integrating hydroprocessors in refinery hydrogen network

1

INTEGRATING HYDROPROCESSORS IN REFINERY

HYDROGEN NETWORK OPTIMIZATION

A thesis submitted to The University of Manchester for the degree of

Doctor of Philosophy (PhD by Published Work)

in the Faculty of Engineering and Physical Sciences

2015

BLESSING UMANA

Centre for Process Integration

School of Chemical Engineering and Analytical Science

2

Contents

List of Figures................................................................................................................... 4

Abstract ....................................................................................................................... 5

Declaration ....................................................................................................................... 6

Copyright Statement ....................................................................................................... 8

Dedication ..................................................................................................................... 10

Acknowledgements ...................................................................................................... 11

The Author ..................................................................................................................... 12

Rationale for Submitting the Thesis in an Alternative Format ........................... 14

List of Published Research Papers ............................................................................. 16

List of Submitted Research Papers ............................................................................ 16

Context of the Research................................................................................................ 17

Chapter 1 Introduction .............................................................................................. 18

1.1 Research Background and Motivation .................................................. 18

1.2 The Aim of the Present Work ................................................................. 24

1.3 Research Strategies ................................................................................... 25

1.3.1 Process Model Development, Regression and Validation of

Hydrogen Consumer Models ............................................................................... 27

1.3.2 Overall network modelling..................................................................... 28

1.3.3 Overall network optimisation ................................................................ 29

1.4 Contribution of Research ........................................................................ 29

Chapter 2 Integrating Hydroprocessors in Refinery Hydrogen Network

Optimization ............................................................................................ 32

2.1 Publication 1 .............................................................................................. 33

Chapter 3 Integrating Hydrocracking Process in Refinery Hydrogen

Network Optimization ........................................................................... 34

3.1 Publication 2 .............................................................................................. 35

Chapter 4 Development of Vacuum Residue

Hydrodesulphurisation/Hydrocracking Models and their

Integration with Refinery Hydrogen Networks ............................... 36

3

4.1 Publication 3 .............................................................................................. 37

Chapter 5 Summary and Future Work ................................................................... 38

5.1 Summary ................................................................................................... 38

5.2 Perspectives and Future Work ............................................................... 42

References ..................................................................................................................... 44

Appendix A: Supplementary Information for Chapter 3 ................................. 46

A.1 Model Development for VGO Hydrocracker Products ...................... 47

Appendix B: Supplementary Information for Chapter 4 ................................. 49

B.1 Model Development for VRDS / HC Products .................................... 50

4

List of Figures

Figure 1.1 Variations in Petroleum Fuel Demand in recent years (IEA) ................ 19

Figure 1.2 Methodology for integration of hydroprocessors in a refinery

hydrogen network ......................................................................................................... 27

5

Abstract

Effective distribution of hydrogen in refinery hydrogen networks is a major

concern for refiners tackling the stringent specifications on maximum sulphur

levels in middle distillates and the increasing global demand of diesel fuel. A

major challenge is the implementation of a shift from conventional to ultra-deep

methods of desulphurisation. Meanwhile, the capacity of secondary conversion

processes such as fluid catalytic cracking (FCC) and hydrocracking in refineries

has steadily increased in converting the bottom of the barrel into high-value

lighter products resulting in increased levels of hydroprocessing, which exerts a

higher demand on refinery hydrogen systems.

Previous methodologies on hydrogen network optimization have been

developed mainly based on the assumption of fixed hydroprocessing

performance with constant hydrogen consumption and light hydrocarbon yields,

in order to reduce the complexity of the optimisation problem. Consequently,

critical interactions among feed and catalyst properties, hydroprocessor

operating conditions, product quality and yields, and hydrogen consumption are

usually neglected. This research work involves three major aspects: 1.

Development of semi-empirical nonlinear lumped hydrodesulphurisation (HDS)

and hydrocracker models that are robust and sufficiently detailed to capture the

behaviour of the process with changes in feed characteristics and operating

conditions. The formation of light hydrocarbons during HDS reactions have been

accounted for. Hydrocracker conversion models and five/six-lumped product

yield models for vacuum gas oil (VGO) and vacuum residue (VR) feedstocks

have been developed from a combination of first principles and empirical

methods based on several process parameters. The proposed models are

validated with different feedstocks and shows good agreement with industrial

data. 2. Integration of HDS and hydrocracker performance models into refinery

hydrogen network models to explore existing interactions between processes

and the hydrogen network, and their combined effect on the overall network

objective. 3. Optimization of the overall superstructure under different operating

scenarios to facilitate the efficient distribution and utilization of hydrogen and

the maximization of clean high-value products.

The integrated superstructure network model is developed and optimized

within the General Algebraic Modelling System (GAMS). The model is

representative of the dynamic interactions between hydrodesulphurisation and

hydrocracking processes in the refinery hydrogen network as demonstrated by

the reproducibility of industrial refinery data. Thus, this work presents a holistic

and realistic implementation of refinery hydrogen management technique.

6

University of Manchester

PhD by Published Work Candidate Declaration

Candidate Name: Blessing Umana

Faculty: Engineering and Physical Sciences

Thesis Title: Integrating Hydroprocessors in Refinery Hydrogen

Network Optimization

Declaration

I declare that no portion of the work referred to in the thesis has been submitted

in support of an application for another degree or qualification of this or any

other university or other institute of learning.

The thesis contains two original paper published in peer-reviewed journals

[Publication 1] and [Publication 3], and one submitted paper [Publication 2]. I

confirm that the materials covered in Publication 1 to 3 in the thesis, including

model development and validation, data calculation and integration analyses are

the results of my original contribution. These studies were carried out under the

supervision of the co-authors of the research papers, Dr Nan Zhang (principal

supervisor) and Prof Robin Smith (co-supervisor). The co-authors provided

expert advice and guidance in the paper development. I certify that I have

7

obtained permission from the co-authors for incorporating the published

materials in my thesis.

I declare that to the best of my knowledge, my thesis does not infringe upon

anyone’s copyright nor violate any proprietary rights and that any ideas,

techniques, quotations or any other material from other authors’ work included

in my thesis, published or otherwise are fully acknowledged in accordance with

the standard referencing practices.

All the work presented in this thesis has been completed whilst a registered

student at The University of Manchester.

I confirm that this is a true statement and that, subject to any comments above,

the submission is my own original work.

Signed: Blessing Umana

8

Copyright Statement

i. The author of this thesis (including any appendices and/or schedules

to this thesis) owns certain copyright or related rights in it (the

“copyright”) and s/he has given The University of Manchester certain

rights to use such copyright, including for administrative purposes.

ii. Copies of this thesis, either in full or in extracts and whether in hard or

electronic copy, may be made only in accordance with the Copyright

Designs and Patents Act 1988 (as amended) and regulations issued

under it or, where appropriate, in accordance with licensing

agreements which the University has from time to time. This page

must form part of any such copies made.

iii. The ownership of certain Copyright, patents, designs, trademarks and

other intellectual property (the “Intellectual Property”) and any

reproductions of copyright works in the thesis, for example graphs

and tables (“Reproductions”), which may be described in this thesis,

may not be owned by the author and may be owned by third parties.

Such Intellectual Property and Reproductions cannot and must not be

made available for use without the prior written permission of the

owner (s) of the relevant Intellectual Property and / or Reproductions.

9

iv. Further information on the conditions under which disclosure,

publication and commercialization of this thesis, the Copyright and

any Intellectual Property and / or Reproductions described in it may

take place is available from the Head of School of Chemical

Engineering and Analytical Science.

10

Dedication

To my source, my hope, my strength and my inspiration - Jesus, who constantly

thinks of giving me a glorious future, Jer. 29:11

11

Acknowledgements

I sincerely thank my supervisor, Dr. Nan for his sound advice, critical reviews,

logical and coherent assessment of this work, thought-provoking questions

during meetings, opportunities to present my work at local and international

conferences, and invaluable contributions towards the completion of this thesis.

To my co-supervisor, Prof. Robin, you are the starting point of this journey.

Thank you.

I am indebted to my parents, who have been an everyday source of hope and

inspiration in the achievement of my goals. Mummy, thank you so much for

your prayers. Daddy, thank you for believing in me. To my brother, I owe you

the lessons I have learnt in attaining this height.

Thank you, my husband for your immeasurable support and words of

motivation.

To my children, I thought it would be impossible to finish this race, but your

birth during this period of PhD has brought forth fruitfulness and perfection in

my career.

To all members of CPI, and other PhD/postdoc researchers, I treasure every

moment with you.

12

The Author

Having obtained a Bachelor’s Degree in Chemical Engineering and acquiring

two years’ working experience in plant operations, the author proceeded to

study for a Master’s Degree in Advanced Chemical Process Design at the Centre

for Process Integration, University of Manchester. The author’s dissertation,

which focused on the simultaneous integration of process heat-recovery

networks and process utilities within the total site, was graded distinction. A

systematic methodology was proposed to integrate processes with high

temperature requirements with process utility systems based on Pinch

technology in refinery applications. This research experience motivated further

interests in refinery hydrogen distribution systems, hydrogen pinch analysis,

and new developments in the proliferation of integrated hydrogen networks,

with a view to optimizing the hydrogen distribution network, which is a major

contributor to refinery profitability.

The present doctoral research involves the modeling and optimization of

integrated multicomponent superstructures that takes into account all possible

interactions between hydrogen producing and hydrogen consuming processes in

refinery operations. The contribution of this work is crucial to enabling refiners

embark on a holistic and global approach in refinery optimisation decisions.

13

During the course of the research work, the author has acquired a number of

professional skills, including extensive modeling in GAMS, presentation at local

and international conferences, and refinery consultancy at PIL.

14

Rationale for Submitting the Thesis in an Alternative Format

This thesis has been presented in an alternative format for a coherent and

thorough organization of the contents of the work, which consists of three major

aspects, namely, integration of hydrodesulphurization processes, integration of

hydrocracking processes, and integration of vacuum residue hydroconversion

processes in refinery hydrogen networks. These aspects of this work have been

prepared in either published or manuscript formats.

The content of this work provides additional knowledge to the body of literature

on the subject of refinery hydrogen management. Thus, the alternative

presentation format allows other researchers in the field, including industrialists,

to easily access the proposed methodology and outcomes of the work.

Consequently, the format presented in this work could initiate an extensive

application of novel and practical tools to refinery hydrogen management

strategies.

The thesis consists of two peer-reviewed journal publications and one submitted

manuscript for publication, conforming to the standards of an alternative-thesis

format at the University of Manchester. Therefore, the developed methodology

and research outcome have been thoroughly validated and acknowledged

through a rigorous peer-review process. Moreover, the contents of this work

15

have been presented and critiqued at various high-profile international

conferences, including PIRC, EFCE and AIChE, thus, proving the acceptability of

the research outcome to a wide range of audience from both academic and

industrial backgrounds.

16

List of Published Research Papers

1. Umana B, Shoaib A, Zhang N, Smith R. Integrating Hydroprocessors in

Refinery Hydrogen Network Optimization. Applied Energy. 2014;

133:169-182.

2. Umana B, Zhang N, Smith R. Development of VRDS/HC models and their

integration with refinery hydrogen networks . Industrial and Engineering

Chemistry Research. 2016; DOI: 10.1021/acs.iecr.5b04161.

List of Submitted Research Papers

1. Umana B, Zhang N, Smith R. Integrating Hydrocracking Process in the

Modelling and Optimization of Refinery Hydrogen Network. Journal of

Cleaner Production. Submitted (2016).

17

Context of the Research

This thesis is organised into five chapters. Chapter 1 presents the rationale for

undertaking this research. It reviews current underlying trends in the refining

industry and previous methodologies in refinery hydrogen management from

graphical analysis to mathematical methods of refinery hydrogen network

optimization. The purpose of the research, strategies to implement the research

aims, its contributions to refinery operations and the research significance in an

industrial context are also presented. Chapter 2 discusses in-depth on HDS

model development and modifications for diesel, kerosene, and naphtha

feedstocks. These models are integrated and optimized in the overall refinery

framework. In Chapter 3, semi-empirical nonlinear process models to predict

VGO conversion and yield in a hydrocracker are developed and integrated in a

multicomponent hydrogen network. The integrated refinery hydrogen network

is optimized for optimum hydrogen distribution and profit. An extensive study

on VRDS/HC conversion and yield model is developed, integrated and

optimized in Chapter 4. Case scenarios during optimization have also been

carried out. The results obtained are peculiar to operational trends in the refining

industry. In Chapter 5, the work is summarized based on two distinct

contributions: the development of hydroprocessor models and the optimization

of the integrated refinery hydrogen network. Recommendations for future work

are also proposed.

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Chapter 1 Introduction

1.1 Research Background and Motivation

The lack of representative refinery hydrogen network models presents a

challenge in the optimum distribution of hydrogen in the refinery and its

efficient utilization in hydroprocessors. This anomaly is often overlooked in the

evaluation of refinery hydrogen distribution systems when considering

profitability as an objective. In recent times, refiners have been confronted with

the onerous challenge of upgrading fuels from heavier, sour crudes to lighter,

sweet crudes in order to meet tightening environmental and legislative

specifications. The increasing moral expectations and stricter regulations are

triggering a shift from conventional desulphurisation methods to ultra-deep

desulphurisation methods. Notwithstanding the environmental benefits of these

guidelines on sulphur limits, meeting the required stringent specifications

presents a major operational and economic challenge in the petroleum refining

industry. The principal constraint is the removal of refractory sulphur

compounds particularly those containing alkyl side chains in the 4- and 6-

positions in dibenzothiophene molecule, which are difficult to desulfurize under

conventional desulfurization conditions.

19

Another challenge confronting refinery profitability is the increasing steady

growth in the global demand for middle distillates, as against the decreasing

demand for low value product, such as fuel oils as shown in Figure 1.1.

Fuel oil

Others

Gasoline

Middle

distillates

10

15

20

25

30

35

40

45

1990 1995 2000 2005 2010 2015 2020

Wo

rld

oil

pro

du

ct d

eman

d, %

Year

Figure 1.1 Variations in Petroleum Fuel Demand in recent years (IEA)

According to projections given by the International Energy Agency (IEA),

middle distillates such as jet fuel, kerosene, diesel and other gas oils would

remain the main driver of world oil demand in the coming years [1]. As a result

of these trends, there has been an increase in heavy oil hydroprocessing due to

the decrease in fuel oil demand and increase in distillate demand. At the same

time, the quality of crude and diesel feed streams available to refiners is

declining [2]. Consequently, refiners are faced with increasingly difficult task of

20

producing high quality diesel product from low quality feedstocks. Conversion

processes such as fluid catalytic cracking (FCCU) and hydrocracking with

greater flexibility to handle heavy and lower quality feedstocks is therefore

required to produce lighter and high value products. This impacts refinery

profitability as refiners are investing in additional capital intensive hydrogen

production facilities such as steam reforming, or faced with increasing OPEX of

importing hydrogen to meet their requirements. Furthermore, the reduction of

aromatics in gasoline through benzene saturation constrains catalytic reformer

operation and removes some of the traditional sources of hydrogen available to

refineries.

The aforementioned trends tend towards increasing the level of hydroprocessing

in refineries despite limited hydrogen availability, thus creating strict hydrogen

balances in hydrogen distribution systems. Due to these developments, exigent

efforts towards resolving the hydrogen imbalance are paramount. The efficient

distribution of hydrogen is a prerequisite to balancing hydrogen production and

consumption in refineries. Exploiting the interactions between hydrogen

consumers and hydrogen producers in the hydrogen distribution system can

provide quantitative insights into optimum hydrogen requirements.

Hydrogen is critical to the production of less dense clean fuels by hydrotreating

and hydrocracking processes, and its use has increased with the introduction of

21

ultra-low sulphur diesel (ULSD) and the upgrading of low quality heavy crudes

or bottom of barrel products, such as atmospheric or vacuum residues. The

benefits derived from the increased use of hydrogen in these processes are

enormous. Hydrogen has a significant effect on process performance and

network profitability [3]. A unique variable that determines the concentration of

hydrogen present in the system is the hydrogen partial pressure, often

represented by the recycle hydrogen purity. This single variable can be

manipulated to improve product quality, increase conversion, increase catalyst

life in hydroprocessing units, minimize coke formation, reduce operating

severity, increase throughput, and maximize profitability. A variable that is

critical to maintaining the H2 partial pressure in the reaction system is the H2-oil

ratio. In addition to affecting the H2 partial pressure, the gas rate is important as

it acts to strip volatile products from the reactor liquids, and thus affects the

concentration of various components in the reactive liquid phase [4]. Similar to

H2 partial pressure, an increase in H2-oil ratio ensures adequate conversion and

minimizes the amount of carbon deposited on catalyst due to efficient physical

contact of the hydrogen with the catalyst and hydrocarbon. The extent of

conversion or coke reduction depends on the combination of operating

conditions at appropriate values. Efficient hydrogen utilization in these

processes is usually realized through its distribution amongst processes in the

network.

22

The hydrogen network is a system of interconnected units consisting of

hydrogen production units, hydrogen consuming units, purification units,

compression units, the fuel system and the hydrogen distribution headers. In a

hydrogen network, there are several hydrogen producers and consumers, which

are referred to as sources and sinks, respectively. A source from one process

becomes a sink to another process in which it is required. Hydrogen containing

streams such as hydrotreater off-gases or excess hydrogen streams that are sent

to fuel or fed to hydrogen plants could be routed to purifiers for recovery or

increase in hydrogen purity for reuse in consumers. The most commonly used

processes for hydrogen purification are the pressure swing absorption (PSA),

membrane units and cryogenic separation technique. Oftentimes, pressure

requirements by hydrogen consumers or purifiers are satisfied using

compressors. The hydrogen network requirement is further satisfied by utilities,

such as hydrogen plant, or by imports. Normally, these units are considered as

complementary within the distribution framework and optimisation process;

otherwise overall refinery profit margins could be depleted.

Several authors have introduced concepts and methodologies in analysing and

designing mass distribution systems. El-Halwagi and Manousiouthakis [5]

pioneered works in synthesizing cost effective mass integration networks

utilizing different materials for route between processes with the objective of

identifying target resource requirements, optimizing the allocation of material

23

streams and yield enhancement. Alves [6] implemented the design of hydrogen

distribution systems with the objective of minimizing hydrogen utility

requirements. This study has been extended to other areas of research, including

mathematical formulations for equipment constraints, such as the purifiers,

compression units and piping system, which presents a thorough approach in

the optimization of hydrogen networks [7]. Other authors resolved the

limitations of complexity of the hydrogen network by transforming the

optimisation problem into two small scale MINLP models that are solved

sequentially [8]. A major assumption in these approaches is the binary mixture of

hydrogen and methane in the stream. Other impurities such as light

hydrocarbons and compounds of sulphur and nitrogen in combination with

hydrogen which are usually present are not considered. Such assumptions could

result in loss of accuracy and does not account for the effect of impurities on the

hydrogen partial pressure in a system. Singh [9] and Jia [10] developed a

systematic methodology to incorporate the impact of impurities by integrating a

high pressure flash model to account for the vapour-liquid equilibrium

characteristics within hydrogen consumers. These methodologies have neglected

the performance of hydroprocessors in the overall network optimization by

fixing the process hydrogen consumption for different feedstock hydrotreating

and hydrocracking processes in the refinery hydrogen network, which results in

overly optimistic solutions that cannot be implemented in a real refinery. With

the growing demand of middle distillates and restrictions on product quality,

24

resulting in increase in hydrogen consumption, hydroprocessor integration in

the overall optimization of hydrogen networks becomes a necessity. The existing

interactions between hydroprocessors and the hydrogen network have not been

carefully exploited, and therefore present a relatively new research

methodology. Consequently, there is need to define hydroprocessor

performance with representative models that improve interactions with the

network to produce sustainable designs of refinery hydrogen network.

Sustainable, in this sense, refers to “optimum” or “cost effective” as opposed to

its use in the natural sense to mean “to maintain” or “to keep in use without

depletion or deterioration for longer periods.” Resources are always limited,

though they form an integral part of system management and optimization. The

optimum distribution of hydrogen resource and its efficient utilization by

hydrogen consuming units within a refinery framework remains unresolved.

Such an integrated approach to network modelling would provide realistic

targets for hydrogen requirements, if implemented during the early stages of

design. There is also the additional benefit of simultaneously optimizing several

processes and the hydrogen network in a single framework.

1.2 The Aim of the Present Work

The aim of the present research is to quantitatively investigate existing

interactions between hydroprocessors, conventional hydrogen resource supply,

25

and the expected outcome on the overall network optimisation. The work

involves the development of process models that are capable of capturing the

relationship between input parameters, operating conditions, and products yield

and quality. The resulting models derived from a combination of physical

modelling and empirical approaches are validated using experimental or

industrial data. The robustness of these models is demonstrated in their

integration in the multicomponent hydrogen network model. The resulting

superstructure is representative of the interactions between process variables

and the hydrogen distribution system and enables efficient distribution of

hydrogen for optimum hydrogen utilization and profit objectives. The overall

integrated semi-empirical non-linear hydroprocessor- hydrogen network model

is formulated as a large-scale NLP on the General Algebraic Modelling System

(GAMS) platform and optimized using a CONOPT solver.

1.3 Research Strategies

This section addresses the procedures undertaken in the design of integrated

refinery hydrogen networks from the development of hydroprocessor models

through to the integration of these models and the overall network optimization.

The implementation of such integrated approach to model the interactions

between hydroprocessors in the refinery hydrogen network is key to achieving

optimal network designs. Moreover the stringent environmental regulations on

allowable sulphur content in product, availability of heavier feedstocks,

26

increasing demand for middle distillates makes it necessary to accurately

represent refinery processes with models that capture the fundamental

chemistry and retains good predictive capabilities. Therefore, a fundamental first

step in the design of integrated hydrogen networks is the development of

hydroprocessor models that can ultimately provide realistic hydrogen

requirements and corresponding profit levels. Three aspects of hydroprocessor

model development are implemented in this work:

1. Modelling of HDS reactions for diesel, naphtha, kerosene and vacuum gas

oil feeds. This includes development of models for the removal of sulphur

and production of associated light hydrocarbons.

2. Modelling of hydrocracking reactions for VGO feed

3. Modelling of HDS, conradson carbon removal (CCR), deasphaltenization

(HDA), and hydrocracking reactions for VR feed

These models are integrated in the refinery hydrogen network resulting in an

overall superstructure model that is capable of simulating the operational

performance of the integrated hydrogen network. The integration optimisation

methodology framework is presented in Figure 1.2 below, illustrating the

integration of hydrotreater and hydrocracker models in the optimisation of

refinery hydrogen networks.

27

Data collection and

reconciliation

Process model development,

regression and validation

Overall network modelling

Overall network optimization

Figure 1.2 Methodology for integration of hydroprocessors in a refinery

hydrogen network

1.3.1 Process Model Development, Regression and Validation of Hydrogen

Consumer Models

The interactions between hydrogen consumers and the refinery hydrogen

network cannot be captured without accurate representation of hydrogen

consumers with process models. Consequently, it becomes imperative to

develop process models that are capable of simulating existing data trends, and

thus facilitates the integration of refinery processes in the hydrogen network.

In this work, semi-empirical nonlinear process models for HDS and

hydrocracking processes derived from first principles and empirical sources will

be developed to predict sulphur levels in liquid products, light hydrocarbon

28

yields associated with HDS processes, hydrocracker conversion for VGO and VR

feeds, hydrocracker product yields, hydrogen consumption and overall

hydrogen network requirements. In the upgrading of heavy feedstocks, in

particular vacuum residue, the extent of conversion is limited by a number of

feedstock characteristics such as asphaltene, sodium and conradson carbon

content. The presence of large molecules indicates a significant complexity of the

reactions occurring during hydroprocessing of heavy feeds. Due to the

increasing involvement of asphaltene molecules, the complexity increases from

VGO toward VR and topped heavy crude. This work attempts to adequately

represent these heteroatoms in the conversion model for VR feed. The yield

models will be regressed on plant data and validated using other experimental

or industrial data. The models show good agreement with the experimental data

over a range of operating conditions.

1.3.2 Overall network modelling

The semi-empirical nonlinear hydroprocessor models are integrated in the

refinery hydrogen network model to study the effect of changing process

variables, such as H2 partial pressure, H2-oil ratio, and temperature on HDS

reactions, feed conversion, and product yields in hydroprocessors. The

integration of these models allows the refiner to exploit significant interactions

within the hydrogen network. The objective function is to minimize hydrogen

production cost and maximize profit.

29

1.3.3 Overall network optimisation

The resulting superstructure model is solved using a robust large scale

optimisation solver, CONOPT due to nonlinearities in the process models. The

GRG method, often used for large scale optimisation problem, achieves

reliability and speed for models with a large degree of nonlinearity, however,

CONOPT is preferable for highly nonlinear models where feasibility is difficult

to achieve. Changes in feed flow, hydrogen oil ratio, hydrogen partial pressure,

and reactor temperature result in different feed conversions and subsequently

changes to hydrocracker product yields. The effects of these changes are seen in

the hydrogen consumption levels and overall hydrogen requirements in the

network, which impacts profitability.

1.4 Contribution of Research

The main achievement of this work is the development of a hydrogen

superstructure comprising of process performance models that are capable of

reproducing industrial data related to product properties and yields. These

models are characterized by the feed properties, operating conditions in the

hydroprocessor, product properties and interconnecting parameters that

describe the links between them. The predictive ability of the models is enhanced

by introducing parameters that are qualitatively significant to the feasible

implementation of the model. The integration of these models in a refinery

30

hydrogen network model allows the refiner to assess critical interactions that

exists between hydroprocessors and the hydrogen network. The model is

capable of simulating the non-linear relationship between recycle purity, feed

rate, product properties and operating parameters. Some of these operating

parameters, such as recycle hydrogen purity, hydrogen partial pressure, and

hydrogen-oil ratio are critical in establishing the interactions between the

refinery hydrogen network and the hydrogen consumers. As a result, the refiner

can evaluate the effect of varying these operating parameters on the overall

integrated network objective. The results obtained can be used in the early stage

analysis and design of refinery hydrogen distribution systems.

The research strategies and outcomes of this work can serve as a guide to

refiners on the optimization of hydrogen production and utilization in refinery

hydrogen networks. The model development and integration approach to

refinery hydrogen network optimization can assist refiners in the identification

of constraints that limits profitability and the alleviation of such constraints

through the implementation of improvement initiatives, especially those that do

not require capital investment. This work can also assist refiners in exploring

various degrees of freedom, including decreased hydrogen production capacity,

target hydrogen partial pressures, process changes in hydroprocessors, and

catalysts properties for the realization of the refinery optimisation objectives.

Also, the developed models can be used by refiners to test potential

modifications in the refinery hydrogen network. There is also the potential

31

benefit of affecting refinery profitability through hydrogen management

techniques developed in this work compared to simply reducing hydrogen

production or import costs in a hydrogen network with stand-alone processes.

32

Chapter 2 Integrating Hydroprocessors in Refinery Hydrogen

Network Optimization

This paper investigates the existing interrelationship between hydrogen

consumers and the refinery hydrogen distribution system through the

development of models that characterizes the process and provides a linkage to

the overall hydrogen network. The work presents a new and detailed

preliminary approach to the effective distribution of hydrogen between

hydrogen consumers based on HDS requirements in hydrotreaters and

hydrogen producers. Light hydrocarbons associated with the removal of sulphur

are also generated. In this work, the empirical model of Choudhary et al [11] has

been modified to reflect the nature of feed and product properties as a function

of the prevailing operating conditions, by introducing a feed quality parameter.

The model of Hasenberg and Campagnolo [12] has also been modified and

calibrated based on a refinery industrial data. The model comprises of sulphur

conversion, reaction pressure, LHSV, temperature, and parameters relating to

the yield distribution among different hydrocarbons. The model fits well with

the industrial data, and the results are in agreement with industrial data. These

models are integrated in a refinery hydrogen network to study the effects of

operational changes in hydroprocessors on the overall network optimization. Dr.

Nan Zhang is responsible for the critical review of this paper.

33

2.1 Publication 1

Umana B, Shoaib A, Zhang N, Smith R. Integrating Hydroprocessors in

Refinery Hydrogen Network Optimization. Applied Energy. 2014;

133:169-182 (Published).

P33

Applied Energy 133 (2014) 169–182

Contents lists available at ScienceDirect

Applied Energy

journal homepage: www.elsevier .com/locate /apenergy

Integrating hydroprocessors in refinery hydrogen network optimisation

http://dx.doi.org/10.1016/j.apenergy.2014.06.0800306-2619/� 2014 Elsevier Ltd. All rights reserved.

⇑ Corresponding author.E-mail address: [email protected] (N. Zhang).

Blessing Umana a, Abeer Shoaib b, Nan Zhang a,⇑, Robin Smith a

a Centre for Process Integration, School of Chemical Engineering and Analytical Science, The University of Manchester, PO Box 88, Sackville Street, M60 1QD, UKb Department of Chemical and Refining Engineering, Faculty of Petroleum and Mining Engineering, Suez Canal University, Suez, Egypt

h i g h l i g h t s

� Correlations for sulphur in liquid products are modified for hydrotreaters.� Correlations for light hydrocarbons in hydrotreaters are developed.� Effects of process and operational variables on H2 production are investigated.� An NLP algorithm is developed using CONOPT solver, demonstrated with a case study.

a r t i c l e i n f o

Article history:Received 11 February 2014Received in revised form 28 June 2014Accepted 30 June 2014Available online 12 August 2014

Keywords:Process model integrationHydrogen utilisationHydrogen networkDesignOptimisation

a b s t r a c t

Recent developments on sulphur specifications in middle distillate fuels are exerting a higher demand onrefinery hydrogen systems. Previous methodologies on hydrogen network optimisation have been devel-oped mainly based on the assumption of fixed hydroprocessing performance with constant hydrogenconsumption and light hydrocarbon yields in hydrogen consumer models, in order to reduce the com-plexity of the problem. As a result, interactions among process operating conditions, product qualityand yields, and hydrogen consumption are usually neglected.

The present work is an integrated approach for refinery process and hydrogen network design. Empir-ical correlations for sulphur prediction in liquid products are modified and adopted to predict hydrogenconsumption in hydrotreaters. The model is validated with different feedstock properties and shows goodagreement with experimental data. Light hydrocarbon yields in hydrodesulphurisation reactions are alsopredicted and integrated in the network model. Modelling and optimisation of the overall network is per-formed and the effects of process and operational variables on performance indicators and hydrogen pro-duction requirements are investigated. As a Nonlinear programming model, the overall network model isoptimised with the CONOPT solver in General Algebraic Modelling System (GAMS). As demonstrated in acase study, by integrating hydrotreating models into multicomponent hydrogen networks, the focus ofrefinery hydrogen management can now be shifted from minimising hydrogen consumption to optimis-ing hydrogen utilisation to improve refining profitability.

� 2014 Elsevier Ltd. All rights reserved.

1. Introduction

A hydrogen system consists of hydrogen production, hydrogenconsumption, hydrogen purification, hydrogen compression, a fuelsystem and a distribution network itself. The primary sources ofhydrogen in a refinery are catalytic reformers, ethylene plants,hydrogen plants and hydrogen imports. The most common hydro-gen consumers are hydrotreaters and hydrocrackers. Hydrogencontaining streams such as hydrotreater off-gases or excesshydrogen streams that are currently being sent to fuel or feed tohydrogen plants could be routed to purifiers for hydrogen recovery

or increase in hydrogen purity for reuse in hydrogen consumers.The most commonly used processes for hydrogen purification arepressure swing absorption, membrane units and cryogenic separa-tion. Usually, pressure requirements by hydrogen consumers orpurifiers are satisfied using compressors. Normally, these unitsare considered highly interactive whole within a distributionframework, otherwise refinery margins are depleted.

Today’s refineries face an increasing challenge of meeting grow-ing demand for cleaner fuels. The need to meet required end prod-uct specifications from crude oil has necessitated the increased useof hydrogen in hydroprocessing operations, with existing hydrogenproduction capacities often being a bottleneck. In recent times,stricter regulations on sulphur specifications and implementationstrategies have been imposed on refiners, hence presenting a major

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Nomenclature

Ea activation energy (kJ/Kmol)FF feed flow (t/h)LP liquid product (t/h)MWH2S molecular weight of H2SMWS molecular weight of SPH2 recycle hydrogen partial pressure (bar)R universal gas constantFmix flowrate of mix (t/h)Fmu flowrate of makeup (t/h)Fre flowrate of recycle (t/h)Fri flowrate of reactor inlet (t/h)Ffeed flowrate of feed (t/h)Ffi flowrate of flash inlet (t/h)Yfs amount of sulphur in the product (ppmw)Yvap vapour phase purity (mass fraction)Yliq liquid phase purity (mass fraction)Fliq flowrate of liquid (t/h)Fpu flowrate of purge (t/h)Fpr flowrate of external recycle (t/h)FSF flowrate of site fuel (t/h)FH2P flowrate of hydrogen producer (t/h)FH2C flowrate of hydrogen consumer (t/h)

ComponentsC1 methaneC2 ethaneC3 propaneC4 butaneC5 pentanePC pseudocomponent

Indicesi all componentsj hydrogen consumerj1 hydrogen consumerk hydrogen producer

SuperscriptsL lower boundU upper bound

170 B. Umana et al. / Applied Energy 133 (2014) 169–182

challenge in deep desulphurisation of petroleum fractions. Refineryprocesses such as hydrotreating, hydrocracking and catalyticdewaxing are dependent on the operating severity and availabilityof hydrogen at adequate high pressure and purity. However, theseprocesses are often not run under optimised conditions, usingexcess or inadequate hydrogen, and allowing hydrogen and associ-ated impurities to be sent to purge and used as fuel. Consequently,effective hydrogen utilisation has become very important to refin-ery hydrogen management. The present strategy would addresstwo major issues: 1. integrating hydrotreating performance inmulticomponent hydrogen network optimisation; 2. evaluatingthe optimum hydrogen requirements for sulphur removal and lighthydrocarbon production based on quality constraints. Such anapproach in the design of integrated refinery hydrogen networkscould present opportunities to exploit various additional degreesof freedom in a network for improved hydrogen utilisation.

2. Review of previous research

Over the years, the pursuit of hydrogen management programsin the design of refinery hydrogen networks has been studied intwo categories:

I. Graphical targeting methodsII. Mathematical programming methods

Towler et al. [1] developed the first systematic approach to ana-lyse hydrogen distribution systems by proposing a graphical tool toprovide an overview of the costs and values associated with recov-ering hydrogen from hydrogen producing or consuming processes.Using this concept of cost and value, the driving force for hydrogentransfer is the difference between the cost of hydrogen availableand the value added to refinery products. Although, this methodassesses the economic trade-offs in recovery cost and value added,it does not provide a systematic approach to the distribution ofhydrogen in a refinery hydrogen network. Therefore, the conceptof hydrogen recovery was redefined by Alves [2] to target the min-imum hydrogen utility flowrate requirements of hydrogen net-works using pinch analysis applications of heat exchanger

networks [3]. Hydrogen pinch analysis gained significance inindustries and was extended to other areas of research [4].

Some underlying practical constraints exist in the application ofgraphical methods to the design of hydrogen distribution net-works. Hallale and Liu [5] extended Alves [2] Linear Programming(LP) technique to a Mixed Integer Non Linear Programming (MIN-LP) formulation that takes into account pressure constraints. Liuand Zhang [6] developed an automated design superstructureapproach that demonstrates the choice of purifiers as well as theirintegration in the hydrogen networks. The objective function forthe MINLP problem could be minimum hydrogen utility, operatingcosts or the total annualised cost of the network. Ahmad et al. [7]extended the MINLP model developed by Liu [8] to multi-periodhydrogen network designs. Kumar et al. [9] introduced variableinlet and outlet pressure configuration to the network modeldeveloped by Hallale and Liu [5] to obtain realistic solutions. Liaoet al. [10] proposed a systematic approach for the location of com-pressors and purifiers that accounted for other structural possibil-ities. Although some of these methods take into considerationpractical constraints, other constraints such as the concentrationsof hydrogen sulphide (H2S) in hydrogen streams and their effectson processing equipment are neglected. Recently, Zhou et al. [11]incorporated an H2S removal unit in a hydrogen network to allowreuse of hydrogen rich streams. The targeting and mathematicaldesign approaches have shown the importance of hydrogen savingin distribution systems.

A major assumption in the aforementioned approaches is that ofa binary mixture of hydrogen and methane, rather than a multicom-ponent stream consisting of a mixture of impurities such as lighthydrocarbons, and compounds of sulphur and nitrogen in combina-tion with hydrogen. Such assumptions could result in loss of accu-racy and does not account for the influence of impurities on thehydrogen partial pressure in a system. Singh and Zhang [12] devel-oped a systematic methodology to incorporate the impact of impu-rities, including hydrogen sulphide (H2S) and methane (CH4,) byintegrating a high pressure flash model to account for vapour-liquidequilibrium characteristics within hydrogen consumers and theeffect on an overall hydrogen network. This method, however,requires a series of iterations between simulation and optimisation

B. Umana et al. / Applied Energy 133 (2014) 169–182 171

resulting in increased computational effort, which may limit appli-cability in real systems. Jia and Zhang [13] introduced a more effi-cient approach to multicomponent optimisation of refineryhydrogen networks by assuming constant vapour-liquid equilib-rium ratios for slight changes in the flash inlet stream composition.

The Non-Linear Programming (NLP) methodology for multi-component hydrogen network neglects the performance of hyd-rotreaters by fixing the hydrogen consumption. Consequently,the effect of variations in hydrogen partial pressure, hydrogen–oil-ratio, operating conditions and hydrogen consumption onrequired sulphur specifications, and the overall network perfor-mance is not considered. Moreover, light hydrocarbon productionsassociated with HDS reactions are not integrated in the hydrogennetwork model. With the objective being the minimum hydrogenproduction flowrate, hydrogen requirements may be misleadingas constraints on maximum allowable sulphur in the productstream are ignored.

This paper aims at providing a framework to assess the interac-tions between hydroprocessing reactions and hydrogen distribu-tion systems by exploiting key degrees of freedom to improveoverall network performance. It also demonstrates the impacts ofchanges in light hydrocarbon composition in the recycle and purgestreams from hydrogen consuming processes on the hydrogenrequirements. An integrated approach in the design of refineryhydrogen networks could maximise the efficient utilisation ofhydrogen and the effective operability of hydroprocessors.

3. Integrated design of refinery process networks

Hydrogen sources such as hydrogen plants, catalytic reformers,and off-gas from hydrogen consuming processes affect hydrogenconsumers by providing hydrogen at different flowrates and puri-ties. Changes in composition of liquid hydrocarbon feedstock andmake-up hydrogen could result in changes in the composition ofrecycle and purge streams from hydroprocessors. Consequently,light hydrocarbons and other impurities are formed that couldaffect hydrogen inlet purity, thus affecting process performance.In the same manner, changes in operating conditions in the processalso affect the amount of light hydrocarbons formed and hydrogenproduction requirements. With the recent trends in fuel specifica-

Fig. 1. An extended hydrogen consumer model show

tions and increasing load on hydrogen distribution systems, itbecomes imperative to integrate hydrotreating performance inthe overall optimisation of refinery hydrogen networks. An impor-tant aspect of this work is the consideration of two key hydrotreat-ing process performance indicators, namely sulphur removal andlight hydrocarbon yield.

Fig. 1 demonstrates schematically the impacts from key processvariable inputs and operating parameters on the outlet conditionsin a typical hydrodesulphurisation (HDS) unit.

A liquid hydrocarbon feed stream is mixed with a gas rich inhydrogen, heated and fed to the HDS reactor. The reactor operatesat a desired hydrogen partial pressure depending on targets for max-imum sulphur. Hydrogen is consumed in the removal of sulphur toyield light hydrocarbons including methane, ethane, propane,butane and pentane, and hydrogen sulphide is produced duringthe reaction. The reactor effluent is cooled and routed to a high pres-sure flash separator. Part of the gas released from the separator couldbe recompressed and purified depending on hydrogen purity andpressure requirements of the reactor. The gas is recycled to the reac-tor inlet and mixed with the make-up stream. The remainder of thegas stream is purged to prevent build-up of contaminants in therecycle loop. Normally, the purge stream is either reused as externalrecycle or sent to the fuel system. The liquid stream containing unre-acted sulphur compounds, dissolved hydrogen, and light hydrocar-bons, is sent to a downstream separator section.

In the network, there are three key locations to an HDS process,namely, reactor inlet, HDS reactor, and flash separator. The interde-pendence of these factors and their intermediate streams in ahydrogen network are achieved by exploiting the behaviour ofthe system. Consequently, models of hydrogen consumers thatare sufficiently detailed to capture these important operating fea-tures of the system are required in the early stage of design of arefinery hydrogen network. The methodology developed in thiswork proposes a generic representation of process models embed-ded into a hydrogen network superstructure to yield an integratedprocess and network configuration. Three key challenges exist inthis methodology: development of hydroprocessor models thatcan predict process performance and hydrogen consumption; inte-gration of process models in the hydrogen consumer model; andoptimisation of hydrogen networks with process models.

ing different interactions in the network model.

Fig. 2. Model fitting with diesel experimental data.

Fig. 3. Model fitting with vacuum gas oil experimental data.

Fig. 4. Model fitting with kerosene experimental data.


3.1. Development of hydroprocessor models

Generally, hydroprocessor models are developed to accuratelypredict the behaviour of a system from changing input or operatingvariables in a hydrogen consumer. In this work, a process–productmodel is modified to represent the characterisation of productstream composition as a function of liquid feedstock properties,HDS reactivities and operating conditions. Two aspects of hydro-processing models are studied: namely sulphur removal and lighthydrocarbon formation.

3.1.1. Sulphur removalSulphur compounds are one of the most important impurities in

various petroleum fractions that cause deleterious effects on pro-cesses and the environment. Often sulphur compounds in distillateand residue fractions could exist as mercaptans, thiols, sulphides,disulphides, polysulphides, thiophenes and their derivatives suchas benzothiophenes (BTs) and dibenzothiophenes (DBTs). A num-ber of authors have reported the difficulty in removal of sulphurfrom DBTs to mercaptans in descending order and proposed empir-ical models to predict HDS performance. Shih et al. [14] proposed atemperature correlation to predict the temperature requirementsfor the desulphurisation of distillates to 0.05 wt% sulphur undertypical HDS conditions. Ho [15] developed a property-reactivitycorrelation between feedstock quality and HDS reactivity. Chemo-metric analysis was applied to identify important feedstock prop-erties that affect HDS reactivity.

Based on the above work, the key variables affecting the compo-sition of residual sulphur in liquid products are given as follows:

Sproduct ¼

Feed propertiesTS;RS;nitrogen and aromatic content;API

Process Operating ConditionsT; P; LHSV ; recycle purity; hydrogen� oil ratio

Process ChemistryHDS reactivity; inhibition constants

8>>>>>>>><>>>>>>>>:

9>>>>>>>>=>>>>>>>>;

TS, RS, T, P, and LHSV are total sulphur, refractory sulphur, temper-ature, pressure and liquid hourly space velocity, respectively. Itcould be inferred that process yields and product properties largelydepend on the feed properties, operating conditions and HDS chem-istry. Such prediction of properties may be inaccurate without thefundamental chemistry models. Based on the principles of first-order kinetics, Choudhary et al. [16] derived an empirical equationto demonstrate that the desulphurization chemistry of heavy oilswas strongly inhibited by three and larger ring aromatic hydrocar-bon content rather than DBTs in the feed.

Sprod;j ¼ Sfeed;j � exp�kj

ðKÞ � ð3þ RjÞ

� �PH2;i;j /LHSVj

� �� ð1Þ

where Sprod;j = sulphur in the product from hydrotreater j, ppmw;Sfeed;j = sulphur in the feed to hydrotreater j, ppmw; kj = rate con-stant of HDS reaction in hydrotreater j, h�1; K = 3 + ring aromaticinhibition constant, 3þ Rj = 3 + ring core aromatic content to hyd-rotreater j, ppmw; PH2;i;j = recycle hydrogen partial pressure in hyd-rotreater j, bar; and a = pressure dependent term. An excellentcorrelation was observed between the sulphur conversion (644 Kand 6.9 MPa) for eight different heavy oil feeds and 3 + ring aro-matic content. The model was tested on diesel, 75% straight rungas oil (SRGO) and 25% light cycle oil (LCO), vacuum gas oil (VGO)and kerosene feedstock at reactor operating conditions of 633 K,60 bar and 1 h�1; 653 K, 53 bar and 2 h�1; 623 K, 60 bar and 5 h�1

respectively. To predict the sulphur content in the product, thepressure dependence parameter, a was tuned based on the avail-able experimental data from Knudsen et al. [17], Ancheyta [18]

and El Kady et al. [19] respectively. The parameters were obtainedby non-linear least square fitting on a MATLAB platform. The resultsfrom the model showed a fair fit with experimental data as shownin Figs. 2–4.

The fit obtained may be a result of the model restrictions tohighly aromatic feeds that contain large amounts of aromatic sul-phur. Based on the graphical results from the model in Eq. (1), ageneralised form of the model is produced that is capable of pre-dicting sulphur content in the product to a reasonable accuracy.A second parameter b was introduced to the model to reflect thenature of the feed, structural characteristics of the sulphur

Table 1Parameters obtained from the modified HDS model.

Hydrotreaters Sfeed (ppmw) Sprod (ppmw) 3 + R (ppmw) T (K) P (bar) LHSV (h�1) a b

VGO 20,000 8393.58 84,000 653 53 2 907.588 1.738KHT 2900 208.68 2000 623 60 5 13.329 0.825DHT 15,200 31.49 32,000 633 60 1 80.735 0.598

Table 2Comparison of results obtained between the original and modified model.

Sprod (experiment)ppmw

Sprod (original model)ppmw

Difference (experiment–original) ppmw

Error% Sprod (modified model)ppmw

Difference (experiment–modified) ppmw

Error%

Diesel55 100.01 �45.01 �81.83 53.34 1.66 3.0345 60.51 �15.51 �34.48 44.63 0.37 0.8335 36.62 �1.62 �4.62 37.34 �2.34 �6.6830 22.16 7.84 26.15 31.24 �1.24 �4.1428 13.41 14.59 52.12 26.14 1.86 6.6422 8.11 13.89 63.13 21.87 0.13 0.5920 4.91 15.09 75.46 18.30 1.70 8.50

Vacuum gas oil8000 11703.97 �3703.97 �46.30 8396.69 �396.69 �4.96

10,000 11831.43 �1831.43 �18.31 9904.34 95.66 0.9612,000 11960.28 39.72 0.33 11682.70 317.30 2.6414,000 12090.53 1909.47 13.64 13780.36 219.64 1.5716,000 12222.21 3777.79 23.61 16254.67 �254.67 �1.59

Kerosene370 687.12 �317.12 �85.71 387.55 �17.55 �4.74310 334.47 �24.47 �7.89 284.38 25.62 8.26230 162.81 67.19 29.21 208.68 21.32 9.27130 79.25 50.75 39.04 153.13 �23.13 �17.79100 38.57 61.43 61.43 112.37 �12.37 �12.37


compounds present in the feed and the product characteristics.With the introduction of b, Sprod is no longer directly proportionalto Sfeed.

Sproduct;j ¼ Sbfeed;j � exp

�kj

ðKÞ � ð3þ RjÞ

� �PH2;i;jaj

LHSVj

� �� ð2Þ

Table 1 shows the differences in parameters obtained from thethree different feedstocks to diesel hydrotreater (DHT), vacuumgas oil hydrotreater (VGOHT) and kerosene hydrotreater (KHT).

As observed, the b value increases from 0.598 for DHT to 1.738for VGO. Although, it is expected that the b value for KHT is lessthan that for DHT due to density difference, the amount of sulphurin KHT product is high compared to DHT product, thus attributingb value to the difficulty in the removal of sulphur in KHT. Thistrend could be assigned to the decreased residence time and lowreactor temperature in KHT. With reference to other feedstocks,it is possible to deduce that b increases with the complexity ofthe feed and structural forms of sulphur compound present inthe feed if operating conditions are maintained. In view of this,the introduction of b in the model enhances the accuracy of theempirical model.

Also, the a value in KHT is small in comparison with the otherhydrotreaters. An inference could be derived from the resultingcoefficients which state ‘‘the correlation between the a value andpolyaromatic content of the feed is directly related.’’ Since the aterm is associated with pressure, an extensive study that involveswide range of operating conditions may be conducted to assessvariations in a value. The modified model was tested on the sameexperimental data, and resulted in a better fit in all feedstock.

Table 2 shows the comparison in errors obtained from the twomodels. As seen the maximum percentage error obtained from themodified model in all feedstock is 17% compared to 85% for the

original model and the average error is 39% and 5% for the originaland modified model respectively.

The fit obtained from the modified models are shown inFigs. 5–7.

3.1.2. Light hydrocarbon productionPrevious work by Singh and Zhang [12] and Jia and Zhang [13]

was based on fixed hydrocarbon production and hydrogen con-sumption. Light hydrocarbons are produced from hydrotreating(example in the desulphurisation of distillates and residues) andhydrocracking reactions. Light gas refers to any gaseous or semi-gaseous molecule with a molecular weight that is less than orequal to that of pentane. Hasenberg and Campagnolo [20] imple-mented a light hydrocarbon model to obtain C1–C5 hydrocarbonsin an atmospheric residue unit. In this work, an empirical modelto predict the production of light hydrocarbons during HDS reac-tions has been developed based on refinery data after several trialsand implemented. The model captures key operating conditions ina hydrotreater such as sulphur conversion, reaction pressure, LHSVand temperature. The light hydrocarbon model in Eqs. (3)–(5) wasdeveloped and implemented based on refinery industrial data withthree consecutive periods having slightly different operating con-ditions. Due to confidentiality issues, it has been difficult to obtainmore data points from this refinery or similar industrial data.Experimental data of light hydrocarbon production from hydro-treating process are also quite limited. In future work, the validityof this model would be tried and tested with more industrial dataas soon as they are available to us.

Cformed;i;j ¼ ci;j � aconv j � k0;i;j � exp �Eai;j

RTj

� �Pj � eLHSVj

� �� 8 i ¼ C1 ! C5 ð3Þ

Fig. 5. Modified model fitting with diesel experimental data.

Fig. 6. Modified model fitting with vacuum gas oil experimental data.

Fig. 7. Modified model fitting with kerosene experimental data.


aconv j ¼ 1� Sprod;j

Sfeed;j

� �ð4Þ

H2Sj ¼MWH2S

MWS� Sfeed;j � FFj

� � Sprod;j � LPj� �

ð5Þ

where Cformed,i,j = amount of light hydrocarbons i formed in hydrotr-eater j, t/h, ci,j = yield coefficient of light hydrocarbons formed inhydrotreater j, t/h, aconvj = sulphur conversion in hydrotreater j,ko,i,j = temperature dependence parameter, otherwise referred to asthe frequency factor for light hydrocarbons i formed in hydrotreaterj, and e is a pressure dependent term. Eq. (5) describes the formationof H2S in hydrotreater j obtained from stoichiometry as in [20].

Fig. 8 shows the amount of light hydrocarbons produced in anindustrial diesel hydrotreater at low operating pressures, approx.240 Nm3/m3, 592 K, and 2.24 h�1.

The result from the model showed good agreement with therefinery data as illustrated in Fig. 9.

A generalised reduced gradient approach is used to estimate theparameters as shown in Table 3.

The yield parameter, ci;j in Table 3 defines the amount of lighthydrocarbons formed. Butane has a high yield compared to otherhydrocarbons. This behaviour could be inferred from the tendencyof heavier hydrocarbons to form butanes during cracking, althoughhydrocracking effect is not considered in the hydrotreating reac-tions. In the context of this paper, the major function of a hydrotr-eater is to consume hydrogen for desulphurisation reactions atcertain flowrates and purity to produce low sulphur products at

fixed sulphur requirements. The unused hydrogen remaining inthe reactor outlet stream is usually recycled with associated lightgases and hydrogen sulphide. From Eq. (3), one of the key con-straints in the production of light hydrocarbons is the maximumsulphur specification in the product stream, which sets the amountof sulphur removed, and subsequently the amount of hydrogenconsumed and light hydrocarbons formed. For hydrogen consum-ing processes, increasing or decreasing the maximum sulphur ina hydrotreated product could affect the recycle hydrogen purityin Eq. (2).

3.2. Integration of reaction correlations in hydrogen networkmodelling

An extended hydrogen consumer model is developed from inte-grating process models for sulphur removal and formation of lighthydrocarbons in the hydrogen consumer. The integration method-ology applied to this work opens up opportunities to exploit vary-ing degrees of freedom that could improve the performance of theoverall integrated optimisation framework. This work presents themathematical formulation and optimisation of integrated multi-component network under variable operating conditions and prod-uct constraints to satisfy product properties.

3.2.1. Reactor inlet constraintsThe reactor inlet constraints include the overall mass and mul-

ticomponent hydrogen balance which ensure that each hydrogensink is supplied with the flowrate and purity requirements of thehydrogen consumer. The inlet of a reactor is made up of threestreams, namely liquid feedstock, make-up hydrogen and recycleshydrogen streams. The material balance at the mix point is givenby:

Fmix;j ¼ Fmu;j þ Fre;j 8 j ð6Þ

Fmix;j � Ymix;i;j ¼ Fmu;j � Ymu;i;j þ Fre;j � Yre;i;j 8 i 8 j ð7Þ

where F, Y and subscript j represents the flowrate of a stream, pur-ity of a stream in mass fraction and hydrotreater respectively. Sub-script i represents other components in the stream such as H2, H2S,CH4, C2H6, C3H8, C4H10, C5H12 as well as pseudo-componentsobtained from simulated distillation profile and bulk density of feedstreams.

Similarly, the amount of gas supplied to the reactor inlet mustbe equal to the flowrate at the mix point and the flowrate of liquidfeedstock for all hydrotreaters.

Fri;j ¼ Fmix;j þ Ffeed;j 8 j ð8Þ

Fig. 8. Light hydrocarbons formed in different periods.

Fig. 9. Model fitting to the refinery data in Fig 8.

Table 3Parameters obtained from the light hydrocarbon model.

Light hydrocarbons/model parameters ci,j (t/h) Ko Ea (kJ/Kmol)

Methane 4.0066 0.1550 365.3463Ethane 8.5276 0.0994 452.7053Propane 25.2526 0.1266 407.6503Butane 28.8097 0.1153 424.7202Pentane 12.4948 0.4589 152.6564


Fri;j � Yri;i;j ¼ Fmix;j � Ymix;i;j þ Ffeed;j � Yfeed;i;j 8 i 8 j ð9Þ

Since the feed flowrate is constant, and the mass composition ofhydrogen in the feed is zero, the pure hydrogen flowrate,Fri;j � Yri;i;j; is equivalent to the product of flowrate and purity atthe mix point, 8 i = H2. Initially, the flowrate and purity of the mix-ture are fixed in order to simulate the base case conditions.

3.2.2. Reaction modelTogether with Eqs. (2)–(5), the following constraints define the

overall mass and component balances around the hydrotreater. Amajor feature of this work is the variation in hydrogen consump-tion and light hydrocarbon production as a result of differenthydroprocessing requirements. These requirements affect thehydrogen partial pressure (referred to as the product of operatingpressure and recycle hydrogen purity) of hydrotreating units as

shown in Eq. (2). Changing recycle hydrogen purity would alsoaffect the hydrogen–oil ratio at the reactor inlets as shown in Eq.(7) and consequently, the light hydrocarbons formed and hydrogenconsumed in Eqs. (10) and (11).

Ffi;j � Yfi;i;j ¼ Fri;j � Yri;i;j � RHi;j 8 i ¼ H2 8 j ð10Þ

Ffi;j � Yfi;i;j ¼ Fri;j � Yri;i;j þ Zi;j 8 i ¼ C1 ! C5;H2S 8 j ð11Þ

The variables RHi;j and Zi;j are defined as consumption of hydrogenand production of light hydrocarbons including H2Sj respectively.The values of RHi;j and Zi;j are obtained from the process modelsin Eqs. (2)–(5). The amount of H2Sj formed is calculated in Eq. (5)and the stoichiometric hydrogen consumed from H2S formation isderived from the ratio of molecular weights of hydrogen and hydro-gen sulphide. Similarly, the amount of hydrogen consumed in theformation of light hydrocarbons is obtained from stoichiometry.The total hydrogen consumed is calculated from Eq. (12).

RHi;j ¼ RH2S;i;j þ RCformed;i;j8 i ¼ H2 8 j ð12Þ

where RH2S;i;j = amount of hydrogen consumed to form H2Sj andRCformed;i;j

= amount of hydrogen consumed in the formation of lighthydrocarbons in hydrotreater j. From Eq. (2), sulphur requirementsin the product are assigned upper bound and lower bound con-straints in each hydrotreater depending on the scope of optimisa-tion as in Eq. (13).


YLfs;j 6 Yfs;j 6 YU

fs;j 8 j ð13Þ

As a result, hydrogen–oil ratio is allowed to vary to expand thesearch region of the optimisation, but controlled by maintainingthe recycle flowrate. Also, if there are changes in composition ofthe make-up streams from different hydrogen producing sources,changes in composition of recycle and purge streams from lighthydrocarbons formed and hydrogen consumed in hydrotreatingreactions, fixing reactor inlet conditions would not be useful tothe hydrogen network optimisation. A key advantage of the presentapproach is that hydrogen–oil ratio and hydrogen partial pressureare allowed to vary, which is beneficial in exploiting optimisationscenarios.

3.2.3. Flash modelThe outlet stream obtained from the reactor is routed to the

high pressure flash separator for the distribution of reactor prod-ucts into vapour and liquid phases. The vapour phase could beinternally recycled in a complete recycle operation or externallyrecycled in the form of purge to other hydrogen consumers orsent to the fuel system. The liquid phase are either routed to alow pressure separator for further recovery of hydrogen andlight gases or prepared for fuel. Constant equilibrium distribu-tion coefficient (K-value) strategy proposed by Jia and Zhang[13] within narrow composition interval is applied in this work.Accurate prediction of K-values could provide effective reactormodelling and improve the accuracy of process simulation andperformance. Assuming vapour and liquid leaving a flash unitare in equilibrium:

Yvap;i;j ¼ Yliq;i;j � Ki;j 8 i 8 j ð14Þ

Mass balance around the flash separator unit is given by the follow-ing equations:

Ffi;j ¼ Fre;j þ Fliq;j þ Fpu;j 8 j ð15Þ

Ffi;j � Yfi;i;j ¼ Fre;j � Yre;i;j þ Fliq;j � Yliq;i;j þ Fpu;j � Ypu;i;j 8 i 8 j ð16Þ

Ffi;j ¼ Fvap;j þ Fliq;j 8 j ð17Þ

Fvap;j ¼ Fre;j þ Fpu;j 8 j ð18Þ

Yvap;i;j ¼ Yre;i;j ¼ Ypu;i;j 8 i 8 j ð19ÞX

i

Yvap;i;j ¼ 1 8 j ð20Þ

Xi

Yliq;i;j ¼ 1 8 j ð21Þ

Fre;j ¼ FH2C;j1;j 8 j ¼ j1 ð22Þ

Fpu;j ¼ Fpr;j þ FSF;j 8 j ð23Þ

Fpr;j ¼ FH2C;j1;j 8 j–j1 ð24Þ

3.2.4. Hydrogen network modelNormally, the makeup hydrogen to the hydrogen consumers

comes from hydrogen producing sources as in Eq. (25). The rela-tionship between hydrogen producers and consumers in a networkis represented by the network mass balance:X

k

FH2P;k;j ¼ Fmu;j 8 j ð25Þ

Xk

FH2P;k;j þX

j1

FH2C;j1;j ¼ Fmix;j 8 j ð26Þ

Xk

ðFH2P;k;j � YH2P;i;kÞ þX

j1

ðFH2C;j1;j � YH2C;i;j1Þ ¼ Fmix;j � Ymix;i;j 8 j

ð27Þ

Eq. (26) shows that the sum of hydrogen production flowrates fromvarious hydrogen producers, k, externally recycled gas flows fromother consumers, jl, and internally recycled gas flows within con-sumer, j, is equal to the gas flowrate at the reactor inlet mix pointfor consumer, j. Combining with Eq. (27) for multicomponent massbalance, the complete mass balance between hydrogen producersand consumers is obtained.

In a hydrogen network, hydrogen consumers require hydrogenat certain flowrates and purities. The purge gas from the high pres-sure separators of various hydrogen consumers are either reused inother consumers or sent to a site fuel system. The relationshipbetween hydrogen consumers and site fuel system is representedby the following mass balance equations:

Fpu;j ¼ FSF;j þX

j1

FH2C;j;j1 j–j1 8 j ð28Þ

Fpu;j � Ypu;i;j ¼ FSF;j � YSF;i;j þX

j1

ðFH2C;j;j1 � YH2C;i;jÞ j–j1 8 j ð29Þ

Ypu;i;j ¼ YSF;i;j ¼ YH2C;i;j 8 i 8 j ð30Þ

Eq. (28) shows that the purge gas from the flash outlet could be rou-ted to other consumers, as an external recycle or to the fuel gassystem.

Normally, the flowrate from a hydrogen producer are subject tocertain maximum or minimum limits as shown in Eq. (31).

FLH2P;k 6

Xj

FH2P;k;j 6 FUH2P;k ð31Þ

By combining Eqs. (1)–(31), a process network model is formulated thatcomprises non-linear empirical models for prediction of sulphur inproduct, light hydrocarbon formation and hydrogen consumption,and the mass balances of hydrogen producers and consumers in a net-work. The objective function is to minimise operating cost that accountsfor the hydrogen production cost and fuel gas value as in Eq. (32).

Objective ¼ MINX

k

ðFH2P;k � UH2 Þ �X

j

ðFSF;j � USF;jÞ" #

ð32Þ

where UH2 and USF , represent the unit prices of hydrogen and fuelgas respectively. Other costs that are integral in refinery processeconomics have not been considered in this present work. A signif-icant aspect of this work is to develop a strategy for hydroprocessorintegration in multicomponent hydrogen networks and theirimpacts on hydrogen production requirements and fuel gas loss.The additional process constraints proposed in the formulation ofthis methodology are expected to give more realistic solutions.

4. Integrated optimisation framework for refinery processnetworks – A multicomponent strategy

The proposed methodology which incorporates non-linearempirical hydrotreater models to predict the effect of changingprocess variables on hydrotreater performance and its interactionswith the multicomponent hydrogen network model is shown inFig. 10.

The methodology can be summarised into three major steps.

4.1. Integration of process models in a multicomponent framework

The non-linear process models described in Section 3.1 are inte-grated in the multicomponent hydrogen framework with flash


integration. Strictly speaking, the ultimate aim of designing a refin-ery process network is to produce distillate fuels that meet envi-ronmental, as well as economic objectives. Consequently,neglecting the hydroprocessors in the optimisation of hydrogennetworks may result in solutions that are not applicable to real sys-tems. By considering the integration of process performance inhydrogen network optimisation, the interactions between processrequirements and qualitative hydrogen distribution can be esti-mated. In this step, operating conditions and hydrogen–oil ratioare fixed for hydrogen consuming processes; the integrated pro-cess models in the hydrogen consumer define the amount ofhydrogen consumed in the process. Flash calculations using con-stant K-value strategy for slight compositional changes were inte-grated in the network to improve the network behaviour andreduce complexity. However, changes in feed source, hydrogensource could result in large variation in K-values which could affectthe accuracy of the solutions.

4.2. Optimisation with varying process operating conditions

Normally, operating conditions in refinery process changes areallowed to vary within specific limits that are suitable for processimprovement. For example, changes in feedstock composition,hydrogen inlet conditions, and reactor temperature result in vary-ing reaction conversions for different sulphur specifications andsubsequently changes to light hydrocarbon production. The effectof these slight changes are described in the resulting variation inhydrogen consumption, flowrate and purity of recycle and purgestreams and overall hydrogen production in the network. Byexpanding the variable search in the network, such as hydrogen

Optimization with variable conditions

Data

Integrated multicomponent network design under varying

operating conditions

Define range of operating conditions

Refinery process network optimisation with integrated non linear process models for varying

process performance

Fig. 10. Integrated optimisation frameworks for refinery process networks.

oil ratio and hydrogen partial pressure, more degrees of freedomcould be exploited to satisfy the objective function, while obtainingoptimal process performance.

4.3. Operational optimisation: Integrated multicomponent design ofrefinery process networks

In addition to allowing boundary tolerance in hydrogen oil ratioand hydrogen partial pressure, a mix of lower reactor temperatureand purity requirements for the same amount of sulphur in prod-uct may be worth exploring for different hydrogen consumers.Consequently, an integrated multicomponent hydrogen networkfor improved operating conditions could be developed dependenton experimental models and limitations. Relaxing operating condi-tions in one hydrogen consumer would normally improve theinteractions between other hydrogen consumers, and subse-quently benefit hydrogen producer supply for an efficient operat-ing mix. Therefore, an optimal combination of flowrate, hydrogenpurity and operating conditions is required to meet desiredspecifications.

5. Case study

5.1. Base case

The hydrogen network base case is presented here to illustratethe applicability of the proposed approach developed for the inte-grated design and optimisation of multicomponent networks. Thecase study describes the simultaneous integration of process mod-els and multicomponent hydrogen network models in the optimi-sation of hydrogen network requirements. The objective of thisstudy is to determine the optimum hydrogen production flowratefor different study scenarios.

The hydrogen network base case consists of two hydrogen pro-ducers: Hydrogen plant, H2 Plant; catalytic reformer, CCR; and fourhydrotreaters: naphtha hydrotreater, NHT; cracked naphtha hyd-rotreater, CNHT; diesel hydrotreater, DHT; and vacuum gas oilhydrocracker, VGOHC. Table 4 shows the detailed feed stream datafor base case.

The components listed in Table 4 are pseudo-componentsderived from the corresponding stream physical properties. Non-linear process models developed in Section 3.1 are integrated inthe multicomponent hydrogen network under fixed operating con-ditions as shown in Table 5, fixed hydrogen–oil ratio, and varyinghydrogen consumption for different hydrotreaters based on sul-phur requirements in the product resulting in an integrated basecase flowsheet in Fig. 11. Hydrogen cost from import is £ 3000/yr[21].

The hydrogen production flowrate obtained from the integratedmulticomponent process network is 11.771 t/h for fixed inlethydrogen conditions. The network diagram in Fig. 11 shows thatrequired sulphur specifications are not compromised while opti-mising hydrogen allocation to different hydrotreaters. In the previ-ous integrated flash methodology without consideration to processor environmental demands [13], and therefore fixing hydrogenconsumption, approximately 22% of hydrogen was spent in the fuelsystem. This present work demonstrates the utilisation of allhydrogen produced within the processes; hence no hydrogenwas routed to the fuel system as indicated in Figs. 11–13. However,hydrogen not required by the process from the integrated con-sumer models is dissolved in liquid product, which could be sub-ject to further low pressure separation. Integrating the hydrogenconsuming processes in the hydrogen network model opens upopportunities to exploit various degrees of freedom available tooptimise the process. For example, recycle hydrogen purity and

Table 4Detailed feed data for base case.

Hydroprocessors NHT CNHT DHT VGOHC

Feed flowrate (t/h) 175.041 75.886 337.563 260.044Compositions (mass fraction)H2 0.0000 0.0000 0.0000 0.0000C1 0.0000 0.0000 0.0000 0.0000C2 0.0000 0.0000 0.0000 0.0000C3 0.0000 0.0000 0.0000 0.0000C4 0.0000 0.0000 0.0000 0.0000C5 0.0000 0.0000 0.0000 0.0000H2S 0.0000 0.0000 0.0000 0.0000PC1-NHT 0.1627 0.0000 0.0001 0.0000PC2-NHT 0.2051 0.0000 0.0002 0.0000PC3-NHT 0.2207 0.0000 0.0002 0.0000PC4-NHT 0.2686 0.0000 0.0003 0.0000PC5-NHT 0.1429 0.0000 0.0002 0.0000PC1-CNHT 0.0000 0.0916 0.0000 0.0000PC2-CNHT 0.0000 0.1675 0.0000 0.0000PC3-CNHT 0.0000 0.2422 0.0000 0.0000PC4-CNHT 0.0000 0.2031 0.0000 0.0000PC5-CNHT 0.0000 0.2957 0.0000 0.0000PC1-DHT 0.0000 0.0000 0.0745 0.0000PC2-DHT 0.0000 0.0000 0.1743 0.0000PC3-DHT 0.0000 0.0000 0.2630 0.0000PC4-DHT 0.0000 0.0000 0.3502 0.0000PC5-DHT 0.0000 0.0000 0.1371 0.0000VGOHC-GA1 0.0000 0.0000 0.0000 0.0062VGOHC-GA2 0.0000 0.0000 0.0000 0.0133VGOHC-GA3 0.0000 0.0000 0.0000 0.0281VGOHC-GA4 0.0000 0.0000 0.0000 0.0192VGOHC-GA5 0.0000 0.0000 0.0000 0.0140VGOHC-NAP1 0.0000 0.0000 0.0000 0.0213VGOHC-NAP2 0.0000 0.0000 0.0000 0.0358VGOHC-NAP3 0.0000 0.0000 0.0000 0.0825VGOHC-NAP4 0.0000 0.0000 0.0000 0.0738VGOHC-NAP5 0.0000 0.0000 0.0000 0.0394VGOHC-DIE1 0.0000 0.0000 0.0000 0.0756VGOHC-DIE2 0.0000 0.0000 0.0000 0.1242VGOHC-DIE3 0.0000 0.0000 0.0000 0.1872VGOHC-DIE4 0.0000 0.0000 0.0000 0.1831VGOHC-DIE5 0.0000 0.0000 0.0000 0.0964

Table 5Base case operating conditions of hydroprocessors.

Hydroprocessors T (K) P (bar) LHSV (h�1)

NHT 623 60 5CNHT 653 65 1.5DHT 633 60 1VGOHC 653 52.96 2


hydrogen–oil ratio are obtained from the required sulphur conver-sion and process operating conditions, and these set the hydrogenconsumption and makeup hydrogen requirements of the hydro-processor, which translates into the hydrogen production flowrateof the network. The amount of total hydrogen consumed, sulphurconsumed and light hydrocarbons formed are mainly based onthe HDS and light hydrocarbon process models coupled with stoi-chiometric relationships of HDS process for different sulphur dis-tribution embedded in the hydrogen consumer.

5.2. Optimisation with varying hydrogen inlet conditions

For a multicomponent network configuration with fixed hydro-gen flowrate at the reactor inlet as in Fig. 11, there are limitationsto manipulating the degrees of freedom existent in the integratedframework. For example, product requirements could be achievedat lower make up requirements when there are opportunities totune the inlet hydrogen flow for a given property. In this section,

reactor inlet hydrogen flowrates have been allowed to vary withinbounds, while maintaining the recycle flow as illustrated in Fig. 12.

Fig. 12 shows that hydrogen production flow obtaineddecreases by approximately 2% to 11.547 t/h. As noticed, equilib-rium has been affected, thus resulting in a reduction of dissolvedhydrogen in some hydrogen consumers where makeup hydrogenis decreased compared to base case. Note that the amount of lighthydrocarbons produced is same for both fixed and varying inlethydrogen flow. With varying inlet configuration for hydrogen con-sumers, makeup hydrogen requirements could be decreased forsome hydrotreaters, resulting in a decrease in hydrogen productionflowrate. Table 6 describes the comparison between fixed andvarying inlet conditions.

5.3. Operational optimisation of multicomponent hydrogen network

Suppose it is required to optimise operating temperature, whilemaintaining the same amount of sulphur in product as described inFig. 13.

The resulting multicomponent integrated network shows adecrease in H2 import by 9% from the fixed base case. As observed,dissolved hydrogen has been reduced considerably in most hydro-gen consumers. Slightly lower hydrogen outlet partial pressureshave been obtained for allowable decrease in reaction severity.Table 7 describes the changes in the overall network.

Table 7 shows the change in import obtained from varying H2

inlet only compared with sequential temperature optimisation.An approximate 7% decrease in H2 import proves the overall ben-efit of temperature and purity optimisation.

5.4. Environmental regulatory effects on multicomponent hydrogennetwork

Sometimes environmental regulators demand a more stringentsulphur specification, especially in diesel hydrotreaters. For exam-ple, the case of further sulphur reduction to 6 ppm as in DHT pro-cess in Table 8. Is it really worthwhile to restrict sulphur limits tolower levels?

For every reduction in sulphur, there is a simultaneous increasein temperature, as well as slight increases in outlet hydrogen par-tial pressure. However, the makeup H2 requirement only increasesinsignificantly and thus the H2 import flow. Note that an increasein light hydrocarbons formed is likely; hence building up impuri-ties in the system as seen in Fig. 14.

In some cases, the light hydrocarbon slope tends to be steeperdepending on the feedstock involved and rate of desulphurisation.It is usually desirable to operate within an operational safety enve-lope (OSE) such that a reduction in diesel sulphur specifications toa minimum is checked against increase in operating severity, forexample reaction temperature, build-up of light hydrocarbons inthe recycle loop and the overall effect on hydrogen productionflow. However, note that increase in reaction temperature couldnecessitate a reduction in catalytic activity. If deactivation rate isinversely proportional to the hydrogen partial pressure, then cata-lyst deactivation is expected to follow a downward trend underincreasing hydrogen partial pressure conditions. However, rate ofdeactivation is still evident due to increasing temperature as inFig. 15.

The approach adopted in the constrained optimisation is usefulin allowing effective use of hydrogen in hydrogen consuming pro-cesses. An optimal integrated multicomponent network have beenobtained that allows varying hydrogen inlet conditions, hydrogenconsumption based on sulphur specification constraints, and amix of operating conditions that improves interaction betweenhydrogen use and the overall optimisation of the multicomponenthydrogen network.

Fig. 11. Optimised integrated multicomponent hydrogen network under fixed inlet hydrogen conditions.

Fig. 12. Integrated multicomponent network under varying inlet hydrogen conditions.


5.5. NLP hydrogen network optimisation

The NLP network model is optimised with the CONOPT solver inGAMS. With fixed hydrogen–oil ratio and operating conditions, thehydrogen plant production of the integrated multicomponenthydrogen network is 11.77 t/h as shown in Fig. 11. By extendingthe integrated multicomponent network to capture varying inlethydrogen conditions under different sulphur requirements, thehydrogen production flowrate was decreased to 11.55 t/h, result-ing in a savings of 2%. In another scenario, varying operating con-

ditions (temperature) for all hydrogen consuming processes atlower hydrogen purities was explored for the same sulphur speci-fication resulting in an additional benefit of approximately 7%. Fur-ther optimisation based on reduction of sulphur target from15 ppm to 10 ppm was investigated in DHT process. The resultshows that the effect on hydrogen import cost was quite significantcompared to a further decrease to 8 ppm and 6 ppm, whichincurred zero costs in hydrogen import requirements as presentedin Table 8. Hence, there may be no real benefit in exploring lowersulphur requirements.

Fig. 13. Integrated multicomponent networks with optimised CNHT.

Table 6Comparison of fixed and varying H2 inlet conditions.

Cases Fixed and varying inlet H2 for fixed H2 consumption

Hydrogen consumers NHT CNHT DHT VGOHC

Fixed Vary Fixed Vary Fixed Vary Fixed Vary

Fixed reaction conditions 623 623 653 653 633 633 653 653Fixed sulphur in product (wt%) 0.015 0.015 0.015 0.015 0.0015 0.0015 0.100 0.100Makeup hydrogen (t/h) 0.556 1.334 0.649 0.827 2.496 2.457 8.404 7.275External recycle H2 to consumer inlet (t/h) 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000Chemical hydrogen consumed (t/h) 0.398 0.398 0.426 0.426 1.845 1.845 0.908 0.908H2 to external recycle/fuel system (t/h) 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000Dissolved hydrogen (t/h) 0.159 0.937 0.224 0.401 0.651 0.613 7.494 6.368C1 formed (t/h) 0.021 0.021 0.021 0.021 0.020 0.020 0.011 0.011C2 formed (t/h) 0.036 0.036 0.038 0.038 0.034 0.034 0.018 0.018C3 formed (t/h) 0.161 0.161 0.167 0.167 0.153 0.153 0.082 0.082C4 formed (t/h) 0.152 0.152 0.162 0.162 0.144 0.144 0.078 0.078C5 formed (t/h) 0.082 0.082 0.085 0.085 0.077 0.077 0.042 0.042

H2 production flowrate – Fixed case (t/h) 11.771H2 production flowrate – Varying case (t/h) 11.547Cost of H2 saved from fixed case (£/yr) 5,376,000

Table 7Comparisons of simultaneous variation of H2 inlet and reaction temperature conditions.

Cases Varying inlet H2 and varying T for same sulphur in product

Vary reaction conditions NHT CNHT DHT VGOHC

Vary H2 inlet Vary T Vary H2 inlet Vary T Vary H2 inlet Vary T Vary H2 inlet Vary T

Reactor operating temperature (K) 623 623 653 652.93 633 633 653 653Fixed sulphur in product (wt%) 0.015 0.015 0.015 0.015 0.0015 0.0015 0.100 0.100Makeup hydrogen (t/h) 1.334 1.240 0.827 0.823 2.457 2.457 7.275 6.640External recycle H2 to consumer inlet (t/h) 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000Chemical hydrogen consumed (t/h) 0.398 0.398 0.426 0.426 1.845 1.845 0.908 0.908H2 to external recycle/fuel system (t/h) 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000Dissolved hydrogen (t/h) 0.937 0.843 0.401 0.397 0.613 0.613 6.368 5.731Pure recycle H2 at the outlet 6.319 6.319 8.990 8.988 39.679 39.679 10.971 10.971C1 formed (t/h) 0.021 0.021 0.021 0.021 0.020 0.020 0.011 0.011C2 formed (t/h) 0.036 0.036 0.038 0.038 0.034 0.034 0.018 0.018C3 formed (t/h) 0.161 0.161 0.167 0.167 0.153 0.153 0.082 0.082C4 formed (t/h) 0.152 0.152 0.162 0.162 0.144 0.144 0.078 0.078C5 formed (t/h) 0.082 0.082 0.085 0.085 0.077 0.077 0.042 0.042

H2 plant flowrate (t/h) – Case I – varying H2 inlet 11.547H2 plant flowrate (t/h) – Case II – varying H2 inlet, T 10.755Cost of H2 saved from Case I (£/yr) 19,008,960


Table 8Effect of sulphur restriction in DHT process on the overall network.

Cases Different sulphur content in productHydrogen consumer DHT

Reactor operating temperature (K) 633 639.93 643.48 647.84Varying sulphur in product (wt%) 0.0015 0.0010 0.0008 0.0006Makeup hydrogen (t/h) 2.457 2.458 2.458 2.459External recycle H2 to consumer inlet (t/h) 0.000 0.000 0.000 0.000Chemical hydrogen consumed (t/h) 1.845 1.845 1.846 1.846H2 to external recycle/fuel system (t/h) 0.000 0.000 0.000 0.000Dissolved hydrogen (t/h) 0.613 0.613 0.613 0.613Pure recycle H2 at the outlet 39.679 39.720 39.743 39.768H2S formed (t/h) 5.448 5.450 5.451 5.451H2 import flowrate (t/h) 10.757 10.758 10.758 10.758Cost of H2 increase (£/yr) 24,000 0 0

Fig. 14. A schematic representation of quality trend in hydrotreaters.

Fig. 15. Temperature vs. deactivation rate.


By integrating hydrotreating models into multicomponenthydrogen networks for improved network performance, optimisa-tion of hydrogen use, and exploitation of operating variables, thefocus of refinery hydrogen management can now be shifted fromminimising hydrogen consumption to optimising hydrogenutilisation.

A summary of the GAMS calculations for the overall integratedmulticomponent network is as follows:

Computer resource
64-bit operating system,6 GB RAM, 2.4 GHz Intel Core i5-2430M
No. of variables
38 No. of equations 70 CPU time 0.68 s (varies for different cases)
6. Conclusions

Until now studies based on refinery hydrogen managementhave fixed hydroprocessor performance in the optimisation of ahydrogen network, thereby neglecting the crucial interactionsbetween hydroprocessors and multicomponent hydrogen net-works. The proposed methodology accounts for this deficiency inthe optimisation of integrated multicomponent process networks.The interactions between varying hydrogen inlet conditions ofhydrogen consuming processes, optimising operating conditionsand constrained process performance indicators have been investi-gated. The effect on light hydrocarbon generation produced fromHDS processes and catalyst activity have also been analysed. Apotential saving of 2% was realised from varying H2 inlet condi-tions and a further reduction of 7% was achieved by optimisingtemperature. Exploiting such degrees of freedom in the networkopens up opportunities for allocating optimum operating condi-tions to hydroprocessors, thereby increasing hydrogen utilisationefficiency and optimising hydrogen production flowrate. By allow-ing simultaneous consideration of hydroprocessor integration,multicomponent hydrogen network optimisation, and varyingoperating conditions, an actual and effective hydrogen optimisa-tion methodology has been implemented.

Acknowledgement

The authors would like to acknowledge Petroleum TechnologyDevelopment Fund for their financial support granted.

References

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[2] Alves J. Analysis and design of refinery hydrogen distribution systems. Ph.D.thesis, Department of Process Integration. University of Manchester Instituteof Science and Technology, Manchester, UK; 1999.

[3] Linnhoff B, Mason DR, Wardle I. Understanding heat exchanger networks.Comput Chem Eng 1979;1979(3):295.

[4] Zhao Z, Liu G, Feng X. The integration of the hydrogen distribution system withmultiple impurities. Chem Eng Res Des 2007;85:1295–304.

[5] Hallale N, Liu F. Refinery hydrogen management for clean fuels production.Adv Environ Res 2001;6:81–98.

[6] Liu F, Zhang N. Strategy of purifier selection and integration in hydrogennetworks. Chem Eng Res Des 2004;82:1315–30.

[7] Ahmad MI, Zhang N, Megan J. Modelling and optimisation for design ofhydrogen networks for multi-period operation. J Clean Prod 2011;19:204–11.

[8] Liu F. Hydrogen integration in oil refineries. PhD. Thesis, Department ofProcess Integration, University of Manchester Institute of Science andTechnology, Manchester, UK; 2002.

[9] Kumar A, Gautami G, Khanam S. Hydrogen distribution in the refinery usingmathematical modelling. Energy 2010;35:3763–72.

[10] Liao Z, Wang J, Yang Y, Rong G. Integrating purifiers in refinery hydrogennetworks: a retrofit case study. J Clean Prod 2010;18:233–41.

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[11] Zhou L, Zuwei L, Wang J, Jiang B, Yang Y. Hydrogen sulphide removal processembedded optimization of hydrogen network. Int J Hydrogen Energy2012;37:18163–74.

[12] Singh BB, Zhang N. Impact of gas phase impurities on refinery hydrogennetwork management. 2005 AIChE Spring National Meeting: ConferenceProceedings. Atlanta, GA, USA; April 2005. p. 1469–80.

[13] Jia N, Zhang N. Multi-component optimization for refinery hydrogen networks.Energy 2011;36(8):4663–70.

[14] Shih SS, Mizrahi S, Green LA, Sarli MS. Deep desulphurisation of distillates. IndEng Chem Res 1992;31:1232–5.

[15] Ho TC. Property-reactivity correlation for HDS of middle distillates. Appl CatalA, General 2003;244:115–28.

[16] Choudhary TV, Parrot S, Johnson B. Unravelling heavy oil desulphurisationchemistry: targeting Clean fuels. Environ Sci Technol 2008;42:1944–7.

[17] Knudsen KG, Cooper BH, Topsoe H. Catalyst and process technologies for ultra-low sulphur diesel. Appl Catal A 1999;189:205–15.

[18] Ancheyta J. Modelling and simulation of catalytic reactors for petroleumrefining. John Wiley & Sons 2011.

[19] El Kady FY, Mohamed LK, Shaban SA. Hydrotreatment of kerosene petroleumfraction to improve its properties. Int J Acad Res 2010:2.

[20] Hasenberg DM, Campagnolo JF. Modelling and simulation of a reaction forhydrotreating hydrocarbon oil. Philips Petroleum Company; 1997.

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http://www.h2carblog.com

34

Chapter 3 Integrating Hydrocracking Process in Refinery

Hydrogen Network Optimization

This paper explores the impact of varying operating conditions on

hydrocracking process performance and refinery profitability. A semi-empirical

non-linear conversion and yield model have been developed to evaluate the

interrelationship between the hydrocracker process variables and the hydrogen

requirements of the network. The conversion model and light naphtha (LN)

yield model has been derived from first principles modelling of reaction systems.

Other yield of products obtained from VGO hydrocracking is then derived from

LN yield and are based on industrial or experimental data. The developed

models are validated on another industrial data. In addition to the models

proposed in Publication 1, these models are integrated in the refinery hydrogen

network to obtain an overall superstructure consisting of hydroprocessor

models, material balances around the reaction and separation systems, and flash

model. The integrated network is optimized with a CONOPT solver, and the

effects of varying operating conditions can be seen in the product yields and

profitability of the network. The results prove the effectiveness of the model in

accurately predicting existing trends in refinery operations. Consequently, the

model can be used to estimate hydrogen production requirements and profit

based on the feed characteristics and prevailing operating conditions. Dr. Nan

Zhang is responsible for the critical review of this paper.

35

3.1 Publication 2

Umana B, Shoaib A, Zhang N, Smith R. Integrating Hydrocracking

Process in Refinery Hydrogen Network Optimization. Journal of Cleaner

Production. 2015; (Submitted)

P35

1

INTEGRATING HYDROCRACKING PROCESS IN REFINERY HYDROGEN

NETWORK OPTIMIZATION

Blessing Umana, Nan Zhang*, Robin Smith

Centre for Process Integration, School of Chemical Engineering and Analytical Science,

The University of Manchester, PO Box 88, Sackville Street, M60 1QD, UK

ABSTRACT

A method for integrating hydrodesulphurization process models in refinery

hydrogen network models was developed [17]. The present work investigates

the integration of hydrocracking processes in the optimization of hydrogen

networks. The authors have developed process models that could predict

hydrocracker conversion and yields. The integration of these models in the

hydrogen network optimization is carried out on a General Algebraic Modelling

System (GAMs) platform. The effects of these interactions are visible in the

expected results. The findings demonstrate that hydrogen management

programs and process integration initiatives are profitable in the implementation

of actual and effective hydrogen networks.

* Corresponding author: [email protected]

Keywords: Hydrocracking, Process models integration, hydrogen

utilization, hydrogen network, optimization


2

List of Tables

Table 1 Experimental Data for Feed and Products obtained from the Refinery ... 19

Table 2 Boiling range of hydrocracked products ...................................................... 19

Table 3 Feed and operating data in the refinery ........................................................ 22

Table 4 Yield comparison of Industrial and Model predictions I ........................... 24

Table 5 Comparison of feed, operating conditions and parameters between two

industrial data ................................................................................................................. 26

Table 6 Yield comparison of industrial and model predictions II .......................... 28

Table 7 Detailed feed data for base case ..................................................................... 37

Table 8 Comparison of VGO product yields at fixed and varying inlet H2

conditions ........................................................................................................................ 41

Table 9 Sulphur distribution in VGO hydrocracked products ................................ 42

Table 10 Effects of varying temperature on VGO hydrocracker process

performance .................................................................................................................... 42

Table 11 Comparison of fixed and varying inlet H2 conditions for maximum

profit ................................................................................................................................. 45

Table 12 Effect of increasing feed flow on network profitability ............................ 46

List of Figures

Figure 1 Simplified flow diagram of a one stage once-through hydrocracker

configuration ................................................................................................................... 10

Figure 2 Comparison of Industrial and Predicted LN yields [20] .......................... 23

Figure 3 Comparison of Industrial and Predicted yields of hydrocracked VGO . 24

Figure 4 Comparison of Industrial and Predicted LN yields [23] .......................... 27

Figure 5 Comparison of Industrial and Predicted yields for hydrocracked VGO

[23] .................................................................................................................................... 28

Figure 6 Methodology for integration of hydrocrackers in a refinery hydrogen

network ............................................................................................................................ 34

Figure 7 Integrated hydrogen network at fixed operating conditions ................... 38

Figure 8 Integrated hydrogen network under varying inlet H2 conditions .......... 40

Figure 9 Integrated hydrogen network under varying inlet H2 conditions for

maximum profit ............................................................................................................. 44

3

Highlights

A six-lump model for predicting conversion in hydrocracking process based

on feed characteristics and operating conditions has been developed.

Integration of the developed hydrocracker model with a hydrogen network

model is established.

Impacts of operating temperature and pressure on product yields are

quantified.

1. Introduction

Hydrocracking is a catalytic process that converts heavier products into valuable

lower boiling products primarily using cracking, isomerization, and aromatic

saturation reactions in the presence of hydrogen. This is one of the major

conversion processes used in modern refining. Hydrocracking is a versatile

process because of its capability to process a wide variety of feedstocks and

produce superior quality products, namely: high-quality middle distillates,

naphtha with high naphthenic content for reformer feed, lube oil base stocks,

feedstocks for FCC units and ethylene crackers [1].

In the last two decades, the primary driver for growth in the demand for fuels

has been middle distillate, whilst global growth in gasoline demand has been

modest. More recently, the International Energy Agency (IEA) has projected a 48

% increase in middle distillate fuel demand, dominated by diesel in 2020 [2]. The

surging interest in the demand for diesel could be traced to the increased

consumption for transportation and power generation in developing nations.

4

Now, with the tightening of sulphur specifications in diesel fuel to ultra-low

levels, refiners are faced with the challenge of meeting the demand for ultra-low

sulphur diesel (ULSD), which is expected to continue for some time.

Consequently, refiners are investing heavily in middle distillate conversion

units, such as hydrocrackers, as well as in hydrotreating units necessary to

produce ULSD. This increased reliance on hydrocrackers for clean middle

distillate fuel demand has also led to a rise in hydrogen consumption, thus

stretching the existing hydrogen production capacity, and thereby increasing the

cost and creating a deficit in the hydrogen balance of a refinery. Hydrogen is

often produced or purchased in excessive quantity to have sufficient supply

available to hydrogen consuming units. Future trends and legislative

requirements are expected to further increase hydrogen consumption generating

an additional operating cost. With limited and expensive hydrogen availability

for refinery hydrogen consumers, it becomes important to optimize hydrogen

consumption in an overall refinery hydrogen distribution system in order to

utilize hydrogen effectively for maximum profit. Therefore, the development of

process models is a requisite to optimizing hydrogen consumption for an

effective hydrogen management system.

The present strategy would address two major issues: 1. Development of

hydrocracker models that are robust and sufficiently detailed to capture the

behaviour of a process with changes in operating conditions. 2. Integration of

5

hydrocracker performance into hydrogen networks to exploit the interactions

between hydrocrackers and hydrogen networks, and their effect on the overall

network. The resulting superstructure would facilitate the efficient utilization of

hydrogen resources for improved process operation.

2. Review of previous research

The pursuit of hydrogen management programs in refinery hydrogen networks

has been designed to maximize hydrogen recovery and minimize hydrogen

utility flowrate without considering the interactions with hydroprocessors under

these objectives. This approach is particularly important in the early stages of

network design. Towler et al [3] developed the first methodical approach to

analysing hydrogen distribution systems by graphically illustrating the cost and

value concept as the driving force for hydrogen transfer between hydrogen

resources and refinery products. However, this method does not provide a

systematic approach to optimizing hydrogen distribution systems. Alves [4]

proposed the concept of hydrogen pinch by extending the pinch analysis method

of Linnhoff et al [5] to a refinery hydrogen network. A hydrogen pinch shows

the minimum theoretical hydrogen needed from sources to sinks, such that any

further reduction in flowrate would create a negative hydrogen surplus, making

the hydrogen distribution problem infeasible. Hydrogen pinch analysis was

6

quickly adopted in the refining industry and was extended to cover more aspects

of refinery hydrogen management [6].

Some practical limitations exist in the application of two-dimensional graphical

methods to the design of hydrogen distribution networks. Hallale and Liu [7]

extended Alves [4] Linear Programming (LP) technique to a Mixed Integer Non-

Linear Programming (MINLP) formulation to account for pressure constraints.

Liu and Zhang [8] developed a strategy to integrate purifiers in refinery

hydrogen networks. Ahmad et al [9] extended the MINLP model developed by

Liu [10] to multi-period hydrogen network designs. The concept of variable inlet

and outlet pressure configuration was introduced by Kumar et al [11]. Liao et al

[12] accounted for the optimum location of compressors and purifiers in the

optimization of hydrogen distribution networks.

While these methods take into consideration practical constraints, other

constraints such as the actual requirements of hydrogen consuming refinery

processes are neglected. More importantly, the building of process models

within refinery processes to predict the yields, physical and performance

properties of various products as a function of plant operating conditions,

catalyst and feedstock source have been ignored in previous research. Zhang [13]

evaluated the impact of crude oil composition and refining chemistry on the

performance and physical properties of products by developing a consistent and

accurate characterization method for use in the development of molecular-level

7

process models. Peng [14] proposed a Molecular type homologous series

(MTHS) matrix representation that provides a convenient way of describing the

composition, reactions and properties of complex petroleum mixtures. In

addition to hydrocarbons, sulphur and nitrogen compounds were also included

in the matrix. Sun [15] developed a strategy to analyze the interactions between

one diesel hydrotreater and the hydrogen network by using the hydrogen

surplus obtained from hydrogen pinch analysis to simulate the performance of

the hydrotreater with increasing throughput. The author further developed a

molecular model for hydrocracking based on MTHS representation. These

methods of analysing oil mixtures at such a molecular level are very computing

extensive, time-consuming and practically difficult to integrate with the overall

hydrogen network model.

The above mentioned approaches treats hydrogen streams as a binary mixture of

hydrogen and methane. To address this limitation, Singh and Zhang [16]

incorporated the impact of impurities, by integrating a flash model to account

for vapour-liquid equilibrium characteristics within hydrogen consumers and

their effects on an overall hydrogen network. Due to the series of iteration

involved, Jia and Zhang [17] introduced a more efficient approach to

optimization of refinery hydrogen networks by assuming constant vapour-liquid

equilibrium coefficients in the flash calculation.

8

Still, the main focus of the above methodologies is to reduce overall hydrogen

consumption. The effects of different hydrogen supply conditions on the yields

and quality of refining products are not considered, such as the effect of

variations in reaction hydrogen partial pressures, hydrogen-oil-ratio, and other

operating conditions on required product specifications. During

hydrodesulphurization (HDS) reactions, light hydrocarbons are formed

simultaneously from hydrogenolysis and hydrogenation reactions. The actual

prediction of these light ends is not accounted for in the hydrogen network

optimization. With the objective being minimum hydrogen production flowrate,

hydrogen requirements may be misleading and limiting as constraints on

maximum allowable sulphur in product streams are neglected. Also, the

economic potential of the process may be affected as hydrogen giveaways is

common practice in such methodologies. In a recent paper [18], a methodology

for integrating hydrodesulphurization process models in the optimization of

hydrogen networks was proposed. Models for the prediction of sulphur removal

and production of light hydrocarbons were modified to represent the effect of

network dynamics on process performance. The effect of changing sulphur

targets on light hydrocarbon composition and their corresponding hydrogen

requirements was also investigated. An Integrated Framework for assessing the

interactions between hydrotreating reactions and hydrogen distribution systems

in order to maximize efficient utilization of hydrogen and ensure adequate

operability of hydroprocessors was proposed. With growing demand for middle

9

distillates, hydrocracking processes have become vital to the profitability of oil

refineries and put stress on their hydrogen networks. Following the previous

work on hydrotreating processes [18], the purpose of this paper is to develop an

integrated framework to assess further the interactions between hydrocracking

reactions and hydrogen distribution systems by exploiting key degrees of

freedom to improve overall network performance and profitability. Such an

integrated approach, if employed during the early stages of the design of

refinery hydrogen networks could maximize the efficient utilization of hydrogen

by hydrogen consumers and ensure their optimal operation in the overall

network.

3. Integrated design of hydrocracking processes and hydrogen networks

Efficient distribution of hydrogen within a refinery hydrogen network is a major

concern in tackling the hydrogen deficit problem. More importantly, the

interactions between hydrogen consuming units in the overall network are

critical to determining refinery profitability. The proposed methodology focuses

on the integration of a hydrocracker unit in a refinery hydrogen network. A

fundamental aspect of this work is the optimization of hydrocracker operations

for production of maximum distillates and minimum light ends. Depending on

the mode of operation, a typical conversion of 40-80 % can be achieved in once-

through commercial hydrocrackers. In case a high conversion is necessary,

10

unconverted products are recycled (partially or totally) back to the reactor. Such

a configuration can be used to maximize the conversion of heavy ends to middle

distillates. Figure 1 illustrates the one stage once-through hydrocracking scheme

employed in this work.

Figure 1 Simplified flow diagram of a one stage once-through hydrocracker

configuration

The single stage process is commonly used in the conversion of vacuum gas oils

into middle distillates and allows for high selectivity. The conversion is typically

around 50-60 %. The single stage configuration is a combination of hydrotreating

and hydrocracking reactions in one reactor, and hence the catalyst would usually

perform both a hydrogenation and a strong cracking function. The unconverted

material is low in sulphur, nitrogen and other impurities and can be used as

Reactor

HP

Flash

Fractionator

Fuel oilFCC feedEthylene feedLube oil base

Make up

Fresh feed

Recycle H2

Single-stage product

Product gas

Light naphtha

Heavy naphtha

Jet fuel / Kerosene

Diesel

T, P, WHSV, Cat.

SA

One stage

reactor

11

either feed for fluid catalytic cracking units (FCCU) or a blending component for

fuel oil production.

Two levels of hydrocracking severity exist in the description of conversion for

different flow schemes: mild or conventional hydrocracking. In mild

hydrocracking, the process conditions are very similar to those of a vacuum gas

oil desulphurization unit for conversion to significant yields of lighter products.

Typically, a one stage reactor without recycling is used in mild hydrocracking,

and it operates between temperatures (350-4400C), pressures (35-70 bars) and

LHSV (0.3-1.5 h-1). The major characteristics of this process are the production of

high yield of fuel oil and low hydrogen consumption as the process operates at

approximately half of the hydrogen pressure required in conventional

hydrocracking [1]. The quantity and quality of hydrocracker yields obtained are

determined by the combination of operating conditions and catalyst composition

that characterizes the process. The interactions between these process conditions,

feed quality, catalyst properties, product yields and product quality are not

adequately represented without the use of robust process models. The

methodology developed in this work proposes a generic representation of

hydrocracking models embedded into a hydrogen network to yield an

integrated process network. There are three key steps that constitute the

methodology: Development of hydrocracker models that are sufficiently detailed

to capture the dynamic interactions between hydrogen consuming processes and

12

the hydrogen distribution network; Integration of hydrocracker models in the

hydrogen network model to assess the consequence of interactions on overall

network objective; and Scenario optimization of integrated hydrogen networks.

3.1 Model development and validation

Modelling and simulation of hydrocracking units are a difficult task due to a

large number of components in the feedstock, an extremely large number of

reactions, and the complexities associated with measuring feed and product

compositions. Hydroprocessing models are developed to predict the behaviour

of a system from changing feed input or operating variables in a hydrogen

consumer. The modelling methodologies for cracking processes, such as fluid

catalytic cracking and hydrocracking, is often classified into two categories,

namely, lumping models and mechanistic models [19]. Lumping models can be

sub-divided into two groups: models based on wide distillation range fractions

and models based on pseudocomponents (also called discrete lumping). In

discrete lumping approach, the individual components in the reaction mixture

are divided into discrete pseudocompounds (lumps) based on the true boiling

point (TBP), molecular weight (MW), or carbon number (CN) [20]. The

molecules with similar TBP, MW or CN are treated as cracking with a particular

rate constant. Stangeland [21] developed a discrete lumping approach for

predicting hydrocracker yields using correlations based on the boiling points of

each of the pseudocomponents that characterize the cut. The model includes

13

three parameters: one parameter describes the effect of boiling point on the rate

constant; two other parameters determine what products would be generated as

each cut cracks. The predicted yields based on these parameters were obtained at

certain conversion levels. The major disadvantage of this method is that a change

in the specification of the hydrocracker product, or in the number of products

requires reformulating the model and refitting the data [22]. Mohanty et al [23]

adopted Stangeland’s [21] kinetic model for the simulation of a two stage

vacuum gas oil hydrocracker unit. The model assumed that each

pseudocomponent could only form lighter products by a pseudo-homogenous

first order reaction. Calculated yields, hydrogen consumption and reactor

temperature profile were obtained from the model, which showed good

agreement with plant data. Bhutani et al [20] applied the discrete lumped model

approach to kinetic modelling of an industrial hydrocracking unit. The model

was validated and used to analyse the behaviour of an industrial unit with

respect to certain process variables. Also, their study included the optimization

of the hydrocracker unit for multiple objectives. These models described so far

depend on certain feed conversion levels. This work uses a combination of the

discrete lumped approach and end-boiling point based correlations for

predicting conversion and product yields from industrial data. Two aspects of

hydrocracker models are studied: conversion model and hydrocracker yields

model.

14

3.1.1 Feed Conversion model

A hydrocracker conversion model derived from steady state reaction kinetics has

been developed to describe the effects of feed and catalyst properties, interface

variables such as hydrogen-oil ratio, process operating conditions and product

requirements on conversion level.

As discussed before, a conversion of 40 – 80 % of the feed can be achieved in

commercial hydrocrackers. However, if high conversion is required, the bottom

product is recycled back to the reactor for complete conversion, which can be

used to maximize a diesel product. Another factor that affects the degree of

conversion is the hydrogen severity, also referred to as the ratio of mass

flowrates of hydrogen to feed. Hydrogen severity ranges from (1.5 – 4) wt% of

the feed [1]. In mild hydrocracking, it can be assumed as 1.5 wt% of feed and in

conventional hydrocracking as 3.0 wt% of feed [1]. In this work, a one stage

once-through process with an assumed hydrogen severity of 4 wt% is modelled

with industrial data [20]. Some of the assumptions in the development of a

kinetic model in [21] and [23] are considered in this study. The exceptions are the

characteristics of hydrogen stream described in the overall optimization

framework and the built-in dependence of the average residence time on the

ratio of mass flowrates of hydrogen to feed.

Consider a first order reaction kinetics,

(1)

15

Converting rate law from to

(2)

(3)

At steady state, accumulation = 0;

(4)

(5)

(6)

(7)

(8)

where = rate of reaction of reactant A in mol l-1s-1; = reaction rate constant;

= concentration of reactant A; = initial concentration of reactant A; =

residence time in s; = weight hourly space velocity in h-1; = Amount

of catalyst in kg; = mass flowrate of reactant A in kgs-1; = mass flowrate of

hydrogen in kgs-1; = conversion of reactant A; inflow rate of A in

molecules per second; outflow rate of A in molecules per second; =

volume of the reactor in l; = instantaneous reaction rate of A in a given

differential volume.

For a higher rate of hydrogen flow, a shorter residence time is obtained

(9)

16

(10)

For a given space velocity, the average residence time of the catalyst and the

coke content of the catalyst emerging from the reactor decreases with increasing

hydrogen/oil ratio, . To reach a high conversion, the cracking would be

conducted at low space velocity and high temperature, while maintaining a high

hydrogen circulation flow in the reactor section.

(11)

The conversion also increases with the cracking rate constant, . The rate

constant is highly dependent on the nature of hydrocracking feed, catalyst

and operating conditions in the reactor.

The temperature effect of the specific reaction rate could be correlated with the

Arrhenius equation:

(12)

where frequency factor (h-1), and represents the apparent activation energy.

Li [24] reported that the relative deviation of

is not greater than 3 %

17

when and vary in a reasonable range. So the apparent activation energy was

set to a constant value [24] of (E = 108 KJ/mol).

The rate of hydrocracking for pure normal alkanes increases in the ratio

1/32/72/120 for C5/C10/C15/C20 [26]. As a result, there is a strong increase in the rate

constant as boiling point increases. In the present conversion model, is

calculated as the ratio of boiling points of the heaviest pseudocomponent in the

product to the feed end point, in 0F to reflect the overall product range. Also,

normal paraffins of a given molecular weight crack more quickly than their

corresponding isoparaffins or cyclic compounds [20]. For example, the rate of

cracking of n-paraffins is more than the rate of cracking of aromatics,

cycloparaffins, and isoparaffins of the same carbon number. Based on this effect

of varying hydrocarbon classes on the rate of hydrocracking, the term, “feed

quality parameter (FQP)” represented as is introduced in the conversion

model to differentiate feed types. Under hydrocracking reaction conditions, each

of the hydrocarbon classes exhibits different crackability. As a result, an attempt

to establish their effects on the cracking behaviour of different feedstocks is

made. Similar to a feed characterization factor, this parameter describes the

tendency towards a paraffinic or aromatic feed. While not largely definitive, it

can be observed that higher specific gravity feeds tend to be more naphthenic

(feed quality parameter < 12.5) and lower specific gravity feeds incline to a

paraffinic nature (feed quality parameter > 12.5). Other hydrocarbons, such as

18

the highly aromatic types tend to exhibit values of 10.0 or less. It is important to

note that the feedstock endpoint affects the catalyst deactivation rate. Higher

boiling point feeds have a high tendency to form coke, and thus would require

higher pressures, larger reactors, and temperature-stable catalysts. The equation

for overall feed conversion is given as:

(13)

where = conversion; = specific surface area of catalyst, m2/g; = pressure,

atm; = pressure dependent parameter; = rate constant, h-1; = ratio of end-

boiling points of product to feed; = hydrogen-oil ratio; = specific gravity; =

feed quality parameter; = weight hourly space velocity, h-1.

3.1.2 Hydrocracker Yield Models

A six-lumped experiential yield model for gases (GA), light naphtha (LN), heavy

naphtha (HN), kerosene (KER), diesel (DIE) and unconverted oils (UCO), as a

general function of conversion has been developed. The unconverted fraction

(UCO) is the difference of the overall expected yield fraction (usually 1) and the

sum of yield fraction for the five product sets. Table 1 and Table 2 show the

experimental data for feed and products obtained from a refinery [20] and the

boiling point range of the hydrocracked products.

19

Table 1 Experimental Data for Feed and Products obtained from the Refinery

Feed HTEffluent LN HN KS LD HD UCO

IBP 310 34.7 101.1 163.9 199.2 305.2 315.5

5% 363.5 42.5 107.3 179.7 206.6 321.8 361

10% 383.5 46.8 109 183.5 219 326 380.5

20% 407.5 52 111.1 191 403

30% 424.5 56.5 113.6 198.1 227.8 333 417.5

40% 429

50% 456.5 66 119.1 216 244.6 342.4 440.5

60% 453

70% 487 73.2 126.6 237.8 258 356 467

80% 504 483.5

90% 526.5 85.1 138 263.7 272.2 373.4 507

95% 543 91.9 142.7 272.5 278 378.6 526.5

FBP 578 101.4 160.5 277.2 280 380.6 566

ρ 150C 0.933 0.883 0.68 0.75 0.81 0.82 0.83 0.84

Table 2 Boiling range of hydrocracked products

94.46 214.52 213.98 320.9 327.02 530.96 390.56 717.08 599.9 1050.8

UCOProducts LN HN KER DIE

47930

Boiling range (0F)

Mass flowrate (kg/h) 11400 17200 34030 25880

The feed and products are characterized into 58 pseudocomponents using oil

manager in Aspen Hysys. Light components such as methane, ethane, propane

and butane were grouped into pseudocomponent 1. The available experimental

assay data in Table 1 is used to evaluate pseudocomponent composition of

industrial products such as LN, HN, KS, and Diesel. LD (light diesel) and HD

(heavy diesel) are grouped into one common name, Diesel. The mass flowrates

of products are obtained from [20].

20

The model for the yield of each pseudocomponent in the LN range as a function

of conversion, relative rate constant and selectivity to LN fraction (Equation 14)

is first calibrated and validated with a set of industrial data [23].

(14)

Stangeland [21] proposed an expression that could reflect the differences in feed

composition and catalyst character using a minimum number of parameters:

(15)

where = TBP/1000 in 0F and = 1. For any value of the parameter ‘A’, k (0) = 0

and k (1) = 1. Above = 1, is greater than one, unless A is negative. Since A

usually lies in the range of (0 – 1), varies from a linear to a cubic function.

Parameter ‘A’ could be said to define the shape of the yield curve in the LN

boiling range. Other yield models are based on the predictions from LN yield.

The mass fraction of butane and lighter components represented as

Pseudocomponent 1 is described as the difference between conversion and total

product yield. The result obtained showed reasonable accuracy with industrial

data.

(16)

where = pseudocomponent 1; = conversion; are the

yields of light naphtha, heavy naphtha, kerosene and diesel respectively.

21

The total yield of light naphtha is then given as the sum of the yields of

pseudocomponents in the light naphtha range.

(17)

A maximum of ten parameters (A, SP, β, α, ) is

obtained for the conversion and yield models for six product lumps. is the

number of pseudocomponents in the light naphtha range. As parameter ‘A’

affects the shape of the yield curve, it varies to some extent with different

feedstocks. The proposed model is implemented in a software package for

nonlinear regression, based on the least squares method, to obtain optimized

kinetic parameters for the modified model. The results are plausible; in

particular the feed quality parameter obtained changes with the specific gravity

of different feedstocks similar to Watson’s characterization factor [25]. However,

these tendencies apply if the initial guess is close enough to the final value, a

fundamental limitation of deterministic solution methods. In the future, the

PONA feed composition would be correlated with the feed quality parameter, β,

to validate its relationship with specific gravity. Parameters ‘SP’ represent the

selectivity to LN yield and ‘α‘ parameter is associated with the catalyst surface

area. Parameters and represent coefficients in the yield model for HN, KER

and DIE products. By using the obtained parameters, it is possible to determine

the grouped product distribution for VGO hydrocracked products and the

22

pseudocomponent composition for the LN lump. Table 3 shows the feed,

operating data and the resulting parameters for the refinery in [20].

Table 3 Feed and operating data in the refinery

Feed properties

Specific gravity

Product properties

Final pseudocomponent boiling point of product (0F)

μ (coefficient in yield model for HN, KER and DIE)

γ (coefficient in yield model)

3.6810; 2.7983; 2.1729

0.5524; 0.6184; 0.5884

Operating conditions

A (relative rate function dependent parameter)

SP (selectivity parameter)

β (feed quality parameter)

α (surface area dependent parameter)

Temperature (K)

Pressure (atm)

LHSV (h-1)

Hydrogen severity (assumed)

Catalyst surface area (m2/g)

Parameters

0.9330

1012.8

672

170

1.43

0.04

1.1767

115

0.9927

3.0182

10.8960

The LN predictions from the model showed good agreement with industrial

data as shown in Figure 2 below.

23

0

0.005

0.01

0.015

0.02

0.025

0.03

57.7 76.2 93.3 111.0 128.8 146.7 163.7 180.8 198.5 211.0

LN

yie

ld (

-)

TBP(0F)

LN yield (Industrial)

LN yield (model)

Figure 2 Comparison of Industrial and Predicted LN yields [20]

The yield models for all other products are obtained using a successive

exponential-polynomial distribution as given in the following equations:

(18)

(19)

(20)

where represent the sum of the yields for LN, HN, KER and

DIE respectively; are the ratios of the pseudocomponent end-

boiling point for HN, KER and DIE and the heaviest pseudocomponent boiling

24

point of feed. The total yield of naphtha forms the basis for the lump prediction

of other hydrocracker yields as shown in Figure 3 below.

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

GAS LN HN KER DIE UCO

Industrial yield (-)

Predicted Yield (-)

Figure 3 Comparison of Industrial and Predicted yields of hydrocracked VGO

Table 4 shows the comparison between Industrial and Predicted yields.

Table 4 Yield comparison of Industrial and Model predictions I

Products /

Unconverted

feed

Industrial

yield (-)

Predicted

Yield (-)Difference % difference

GAS 0.025 0.026 -0.001 -4.000

LN 0.082 0.080 0.002 2.395

HN 0.123 0.123 0.000 0.065

KER 0.243 0.242 0.001 0.384

DIE 0.185 0.185 0.000 0.078

UCO 0.343 0.345 -0.002 -0.644

25

The maximum percentage error obtained from the developed model is

approximately -4 % (due to a small base value of GAS), and the average

percentage error is approximately 1.3 %.

Having obtained these correlations, an attempt is made to validate the model by

reproducing similar results on a different industrial hydrocracking data [23].

Table 5 shows the feed, operating data and resulting parameters obtained from

another industrial data and its comparison of parameters with the previous

industrial data [20]. An attempt is made to validate the model by reproducing

similar results on another industrial hydrocracking data [23]. Table 5 shows the

feed, operating data and resulting parameters obtained from another industrial

data and its comparison of parameters with the previous industrial data [20].

26

Table 5 Comparison of feed, operating conditions and parameters between two

industrial data

Bhutani

et al [19]

Mohanty

et al [18]

0.9330 0.8927

1012.8 936.5

672 672

170 172

1.43 1.83

0.04 0.04

115 124.7

0.993 0.994

3.262 6.373

10.739 11.668

0.925 0.638

A (relative rate function dependent parameter)

SP (selectivity parameter)

β (feed quality parameter)

α (surface area dependent parameter)

Temperature (K)

Pressure (atm)

LHSV (h-1)

Hydrogen severity (assumed)

Catalyst surface area (m2/g)

Parameters

Operating conditions

Feed properties

Specific gravity

Product properties

Final pseudocomponent boiling point of product (0F)

Parameters μ and γ for HN, KER and DIE, in the case of Mohanty [23] are 0.2983;

0.7083; 0.8007; and 1.7171; 2.7420; 2.4520; respectively. The relative rate function

dependent parameter ‘A’ gives information on the shape of the curve. Since A

usually lies in the range of 0 to 1, the relative rate expression also varies from a

linear to a cubic function. The selectivity parameter defines the relative

production of light naphtha to the formation of other products. Feedstocks with

lower endpoints would have a higher selectivity to form lighter fractions

compared to high endpoints. As observed in Table 5, similar to Watson

Characterization Factor, the feed quality parameter increases with a decrease in

low specific gravity. Thus, it is possible to describe the nature of the feed as a

27

function of its paraffinicity. Although, these parameters are linked to certain

degrees of significance, it is not completely definitive. However, the different

parameters obtained from the data have illustrated the usefulness of the model

in a wider context. Figure 4 below shows the model behaviour in predicting LN

yields from [23].

0

0.01

0.02

0.03

0.04

0.05

0.06

81.5 126.5 171.5 216.5

LN

yie

ld (

-)

TBP(0F)

LN yield (Industrial)

LN yield (Model)

Figure 4 Comparison of Industrial and Predicted LN yields [23]

The result from the LN yield model showed good agreement with industrial

data. Although, only four data points were provided in the LN range, the

parameters obtained were relevant for comparison with the original data. The

yields of other fractions obtained are shown in Figure 5.

28

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

GAS LN HN KER DIE UCO

Industrial yield (-)

Predicted Yield (-)

Figure 5 Comparison of Industrial and Predicted yields for hydrocracked VGO

[23]

Table 6 shows the model predictions obtained from the industrial data in [23].

Table 6 Yield comparison of industrial and model predictions II

Products /

Unconverted

feed

Industrial

yield (-)

Predicted

Yield (-) Difference % difference

GAS 0.035 0.036 -0.001 -2.857

LN 0.086 0.085 0.001 1.052

HN 0.075 0.075 0.000 0.000

KER 0.191 0.191 0.000 0.063

DIE 0.253 0.253 0.000 0.006

UCO 0.373 0.360 0.013 3.485

The maximum percentage error obtained is approximately 3.5 %, and the

average percentage error is approximately 1.3 %.

29

3.2 Integration of hydrocracker models in hydrogen networks

The previous methodology [18] addressed the integration of sulphur removal

models with the production of light hydrocarbons in HDS processes. This work

presents the mathematical formulation and optimization of integrated hydrogen

networks under variable operating conditions and product quality / yield

constraints. In this section, we model the mass balance around the hydrocracker

and the column. Other relevant equations, such as the reactor inlet constraints,

flash calculations, and hydrogen network model are presented in previous work

[18].

3.2.1 Hydrocracker model

Combining Eqs. (13) to (20) and the sulphur removal / light hydrocarbon

production models in [18], the following constraints define the overall mass and

component balances around the hydrotreaters / hydrocracker, .

(21)

(22)

represent the pure hydrogen flowrate to the flash inlet and

represent the pure hydrogen flowrate to the reactor inlet .

are the light hydrocarbons produced from methane to pentane. The

variables and are defined as consumption of hydrogen in all

hydroprocessors and production of light hydrocarbons in hydrotreaters

30

including respectively. The total hydrogen consumed in hydrocrackers is

calculated as follows:

(23)

where = amount of hydrogen consumed to form and =

amount of hydrogen consumed in the formation of light hydrocarbons from

hydrodesulphurisation reactions. is the hydrogen consumed due to

cracking reactions in the hydrocracker, and could be obtained as functions of the

hydrogen-oil ratio at the inlet of hydrocracker and conversion.

Note also that the products obtained from cracking reactions contain some

amount of residual sulphur. Assuming that the remaining sulphur is present in

only the liquid phase, the product sulphur levels in pseudocomponent, for each

pseudocomponent boiling range, in VGO hydrocracker, can be estimated by

incorporating product yields and properties in Eq. (24).

(24)

= ratio of boiling points of pseudocomponents, to the heaviest

pseudocomponent boiling point in the feed for each product range, ; =

total sulphur in the entire product range; = yield of products, obtained

from hydrocracker, As shown in Eq. (24), in the HDS model representing the

nature of feed and product characteristics has been replaced with which

represents the product characteristics in each pseudocomponent boiling range.

31

The sulphur in each pseudocomponent fraction relates directly to the yields

obtained for each fraction. Light fractions would usually contain the most

reactive sulphur compounds while the most refractory sulphur compounds

concentrate in the heavy fractions. The sulphur in the unconverted fraction is

obtained by subtracting the sulphur in each pseudocomponent range from the

total sulphur in the product in Eq. (25).

(25)

The total sulphur distribution in each product range is the sum of the individual

sulphur amount obtained from Eq. (24).

In a hydrocracker, the light hydrocarbons formed are grouped into the lowest

pseudocomponent number. In some cases, they usually reflect an approximate

quarter fraction of light naphtha yield. The hydrogen consumed in the

hydrocracker is a combination of the chemical hydrogen consumed from feed

conversion to obtain hydrocracked product yields and the chemical hydrogen

consumed due to hydrotreating reactions.

A major feature of this work is the integration of conversion models as a function

of hydrogen/oil ratio in the overall network model, thus allowing interactions

between the hydrogen distribution system and the process operating system in

the formation of light gases, LN, HN, KER and DIE products from VGO

hydrocracking.

32

3.2.2 Column Mass Balance

The liquid outlet from the high pressure separator consisting of VGO

hydrocracked products is routed to a fractionating column.

(26)

where are flowrates of butane (representing

light hydrocarbons), light naphtha, heavy naphtha, kerosene, diesel and

unconverted oil respectively. The flowrates of naphtha, kerosene and diesel are

functions of the total flowrate of liquid product and their respective yield

fractions. An overall mass balance around the reaction-separation system is also

included in the model.

(27)

where is the flowrate of feed to hydroprocessor, ; is the flowrate of

the makeup stream to hydroprocessor, and is the flowrate of recycle to

hydroprocessor,

By combining these equations, An integrated hydrogen network model is

formulated that comprises non-linear empirical process models for prediction of

sulphur in products, light hydrocarbon formation, feed conversion, yields of

hydrocracked products and hydrogen consumption; and mass balances of

33

hydrogen producers and consumers in a network is developed. The objective

function is to minimize operating cost that accounts for the hydrogen production

cost and fuel gas value as in Eq. (28).

(28)

where and represent the flowrates of hydrogen from the hydrogen

producer and flowrates of fuel from the hydrogen consumers; and

represent the unit prices of hydrogen and fuel gas respectively. Other costs that

are integral in refinery process economics are not considered in this present

work. In most cases where hydrocracking processes are considered, refiners

would usually be concerned with how much value they can obtain from

products. The objective function is to maximize profit as in Eq. (29).

(29)

where are the flowrates of liquid products from

naphtha, kerosene and diesel hydrotreaters respectively;

are flowrates of butane, also referred to as light

hydrocarbons lumped into pseudocomponent 1; liquid products: light naphtha,

heavy naphtha, kerosene and diesel from VGO hydrocracker respectively;

are unit prices of butane, naphtha, kerosene and diesel

respectively. The additional process constraints proposed in the formulation of

34

this methodology are expected to give more realistic solutions as demonstrated

in the case study.

4. Integrated optimization framework for hydrogen networks

An extended methodology framework in Figure 6 has been proposed to

illustrate the integration of hydrotreater as well as hydrocracker models in the

optimization of hydrogen networks. The methodology captures the effect of

changing process variables, such as hydrogen/oil ratio on conversion and

product yields in a hydrocracker.

Data collection

Hydrocracker process

model regression and

Overall network

modelling

Overall network

optimization

Figure 6 Methodology for integration of hydrocrackers in a refinery hydrogen

network

The methodology can be summarised into three major steps:

35

4.1 Hydrocracker process model regression and validation

The non-linear process models developed from first principles steady state

kinetics in Section 3.1 is used to predict feed conversion and six-lumped product

yields in a hydrocracker. The process model qualitatively and quantitatively

describes the effect of feed characteristics, process operating conditions and

product properties on the conversion and product yields. The model is regressed

and validated with industrial data.

4.2 Overall network modelling

The nonlinear process models are integrated into the hydrogen network model.

The overall network model can be set up in an optimization environment such as

GAMS. The hydrogen inlet flow to process units is allowed to vary to

accommodate the effects of changing operating conditions on overall network

performance.

4.3 Overall network optimization

The validated process models are integrated in a hydrogen network model to

exploit the interactions between changing hydrocracker process conditions and

network performance. Depending on the hydrocracking process objective,

changes in feed flow, hydrogen oil ratio, and reactor temperature would result in

different feed conversions and subsequently changes to hydrocracker product

yields. The effects of these changes are reflected in hydrogen consumption

36

levels, product distribution from hydrocrackers, overall hydrogen requirements

in the network, and the overall profitability of the whole system.

5. Case study

5.1 Base case

A base case is presented here to illustrate the integration of hydrocracking

processes in refinery hydrogen network optimization. The objective is to

determine the optimum hydrogen production flowrate for different case

scenarios and maximum profit.

The hydrogen network base case consists of two hydrogen producers: a

Hydrogen plant, H2Plant; a catalytic reformer, CCR; three hydrotreaters: a

naphtha hydrotreater, NHT; a kerosene hydrotreater, KHT; a diesel

hydrotreater, DHT; and a vacuum gas oil hydrocracker, VGOHC. The detailed

feed stream data for the base case is shown in Table 7.

37

Table 7 Detailed feed data for base case

Hydroprocessors NHT CNHT DHT VGOHC

Feed flowrate (t/h) 175.04 75.89 337.56 301.00

H2 0.0000 0.0000 0.0000 0.0000

C1 0.0000 0.0000 0.0000 0.0000

C2 0.0000 0.0000 0.0000 0.0000

C3 0.0000 0.0000 0.0000 0.0000

C4 0.0000 0.0000 0.0000 0.0000

C5 0.0000 0.0000 0.0000 0.0000

H2S 0.0000 0.0000 0.0000 0.0000

PC1-NHT 0.1627 0.0000 0.0001 0.0000

PC2-NHT 0.2051 0.0000 0.0002 0.0000

PC3-NHT 0.2207 0.0000 0.0002 0.0000

PC4-NHT 0.2686 0.0000 0.0003 0.0000

PC5-NHT 0.1429 0.0000 0.0002 0.0000

PC1-CNHT 0.0000 0.0916 0.0000 0.0000

PC2-CNHT 0.0000 0.1675 0.0000 0.0000

PC3-CNHT 0.0000 0.2422 0.0000 0.0000

PC4-CNHT 0.0000 0.2031 0.0000 0.0000

PC5-CNHT 0.0000 0.2957 0.0000 0.0000

PC1-DHT 0.0000 0.0000 0.0745 0.0000

PC2-DHT 0.0000 0.0000 0.1743 0.0000

PC3-DHT 0.0000 0.0000 0.2630 0.0000

PC4-DHT 0.0000 0.0000 0.3502 0.0000

PC5-DHT 0.0000 0.0000 0.1371 0.0000

PC1-VGOHC 0.0000 0.0000 0.0000 0.0037

PC4-VGOHC 0.0000 0.0000 0.0000 0.0046

PC7-VGOHC 0.0000 0.0000 0.0000 0.0062

PC22-VGOHC 0.0000 0.0000 0.0000 0.0654

PC23-VGOHC 0.0000 0.0000 0.0000 0.0616

PC24-VGOHC 0.0000 0.0000 0.0000 0.0556

PC25-VGOHC 0.0000 0.0000 0.0000 0.0500

PC26-VGOHC 0.0000 0.0000 0.0000 0.0362

PC27-VGOHC 0.0000 0.0000 0.0000 0.0217

PC28-VGOHC 0.0000 0.0000 0.0000 0.0268

Compositions (Mass %)

The composition for VGO feed is condensed for brevity purposes. The feed

properties, operating conditions in VGO hydrocracker, parameters obtained for

the conversion and yield models are listed in Table 5. The nonlinear

38

hydrocracker process model developed in Section 3.1 along with hydrotreater

models (refer to HDS and light hydrocarbon models in [18]) is integrated in the

hydrogen network under fixed operating conditions. The prices for VGO

feedstock, hydrogen, butane, naphtha, kerosene and diesel are £562.91/ton [38],

£3000/ton [35], £385.95/ton, £594.81/ton, £675.95/ton, and £593.3/ton [36]

respectively. For the objective of minimum hydrogen, an integrated hydrogen

network at fixed operating conditions in is shown in Figure 7.

H2

Plant

NHT

7.294 t/h

86.6 %

10.782 t/h

83.4 %

46.811 t/h

84.8 %

11.785 t/h

92.56 %

15.47 t/h

21.5 %

2.695 t/h

92.56 %

9.09 t/h

92.56 %

To

external

recycle

0.000 t/h

0.000 t/h

83.4 %

0.000 t/h

84.8 %

0.000 t/h

21.5 %

0.159 t/h

0.224 t/h

0.651 t/h

1.326 t/h

CNHT

DHT

VGOHC

Figure 7 Integrated hydrogen network at fixed operating conditions

39

The hydrogen production is 11.785 t/h. By integrating hydrogen consuming

processes in the hydrogen network model, there are opportunities to exploit

additional degrees of freedom for optimization. For example, product quality

requirements from hydrotreating units set the levels of hydrogen-oil ratio

demanded by the processes, and consequently the amount of hydrogen

consumed, which leads to the hydrogen production flowrate of the network. It is

important to allow hydrogen inlet flowrates and purities to vary to exploit the

interactions between the processes and the hydrogen network.

5.2 Optimization with varying hydrogen inlet conditions

Selectivity and yield of a particular distillate fraction can be improved through

the manipulation of operating conditions in reactors and fractionators. For a

network configuration with fixed hydrogen flowrate at a reactor inlet as in

Figure 7, there are limitations to exploiting the interactions between process

performances and operating conditions in the network. By controlling the

degrees of freedom existent in the integrated framework, it is possible to

conserve the amount of hydrogen made available to hydroprocessing reaction,

while achieving certain yield and quality specifications. In this case, reactor inlet

hydrogen flowrates are allowed to vary as illustrated in Figure 8.

40

H2 Plant

NHT

7.294 t/h

86.6 %

10.782 t/h

83.4 %

46.811 t/h

84.8 %

11.649 t/h

92.56 %

15.47 t/h

21.1 %

2.589 t/h

9.032 t/h

92.56 %

To

external

recycle

0.000 t/h

86.6 %

0.000 t/h

83.4 %

0.000 t/h

84.8 %

0.000 t/h

21.1 %

0.150 t/h

0.224 t/h

0.575 t/h

1.311 t/h

CNHT

DHT

VGOHC

0.018 t/h

92.56 %

Figure 8 Integrated hydrogen network under varying inlet H2 conditions

Figure 8 shows a decrease in hydrogen production requirements by

approximately 1 % as a result of a decrease in makeup hydrogen requirements

and hydrogen-oil ratio. The effects of a decrease in hydrogen-oil ratio are seen in

the changes in conversion and product yields in the hydrocracker as shown in

Table 8. Table 8 describes the significance of integrating hydrogen consumer

models in the hydrogen network model at fixed and varying inlet H2 conditions.

41

Table 8 Comparison of VGO product yields at fixed and varying inlet H2

conditions

NHT CNHT DHT

Fixed Fixed Fixed Fixed Vary

623 653 633 672 672

0.015 0.015 0.0015 66.925 66.636

0.556 0.649 2.494 8.414 8.288

0.398 0.426 1.845 7.088 6.977

0.159 0.224 0.651 1.326 1.311

0.021 0.021 0.020

0.036 0.038 0.034

0.161 0.167 0.153

0.152 0.162 0.144

0.082 0.085 0.077

9.719 9.593

0.0860 0.0856

0.1388 0.1365

0.2248 0.2221

0.3074 0.2500

0.1068 0.1621

0.3308 0.3336Yield of unconverted fraction (-)

H2 production flowrate - fixed case (t/h) 11.785

H2 production flowrate - Varying case (t/h) 11.649

Yield of diesel (-)

C3 formed 0.048

C4 formed 0.045

C5 formed 0.024

Pure hydrogen inlet flowrate (t/h)

Yield of light naphtha (-)

Yield of heavy naphtha (-)

Yield of full naphtha range (-)

Yield of kerosene (-)

C2 formed 0.011

Cases Effects of Fixed and Varying inlet H2 on product yields

Hydrogen consumersVGOHC

Fixed reaction conditions (Temperature, K)

Fixed sulphur in product /calculated conversion for VGOHC(wt%)

Makeup hydrogen (t/h)

Chemical hydrogen consumed (t/h)

Dissolved hydrogen (t/h)

C1 formed 0.006

Light hydrocarbons produced from HDS (t/h)

From Table 8, it is observed that a decrease in hydrogen inlet flowrate results in

a decrease in hydrocracking conversion and product yield redistribution while

maintaining constant temperature and fixed sulphur requirements in product in

both cases. This decrease in hydrogen inlet flowrate results in a corresponding

decrease in lighter products, increase in diesel formation, increase in

unconverted fraction and an overall decrease in hydrogen requirements. With

fixed sulphur requirements in hydroprocessors, the amount of light

hydrocarbons formed from HDS reactions is essentially the same for both fixed

42

and varying configurations. Note also that the remaining sulphur in the liquid

effluent is redistributed among the products. For a 1.4 wt. % of sulphur in the

liquid effluent, Table 9 shows the sulphur distribution among the products

obtained from VGO hydrocracking before and after optimization.

Table 9 Sulphur distribution in VGO hydrocracked products

LN HN KER DIE UCO

Sulfur, wt % 0.00004 0.00016 0.00185 0.00576 1.40978

After Optimization Sulfur, wt % 0.00004 0.00015 0.00175 0.00624 1.40940

Products

Before Optimization

As shown in Table 9, most of the sulphur remaining in the liquid effluent are

refractory in nature, and hence concentrate in the heaviest boiling fraction.

5.3 Optimization with varying temperature

Table 10 describes the effects of simultaneously varying temperature and

hydrogen flow at constant feed flow on product yield pattern.

Table 10 Effects of varying temperature on VGO hydrocracker process

performance

301 301 301

672 670 668

66.636 65.291 63.912

6.977 6.834 6.689

9.593 9.572 9.551

0.086 0.084 0.082

0.136 0.126 0.114

0.250 0.241 0.186

0.162 0.209 0.212

11.649 11.626 11.603

Yield of heavy naphtha (-)

Yield of kerosene (-)

Yield of diesel (-)

Hydrogen Production flowrate (t/h)

Feed flow (t/h)

Variable reaction conditions (Temperature, K)

Calculated conversion for VGOHC (wt%)


Pure hydrogen flowrate (t/h)

Yield of light naphtha (-)

43

By decreasing temperature in VGO hydrocracker, the amount of hydrogen

consumed is decreased resulting in a decrease in the yield of light fractions and

an increase in diesel fraction. The decrease in VGO conversion decreases the

chemical hydrogen consumed and affects the yield of light and heavy ends. In

most cases, the range of variation for inlet hydrogen is further extended to

accommodate changing operating conditions.

5.4 Optimization for maximizing profit at varying inlet H2 conditions

For the objective of maximizing profit, the resulting trend is quite different. The

fixed case in Figure 7 remains the same. Figure 9 shows the results obtained from

varying inlet H2 when it is required to maximize profit.

44

H2 Plant

NHT

7.294 t/h

10.782 t/h

83.4 %

46.811 t/h

84.8 %

11.921 t/h

92.56 %

15.47 t/h

21.1 %

2.589 t/h

92.56 %

9.226 t/h

92.56 %

To

external

recycle

0.000 t/h

0.000 t/h83.4 %

0.000 t/h84.8 %

0.000 t/h

21.1 %

0.150 t/h

0.224 t/h

0.575 t/h

1.34 t/h

CNHT

DHT

VGOHC

0.018 t/h

92.56 %

Figure 9 Integrated hydrogen network under varying inlet H2

conditions for maximum profit

In this case, an increase in makeup H2 to the VGO hydrocracker results in an

increase in overall hydrogen-oil ratio to the hydrocracker, therefore increasing

hydrogen production requirements by approximately 3 %, while increasing

profit.

45

Table 11 Comparison of fixed and varying inlet H2 conditions for maximum

profit

NHT CNHT DHT

Fixed Fixed Fixed Fixed Vary

623 653 633 672 672

0.015 0.015 0.0015 66.93 67.21

0.556 0.649 2.494 8.414 8.540

0.398 0.426 1.845 7.088 7.200

0.159 0.224 0.651 1.326 1.340

0.021 0.021 0.020

0.036 0.038 0.034

0.161 0.167 0.153

0.152 0.162 0.144

0.082 0.085 0.077

9.719 9.845

6.665 6.696

69.699 70.554

95.326 98.928

47.136 44.076

91.265 89.974

H2 production flowrate - Varying case (t/h) 11.921

Overall Profit obtained from varying case (£B/yr) 3.761

Profit increase (%) 0.17

Flowrate of diesel (t/h)

Flowrate of unconverted fraction (t/h)

H2 production flowrate - fixed case (t/h) 11.785

Overall Profit obtained from fixed case (£/yr) 3.755

Flowrate of kerosene (t/h)

C2 formed (t/h) 0.011




Hydrogen oil ratio (t/h)

Flowrate of butane (t/h)

Flowrate of naphtha (t/h)

0.006

Cases Effects of Fixed and Varying inlet H2 on product yields

Hydrogen consumersVGOHC


Fixed sulphur in product /calculated conversion for VGOHC(wt%)

Makeup hydrogen (t/h)


Dissolved hydrogen (t/h)

Light hydrocarbons produced from HDS

C1 formed (t/h)

An increase in hydrogen-oil ratio by approximately 1 % decreases diesel

formation by 6 %, and increases light end production. An increase in conversion

is also obtained, resulting in a 1 % decrease in unconverted fraction. The overall

profit is increased by 0.17 %.

5.5 Optimization with varying feed flow for maximum profit

If the objective for hydrocracking is to maximize diesel, optimization of feed rate

and temperature is necessary to achieving this objective [37]. Increasing feed

46

flow under varying inlet H2 conditions favours diesel formation and growth in

profit as shown in Table 12.

Table 12 Effect of increasing feed flow on network profitability

301 303 305

672 672 672

67.21 66.97 66.73

7.200 7.152 7.104

0.0327 0.0324 0.0320

70.554 70.276 69.983

98.928 96.498 94.002

44.076 46.955 49.892

11.921 11.876 11.830

3.7608 3.7611 3.7613

0.008 0.005Profit increase (%)

Process VGOHC

Feed flow (t/h)


Calculated conversion for VGOHC (wt%)

H2 production flowrate (t/h)

Overall Profit obtained from fixed case (£B/yr)


Pure hydrogen flowrate (t/h)

Flowrate of naphtha (t/h)

Flowrate of kerosene (t/h)

Flowrate of diesel (t/h)

Often, refiners target the maximization of high-value products such as diesel and

the minimization of low demand products such as light ends including naphtha

and kerosene. Given this, the operation of the hydrocracker can be optimized for

maximum profit with changing feed flow.

So far, the different scenarios studied in the optimization of integrated networks

for minimum hydrogen and maximum profit has proved useful in

understanding the hydrocracking scheme to achieve a target yield pattern. The

approach adopted in the constrained optimization is useful in allowing effective

use of hydrogen in the minimization and maximization of certain product yields.

In downstream process industries, refiners are usually concerned with

minimizing light ends and maximizing diesel formation. Some of the procedures

47

listed above have resulted in the simultaneous minimization and maximization

of light fractions and middle distillates (diesel) respectively. An integrated

network have been obtained that allows varying hydrogen inlet conditions,

varying operating conditions such as temperature, sulphur in product

constraints, and a mix of operating conditions that improves interaction between

process variables and product specifications / yield pattern.

5.7 NLP hydrogen network optimization

The NLP network model is optimised with the CONOPT solver in GAMS. With

fixed hydrogen-oil ratio and operating conditions, the hydrogen plant

production of the integrated hydrogen network is 11.79 t/h as shown in Figure 7.

By extending the integrated hydrogen network to capture varying hydrogen

inlet conditions under fixed sulphur requirements in hydrotreaters, conversion

and product yields in hydrocrackers, the hydrogen production flowrate was

decreased to 11.65 t/h, resulting in a savings of 1 % and an increase in diesel

yield. In another scenario, it was required to study the effects of varying

temperature and feed flow on VGO hydrocracker process performance. The

results obtained depict the expected trend in refineries: an increase/decrease in

reactor temperature decreases/increases diesel yield respectively;

increase/decrease in feed flow increases/decreases diesel yield respectively. For

the case of maximum profit, further optimization based on varying inlet H2 was

investigated. The effects of increasing hydrogen-oil ratio result in increased

48

conversion and light ends, decreased diesel yields, and a corresponding increase

in profit. By integrating hydrotreating and hydrocracking models into hydrogen

networks for improved overall process performance and optimal hydrogen

utilization, hydrogen management can now be redefined.

A summary of the GAMS calculations for the overall integrated network is as

follows:

Computer resource 64-bit operating system, 6 GB RAM, 2.4 GHz Intel

Core i5-2430M

No of variables 61

No of equations 103

CPU time 5.52 s (varies for different cases)

6. Conclusions

The proposed hydrogen consumer integration methodology in this work targets

the development of conversion and product yield models for hydrocracking

reactions. Although not as rigorous as some kinetic models [20]; the model is

relatively simple and, at the same time, shows a good reproducibility of

experimental data. These models have been integrated in the hydrogen

framework to evaluate the significance of the existing interactions between

process variables in hydrocrackers and their effects on process performance and

the overall hydrogen network. The interactions studied in this work include the

49

effects of varying inlet hydrogen conditions, effect of varying operating

conditions, and the effect of varying feed flow on overall process performance.

Hydrogen is either decreased or increased from the base case depending on the

process objective. In the case of minimizing hydrogen, a savings of 1 % is

obtained, while in the case of maximizing profit, hydrogen requirements are

increased by approximately 3 %. The results are seen in the yield of unconverted

fraction, viable product yields from the hydrocracking process, overall hydrogen

requirements, and optimum profit. Other operating changes in the hydrocracker

produce expected trends obtainable in the industry. By simultaneous

consideration of hydroprocessor integration and hydrogen network

optimization, realistic designs of hydrogen distribution network can be

achieved.

Acknowledgements

The authors would like to acknowledge Petroleum Technology Development

Fund (PTDF) for their financial support granted.

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36

Chapter 4 Development of Vacuum Residue

Hydrodesulphurisation/Hydrocracking Models and

their Integration with Refinery Hydrogen Networks

This paper addresses model development and integration of vacuum residue

hydrodesulphurisation (VRDS) and hydrocracking processes in refinery

hydrogen networks. The conversion and yield models developed for vacuum

residue (VR) hydrocracking process is similar to the vacuum gas oil (VGO)

hydrocracker models in Publication 2. In this paper, VR conversion is a direct

function of asphaltenes conversion, which is indirectly related to hydrogen

partial pressure. Models for sulphur, conradson carbon residue (CCR), and

asphaltenes in product are either developed from first principles or modified

from existing models. The models obtained are in good agreement with

experimental data. The optimized results from the integrated superstructure

show the accuracy of the model in predicting expected industrial trends. Other

variables have also been considered during optimization, for example, when the

availability of hydrogen is limited to exact hydrogen requirements, hydrogen

consumption is decreased. The effects of varying hydrogen inlet flows in all

hydroprocessors on hydrogen production flow and profitability have also been

studied. Although, there are limitations in the operating window, the dynamics

of these interactions produces an efficient and flexible integrated hydrogen

network. Dr. Nan Zhang is responsible for the critical review of this paper.

37

4.1 Publication 3

Umana B, Shoaib A, Zhang N, Smith R. Development of Vacuum Residue

Hydrodesulphurisation-Hydrocracking Models and their Integration with

Refinery Hydrogen Networks. Industrial and Engineering Chemistry

Research. 2015; (Accepted)

P37

Development of Vacuum Residue Hydrodesulphurization−Hydrocracking Models and Their Integration with Refinery HydrogenNetworksBlessing Umana, Nan Zhang,* and Robin Smith

Centre for Process Integration, School of Chemical Engineering and Analytical Science, The University of Manchester, PO Box 88,Sackville Street, Manchester M60 1QD, U.K.

*S Supporting Information

ABSTRACT: In recent years, there has been an increase in vacuum residue hydroprocessing due to the decrease in fuel oildemand and an increase in distillate demand. This work characterizes vacuum residue hydrodesulphurization and hydrocrackingprocesses and their integration with hydrogen networks to evaluate holistic interactions between hydrogen consumers and thehydrogen distribution system. Conversion models for sulfur, conradson carbon residue, asphaltenes, and vacuum residue havebeen developed based on the feed quality, catalyst properties, and process operating conditions. A five-lump yield model isderived incorporating a feedstock characteristic index and true boiling points. The results of the proposed model show reasonableaccuracy with experimental data [Yang et al. J. Fuel Chem. Technol. (Beijing, China) 1998, 5]. A simultaneous optimization ofhydrogen consumer models and the hydrogen network model is executed using the CONOPT solver in the General AlgebraicModeling System environment. Sensitivity analysis is carried out on the integrated framework to demonstrate the influence ofvarying operating conditions on product yields. As expected, the outcomes validate attainable trends in the industry.

1. INTRODUCTIONHydrodesulphurization is a term used to describe processes bywhich molecules in petroleum feedstocks are split or saturatedwith hydrogen gas. It includes hydrotreating, hydrocracking,and hydrogenation of petroleum hydrocarbons. Hydrocrackingis a thermal and catalytic hydrogenation process that convertshigh molecular weight feedstocks to lower molecular weightproducts in the presence of a bifunctional catalyst. The catalystconsists of a metallic part, which provides hydrogenation, andan acid part that promotes cracking. Cracking will break bonds,and the resulting unsaturated products hydrogenate into stablecompounds. Residue conversion processes (fixed, ebullated,and moving bed) use supported palletized catalysts of thebifunctional composition. The fixed bed system is used forlighter and cleaner feedstocks: naphtha, middle distillate,atmospheric gas oils, vacuum gas oils, and atmospheric residuetreatment. With increasing level of complexity in the feedcomposition and density, the ebullated bed reactor systems arewell-suited to process heavy feed streams, particularly feedswith high metal, sulfur, asphaltenes, and conradson carbonresidue (CCR) content.Hydrocracking of heavy oils and residua have become

increasingly important because of the increased globalproduction of heavy and extra heavy crude oils coupled withincreased demand worldwide for low sulfur middle distillatesand residual fuel oils. These trends emphasize the importanceof refinery processes that are capable of converting heavypetroleum fractions, such as vacuum residues, into lighter,valuable, and cleaner products. This increased reliance onvacuum residue (VR) upgrading for clean middle distillate fuelhas also led to a rise in hydrogen consumption, thus stretchingthe existing hydrogen production capacity and creating animbalance between the cost of hydrogen required and value of

products. Anticipated future trends and regulations areexpected to increase further hydrogen consumption. Con-sequently, it becomes imperative to optimize hydrogen use in arefinery hydrogen distribution system. The development ofhydrogen consumer models is a requisite to optimizinghydrogen consumption for an effective hydrogen managementsystem. The present strategy would address two major issues:(1) development of heteroatom conversion models and steady-state lumped yield models that are robust and sufficientlydetailed to capture the behavior of the process with changes inoperating conditions and (2) integration of VR hydro-desulphurization (VRDS) models and hydrogen networkmodels to assess the effects of process performance on thehydrogen distribution network. The resulting superstructurewould facilitate the efficient utilization of hydrogen forimproved process operation.A detailed review of hydrogen network optimization is

presented.2

2. INTEGRATED DESIGN OF VRDS PROCESSES ANDHYDROGEN NETWORKS

Residue hydrodesulphurization can be classified into two majorroutes: noncatalytic and catalytic processes. Noncatalyticresidue process can be categorized into solvent deasphaltingand thermal or carbon rejection processes. Catalytic processesare subdivided into residue fluid catalytic cracking (RFCC) andresidue hydroprocessing. Hydroprocessing is the combination

Received: November 3, 2015Revised: January 25, 2016Accepted: January 27, 2016

Article

pubs.acs.org/IECR

© XXXX American Chemical Society A DOI: 10.1021/acs.iecr.5b04161Ind. Eng. Chem. Res. XXXX, XXX, XXX−XXX

pubs.acs.org/IECR

http://dx.doi.org/10.1021/acs.iecr.5b04161

of hydrotreating and hydrocracking processes, in which residuefeedstock is treated at low temperatures and high hydrogenpartial pressure, usually in the presence of a catalyst. Theincreasing demand for middle distillates has intensified the needfor hydrocracking. Ebullated bed reactors are capable ofperforming both hydrotreating and hydrocracking functionsand are thus referred to as dual purpose reactors. The processscheme of a typical ebullated bed system is shown in Figure 1.In ebullated bed hydroprocessing, the catalyst within the

reactor is not fixed.3 The hydrocarbon feed stream enters at thebottom and flows upward through the catalyst. In this process,oil and catalyst are separated at the top of the reactor and thecatalyst is recirculated to the bottom of the bed to mix with thenew feed. Fresh catalyst is added on top of the reactor, andspent catalyst is withdrawn from the bottom of the reactor.3

The liquid is sent to a high-pressure (HP) flash and routed to afractionator for separation into hydrocracked products. A majoradvantage of this type of reactor is its stirred reactor typeoperation with a fluidized catalyst. Its intrinsic ability to handleexothermic reactions, solid-containing feedstock, and a flexibleoperation while changing feedstocks or operating objectivesmakes it suitable to operate over a wide range of conversionlevels producing high liquid yields. The quantity and quality ofhydrocracker yields obtained are determined by the combina-tion of feed, operating conditions, and catalyst properties thatcharacterize the process. The interactions between theseprocess conditions, feed quality, catalyst properties, productyields, and product quality may not be adequately representedwithout the use of robust process models.The present methodology addresses the development of

VRDS process models and their subsequent integration in thehydrogen network. This work proposes a generic representa-tion of conversion and VRDS models embedded into ahydrogen network model to yield an integrated superstructureof hydrogen distribution−consumer system. There are four keysteps in the development of this methodology: (i) developmentof residue hydrotreating models that are sufficiently detailed tocapture the dynamic interactions between operating conditionsin hydrogen consumer and product quality, (ii) development of

VRDS yield models that are dependent on conradson carbonresidue and vacuum residue conversion levels, (iii) integrationof conversion and yield models in the hydrogen network modelto assess the consequence of interactions between processesand the distribution system on the overall network objective,and (iv) scenario optimization of integrated hydrogen net-works.

2.1. Model Development and Validation. There are twofundamental aspects of VRDS methods studied: residuehydrotreating and residue hydrocracking. Residue hydrotreating(RHT) improves quality for product blending or additionalprocessing, including demetalation, desulphurization, deasphal-tenization, conradson carbon conversion, and saturation.Residue hydrocracking (RHC) increases liquid yields, 1000+F conversion. The overall conversion of vacuum residue isconstant in practice.

2.1.1. Residue Hydrotreating Models. 2.1.1.1. Desulfuriza-tion. By far the most common heteroatom is sulfur, whoseconcentration can reach 6−8% by weight. Sulfur concentrationin products increases with increasing boiling points and ispredominantly present as thiophenic sulfur in condensedstructures (such as benzo, dibenzo, and naphtobenzo), butalso as aliphatic sulfur in sulfide and disulfide type functionalgroups. These functionalities are often used to create linksbetween hydrocarbon clusters. The desulfurization model2 hasbeen modified in this work, as shown in eq 1. Another variable,catalyst concentration, was introduced to the model to describethe influence of catalyst characteristics on sulfur conversion invacuum residue hydroprocessing. The physical contact ofhydrogen with the catalyst ensures adequate conversion andimpurities removal while minimizing carbon deposition.Increasing the hydrogen partial pressure reduces the reactorstart of run temperature as well as the rate of catalystdeactivation. Equation 1 shows the relationship betweenprocess variables and product quality in a vacuum residuehydrodesulphurization process.

α= −

× +× ×β ⎡

⎣⎢⎤⎦⎥S S

kK R

Cexp

( 3 )PH

LHSVprod feed2 cat

(1)

Figure 1. Simplified flow diagram of an ebullated bed process.

Industrial & Engineering Chemistry Research Article

DOI: 10.1021/acs.iecr.5b04161Ind. Eng. Chem. Res. XXXX, XXX, XXX−XXX

B


http://pubsdc3.acs.org/action/showImage?doi=10.1021/acs.iecr.5b04161&iName=master.img-000.jpg&w=388&h=231

Where Sprod is the sulfur content in product, ppmw; Sfeed thesulfur content in feed, ppmw; k the rate constant of HDSreaction, h−1; K the 3+ ring aromatic inhibition constant, 3 + Rthe 3+ ring core aromatic content in feed, ppmw; PH2 therecycle hydrogen partial pressure, bar; α the pressure-dependent term; Ccat the catalyst concentration; and LHSVthe liquid hourly space velocity, h−1. Morawski and Mosio-Mosiewski4 reported the dependence of process parameterstemperature, catalyst content, hydrogen pressure, and LHSV onsulfur, asphaltenes, conradson carbon residue, and vacuumresidue conversion in the experimental data shown in Table 1.Table 1 shows the effect of parameters on the conversion

levels obtained within the following operating ranges: reactiontemperature, 410−450 °C; hydrogen pressure, 12−20 MPa;LHSV, 0.25−0.75 h−1; and Ccat, 1−10 wt %. For purposes ofbrevity, the correlation of sulfur model is presented in Table S1and Figure S1 in the Supporting Information. A simultaneousincrease in sulfur conversion is obtained with a decrease ofsulfur in the product. The modified sulfur model shows goodagreement with the experimental data. The average absoluteerror between experimental and calculated concentrations was0.045%. The model is validated on other data4 for changes intemperature as shown in Table S2 and Figure S2 in theSupporting Information. The result from the model for adifferent case of operating conditions shows good agreementwith the experimental data in Table 1. The average absoluteerror obtained is 2.6%.2.1.1.2. Conradson Carbon Residue Conversion. Several

studies have shown that an important variable in determiningcoke yield is CCR in the feed. CCR conversion depends on thecontent of coke-forming precursors in the feed. Kirchen et al.5

found a linear relationship between the amount of coke formedand microcarbon residue (MCR). The relationship betweenMCR/CCR and different parameters has been studied.Sanford6 and Gray et al.7 reported a linear correlation betweenMCR content of the residue fractions and the aromatic carbon

content. Trasobares et al.8 obtained a similar relationshipbetween CCR and aromatic carbon content at 415 °C. Theremoval of compounds which contribute to CCR is thought tobe due to aromatics saturation and is an indirect way ofstudying aromatic saturation. Beaton and Bertolacini9 indicatedthe effect of aromatic saturation on CCR conversion. CCRreduction comprises the catalytic hydrogenation of aromaticrings and thermal cracking of the naphthenic rings produced byhydrogenation. The reaction is approximately first order withrespect to hydrogen partial pressure. Equation 2 shows the rateequation assuming constant density.

−−

=r

CCR CCR 1LHSV

feed prod

(2)

where CCRfeed is the initial concentration of CCR in feed,ppmw; CCRprod the outlet concentration of CCR in product,ppmw; LHSV the liquid hourly space velocity; and r the rate ofreaction.CCR removal rate can be expressed as follows:

− =r K CPHx n2 (3)

The influence of temperature has been assumed to followArrhenius equation:

= −K K e E RT0

/a (4)

Beaton and Bertolacini9 found that the reaction ofRamsbottom carbon conversion is roughly first order withrespect to hydrogen partial pressure for hydroprocessing of atypical vacuum residue. In this work, CCR conversion isassumed to fit first-order kinetics.

−=

KCCR CCCR

CCRPH

LHSVfeed prod

prod

2

(5)

The overall equation for predicting the amount of CCR inproduct is given as

Table 1. Influence of Process Parameters on Vacuum Residue Conversion

process parameters conversion (wt %)

temperature (°C) LHSV (h−1) pressure (MPa) catalyst content (%) VR sulfur CCR asphaltenes

Effect of Reaction Temperature410 0.5 16 1 27.8 51.8 19.9 13.8420 0.5 16 1 45.1 61.2 32 28.5430 0.5 16 1 61.6 70.5 45.9 44.1440 0.5 16 1 77.5 80.7 64.2 63.5450 0.5 16 1 92.7 91 84.2 83.5

Effect of Liquid Hourly Space Velocity430 0.25 16 1 88.7 84.8 82.5 81.1430 0.38 16 1 74.7 77.4 63.1 61.3430 0.5 16 1 61.6 70.5 45 44.1430 0.63 16 1 50.9 65.4 31.4 30.2430 0.75 16 1 40.9 60.9 18.6 17.2

Effect of Hydrogen Pressure430 0.5 12 1 62.4 69.1 39.9 38.9430 0.5 14 1 62.1 69.8 42.8 41.5430 0.5 16 1 61.6 70.5 45.9 44.1430 0.5 18 1 60.4 71.2 47.5 46.8430 0.5 20 1 59 71.9 49.2 49.4

Effect of Catalyst Concentration430 0.5 16 1 61.6 70.5 45.9 44.1430 0.5 16 5 62.1 80.8 47.3 45.8430 0.5 16 10 62.9 88.4 49.6 48.1



C

http://pubs.acs.org/doi/suppl/10.1021/acs.iecr.5b04161/suppl_file/ie5b04161_si_001.pdf



=+

γ

μ( )CCR

CCR

1 KprodfeedPH

LHSV2

(6)

where γ is the parameter that indicates the aromaticity of thefeed. Table 2 shows the parameters obtained from eq 6. A chartof CCR products in the experiment and model is plottedagainst hydrogen pressure in Figure 2.

The model prediction shows good agreement with theexperimental data. The average absolute error betweenexperimental and calculated concentrations is 0.33%. Themodel in eq 6 was validated using another data with varyingtemperature and constant H2 pressure and LHSV. The resultsare described in Table S3 and Figure S3 of the SupportingInformation. Table 3 shows the parameters obtained from eq 6.The plot of CCR model fit with experimental data at varyingtemperature is shown in Figure 3.2.1.1.3. Deasphaltenization. Asphaltenes are the major

precursors to sludge and sediments. They are very largepolyaromatic compounds with a molecular weight ranging from1 000 to 20 000 and possessing a boiling point above 538 °C.High boiling point fractions contain the so-called resins andasphaltenes fractions, generally defined with high polarity and

aromaticity, combined with large contents of heteroatoms suchas sulfur (S) and nitrogen (N), metals such as vanadium (V)and nickel (Ni), and functional groups. Some metalcompounds, for example, are known to be included in complexstructures as porphyrins.9 Marafi et al.10 showed that only alimited hydrodemetallization (HDM) of a residue could beachieved unless a desirable rate of hydrodeasphaltization(HDAs) is maintained. Most of the metals (V and Ni) whichhave to be removed are associated with asphaltenes entities.Therefore, a high rate of HDAs is a prerequisite for achievinghigh HDMs. Similarly, sulfur is also distributed primarily in theresins and asphaltenes. Asphaltenes is reported in the literatureto consist of a two-dimensional structure of naphthenic,aromatic linkage by aliphatic chains and sulfur bridges.11 Ithas been shown that large polynuclear aromatics thatpredominate in asphaltenes limit the conversion of residuefeedstocks because of the formation of coke and asphaltenicsediments downstream. The linear relationship between CCR,asphaltenes, and 350 °C fraction indicates that coke precursorsreside in the asphaltenes and high boiling fractions.8 Theconversion of asphaltenes into valuable hydrocarbons wouldrequire severe operating conditions at a high temperature andhydrogen partial pressure while using a hydrogenation catalystwith low acidic support to avoid high coke formation. Schabronand Speight12 developed a correlation in their paper relatingasphaltenes content, molecular weight, and heteroatom contentwith CCR and MCR of whole residua. Ancheyta et al.13

reported a first-order kinetic model for two types ofasphaltenes: hard-to-react and easy-to-react asphaltenes.

− = + −β βr A k C C A k C C(1 )A 1 1 A H 1 2 A H2 2 (7)

where A1 is the fraction of the heavy hydrocarbon that reactsslowly, A2 the less refractory fraction that reacts more quickly,CA the asphaltene concentration, CH2

the hydrogen concen-tration, and β the reaction order for hydrogen. Because A1, A2,k1, and k2 are constants, eq 7 can be rearranged and grouped toobtain

− = + − βr A k A k C C[ (1 ) ]A 1 1 1 2 A H2 (8)

− = βr k C CA 0 A H2 (9)

where k0 = A1k1 + (1 − A1)k2.Assuming reaction order with respect to hydrogen

concentration is one, a relationship between asphaltenes in

Table 2. Feed, Operating Data, and Parameters Obtainedfrom CCR Model

Feed PropertiesCCR in feed (ppmw) 158 000

Operating Conditionstemperature (K) 703LHSV (h−1) 0.5

Parametersγ (indicates the aromaticity of the feed) 1.29μ (indicates the order dependence on H2 pressure) 0.34

Figure 2. CCR model fit with experimental data (varying PH2).

Table 3. Feed, Operating Conditions, and ParametersObtained from CCR Model (Varying Temperature)

CCR in feed (ppmw) 158 000Operating Conditions

H2 Pressure (MPa) 16LHSV (h−1) 0.5

Parametersγ (indicates the aromaticity of the feed) 0.99μ (indicates the order dependence on H2 pressure) 10.56

Figure 3. CCR model fit with experimental data (varying temper-ature).



D






product and CCR and sulfur heteroatom contents in feed andproducts can be developed, as in eq 10. The possiblerelationship between CCR content and asphaltenes contentwas studied, and a linear relationship was observed. The CCRcontent decreases as the asphaltenes content decreases. Someauthors have indicated the presence of thiophenic sulfur type inasphaltenes.13 Le Page et al.11 reported the existence of sulfurbridges in asphaltenic structures. Ancheyta et al.13 stated thatthe content of sulfur in asphaltenes is in the range of 6−8 wt %,which is higher than in maltenes (3−5 wt %).

μ

= × + ×

× × ×⎡⎣⎢⎢

⎤⎦⎥⎥

⎡⎣⎢⎢

⎤⎦⎥⎥

A A

SS

Asph (Asph ) (Asph )

expCCRCCR

ln

prod feed 1 feed 2

feed

prod

feed

prod (10)

where Asphfeed is the asphaltenes in feed, ppmw, and Asphprod isthe asphaltenes in product, ppmw.Table 4 shows the feed, operating data, and parameters

obtained from eq 10.

The model shows good prediction of the experimental datawhen correlated with CCR in product and sulfur in productobtained from the model as in Figures 4 and 5.

The values for A1 and A2 are 0.17 and 0.83, respectively. Thesum of these values is 1, as reported in the literature.13 Theaverage absolute error between the experimental and calculatedresults of asphaltenes in the product is 0.7%. Note that thisequation fits data only with varying H2 pressure at fixed reactortemperature, WHSV, and catalyst content. When correlatedwith CCR and sulfur data at varying temperature, it predictstemperatures from 683 to 713 K. Higher values of temperatureare poorly predicted by the model.

2.1.2. Residue Hydrocracking Models. 2.1.2.1. VacuumResidue Conversion. In the characterization of thermalconversion of vacuum residues, few assumptions have beenmade: (1) Thermal reactions are considered to be irreversiblebecause cracked fragments are saturated immediately withhydrogen. (2) The feedstock consists of several pseudo orlumped components that react in the first order.13 A first-orderVR conversion model derived from steady-state reactionkinetics has been developed to describe quantitatively theinteractions that exist between asphaltenes conversion, feed andcatalyst properties, hydrogen partial pressure, and operatingconditions.

τ−

=X

Xk

1VR

VR (11)

The VR conversion also increases with the cracking rateconstant, k; τ is the residence time in seconds. The rateconstant (k) is highly dependent on the characteristics of VRfeed, product properties, and operating conditions in thereactor. In the upgrading of heavy oil, some properties such asheteroatom contents are also very important. Therefore, theirincorporation into the conversion model may yield betterpredictions of VR conversion in hydrodesulphurizationprocesses.

=

⎧

⎨

⎪⎪⎪⎪⎪⎪⎪

⎩

⎪⎪⎪⎪⎪⎪⎪

⎫

⎬

⎪⎪⎪⎪⎪⎪⎪

⎭

⎪⎪⎪⎪⎪⎪⎪

k f

K

T

T

Characteristics of VR feed

Nature of product

Reaction conditions

Heavy oil characterization factor ( )

Asphaltenes content in feed

Boiling point parameter ( )

Asphaltenes content in product

Temperature ( )

Weight hourly space velocity (WHSV)

x

H

Yang and Wang14 developed a feedstock characteristic indexbased on supercritical fluid extraction and fractionation (SFEF)characterization of residue fractions.

ρ=K

M10

H/CH 0.1236 (12)

Table 4. Feed, Operating Data, and Parameters Obtainedfrom Asphaltene Model

Feed Propertiesasphaltenes in feed (ppmw) 52 400

Operating Conditionstemperature (K) 703LHSV (h−1) 0.5

ParametersA1 (refractory fraction) 0.18A2 (less refractory fraction) 0.82μ (exponential coefficient) 0.11

Figure 4. Asphaltenes model fit with experimental data (varying CCRin product).

Figure 5. Asphaltenes model fit with experimental data (varying sulfurin product).



E




whereM is the average molecular weight, ρ the density at 20 °C(g/mL), and H/C the atomic hydrogen-to-carbon ratio. Thisindex is used to correlate properties such as carbon residue andcompositional features, such as saturates, aromatics, resins, andasphaltenes (SARA). According to Wang et al.,15 a vacuumresidue with low H/C atomic ratio and high carbon residue hasa high propensity to produce large amounts of coke. Theauthor reported an increase in coke yield when KH = 6−8; theincreasing rate of coke yield increases gradually with decreasingthe feed KH value and increases more when KH < 6. Shi et al.16

grouped KH values according to their processability: KH > 7.5(adaptable to secondary processing); 6.5 < KH < 7.5(intermediate); KH < 6.5 (difficult in secondary processing).The temperature effect of the specific reaction rate could be

correlated with the Arrhenius equation:

= −k k e E RTT 0

/(13)

where k0 is the frequency factor (h−1) and E represents theapparent activation energy.Although Yang et al.1 reported an increase in density of

vacuum residue subfractions with increasing molecular weight,here the author has replaced density with boiling points ofsubfractions up to vacuum gas oil (VGO). It is assumed that thesimulated distillation data can easily be obtained for theseproducts.

−=

× × × φ ×( )XX

K k T

(1 )

exp

WHSV

T xVR

VR

HAsph

100

(14)

where XVR is the VR conversion; KH the heavy oil character-ization factor; Tx the ratio of boiling points of the heaviestpseudocomponent in the product to the feed end point, in °Fto reflect the overall product range; Asph the asphaltenesconversion; φ the coefficient of exponentiation; and WHSV theweight hourly space velocity in h−1.In eq 14, the value of KH is 5.6 and φ is −1. The value of KH

represents a “difficult to process” feed.According to Morawski and Mosio-Mosiewski,4 the catalyst

concentration and hydrogen pressure has no effect on thehydrocracking of VR. The reaction temperature and LHSVexerted dramatic effects on the hydrocracking of VR, CCR, andasphaltenes content as in Table 1. The authors observed anegative effect on VR conversion with increasing hydrogenpressure as a result of secondary reactions of cracking productsuch as polymerization, alkylation, and hydrogenation. Notethat these experiments were carried out in excess hydrogen tomaintain good agitation in the reactor. For a hydrogen networkwith varying demands from hydrogen-consuming units,availability of hydrogen is an issue; hence, there is a need tomaintain an optimum requirement of hydrogen withoutcompromising on product yields and quality.Figure 6 describes the effect of asphaltene conversion on VR

conversion. Because of the effect of increasing hydrogenpressure, VR conversion is slightly decreasing with increasingconversion of asphaltenes, which is rarely the case in VRhydrocracking reactions. This shows that VR conversion isinsensitive to hydrogen pressure, as implied in Morawski andMosio-Mosiewski.4

The average absolute error between the experimental andpredicted values is 1.67%. Amidst several contradictions in theliterature regarding the influence of hydrogen pressure on VRconversion, the effect of temperature on VR conversion was

consistent. Table 1 describes the outcome of increasingtemperature on asphaltene and VR conversion. A plot of VRconversion and asphaltene conversion is shown in Figure 7 atincreasing temperature.

Figures 6 and 7 show that the VR conversion model iscapable of simulating data with varying H2 pressure andtemperature. The models show flexibility in predictingexperimental data with varying operating conditions.

2.1.2.2. VR Hydrocracker Yield Models. Several authors haveestablished the use of lumps in the kinetic modeling of VGOand heavy oil cracking systems.17 Recently, Gao et al.18

proposed an eight-lump kinetic model for the catalytic crackingof vacuum residue fractions. However, the model does notaccount for feedstock properties and thus makes it difficult tosimulate the model outcomes on a different vacuum residuefeed. This work reports the relationship between distillate yieldproduction and vacuum residue conversion as a function offeedstock properties. The yield correlations are developedbased on plant data and typical operating conditions, thusallowing accurate prediction of yield distribution at a particularseverity. Yang et al.1 reported the yields of products obtainedfrom hydroprocessing fractions of vacuum residue obtained bythe SFEF technique. In this work, we consider the fractionalfeed and product properties of Arabian Medium Crude(SQVR) as shown in Table 4.The hydrodesulphurisation results for each fraction in Table

4 were obtained at 400 °C, 0.25 h−1, and an initial hydrogen

Figure 6. VR conversion model fit with experimental data (increasingPH2).

Figure 7. VR conversion model fit with experimental data (increasingtemperature).



F




pressure (IHP) of 8.5 MPa. Although asphaltenes or CCRconversion are not reported in Table 5, VR conversion wascalculated based on data in Table 1 at 426 °C, 16 MPa, and 0.5h−1. The products obtained from hydroprocessing of thesefractions are shown in Table 6.

Models are developed for gas yield, <200 °C yield, 200−350°C yield, 350−500 °C, and coke yield. The feed and productsare characterized into 20 pseudocomponents, except gas andcoke yields. An excellent correlation was found between CCRconversion and gas yield. Similar results have been reported inthe literature.8 Based on yield correlations obtained from first-principles, an empirical correlation for the prediction of gasyield has been developed as a function of CCR conversion. Themodel for the yield of each fraction in the gas range as afunction of CCR conversion, KH, and selectivity to gas fractionis first calibrated.

=×

YX

KSP

GASCCR GAS

Hparami

i (15)

where XCCR is the CCR conversion, SPGAS the selectivity to gasfraction, param the parameter related to heavy feed character-ization factor, and KHi the heavy feed characterization for eachfraction in the gas range.The model for the yield of each fraction in the naphtha range

was equally calibrated as a function of vacuum residueconversion, selectivity to naphtha fraction, and the relativerate constant for each fraction. Stangeland19 proposed anexpression for the relative rate constant that could reflect thedifferences in feed composition and catalyst character using aminimum number of parameters:

= + −K T k T A T T( ) [ ( )]i i i i i03

(16)

where Ti = TBP/1000 and k0 = 1. For any value of theparameter A, k(0) = 0 and k(1) = 1. Above Ti = 1, Ki is greaterthan 1, unless A is negative. Because A usually lies in the rangeof (0−1), Ki varies from a linear to a cubic function.

=×

<<Y

XK

SP

i200

VR 200paramKi (17)

where SP<200 is the selectivity to <200 fraction and paramK isthe parameter related to relative rate constant.Other yield models are then based on the prediction of

naphtha yield. The results obtained showed reasonable accuracywith industrial data. The total yield of each product range isgiven as the sum of yields of pseudocomponents in each boilingrange. The yield models for all other products are obtainedusing a successive exponential−polynomial distribution as givenby the following equations:

μ μ

γ

− = − + −

+ +

−

− < − < −

< − − < < <

− −

Y Y T Y T

Y T Y T

T

( ) exp( ) exp( )

exp( ) exp( )

200 350 200 200 3504

200 200 3503

200 200 350 200 3502

200 200 200

200 350 200 350

i i i

i i

i (18)

μ μ

γ

− = − −

− −

+

− < − < −

< − − < − −

− −

Y Y T Y T

Y T Y T

T

( ) exp( ) exp( )

exp( ) exp( )

350 500 200 350 5004

200 350 5003

200 350 500 350 5002

200 350 500 350 500

350 5000 350 500

i i i

i i

i (19)

where Y<200 represents the sum of the yields for <200 °C. T<200,T200−350, and T350−500 are the ratios of the pseudocomponentboiling points and the heaviest pseudocomponent boiling pointof feed for <200 °C, 200−350 °C, and 350−500 °Cpseudocomponent range, respectively. Another yield equationwas developed for coke yield based on the amount of carbonresidue in the product, selectivity to coke fraction, and KHc.

=×

YSP

K

CCR

iCcoke

prod coke

Hparami

(20)

where SPcoke is the selectivity to the formation of coke, paramCthe heavy oil characterization parameter for coke fraction, andKHi the heavy feed characterization for each fraction in the cokerange. The reduction of conradson carbon residue minimizesthe amount of petroleum coke produced in a refinery. Similarstudies have shown that the amount of coke formed in thecoking step is a function of the amount of CCR in thehydrocracked product.6

A maximum of 11 parameters (A, SPGAS, SP<200, SPcoke,param, paramK, paramC, μ<200, μ200−350, μ350−500, γ<200, γ200−350,γ350−500, and φ) are obtained for the conversion and yieldmodels for five product lumps. As parameter A affects the shapeof the yield curve, it varies to some extent with differentfeedstocks. To determine the parameters, the developed modelis implemented in a software package for nonlinear regression,based on the least-squares method. Parameters SPGAS, SP<200,and SPcoke represent the selectivity to gas, naphtha, and cokeyield, respectively. Parameters μ and γ represent coefficients inthe yield model for <200 °C, 200−350 °C, and 350−500 °Cproducts. Parameter φ is associated with asphaltene conversion.Table 7 shows the feed, operating data, and resultingparameters for the vacuum residua (SQVR) in Table 5.

Table 5. Experimental Data for SFEF Fractions of SQVR

SQVR, normal pentane as solvent in SFEF

fraction no. 1 2 3 4 5 6 7 8 residue

fraction (m %) 9.9 10.2 10.1 10 10.1 10.1 10 12 18.3d20 (g/cm

3) 0.9432 0.9559 0.9634 0.9714 0.9789 0.9947 1.0232 1.0606MW (g/mol) 558 610 644 653 690 744 900 1128 3047H/C, (mol/mol) 1.67 1.62 1.63 1.63 1.59 1.55 1.46 1.38 1.14

Table 6. Products Obtained from Hydroprocessing of SFEFResidue Fractions

SFEF fractions of SQVR

feed 2 4 6 8 residue

material balance (%)gas 1.02 0.98 1.34 2.04 2.89<200 °C 9.04 8.32 6.57 11.00 2.81200−350 °C 16.88 16.00 16.79 14.57 11.46350−500 °C 31.35 29.43 28.17 19.47 12.33>500 °C 40.83 44.47 46.03 51.32 66.49coke 0.88 0.80 1.10 1.60 4.02



G


The product predictions from the yield models show goodagreement with industrial data. Table 8 shows the comparisonbetween industrial and predicted yields. By using the obtainedparameters, it is possible to determine the grouped productdistribution for VRDS products.

The maximum percentage error obtained from the developedmodels is approximately 0.8%, and the average absolutepercentage error is approximately 0.15%. Overall, the yieldmodels show excellent prediction with the industrial data.Figure 8 shows the graphical representation of the industrialand model yield predictions.2.2. Integration of VRDS Models in Hydrogen Net-

works. In this section, an attempt is made to establish massbalances around the VRDS/HC unit.

2.2.1. Reactor Inlet Constraints. The material balance at themix point is given by

= + + ∀F F F F jj j j jmix, mu, re, pr, (21)

· = · + · + · ∀ ∀F Y F Y F Y F Y i jj i j j i j j i j j i jmix, mix, , mu, mu, , re, re, , pr, pr, ,

(22)

where F, Y, and subscript j represent the flow rate of a stream,purity of a stream in mass fraction, and hydrotreaters/hydrocracker, respectively. Subscript i represents all compo-nents in the stream and pseudocomponents obtained fromsimulated distillation profile and bulk density of feed streams.The material balance around the inlet to the reactor is the

sum of the flow rates of the mix point and liquid feedstock.

= + ∀F F F ji j j jr , mix, feed, (23)

· = · + · ∀ ∀F Y F Y F Y i ji j i i j j i j j i jr , r , , mix, mix, , feed, feed, , (24)

Because the feed flow rate is constant and the masscomposition of hydrogen in the feed is zero, the pure hydrogenflow rate, Fri,j·Yri,i,j, is equivalent to the product of flow rate andpurity at the mix point, ∀i = H2. Initially, the flow rate andpurity of the mixture are fixed to simulate base case conditions.

2.2.2. VRDS Model. When eqs 1−20 are combined withHDS and light hydrocarbon production models2 and VGOhydrocracker models, the overall mass balance can be defined.

· = · − ∀ = ∀F Y F Y R i jHi j i i j i j i i j i jf , f , , r , r , , , 2 (25)

· = · + ∀ = → ∀F Y F Y Z i C C j, H Si j i i j i j i i j i jf , f , , r , r , , , 1 5 2

(26)

The variables Ri,j and Zi,j are defined as consumption ofhydrogen in all hydroprocessors and production of lighthydrocarbons in hydrotreaters including H2Sj, respectively.The values of RHi,j in the hydrocracker could be obtained asfunctions of the hydrogen−oil ratio at the inlet of hydrocrackerand VR conversion. The total hydrogen consumed inhydrocrackers is therefore calculated as

= + + ∀ = ∀R R R i jRH Hi j i j i j, , H S, , C 2i j2 formed, , (27)

where RH2S,i,j is the amount of hydrogen consumed to form H2Sjand RCformed,i,j

is the amount of hydrogen consumed in theformation of light hydrocarbons from hydrodesulphurisationreactions. Note also that the products obtained during crackingreactions contain some amount of sulfur that was not removedduring the reactions. The product sulfur levels in pseudocom-ponent, i for each pseudocomponent boiling range, n in VGOhydrocracker, j can be estimated by incorporating productyields and properties in eq 28.

= × ∀ =S S Y n products obtained from fractionatori j nT

i i j, , prod f , ,i n,

(28)

Ti,n is the ratio of boiling points of pseudocomponents, i, tothe heaviest pseudocomponent boiling point in the feed foreach product range, n; Sprod the total sulfur in the entire productrange; and Yfi,i,j the products, i, obtained from hydrocracker, j.As shown in eq 28, β in the HDS model representing the natureof feed and product characteristics has been replaced with Ti,n,which represents the product characteristics in each pseudo-component boiling range. The sulfur in each pseudocomponentfraction is directly related with the yields obtained for eachfraction. Light fractions would usually contain the most reactive

Table 7. Feed and Operating Data in the Refinery

Feed Propertiesfinal pseudocomponent boiling point of feed (°C) 710

Product PropertiesFinal pseudocomponent boiling point of product (°C) 460

Operating Conditionstemperature (K) 699hydrogen pressure (MPa) 16LHSV (h−1) 0.25

ParametersSPgas (selectivity parameter to gas yield) 0.45SP<200 (selectivity parameter to <200 yield) 0.02SPcoke (selectivity parameter to coke yield) 6.86param (feed characterization parameter in gas yield fraction) 2.18paramK (boiling point parameter in <200 fraction) 2.46paramC (feed characterization parameter in coke fraction) 3.12A (relative rate function dependent parameter) 0.88μ200−350, γ200−350 (yield coefficients for 200−350 °C model) 2.99, 0.70μ350−500, γ350−500 (yield coefficients for 350−500 °C model) 1.33, 0.32

Table 8. Yield Comparison of Industrial Data and ModelPredictions

products/unconverted(UC) feed

industrialyield (−)

predictedyield (−) difference

%difference

gas 0.018 0.018 0.000 −0.779naphtha 0.070 0.070 0.000 −0.006gas oil 0.146 0.146 0.000 −0.025VGO 0.224 0.224 0.000 0.049UC VR 0.521 0.522 −0.001 −0.107coke 0.020 0.019 0.001 3.177

Figure 8. Comparison of industrial and predicted yields.



H



sulfur compounds, whereas heavy fractions are concentratedwith the most refractory sulfur compounds. The total sulfurdistribution in each product range can be given as the sum ofthe individual sulfur amount obtained from eq 28. The sulfur inthe unconverted fraction is obtained by subtracting the sulfur ineach pseudocomponent range from the total sulfur in theproduct in eq 29.

= − + + + +S S S S S S S( )UCO prod gas LN HN ker DIE (29)

2.2.3. Flash Model. The outlet stream from the reactor isrouted to the high-pressure flash separator and distributed intovapor and liquid phases. Assuming vapor and liquid phasesleaving a flash unit are in equilibrium

= · ∀ ∀Y Y K i ji j i j i jvap, , liq, , , (30)

Mass balance around the flash separator unit is given by thefollowing equations:

= + + ∀F F F F ji j j j jf , re, liq, pu, (31)

· = · + · + · ∀ ∀F Y F Y F Y F Y i ji j i i j j i j j i j j i jf , f , , re, re, , liq, liq, , pu, pu, ,

(32)

= + ∀F F F jfi j j j, vap, liq, (33)

= + ∀F F F jj j jvap, re, pu, (34)

= = ∀ ∀Y Y Y i ji j i j i jvap, , re, , pu, , (35)

∑ = ∀Y j1i

i jvap, ,(36)

∑ = ∀Y j1i

i jliq, ,(37)

= ∀ =F F j j1j j jre, H C, 1,2 (38)

= + ∀F F F jj j jpu, pr, SF, (39)

= ∀ ≠F F j j1j j jpr, H C, 1,2 (40)

2.2.4. Column Mass Balance. The liquid outlet from thehigh-pressure separator consisting of VRDS products is routedto the fractionating column.

= + + + +

∀ =

< − −F F F F F F

j hydrocrackers

j j j j j jliq, GAS, 200, 200 350, 350 500, ucVR,

(41)

where FGAS,j, F<200,j, F200−350,j, F350−500,j, and FUCVR are flow ratesof gas, <200 °C, 200−350 °C, 350−500 °C, and unconvertedresidue fractions, respectively. The individual flow rates of gas,naphtha (<200 °C), gasoil (200−350 °C), and VGO (350−500°C) are functions of total flow rate of liquid product and theirrespective yield fractions. An overall mass balance around thereaction−separation system is also included in the model.

+ + = + +

+ + ∀ =

< −

−

F F F F F F

F F j hydrocrackers

j j j j j j

j j

feed, mu, pr, GAS, 200, 200 350,

350 500, ucVR, (42)

2.2.5. Hydrogen Network Model. The interactions existingbetween hydrogen producers and consumers can berepresented with the following mass balance:

∑ = ∀F F jk

k j jH P, , mu,2(43)

∑ ∑+ = ∀F F F jk

k jj

j j jH P, ,1

H C, 1, mix,2 2(44)

∑ ∑· + · = · ∀F Y F Y F Y j( ) ( )k

k j i kj

j j i j j i jH P, , H P, ,1

H C, 1, H C, , 1 mix, mix, ,2 2 2 2

(45)

Equation 44 shows that the sum of hydrogen productionflow rates from various hydrogen producers, k; externallyrecycled gas flows from other consumers, j1; and internallyrecycled gas flows within consumer, j, is equal to the gas flowrate at the reactor inlet mix point for consumer, j. When eq 44is combined with eq 45 for mass balance, the complete massbalance between hydrogen producers and consumers isobtained. In a hydrogen network, hydrogen consumers requirehydrogen at certain flow rates and purities. The purge gas fromthe high-pressure separators of various hydrogen consumers iseither reused in other consumers or sent to a site fuel system.The relationship between hydrogen consumers and site fuelsystem is represented by the following mass balance equations:

∑= + ≠ ∀F F F j j j1j jj

j jpu, SF,1

H C, , 12(46)

∑· = · + · ≠ ∀F Y F Y F Y j j j( ) 1j i j j i jj

j j i jpu, pu, , SF, SF, ,1

H C, , 1 H C, ,2 2

(47)

= = ∀ ∀Y Y Y i ji j i j i jpu, , SF, , H C, ,2 (48)

Normally, the flow rate from a hydrogen producer is subjectto certain maximum or minimum limits as shown in eq 49.

∑≤ ≤F F Fkj

k j kH P,L

H P, , H P,U

2 2 2(49)

When eqs 1−49 are combined, an integrated hydrogennetwork model is formulated that comprises nonlinearempirical process models for prediction of sulfur in theproduct, light hydrocarbon formation from the HDS process,VR feed conversion, yields of VRDS products, hydrogenconsumption, and mass balances of hydrogen producers andconsumers in a network. The objective function is to minimizeoperating cost that accounts for hydrogen production cost andfuel gas value as in eq 50.

∑ ∑= · − ·F U F UObjective min[ ( ) ( )]k

kj

j jH P, H SF, SF,2 2

(50)

UH2and USF represent the unit prices of hydrogen and fuel

gas, respectively. Other costs that are integral in refineryprocess economics have not been considered in this presentwork. In most cases where hydrocracking processes areconsidered, refiners would usually be concerned with howmuch value they can obtain from products. The objectivefunction is to maximize profit as in eq 51.

= + ×

+ + + + ×

+ + ×

+ × + ×

− ×

< <

−

− −

F F U

F F F F U

F F U

F U F U

F U

Objective max[( )

( )

( )

( ) ( )

( )]k

GAS,VGOHC GAS,VR GAS

liq,NHT liq,CNHT naph,VGOHC 200,VR 200

ker,VGOHC 200 350,VR ker

DIE,VGOHC DIE 350 500,VR 350 500

H P, H2 2 (51)



I


FGAS,VGOHC, FGAS,VR, F<200,VR, Fliq,NHT, Fliq,CNHT, Fnaph,VGOHC,FKER,VGOHC, F200−350,VR, FDIE,VGOHC, and F350−500,VR are flow ratesof gas from VGO hydrocracking unit, gas from VRDS unit,naphtha from VRDS unit, naphtha from NHT, cracked naphthafrom CNHT, total naphtha from VGO hydrocracking unit,kerosene from VGO hydrocracking unit, gas oil from VRDSunit, diesel from VGO hydrocracking unit, and VGO fromVRDS unit, respectively. UGAS, U<200, UKER, UDIE, and U350−500are unit prices of gas, <200 or naphtha, kerosene, diesel, and350−500 or VGO respectively. The additional processconstraints proposed in the formulation of this methodologyare expected to give more realistic solutions as demonstrated inthe case study.

3. INTEGRATED OPTIMIZATION FRAMEWORK FORHYDROGEN NETWORKS

An extended methodology framework in Figure 9 has beenproposed to illustrate the integration of hydrotreater and

hydrocracker models in the optimization of hydrogen networks.The optimization methodology describes the effect of changingprocess variables, such as H2 partial pressure and temperature,on sulfur, CCR, asphaltenes, VR conversion, and product yieldsin a VRDS unit.The methodology can be summarized into three major steps:3.1. Process Model Development, Regression, and

Validation. The nonlinear process models developed fromfirst-principles steady-state kinetics in section 2.1 have beensuccessfully used to predict VR feed conversion and five-lumped product yields in a hydrocracker. The process modelqualitatively and quantitatively describes the effect of feedcharacteristics, process operating conditions, and productproperties on the conversion and product yields. Theconversion models are regressed on experimental data,4 andthe yield models are regressed using experimental data1 inTable 5. The resulting trend from each fit shows that theprocess model is robust enough to define the performance of aVRDS.3.2. Overall Network Modeling. The nonlinear con-

version and yield models are integrated into the hydrogen

network model resulting in an integrated superstructure ofprocess and network models. The overall network is modeledin the General Algebraic Modeling System environment. Theinlet hydrogen flow to VRDS is allowed to vary toaccommodate the effects of changing operating conditions onVRDS performance.

3.3. Overall Network Optimization. The process modelsare integrated in a hydrogen network model to exploit theinteractions between changing operating conditions andhydrocracker performance. Depending on the hydrocrackingprocess objective, changes in feed flow, hydrogen oil ratio, andreactor temperature would result in different feed conversionsand subsequently changes to hydrocracker product yields. Theeffects of these changes are seen in the hydrogen consumptionlevels, product distribution from hydrocrackers, and overallhydrogen requirements in the network.

4. CASE STUDY4.1. Base Case. The base case hydrogen network2 is

modified in this work to include a VRDS unit. The hydrogennetwork consists of two hydrogen producers (hydrogen plant,H2Plant); catalytic reformer (CCR); three hydrotreaters(naphtha hydrotreater, NHT; cracked naphtha hydrotreater,CNHT; and diesel hydrotreater, DHT); and two hydrocrackers(vacuum gas oil hydrocracker, VGOHC, and VRDS). Thedetailed feed stream data for the base case and operatingconditions in the hydroprocessing units is as shown in TablesS3 and S4 in the Supporting Information. Nonlinear VRDSprocess models developed in section 2.1 and hydrotreatermodels are integrated into the hydrogen network under fixedand varying operating conditions for the objective of maximumprofit. The prices for VGO feedstock, hydrogen, butane,naphtha, kerosene, and diesel are £562.91/t,20 £3000/t,21

£385.95/t,22 £594.81/t,22 £675.95/t,22 and £593.3/t,22 respec-tively. The objective is to minimize hydrogen at fixed operatingconditions across hydroprocessing units. The hydrogenproduction flow rate is 21.44 t/h. When the hydrogen-consuming processes are integrated in hydrogen networks,the interactions between hydrogen distribution and use inhydroprocessors can be exploited. First, we will consider theoutcomes of manipulating operating variables in the VRDS.Then, we shall consider the simultaneous effect of differentdecision variables on the overall hydrogen network profitability.

4.2. Optimization with Varying Hydrogen PartialPressure in VRDS. The recycle stream is used to maintainthe H2 partial pressure and the physical contact of hydrogenwith the catalyst to ensure adequate conversion and impurityremoval while minimizing carbon deposition. Increasing thehydrogen partial pressure reduces the reactor start of runtemperature as well as the rate of catalyst deactivation. In Table9, H2 partial pressure has been varied at constant temperatureand LHSV to study its influence on product distribution andchemical hydrogen consumption.As expected, increasing hydrogen pressure increases vacuum

residue conversion, decreases unconverted vacuum residue andVGO yields, while increasing the amount of gas, naphtha, andgasoil yields. Other authors have reported an increase in yieldof light fractions with increasing vacuum residue conversion.23

Gillis et al.24 mentioned that a hydrogen-rich environmentwould facilitate very high conversion of residue to liquidproducts, particularly distillate boiling range components,contrary to results reported in Morawski and Mosio-Mosiewski.4 The latter obtained an increase in UCVR, VGO,

Figure 9. Methodology for integration of VRDS unit in a refineryhydrogen network.



J




and gasoline yields and a corresponding decrease in gas and gasoil yields. The authors attributed the behavior of the system tosecondary reactions (polymerization, alkylation, and hydro-genation) of cracking products with increasing hydrogenpressure. Morawski and Mosio-Mosiewski4 explained that thiseffect was due to the excess amount of hydrogen present in thereactor. Figure 10 describes the influence of hydrogen pressureon product yield distribution.

The correlations obtained for asphaltene, CCR, sulfur andVR conversion have been based on experimental data.4 Thechemical hydrogen consumed in the VRDS is obtained fromthe combination of hydrogen consumed due to VR hydro-cracking reactions, HDS reactions, and light hydrocarbonformation.4.3. Optimization with Varying Temperature in VRDS

Process. Table 10 describes the effects of varying temperatureat constant hydrogen partial pressure in VRDS on product yieldpattern.The result in Table 10 shows the effect of decreasing

temperature on product yield distribution at H2 pressure of 160bar and LHSV of 0.5 h−1. The results obtained for gas, naphtha,gas oil, VGO, and VR are similar to the outcomes of theexperimetns of Morawski and Mosio-Mosiewski4. The authors

reported a decrease in VGO at 683 K. This work shows that adecrease in temperature necessitates a corresponding increasein VR and VGO, as expected, and a decrease in the lightfractions. Conversely, an increase in temperature results in acorresponding increase in the yield of light fractions and adecrease in heavier fractions and unconverted vacuum residue.The ratio of decrease in weight fractions for decreasingtemperature is slight in gas and naphtha compared to gas oiland VGO fractions. Morawski and Mosio-Mosiewski4 havenoted the importance of VGO in the production of low sulfurfractions of motor fuels. The result shows that the data1 is verysensitive to small changes in temperature. Although slightchanges in the lighter fraction yield distribution are reported, itis important to note that the resulting trend is plausible. Figure11 describes the results graphically.

In Figure 11, profiles of sectional areas for gas, naphtha, gasoil, and VGO yields and unconverted VR are presented to showthe effect of decreasing temperature. The yield distribution forheavier fractions is very sensitive to temperature compared tothe effects of hydrogen pressure in Figure 10. Beaton andBertolacini9 also predicted similar effects of temperature onproduct yield distribution. The yield of light gases, naphtha, and

Table 9. Effect of Increasing Hydrogen Pressure onConversion and Product Yield Distribution in VRDS Process

H2 pressure (bars) 150 160 170

chemical H2 consumed (t/h) 7.56 7.89 8.13asphaltene conversion (wt %) 31.65 33.34 35.07CCR conversion (wt %) 39.27 40.62 41.92sulfur conversion (wt %) 65.08 66.60 68.06VR conversion (wt %)/yields (wt %) 52.75 53.33 53.93gas 1.95 2.01 2.05naphtha 7.39 7.41 7.44gas oil 34.22 35.83 37.46VGO 9.19 8.08 6.97VR 47.25 46.67 46.07

Figure 10. Effect of hydrogen pressure on product yield distribution inVRDS process.

Table 10. Effect of Decreasing Temperature on Conversionon Product Yield Distribution

temeperature (K) 699 696 693

chemical H2 consumed (t/h) 7.892 7.329 6.626asphaltene conversion (wt %) 33.34 29.24 25.98CCR conversion (wt %) 40.62 35.66 31.11sulfur conversion (wt %) 66.60 63.55 60.62VR conversion (wt %)/yields (wt %) 53.33 48.02 43.00gas 2.01 1.89 1.79naphtha 7.41 7.15 6.99gas oil 35.83 20.21 3.34VGO 8.08 18.77 30.89VR 46.67 51.98 57.00

Figure 11. Effect of temperature on product yield distribution inVRDS.



K




gas oil increased with increasing residue conversion astemperature increased.Although the model showed reasonable accuracy in

predicting the industrial data,1 the extent of conversion islimited by how much change in temperature is accommodatedby the data. A feasible set of solutions can be obtained between693 and 699 K. The simultaneous effects of increases/decreasesin conversion are clearly seen in the chemical hydrogenconsumed and the overall hydrogen production requirementsof the network as shown in Table S5 in the SupportingInformation.4.4. Optimization of Maximum Profit at Varying Inlet

H2 Conditions in VRDS Process. Table 11 describes thecorresponding effect on profit with changes in hydrogen partialpressure.The result shows a slight relative increase of 0.6% in H2

production flow rate from H2 pressures at 150 and 160 bar.From the results, it could be inferred that the product yields arenot very sensitive to variations in hydrogen pressure; thus,differences in annual profit are minimal, similar to resultsobtained in Morawski and Mosio-Mosiewski.4 Under suchscenarios of limited sensitivity, the operator may be able to savehydrogen depending on how much profit giveaways can beaccommodated. In Table 11, the gains in profit outweigh theincrease in hydrogen flow rate with increasing hydrogen partialpressure.4.5. Optimum Profitability in VRDS Process. Morawski

and Mosio-Mosiewski4 reported the significant effects ofreaction temperature on the hydrocracking of VR and CCRand asphaltenes content in products. In Table 12, the effect oftemperature and hydrogen partial pressure on hydrogenproduction flow rate, profit, and the amount of CCR left inthe product is presented.As shown in Table 12, a balance between temperature and

hydrogen partial pressure requirements for the VRDS processcan be obtained, while maintaining the profitability of theprocess. For example, maximum profit is obtained at 699 K and160 bar; the amount of CCR produced is least at 699 K and

160 bar compared to other operating cases. Marafi et al.10

indicated that the content of CCR depends on the content ofcoke-forming precursors in the feed. In this regard, an attemptto correlate CCR content in products with coke formed hasbeen demonstrated in eq 20. An increase in temperature resultsin a corresponding increase in CCR conversion and thus adecrease of CCR in products and coke formed. Other authorshave established a temperature limit before the rapid formationof coke is reached. Font et al.25 reported a rapid decrease inconversion beyond temperatures of 417 °C, especially highlynegative conversions at 450 and 470 °C. The authors indicatedthat the decrease in conversion was attributed to the gradualinfluence of both recombination and coking reactions, as aresult of hydrogen deficit induced by the strong consumption

Table 11. Effect of Variations in Hydrogen Partial Pressure on Profit in VRDS Process

cases effects of fixed and varying inlet H2 on product yields

hydrogen consumers

NHT CNHT DHT VGOHC VR

fixed fixed fixed fixed fixed vary vary

temperature (K) 623 653 633 672 699 699 699hydrogen partial pressure in VR (bars) 150 160 170H2S formed (t/h) 0.329 0.538 5.430 1.726 2.881 3.059 3.131calculated conversion for VGOHC and VR (wt %) 66.88 52.75 53.33 53.93makeup hydrogen (t/h) 1.334 0.649 2.494 8.393 8.175 8.338 8.416chemical hydrogen consumed (t/h) 0.344 0.426 1.845 7.070 7.565 7.892 8.180dissolved hydrogen (t/h) 0.990 0.224 0.651 1.324 0.611 0.445 0.236

Products Formed from VGO Hydrocracking Reactions (t/h)flow rate of light gases 6.34flow rate of naphtha 66.89flow rate of kerosene 90.13flow rate of diesel 44.58

Products Formed from VR Hydrocracking Reactions (t/h)flow rate of light gases 3.61 3.71 3.80flow rate of naphtha 13.64 13.70 13.76flow rate of gasoil 63.20 66.23 69.26flow rate of VGO 16.97 14.94 12.89H2 production flow rate (t/h) 21.44 21.58 21.63overall profit (£B/y) 3.69 3.70 3.71

Table 12. Effect of Sequential Variation in H2 Pressure andTemperature on Profit in VRDS

hydrogen consumer VR

temperature (K) 699 699 696 696 693hydrogen partial pressurein VR (bars)

150 160 160 170 170

asphaltene conversion(wt %)

31.65 33.34 29.24 30.62 27.07

CCR conversion (wt %) 39.27 40.62 35.66 36.86 32.19sulfur conversion (wt %) 65.08 66.60 63.55 64.95 61.95VR conversion (wt %)/yields (wt %)

52.75 53.33 48.02 48.50 43.38

chemical hydrogenconsumed (t/h)

7.56 7.89 6.99 7.19 6.47

Products Formed from VR Hydrocracking Reactions (t/h)flow rate of light gases 3.61 3.71 3.48 3.57 3.39flow rate of naphtha 13.64 13.70 13.19 13.22 12.88flow rate of gasoil 63.20 66.23 37.29 39.98 8.63flow rate of VGO 16.97 14.94 34.63 32.69 55.07H2 production flow rate(t/h)

21.44 21.58 21.24 21.19 21.15

overall profit (£B/y) 3.695 3.700 3.636 3.643 3.573Yccr (wt %) 9.60 9.38 10.17 9.98 10.71



L




of this element while temperature increases. As described byMorawski and Mosio-Mosiewski,4 the primary function is tomaintain the concentration and reactivity of hydrogen donorsin the asphaltenes during high-temperature hydroconversion.This function prevents the growth of polynuclear aromatics andmakes them less likely to come out of solution as either coke ordownstream asphaltenic fouls, even at increased conversion.This work describes the effect of temperature between anallowable range of 693 and 699 K on hydrogen productionrequirements and overall profit. Beyond these temperatures, theresult is a negative conversion, as reflected in the experimentalyield data of Yang et al.1 From the simulations in Table 12, it isconsidered productive to operate at high temperatures of 699 Kand high hydrogen partial pressures of 160 bar, while lessamount of CCR, a precursor to coke formation, is produced.Consequently, the effect on catalyst deactivation could bemeasured as minimal, as a result of reduced coking activity.4.6. Overall Synthesis of Integrated Networks under

Varying H2 Inlet Conditions. Figures 12 and 13 describe the

integrated hydrogen network under fixed and varying operatingconditions, respectively.Figure 13 describes the integrated hydrogen network under

varying conditions when hydrogen availability is 50 t/h.Table 13 shows the effect of variations of H2 inlet conditions

in hydroprocessors on the overall network profitability. Whenhydrogen consumed is increased in the VGOHC and VRDSunit, light fractions increase and heavy fractions decrease,resulting in an overall increase of 6% and 2% in hydrogenproduction flow rate and network profitability, respectively.Where only limited H2 is available, an overall increase in profitof 0.3% is obtained from the base case at fixed inlet H2

conditions. Although the increment in profit is small, thedecrease in hydrogen production requirements is approximately1.3%. Consequently, the refiners can break even in theoperation of hydroprocessing units across the refinery. Table14 shows the effect of accommodating a further variation in H2

inlet conditions on the overall network.Table 14 shows that a further limitation on H2 supply to

20.95 t/h, while expanding the variations in H2 inlet conditions,

Figure 12. Integrated hydrogen networks under fixed operating conditions.



M



results in a decrease in the amount of H2 available to the VGOhydrocracker and hence an increase in flow rate of diesel anddecrease in lighter end hydrocarbons. The VRDS is constrainedon the maximum allowable changes in H2 pressure. The overallnetwork is in deficit of a 0.11% change in profit. A profit loss ofapproximately £4.2 M is incurred compared to a savings inhydrogen of approximately £4.8M. Overall, the savings inhydrogen outweighs the loss in profit. Based on the inferencesfrom Tables 13 and 14, an optimum amount of hydrogen canbe realized with a corresponding growth in profit. A hydrogensupply of 20.95 t/h is considered ideal in the profitmaximization scenarios. Note also that the products obtainedfrom VGO and VRDS at a hydrogen supply of 20.95 t/h ofhydrogen constitutes some amount of sulfur depending on theirboiling range and yield distribution, as shown in Table 15.As expected, sulfur concentrates in the highest boiling range,

in this case, the unconverted VGO and VR fractions. The smallamount of sulfur in the lighter fractions suggests that the easy-

to-react sulfur compounds are dispersed across the lowerboiling range.

5. CONCLUSIONS

Representation of hydrogen consumers with models that definethe process chemistry is fundamental to optimizing the use ofhydrogen in refineries. In this work, process models have beendeveloped for the VRDS process to accurately predict productformation based on significant characteristic variables andparameters. These hydrogen consumer models have beenintegrated in the hydrogen network model to exploitinteractions between hydrogen consumers and the hydrogendistribution network. Of particular interest is the similarity inthe behavior of the models with existing optimization trends inthe refining industry. An increase in hydrogen partial pressureby approximately 7% improves profit by only 0.03%, in contrastwith temperature changes. A decrease in temperature enhancesthe production of heavier hydrocarbons and decreases theformation of light ends. The effect of this decrease or increase

Figure 13. Integrated hydrogen networks under varying H2 inlet conditions.



N



in yields in VGOHC and VRDS and changes in sulfurconversion is seen in the process hydrogen requirements andoverall hydrogen production flow rate of the network. Asensitivity analysis has also been carried out to understand the

effects of limited hydrogen availability on the overall networkprofitability for different case scenarios. Hydrogen savingsrealized from a decrease in hydrogen requirements counter-balances the loss in network profitability. By allowing

Table 13. Effect of Fixed and Varying inlet H2 Conditions on the Overall Network

Table 14. Effect of a Further Limitation of H2 Supply on the Overall Hydrogen Network

hydrogen consumers NHT CNHT DHT VGOHC VR

vary vary vary vary vary vary vary vary vary vary

maximum hydrogen limit for all consumers (t/h) 21.15 20.95 21.15 20.95 21.15 20.95 21.15 20.95 21.15 20.95temperature (K) 623 623 653 653 633 633 672 672 699 699calculated conversion for VGOHC and VR (wt %) 66.82 66.55 53.33 53.28makeup hydrogen (t/h) 1.334 1.327 0.639 0.636 2.457 2.445 8.365 8.251 7.986 7.937chemical hydrogen consumed (t/h) 0.344 0.344 0.426 0.426 1.845 1.845 7.046 6.945 7.705 7.679dissolved hydrogen (t/h) 0.991 0.984 0.214 0.210 0.613 0.599 1.321 1.308 0.281 0.258

Products Formed from VGO Hydrocracking Reactions (t/h)pure hydrogen inlet flow rate 9.67 9.56flow rate of light gases formed 6.33 6.31flow rate of light naphtha 25.32 25.26flow rate of heavy naphtha 40.86 40.28flow rate of total naphtha 66.18 65.54flow rate of kerosene 89.39 86.37flow rate of diesel 45.25 48.03

Products Formed from VR Hydrocracking Reactions (t/h)flow rate of light gases 3.70 3.70flow rate of naphtha 13.68 13.68flow rate of gasoil 66.12 66.10flow rate of VGO 14.91 14.92

H2 prod. flow rate (t/h) - vary when H2 is limited 21.15H2 prod. flow rate (t/h) - vary when H2 is further limited 20.95overall profit (£B/yr) - vary when H2 is limited 3.71overall profit (£B/yr) - vary when H2 is further limited 3.70



O



simultaneous consideration of hydroprocessor integration,hydrogen network optimization, and varying operatingconditions, an actual and effective hydrogen optimizationmethodology can be implemented.

■ ASSOCIATED CONTENT*S Supporting InformationThe Supporting Information is available free of charge on theACS Publications website at DOI: 10.1021/acs.iecr.5b04161.

Additional tables and figures (PDF)

■ AUTHOR INFORMATIONCorresponding Author*E-mail: [email protected] authors declare no competing financial interest.

■ REFERENCES(1) Yang, C.; Zhang, J.; Xu, C.; Lin, S. HydroconversionCharacteristics on Narrow Fractions of Residua. J. Fuel Chem. Technol.(Beijing, China) 1998, 5.(2) Umana, B.; Shoaib, A.; Zhang, N.; Smith, R. IntegratingHydroprocessors in Refinery Hydrogen Network Optimization. Appl.Energy 2014, 133, 169−182.(3) Rana, M. S.; Samano, V.; Ancheyta, J.; Diaz, J. A. I. A review ofrecent advances on process technologies for upgrading of heavy oilsand residua. Fuel 2007, 86, 1216−1231.(4) Morawski, I.; Mosio-Mosiewski, J. Effects of parameters in Ni-Mocatalysed hydrocracking of vacuum residue on composition and qualityof obtained products. Fuel Process. Technol. 2006, 87 (7), 659−669.(5) Kirchen, R. P.; Sanford, E. C.; Gray, M. R.; George, Z. M. Cokingof Athabasca Bitumen Derived Feedstock. AOSTRA J. Res. 1989, 5,225.(6) Sanford, E. C. Conradson Carbon Residue Conversion duringHydrocracking of Athabasca Bitumen: Catalyst Mechanism andDeactivation. Energy Fuels 1995, 9, 549−559.(7) Gray, M. R.; Jokuty, P.; Yeniova, H.; Nazarewycz, L.; Wanke, S.E.; Achia, U.; Krzywicki, A.; Sanford, E. C.; Sy, O. K. Y. TheRelationship between Chemical Structure and Reactivity of AlbertaBitumens and Heavy Oils. Can. J. Chem. Eng. 1991, 69, 833.(8) Trasobares, S.; Callejas, M. A.; Benito, A. M.; Martinez, M. T.;Severin, D.; Brouwer, L. Kinetics of Conradson Carbon ResidueConversion in the Catalytic Hydroprocessing of a Maya Residue. Ind.Eng. Chem. Res. 1998, 37, 11−17.(9) Beaton, W. I.; Bertolacini, R. J. Resid Hydroprocessing at Amoco.Catal. Rev.: Sci. Eng. 1991, 33 (3-4), 281.(10) Marafi, A.; Stanislaus, A.; Furimsky, E. Kinetics and Modellingof Petroleum Residues Hydroprocessing. Catal. Rev.: Sci. Eng. 2010, 52(2), 204−324.(11) Le Page, J. F.; Morel, F.; Trassard, A. M.; Bousquet, J. ThermalCracking under Hydrogen Pressure: Preliminary Step to the

Conversion of Heavy Oils and Residues. Prepr. - Am. Chem. Soc.,Div. Fuel Chem. 1987, 32, no. CONF-8704349.(12) Schabron, J. F.; Speight, J. G. Correlation between CarbonResidue and Molecular Weight. Prepr. - Am. Chem. Soc., Div. FuelChem. 1997, 42 (2), 386−389.(13) Ancheyta, J.; Trejo, F.; Rana, M. S. Asphaltenes: ChemicalTransformation during Hydroprocessing of Heavy Oils; CRC Press: BocaRaton, FL, 2010.(14) Yang, G. H.; Wang, R. A. The Supercritical Fluid ExtractiveFractionation and the Characterization of Heavy Oil and PetroleumResidua. J. Pet. Sci. Eng. 1999, 22, 47−52.(15) Wang, Z. X.; Guo, A. J.; Que, G. H. Coke Formation andCharacterization during Thermal Treatment and Hydrocracking ofLiaohe Vacuum Residuum. 1998. China University of Petroleum;https://web.anl.gov/PCS/acsfuel/preprint%20archive/Files/43_3_BOSTON_08-98_0758.pdf.(16) Shi, T.; Xu, Z.; Cheng, M.; Hu, Y.; Wang, R. CharacterizationIndex for Vacuum Residua and their Subfractions. Energy Fuels 1999,13, 871−876.(17) Sadighi, S.; Ahmad, A.; Seif Mohaddecy, S. R. 6-Lump KineticModel for a Commercial Vacuum Gas Oil Hydrocracker. Int. J. Chem.React. Eng. 2010, 8, No. A1, DOI: 10.2202/1542-6580.2164.(18) Gao, H.; Wang, G.; Xu, C.; Gao, J. Eight-Lump KineticModelling of Vacuum Residue Catalytic Cracking in an IndependentFluid Bed Reactor. Energy Fuels 2014, 28, 6554−6562.(19) Stangeland, B. E. Kinetic model for prediction of hydrocrackeryields. Ind. Eng. Chem. Ind. Eng. Chem. Process Des. Dev. 1974, 13 (1),71−76.(20) U.S. Energy Information Administration 2014. Retrieved fromwww.eia.gov.(21) Blenco G. Hydrogen car revolution (November 2009). www.h2carblog.com.(22) Platts 2013. www.platts.com.(23) Fukuyama, H.; Terai, S. Kinetic Study on the HydrocrackingReaction of Vacuum Residue Using a Lumping Model. Pet. Sci.Technol. 2007, 25, 277−287.(24) Gillis, D.; Wees, M. V.; Zimmerman, P. Upgrading Residues toMaximize Distillate Yields; UOP LLC: Des Plaines, IL, 2009.(25) Font, J.; Moros, A.; Fabregat, A.; Salvado, J.; Giralt, F. Influenceof Fe and FeMo high loading supported catalysts on the coprocessingof two Spanish lignites with a vacuum residue. Fuel Process. Technol.1994, 37, 163−173.

Table 15. Sulphur Distribution among Products in VGO andVRDS Hydroprocessors

VGO Sfeed (wt %) 2.00VR Sfeed (wt %) 2.54VGO Sprod (wt %) 1.42VR Sprod (wt %) 0.85

products fromVGO unit GAS LN HN KER DIE UCO

sulfur (wt %) 0.000 0.000 0.000 0.002 0.006 1.409products from VRDS

unit GASNAPHTHA

GASOIL VGO UVR

sulfur (wt %) 0.000 0.000 0.001 0.001 0.846



P

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38

Chapter 5 Summary and Future Work

5.1 Summary

In the context of refinery hydrogen management, this work represents a major

progress in the design of representative hydrogen network superstructures

based on a fully integrated approach leading to sustainable design of refinery

hydrogen networks. The work has successfully developed semi-empirical

nonlinear steady-state process models for five hydroprocessors that are capable

of predicting product quality, conversion, and product yields.

Models for sulphur removal and associated light hydrocarbons were developed

and validated based on the following feedstocks: diesel (75 % SRGO, 25 % LCO),

VGO, and kerosene in the operating range 623-653 K, 53-60 bars, and 1-5 h-1. The

modification of HDS model in hydrotreaters was implemented to capture the

effect of feed and operating conditions on product characteristics. The model

parameter describes the structural features and hydroprocessing difficulty of

the feed, with reference to the sulphur type in the feed. The HDS model fit was

in good agreement with the experimental data for all three feedstocks.

Hydrocracker Conversion and Yield models were developed and validated

based on VGO feedstocks (0.893 and 0.933 SG) at reactor operating conditions of

672 K, 170-172 bars and 1.43-1.83 h-1. The parameters obtained proved useful in

39

defining the feedstock characteristic and the degree of the function obtained, in

the case of the yield models. The feed quality parameter β embedded in the VGO

conversion model is capable of differentiating a paraffinic feed from a

naphthenic or aromatic feed, similar to Watson characterization factor. The value

of the relative rate dependent parameter describes the tendency of the model to

produce a linear or cubic yield function. Other parameters have shown

significance in their interrelationships with operating variables. The predictions

from the yield models shows good agreement with the experimental data.

Other models for the removal of CCR and asphaltenes in VRDS/HC processes

have also been developed from a first principle approach at 703 K, 0.5 h-1 and 200

bars. VR conversion models have been developed and validated based on

hydrogen pressures ranging from 120 bars to 200 bars and temperatures ranging

from 683 K to 723 K, at constant LHSV and shows good overall agreement with

experimental data. However, the asphaltene model fits well between

temperatures of 683-713 K. The VR yield models were implemented on an

Arabian vacuum residue at reactor operating conditions of 699 K, 160 bars, and

0.25 h--11 and the results have proved the applicability of these models to different

feedstock characteristics. These models were validated based on the feedstocks

and operating conditions discussed above due to limited availability of

experimental data.

40

The VR conversion and yield models show excellent prediction of the

experimental data. The usefulness of these models can be seen in their

constitution and interrelationships between several variables. For example, an

increase in hydrogen oil ratio or hydrogen partial pressure necessitates a

corresponding increase in light ends production and a decrease in the yield of

heavier ends. Similar trends result with increasing temperature and decreasing

feed flow. These tendencies are coherent with industrial behaviour when one or

more operating conditions are varied in a singular direction. Often, a

combination of changes in operating conditions would result in a significant

change in hydrogen consumed, and thus a reversal in the process dynamics.

The ease of implementation of the hydroprocessor models developed within the

integrated refinery hydrogen network would facilitate its application in the

refinery industry, thus providing a useful tool for refiners to make critical,

timely, and cost-saving optimisation decisions. Moreover, the model

characteristics, which reflect the chemistry underlying different reactions, nature

of feed, operating conditions, and catalyst properties proves the versatility of the

model in accurately reproducing refinery data. Therefore this work has

demonstrated the predictability of refinery processes using well-detailed and

effective process models.

41

The implementation of the hydroprocessor models enhances operational

flexibility within the network and helps in exploiting the benefits of the

interactions between hydrogen consuming and hydrogen production processes

in the hydrogen network. These models were implemented within a

multicomponent hydrogen framework to yield an integrated process network,

and solved with a nonlinear optimization algorithm in a GAMs platform. The

effects of changing feed flow, hydrogen partial pressure, hydrogen-oil ratio, and

temperature on process performance and overall hydrogen requirements were

well-captured in the integrated process network. The results of these changes on

product yields depict the expected outcomes in refining operations. In the

VRDS/HC process, other alternatives of operating the refinery at lower hydrogen

requirements were explored. It was established that the savings in hydrogen

obtained outweighs the loss in profit over an annual period. In another scenario,

increasing hydrogen requirements with increasing hydrogen partial pressure

culminated in a modest growth in profit. Here, the gains in profit offset the

increase in hydrogen production. The optimized solutions of the hydrogen

network in the integrated superstructure are sustainable and realistic. This

integrated approach to hydrogen management can be rigorously applied on an

industrial scale as a first step to obtaining optimum hydrogen requirements

considering the constraints in product quality and consumer expectations or

demand on product yields.

42

5.2 Perspectives and Future Work

The outcomes of this thesis have significant implications with regards to our

understanding of the design of sustainable multicomponent hydrogen networks.

The work has shown that the integration of semi-empirical nonlinear process

models in the optimization of refinery hydrogen network models is an economic

and tactful approach to achieving, simultaneously, optimum hydrogen

requirements and profit. The refiner is equipped with a tool to predict optimum

hydrogen distribution between the processes from the onset of the design. This

can be further enhanced by integrating the overall hydrogen network model

with a refinery plant data management system (PDMS) to provide real-time

automated solutions to the modeling and optimization of refinery hydrogen

distribution systems.

The application of the models developed in this work could be extended to

several industrial data to validate the range of operating conditions and data

limitations.

Further work could also study the effect of using a recycle configuration in

hydrocrackers on process performance and overall hydrogen requirements.

Theoretically, this could yield increased conversion and consequently increase in

hydrogen requirements, increase in light hydrocarbons in the system, and

43

therefore exert pressure on the purification system. Such practical constraints as

purifiers or compressors could also be integrated to allow quantitative

description of the effects of different operating scenarios on capital costs. A

hysterical increase in capital costs could topple the benefits of maximizing profit.

44

References

1. Energy Information Administration (EIA):

http://www.eia.doe.gov/oiaf/aeo/index.html.

2. Stanislaus A, Marafi A, Rana M. Recent Advances in the Science and

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Today. 2010; 153:1-68.

3. Long R, Picioccio K, Zagoria A. Optimizing hydrogen production and use.

UOP LLC, A Honeywell Company.

4. Ancheyta J. Modeling and Simulation of Catalytic Reactors for Petroleum

Refining. John Wiley & sons. 2011.

5. El-Halwagi MM, Manousiouthakis V. Synthesis of Mass Exchange

Networks. AiChE Journal. 1989; 35:1233-1244.

6. Alves J. Analysis and design of refinery hydrogen distribution systems,

Ph.D. thesis, Department of Process Integration, University of Manchester

Institute of Science and Technology; 1999. Manchester, UK.

7. Liu F, Zhang N. Strategy of Purifier Selection and Integration in

Hydrogen Networks. Chemical Engineering Research and Design. 2004;

82:1315-1330.

8. Jiao Y, Su H, Hou W. Improved optimization methods for refinery

hydrogen network and their applications. Control Engineering Practice

(2012), http://dx.doi.org/10.1016/j.conengprac.2012.04.010

9. Singh BB. Bi-linear Data Reconciliation and Rigorous Optimisation of

Refinery Hydrogen Networks with Hydrogen Plant. Process Integration

Research Consortium, Manchester, 2006.

10. Jia, N., Refinery Hydrogen Network Optimisation with Improved

Hydroprocessor Modelling. PhD Thesis, University of Manchester,

Manchester, UK, 2010.

11. Choudhary TV, Parrot S, Johnson B. Unravelling Heavy Oil

Desulphurization Chemistry: Targeting Clean fuels. Environ. Sci. Technol.

2008; 42:1944-1947.

12. Hasenberg DM, Campagnolo JF. Modelling and Simulation of a Reaction

for Hydrotreating Hydrocarbon oil, Philips Petroleum Company 1997.

13. Bhutani N, Ray AK, Rangaiah GP. Modelling, Simulation and Multi-

objective Optimization of an Industrial Hydrocracking Unit. Ind. Eng.

Chem. Res. 2006; 45:1354-1372

14. Mohanty S, Saraf DN, Kunzru D. Modelling of a hydrocracking reactor.

Fuel Processing Technology. 1991; 29:1-17.

http://www.eia.doe.gov/oiaf/aeo/index.html

45

15. Qaeder SA, Hill GR. Hydrocracking of gas oils. Ind. Eng. Chem. Pro. Des.

Dev. 1969; 8(1):98 – 105.

16. Stangeland BE. Kinetic model for prediction of hydrocracker yields. Ind.

Eng. Chem., Proc. Des. Dev. 1974; 13(1):71-76.

17. Rapaport IB. Chemistry and Technology of synthetic liquid fuels, 2nd ed.

1962; Israel program for scientific translation Ltd, Jerusalem.

18. Yang GH, Wang RA. The Supercritical Fluid Extractive Fractionation and

the Characterization of Heavy Oil and Petroleum Residua. Journal of

Petroleum Science and Engineering. 1999; 22:47-52.

46

Appendix A: Supplementary Information for Chapter 3

47

A.1 Model Development for VGO Hydrocracker Products

Table A.1 Comparison of Industrial and Model Yield Predictions [13]

Pseudocomponent

(PC) number TBP (0F)

Industrial PC

mass flowrate

(kg/h)

Industrial PC

composition (-)

Sum of product

composition

(Industrial) Ti Ki

Model PC

composition (-)

Sum of product

composition

(Model) Error (-) Error %

1…..6 37.6567 3500 0.0250 LE = 0.0250 0.0359 0.0003 0.0201 LE = 0.0201 0.0049

7 57.7 200 0.0014 0.0551 0.0006 0.0011 0.0003

8 76.2 250 0.0018 0.0727 0.0009 0.0018 0.0000

9 93.3 500 0.0036 0.0890 0.0013 0.0027 0.0009

10 111.0 750 0.0054 0.1059 0.0019 0.0039 0.0015

11 128.8 950 0.0068 0.1229 0.0027 0.0055 0.0013

12 146.7 1150 0.0082 0.1400 0.0037 0.0075 0.0007

13 163.7 1250 0.0089 0.1562 0.0049 0.0098 -0.0009

14 180.8 1150 0.0082 0.1726 0.0064 0.0127 -0.0045

15 198.5 1750 0.0125 Sum of LN 0.1894 0.0081 0.0162 Sum of LN -0.0037

16 211.0 3450 0.0247 0.0815 0.2013 0.0096 0.0191 0.0803 0.0055 1.3971

17 228.6 3900 0.0279 0.2181 0.0119 0.0265 0.0014

18 246.1 3450 0.0247 0.2348 0.0146 0.0251 -0.0004

19 263.9 3050 0.0218 0.2518 0.0177 0.0230 -0.0012

20 281.3 2800 0.0200 0.2684 0.0211 0.0202 -0.0002

21 297.6 2200 0.0157 Sum of HN 0.2840 0.0248 0.0170 Sum of HN -0.0013

22 318.3 1800 0.0129 0.1229 0.3037 0.0300 0.0119 0.1236 0.0010 -0.5757

23 335.0 2900 0.0207 0.3196 0.0347 0.0223 -0.0016

24 350.7 2500 0.0179 0.3347 0.0396 0.0212 -0.0033

25 369.1 2780 0.0199 0.3522 0.0459 0.0201 -0.0002

26 389.2 2730 0.0195 0.3714 0.0535 0.0192 0.0003

27 405.5 2750 0.0197 0.3869 0.0603 0.0187 0.0010

28 422.9 2780 0.0199 0.4035 0.0682 0.0185 0.0014

29 441.5 2760 0.0197 0.4213 0.0773 0.0185 0.0012

30 459.7 2750 0.0197 0.4386 0.0869 0.0189 0.0008

31 477.9 2950 0.0211 0.4560 0.0974 0.0196 0.0015

32 495.6 3200 0.0229 0.4728 0.1084 0.0206 0.0022

33 513.5 3150 0.0225 Sum of KER 0.4900 0.1204 0.0221 Sum of KER 0.0004

34 533.5 2780 0.0199 0.2432 0.5090 0.1346 0.0241 0.2437 -0.0042 -0.2132

35 556.4 2700 0.0193 0.5309 0.1524 0.0136 0.0057

36 575.4 2500 0.0179 0.5491 0.1683 0.0147 0.0032

37 594.2 500 0.0036 0.5670 0.1851 0.0158 -0.0122

38 610.6 1200 0.0086 0.5826 0.2006 0.0168 -0.0082

39 630.5 2500 0.0179 0.6016 0.2205 0.0179 -0.0001

40 645.8 4500 0.0322 0.6162 0.2367 0.0188 0.0133

41 663.5 3250 0.0232 0.6331 0.2565 0.0199 0.0033

42 681.8 3000 0.0214 0.6505 0.2780 0.0210 0.0005

43 699.9 2780 0.0199 Sum of DIE 0.6678 0.3005 0.0220 Sum of DIE -0.0022

44 717.8 2950 0.0211 0.1849 0.6849 0.3239 0.0231 0.1836 -0.0020 0.7096

48

Table A.2 Comparisons of Industrial and Model Predictions [14]

PC

Number TBP (0F)

Industrial PC

composition (-)

Sum of

Product

composition

(Industrial) Ti Ki

Model PC

composition (-)

Sum of

Product

composition

(Model) Error (ppmw)

Error in

total %

1 36.5 0.035 LE = 0.035 0.036 0.001 0.021 0.036 -0.001 -2.976

2 81.5 0.000 0.079 0.001 0.005 -0.005

3 126.5 0.018 0.123 0.003 0.012 0.006

4 171.5 0.030 Sum of LN 0.167 0.006 0.024 Sum of LN 0.006

5 216.5 0.038 0.086 0.211 0.011 0.045 0.085 -0.007 1.052

6 261.5 0.035 Sum of HN 0.255 0.018 0.035 Sum of HN 0.000

7 306.5 0.040 0.075 0.299 0.029 0.040 0.075 0.000 0.000

8 351.5 0.041 0.342 0.043 0.039 0.002

9 396.5 0.045 0.386 0.060 0.047 -0.002

10 441.5 0.050 Sum of KER 0.430 0.082 0.052 Sum of KER -0.002

11 486.5 0.055 0.191 0.474 0.109 0.054 0.191 0.001 0.063

12 531.5 0.059 0.518 0.142 0.057 0.002

13 576.5 0.061 0.562 0.180 0.064 -0.003

14 621.5 0.068 Sum of DIE 0.605 0.225 0.067 Sum of DIE 0.001

15 666.5 0.065 0.253 0.649 0.277 0.065 0.253 0.000 0.006

49

Appendix B: Supplementary Information for Chapter 4

50

B.1 Model Development for VRDS / HC Products

Table B.1 Comparison of Industrial and Model Yield Predictions

PC

Number TBP(0F)

Industrial SFEF

composition (-)

Sum of Product

Composition

(Industrial) Ti Ki

Model SFEF

composition

(-)

Sum of Product

composition

(Model)

Error

(ppmw)

Error in

Total

(%)

6 230 0.0152 0.1756 0.0254 0.0150 0.0002

7 275 0.0137 0.2099 0.0328 0.0145 -0.0008

8 320 0.011 0.2443 0.0416 0.0140 -0.0030

9 365 0.0218 Sum of (<200) 0.2786 0.0518 0.0135 Sum of (<200) 0.0083

10 410 0.0085 0.0702 0.3130 0.0638 0.0131 0.0702 -0.0046 -0.0064

11 455 0.0284 0.3473 0.0778 0.0288 -0.0004

12 500 0.0264 0.3817 0.0939 0.0267 -0.0003

13 545 0.028 0.4160 0.1124 0.0268 0.0012

14 590 0.0289 Sum of (200-350) 0.4504 0.1336 0.0294 Sum of (200-350) -0.0005

15 635 0.0346 0.1463 0.4847 0.1575 0.0347 0.1463 -0.0001 -0.0249

16 680 0.0528 0.5191 0.1844 0.0501 0.0027

17 725 0.0486 0.5534 0.2146 0.0500 -0.0014

18 770 0.047 0.5878 0.2483 0.0475 -0.0005

19 815 0.0386 Sum of (350-500) 0.6221 0.2856 0.0423 Sum of (350-500) -0.0037

20 860 0.0372 0.2242 0.6565 0.3268 0.0343 0.2241 0.0029 0.0492

Table B.2 Effect of hydrogen pressure on sulphur in VR product []

PH2

(MPa)

Conversion

(%) Conversion (-)

Sprod (ppmw)

(experiment)

Sprod

(ppmw)

(model)

Error

(ppmw) Error %

Average

absolute

error %

12 69.1 0.691 7848.6 7852.3 -3.672 -0.047

14 69.8 0.698 7670.8 7668.4 2.414 0.031

16 70.5 0.705 7493 7488.8 4.194 0.056

18 71.2 0.712 7315.2 7313.4 1.769 0.024

20 71.9 0.719 7137.4 7142.2 -4.764 -0.067 0.045

51

Table B.3 Effect of hydrogen pressure on CCR in VR product

PH2 (MPa)

CCR

conversion%

CCR

Conversion (-)

CCRprod

(ppmw)

(experiment)

CCRprod

(ppmw)

(model) Error Error %

Average

absolute

error %

12 39.9 0.399 94958 94866.194 91.806 0.097

14 42.8 0.428 90376 90113.607 262.393 0.290

16 45.9 0.459 85478 86186.390 -708.390 -0.829

18 47.5 0.475 82950 82862.361 87.639 0.106

20 49.2 0.492 80264 79996.010 267.990 0.334 0.331

Table B.4 Effect of hydrogen pressure on Asphaltenes in VR product

PH2(MPa)

Asphconv %

(experiment)

Asphprod

(ppmw)

(experiment)

CCRprod

(ppmw)

(experiment)

CCRfeed/

CCR prod 1

Asphprod

(ppmw)

(model) Error Error %

Average

absolute error

%

12 0.389 32016.4 94958 1.666 -0.666 32266.067 -249.667 -0.780

14 0.415 30654 90376 1.753 -0.753 30483.932 170.068 0.555

16 0.441 29291.6 85478 1.833 -0.833 29022.968 268.632 0.917

18 0.468 27876.8 82950 1.907 -0.907 27804.138 72.662 0.261

20 0.494 26514.4 80264 1.975 -0.975 26773.625 -259.225 -0.978 0.698

Table B.5 Operating data

703

0.5

20

115

5.61

0.68

1.37

Tx

Ψ

Operating data in VRDS/HC unit

T (K)

LHSV (h-1)

P (MPa)

Surface Area of catalyst (m2/g)

KH

52

Table B.6 Comparison of Industrial and Model Predictions for VR conversion

VR conversion (-)

(experiment)

Asph conversion

(%)(experiment) x/(1-x)

VR conversion

(-) (Model) Error Error %

Average

absolute

error (%)

0.624 38.9 1.688 0.628 -0.004 -0.642

0.621 41.5 1.629 0.620 0.001 0.218

0.616 44.1 1.572 0.611 0.005 0.776

0.604 46.8 1.515 0.602 0.002 0.266

0.59 49.4 1.462 0.594 -0.004 -0.650 0.510

integrating hydroprocessors in refinery hydrogen network

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