.3.2 Dynamic Model

3.5 Simulation Studies

3.5.1 Software Used

3.5.2 Simulations

3.5.3 Determination of Model Accuracy

3.6 Summary

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4 MODELING A PILOT SCALE HYDROCRACKING

REACTOR

4.1 Introduction

4.2 Experimental Results

4.3 Modeling a Pilot Scale Hydrocracking Reactor

4.3.1 Reaction Models

4.3.2 Combined Bed Model

4.3.3 Dual Bed Model

4.4 Model Development

4.5 Results and Discussions

4.6 Summary

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66

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71

74

84

5

MODELING AND OPTIMIZATION OF THE

I NDUSTRIAL VGO HYDROCRACKING PROCESS

5.1 Introduction

5.2 Industrial Data

5.3 Modeling of VGO Hydrocracking Process

5.3.1 Reactor Model

5.3.2 Separation Section

5.3.3 Parameter Estimation, Simulation and

Evaluation

5.3.4 Results and Discussion

5.4 Process Optimization

5.4.1 Profit Function

5.4.2 Developing the Optimizer


5.5.1 Profit Achievements

5.5.2 Optimal Decision Variables

5.6 Summary

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108

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115

120


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6

7

DYNAMIC MODELING OF THE PILOT SCALE

REACTOR

6.1 Introduction

6.2 Developing Dynamic model

6.2.1 Dynamic Mass Conservation

6.2.2 Numerical Methods

6.2.2.1 Finite Difference Method (FDM)

6.2.2.2 The Space-time Conservation Element

and Solution Element Method (CE-SE)


6.3.1 Steady State Analysis

6.3.2 Stability Analysis

6.3.3 Dynamic Simulation

6.4 Summary

CONCLUSIONS AND RECOMMENDATIONS

7.1 Summary

7.2 Conclusions

7.3 Recommendations

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REFERENCES

146

Appendices A-E 154-174


xi

LIST OF TABLES

TABLE NO. TITLE PAGE

2.1

3.1

3.2

3.3

3.4

3.5

4.1

4.2

4.3

4.4

4.5

4.6

4.7

4.8

4.9

4.10

Typical feedstocks of hydrocrackers

Characteristics of HDT&HDC catalyst

Properties of fresh VGO and recycle feed

Average properties of hydrocracking product

Variation in properties of fresh vacuum gas oil in

industrial Case

Catalyst Specifications of hydrocracking process in

industrial case

Yield percent of hydrocracking process basis on the fresh

feed


Major reactions in the hydrotreating of VGO

Coefficient values for the hydrogen consumption

Kinetic parameters for complete network of combined

bed model

Kinetic parameters for reduced network of combined bed

model

Kinetic parameters for the complete network of the dual

bed model

Kinetic parameters for the reduced network of the dual

bed model

The AAD% for the different lumping models

ANOVA results of the developed model

15

43

44

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47

48

58

63

70

71

75

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xii

5.1

5.2

5.3

5.4

5.5

5.6

5.7

5.8

5.9

6.1

Bed temperature in reactors vs. operating time

Feed and recycle flowrates of Isomax plant vs. operating

time


The yield of the hydrocracking products for Isomax case

studies

Kinetic parameters for the complete model

Kinetic and deactivation parameters for the reduced

model

Electrical and heating equipment of Isomax unit

Price of Energy, feed and products in the target refinery

Profit and energy expenses of Isomax unit

Comparison between the AAD% of CE-SE & FDM for

the case studies

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99

100

104

108

109

136


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LIST OF FIGURES

FIGURE NO. TITLE PAGE

1.1

2.1

2.2

2.3

2.4

2.5

2.6

2.7

3.1

3.2

3.3

3.4

3.5

3.6

3.7

3.8

3.9

4.1

4.2

4.3

4.4

Flowchart of the research design

Schematic flow plan for single-stage hydrocracking plant

Schematic Flow Plan for Two Stage Hydrocracking Unit

Trickle-bed flow

Schematic diagram of a commercial hydrocracking

reactor

Steps of modeling hydrocracking process

Description of a feed stock as discrete components

Five-lump discrete-lump model proposed by Sanchez

Flowchart of the research methodology of Chapter 4



Simplified process flow diagram of hydrocracking set up

Sections of catalytic reactor bed

Block flow diagram of the hydrocracking process

A simplified scheme of the hydrocracking process in the

commercial case

Schematic representation of series mixed cells

Control volume of the catalyst inside the catalytic bed

Gas yield vs. Temperature.

Naphtha yield vs. Temperature

Distillate yield vs. Temperature

Unconverted oil yield vs. Temperature

6

13

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18

22

24

27

37

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61


xiv

4.5

4.6

4.7

4.8

4.9

4.10

4.11

4.12

4.13

5.1a

5.1b

5.2

5.3a

5.3b

5.4

5.5a

5.5b

5.5c

5.6

5.7

Aromatic content vs. Temperature

The complete four-lump kinetic model

Schematic representations of the developed lumping

models (A) Combined bed model (B) Dual bed model

The reduced 4-lump kinetic network for the combined bed

model

The reduced 4-lump kinetic network for the dual bed

model

Predicted yields, measured yields and deviation plots for

the prediction of gas lump


the prediction of naphtha lump


the prediction of distillate lump


the prediction of VGO lump

(a) The complete eight-lump kinetic model

(b) The reduced eight-lump kinetic model

The scheme of the process flow diagram of Isomax

optimizer

Parity plot for the predicted gas, LPG, LN and HN yields

Parity plot for the predicted Ker., Dis. and offtest yields

Comparison of the optimized and the actual yields of

Isomax plant

Comparison of the optimized and the actual yields of gas

in Isomax plant


LPG, LN and HN in Isomax plant


Kersosene, Diesel and Offtest in Isomax plant

Comparison of the optimized and the actual WABTS

Comparison of the optimized and the actual CFR of

Isomax plant

61

63

67

76

77

80

81

82

83

89

89

90

100

101

110

111

112

113

114

115


xv

5.8a

5.8b

5.8c

6.1

6.2

6.3

6.4

6.5

6.6

6.7

6.8

6.9

6.10

Comparison of the optimized and the actual bed

temperatures of the first Isomax reactor


temperatures of the second Isomax reactor


temperatures of the third Isomax reactor

The space-time mesh for the domain time versus the

catalyst volume

Solving VGO hydrocracking dynamic model using CE-

SE

The response of the dynamic model solved by CE-SE

from (LHSV=0.8 h-1, T=360ºC) to (LHSV=1.05 h-1,

T=390ºC), Co<1


from (LHSV=0.8 h-1, T=3600C) to (LHSV=1.05 h-1,

T=390ºC), Co~1.047


from (LHSV=0.8 h-1, T=3600C) to (LHSV=1.05 h-1,

T=390ºC), Co ~1.057

The response of the dynamic model solved by CE-SE &

FDM from (LHSV=0.8 h-1, T=3600C) to (LHSV= 0.8

h-1, T=3900C), CO max=0.989


FDM from (LHSV=0.8 h-1, T=3600C) to (LHSV=1.05

h-1, T=3900C), CO max=0.979


FDM from (LHSV=1.05 h-1, T=3900C) to (LHSV=0.8

h-1, T=3600C), CO max=0.93

Convergence time and courant number of case studies

vs. time-step-size

Convergence time (■) and AAD% (▲) of case studies

vs. dissipation factor

117

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119

124

129

131

132

132

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134

135

137

138


xvi

LIST OF ABBREVIATIONS

AAD - Absolute Average Deviation, %

ABT - Average bed temperature

ACM - Aspen Custom Modeler

ANOVA - Analysis of variance

AGO - Atmospheric gas oil

BASF - Badische Anilin und Soda Fabrik

CE-SE - Conservation element-solution element

CFR - Combined feed ratio

Co - Courant number

DF - Degree of freedom

FBP - Final boiling point, °C

FCC - Fluid catalytic cracking

FD - Flash drum

FDM - Finite difference method

FEM - Finite element method

FVM - Finite volume method

Frac - Fractionation tower

GS - Stabilizer tower

HCGO - Heavy cycle gas oil

HDA - Hydrodearomatization

HDC - Hydrocracking

HDN - Hydrodenitrogenation

HDS - Hydrodesulfurization

HDT - Hydrotreating


xvii

HPS - High pressure separator

IBP - Initial boiling point, °C

LCO - Light cycle oil

LCGO - Light cycle gas oil

LHSV - Liquid Hourly Space Velocity, h-1

LPG - Liquefied petroleum gas

LPS - Low pressure separator

ODE - Ordinary differential equation

SE - Solution element

SPA - Sponge absorber

Sp.gr - Specific gravity

SQE - Sum of squared error

SRD - Straight Run Diesel

Stab Gas - Stabilizer gas

VGO - Vacuum Gas Oil

WABT - Weighted average bed temperature


xviii

LIST OF SYMBOLS

A - Space-time region A in the domain

B - Price of the unit of product, feed or electricity

C - Mass concentration, kg/m3

C' - Functional mass concentration, kg/m3

Cp - Heat capacity of lumps, kJ/kg.°C

Cpf - Heat capacity of VGO feed, kJ/kg.°C

Cph - Heat capacity of hydrogen, 14.21 kJ/kg.°C

C1 - Methane in light gas

C2 - Ethane in light gas

C3 - Hydrocarbons with 3 carbons in LPG



C6 - Hydrocarbons with 6 carbons in LN

D - Distillate or Diesel

E - Apparent activation energy, kcal/mol

Efht - Fuel consumption of the furnace, m3/h

Efhx - Fuel consumption of the reactor furnace, m3/h

EE-l - Fuel consumption of reboiler, m3/h

Ep-l - Electrical consumption of the pump, kW

ET-502 - Fuel consumption of the turbine 502 in Isomax plant, m3/h

ET-531 - Fuel consumption of the turbine 531 in Isomax plant, m3/h

Effht - Thermal efficiency of the furnace, 0.735

Effhx - Thermal efficiency of the reactor furnace, 0.685

Expen - Energy consumption of Isomax unit


xix

f - Concentration-flow function

F - Feed lump

Fm - Mass flow rate of stream through the reactor, kg/h

G - Gas

hr

- Current density vector in the domain

H2 - Hydrogen

H2S - Hydrogen sulfide

Hf - Heating value of the fuel gas, 5.327´104 kJ/kg

HN - Heavy Naphtha

i - Cell number

j - Discretized volume of the catalyst

k - Reaction rate constant, m3/(h.m3 cat)

kE-l - Energy coefficient of reboiler, kJ/m3

kh - Consumed mass of hydrogen per converted mass of VGO

kp-l - Energy coefficient of pump, kW.h/m3

kT-502 - Fuel coefficient of H2 turbine 502 in Isomax plant, m3/h

kT-531 - Fuel coefficient of feed turbine 531 in Isomax plant, m3/h

k0 - Frequency factor, m3/(h.m3 cat)

K - Kerosene

Kc - Proportional gain, °C/%)],

LN - Light Naphtha

meas - Measured yield 0m - Mass flow rate, kg/h

M - Element of catalyst in the commercial reactor

Mw - Molecular weight

Mws - Molecular weight of sulfur (32)

MwN - Molecular weight of nitrogen (14)

n - Time step-size

nr

- Outward unit normal of a surface element

N - Naphtha

NH3 - Ammonia

Nv - Number of cells (200)

Nt - Number of total experiments


xx

Opc - Operating charges of Isomax unit

p - Number of test runs 0

mP - Mass flow rate of product leaving fractionator, kg/h

Pred - Predicted yield

Profit - Profit of the Isomax plant

Q - Volume flow rate

QE-l - Fluid flow rate of the heavy naphtha reboiler, m3/h

Qp-l - Fluid flow rate of the product in Isomax , m3/h

r - Rate of VGO converted to the hydrocracking products, kg/h

R - Ideal gas constant, 1.987 kcal.kmol-1.K-1

Rl - Reaction rate of lump l, kg/(h.m3 cat)

R1-3 - Reactors of Isomax plant (three parallel reactors)

S - Surface region or Real plant selectivity

Ssp - Set point selectivity

S - Surface region

t - Time domain (h)

tc - Accumulated feed

T - Temperature, K or R

Teff - Temperature of the effluent exchanger, 363°C

TH - Output stream of the furnace, 120°C

u - Feed, distillate, naphtha and gas lumps

UN - Unconverted oil

V - An arbitrary but constant volume element of the domain

Vb - Total Volume of bed, m3

Vcat - Volume of catalyst per cell, m3

W - Weight of catalyst

wt% - Weight percent

x - Catalyst volume domain; Number of Isomax reactor

X - Mass fraction of lumps

Xc - Weight fraction of catalyst in Isomax bed.

Y - Yield of products

a - Consumed mass of hydrogen per mass of VGO (chapter 4) or Catalyst deactivation (chapter 5)

120 ,..bb - Coefficient values for the hydrogen consumption


xxi

s - Area unit normal of a surface element

D - Difference between actual and optimized values

tD - Time-step

vD - Volume step

e - Dissipation factor

'e - Catalyst void fraction

h - Effectiveness factor

n - Catalyst volume element

r - Density, kg/m3

j - Time-Space function in the integral (chapter 6) or Decay function (Chapter 5)

t - Stiff function

actual - Actual value of Isomax plant

Af - Aromatic in feed

Ap - Aromatic in product

b - Catalytic bed

cat - Catalyst

D - Diesel

'Dj - Distillate to lighter lumps

Dh - Diesel in the output stream of hydrotreating bed

e - Electricity

fh - VGO feed or residue in the output stream of hydrotreating bed

f - VGO feed


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LIST OF APPENDICES

APPENDIX. TITLE PAGE

A

B

C

D

E

Hydrogen Consumption for the Pilot Scale VGO

Hydrocracker

AVOVA Analysis

List of Publications in Journals with Impact Factor (ISI)

Abstract of Publications in Journals with Impact Factor

(ISI)

List of Cited Publications in Journals with the Impact

Factor (ISI)

154

157

161

163

173


CHAPTER 1

INTRODUCTION

1.1 Background of Research

The needs for processing heavy feedstock into high value products are

exacerbated by the increasing demand of light oil fractions and decreasing reserves

of light crude oils. This steady growth in fuel demands can only be fulfilled by the

inclusion of heavier feedstock into refinery operations (Hsu and Robinson, 2006),

which can be converted to lighter ones using thermal and/or catalytic processing in

the absence or presence of hydrogen pressure (Gary and Handwerk, 2001). This has

been responded by the introduction of several process licenses for industrial use.

Considering economic aspects, hydrocracking is seen as a suitable option as it

improves the quality and quantity of the refined petroleum products simultaneously

(Balasubramanian and Pushpavanam, 2008), especially if optimum design and

operations are realized. The versatility and flexibility of the process makes it

economically attractive to convert different types of feedstocks into various products

including gas, LPG, naphtha, kerosene and diesel, leading to its widespread

applications.

To enhance the efficiency of the hydrocracking process, the kinetics of the

reactions involved must be fully defined so that the process behavior can be

predicted. This can only be achieved if both the kinetic model and its corresponding

parameters are correctly estimated. Having accurate models, the reactor operations


2

can be configured so that desirable product yield distribution was obtained in the

most economical manner.

Ideally, the kinetic model should take into account all elementary reactions,

which include all the different components in the feedstock (Balasubramanian and

Pushpavanam, 2008). However, the complexity of hydrocracking feed makes it

extremely difficult to characterize and describe its kinetic at a molecular level

(Ancheyta et al., 1999). This has given rise to the use of simplified approach by

combining several similar components into groups called lumps. Using “black-box”

concept, the input-output relationships among the lumps are configured to form a

network of reactions that define the process. These models can then be extended for

variety of tasks to facilitate the hydrocracking process including (Valavarasu et al.,

2005):

1) Design tool

2) Automation and optimizing control

3) Finding the optimum operation

4) Catalyst deactivation

In hydrocracking research, two major lumping methods have been used, i.e.,

continuous and discrete lumping approaches. In the first method, the reactive mixture

is considered to form a continuum mixture with respect to its properties such as

boiling point, molecular weight, carbon number or chemical species (Basak et al.,

2004; Elizalde et al., 2009; Elizade and Ancheyta, 2011); but, in the discrete lumping

approach, the reaction network is reduced to the limited number of reactions among

the lumped components. The lumps, based on compound types present in feedstock

and products (e.g., lumps of diesel, kerosene, gasoline, etc.), are often defined by

boiling point ranges. This approach is attractive for kinetic modeling of complex

mixtures because of its simplicity (Ancheyta et al., 2005).


3

1.2 Problem Statement

Optimal operating conditions are required in an industrial process operation

to ensure profitability. This is nevertheless hampered by the fact that the plant is

subjected to various uncertainties such as variability in feedstock, changing

environmental condition and upsets in the process itself. To effectively handle these

difficulties, the process behavior must be understood and predictable using some

form of process models. Using these models product yields are estimated and

sensitivity analyses can be carried out so that the effect of operating parameters like

reactor temperature, pressure and space velocity on product yields can be sensed.

The model can also be extended for the use in optimization, control, design of new

plants and selection of appropriate hydrocracking catalysts.

A number of works have been presented in the literatures, but a full lump

model for an industrial hydrocracking plant that predicts and optimizes the yield of

all products has not been reported. Furthermore, if the steady state model can be

extended to a dynamic form, it can be used in dynamic optimization and control.

Additionally the prediction of the dynamic behavior of the reactor during start up or

shut down can be facilitated. It is essential that the chemical engineer have a better

understanding from the dynamic relationship among process and operating variables.

In this instance, little knowledge is a dangerous matter for the process operation

(Wozny and Jeromin, 1994). These serve as motivations for this work.


4

1.3 Research Objectives

The main objectives of this research are as follows:

i. Developing a kinetic based steady state model for a pilot scale vacuum gas oil

(VGO) hydrocracking unit by using the discrete-lumping approach.

ii. Developing a practical steady state model and simulator for a commercial VGO

hydrocracking unit.

iii. Applying discrete-lumping approach in the constrained optimization of a

commercial VGO hydrocracking plant.

iv. Developing a new dynamic model for a pilot scale VGO hydrocracking reactor

by applying the “conservation element-solution element” or “CE-SE” method.

1.4 Scope of Study

The scope of this work consists of the following actions:

I. Gathering the experimental data from a pilot scale VGO hydrocracking plant

II. Developing a steady state lumped kinetic-based model for the pilot scale

plant and comparing the effect of hydrogen consumption on the accuracy of

the yield prediction.

III. Gathering the industrial data from a commercial VGO hydrocracking plant.

IV. Developing a commercial full lump kinetic-based model including of catalyst

deactivation to simulate the yield of all precious products such as diesel and

gasoline.


5

V. Optimizing the operating parameters of an industrial VGO hydrocracking

plant to maximize the profit of the process.

VI. Developing a dynamic model for the pilot scale plant using the lumping

approach. To simulate and verify the dynamic behavior of the pilot plant.

Figure 1.1 illustrates the flow of this research for covering all the mentioned

scopes of study.


6

Figure 1.1 Flowchart of the research design

MODELING OF VACUUM GAS OIL HYDROCRACKING PROCESS BY USING LUMPING APPROACH

Literature Review

i) Studying hydrocracking and hydrotreating process ii) Studying modeling and optimizing approaches in literature

Data Gathering

i) Pilot scale experiments to develop an improved lumped model ii) Gathering actual data from refineries to develop practical lumped models

Pilot scale model

i) 4-lump combined-bed model ii) 4-lump dual-bed model

Commercial scale model

8-lump practical model (full lump model) including catalyst deactivation

Dynamic model

i) Developing 4-lump dynamic model

ii) Solving the dynamic model using conventional approach

iii) Solving the dynamic model using CE-SE approach

Process Optimization

i) Adding cost elements to the 8-lump practical model

ii) Profit maximization of the commercial plant by using the practical model


7

1.5 Significance of the study

The work proved the applicability of lump modeling approach in representing

industrial hydrocracking effectively. Two model configurations for the pilot scale

plant have been investigated and their applications in process analysis and

optimization have been conducted. A simple and practical optimization strategy has

been put forward which can serve as a useful guide in plant operation. The model has

also been extended to include dynamic capability using a powerful, but less common,

CE-SE method. This allows further investigations that require dynamic process

information such as process control, dynamic optimization, alarm analysis and other

control-safety related studies.

In short, this work has brought forward the following contributions:

i. Introducing discrete lumping models for a pilot scale hydrocracking plant.

The main advantage of that over those previously reported in the literatures is

consideration of the hydrogen consumption in kinetic equations and overall

mass balance. This model can predict the yield of hydrocracking products and

hydrogen consumption reasonably well.

ii. Introducing an applicable approach to simulate the product yields of

commercial hydrocracking units during their cyclic operation.

iii. Investigating selective modeling of the catalyst life by using the commercial

data.

iv. Introducing a simple and applicable approach to access the maximum profit

in a commercial hydrocracking unit and/or the similar refinery processes.

v. Incorporating a discrete-lumped kinetic approach to simulate the dynamic

behavior of hydrocracking plants with accurate and robust results.


8

vi. Developing CE-SE solving approach to be used for simulating the dynamic

behavior of a VGO hydrocracking reactor. Its accuracy is studied against the

conventional solving method.

1.6 Layout of the Thesis

This thesis is divided into 7 chapters. Following this introductory chapter,

literature review is presented in chapter 2. Then in chapter 3, the research

methodology and the required data to develop pilot and industrial scale lumping

models have been presented.

In chapter 4, after discussing on the results of the pilot scale experiments, it is

explained how by using lumping approach, 4-lump models are developed for the

pilot scale plant. Two different steady state strategies (combined-bed and dual-bed)

have been developed and compared together in this chapter.

Chapter 5 is allocated to develop an applicable discrete lumping model for

being used to predict the product yields of an industrial plant. At first the gathered

data from the understudy commercial plant is presented. Then, a full lump industrial

model is developed, and finally it is used to optimize the profit of the commercial

hydrocracking plant by manipulating the significant process variables.

Then in chapter 6, due to the importance of the dynamic model to control the

performance of hydrocracking process, the steady state results of chapter 4 are

applied to develop a dynamic lumped model. It is shown that this model is capable of

simulating the response of the pilot scale model to the process disturbances. To solve

the developed dynamic model, modern of art conservation element-solution element

(CE-SE) method is used and its accuracy is compared against a conventional solving

approach.


146

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