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DEVELOPMENT OF VRDS/HC MODELS AND THEIR INTEGRATION WITH REFINERY HYDROGEN NETWORKS Blessing Asuquo Umana, Nan Zhang, and Robin Smith Ind. Eng. Chem. Res., Just Accepted Manuscript • DOI: 10.1021/acs.iecr.5b04161 • Publication Date (Web): 27 Jan 2016 Downloaded from http://pubs.acs.org on February 17, 2016

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Industrial & Engineering Chemistry Research

DEVELOPMENT OF VACUUM RESIDUE HYDRODESULPHURIZATION/HYDROCRACKING MODELS AND THEIR INTEGRATION WITH REFINERY HYDROGEN NETWORKS *

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 In recent years, there has been an increase in vacuum residue hydroprocessing due to the decrease in fuel oil demand and an increase in distillate demand. This work characterizes vacuum residue hydrodesulphurization (VRDS) and hydrocracking (HC) processes and their integration with hydrogen networks to evaluate holistic interactions between hydrogen consumers and hydrogen distribution system. Conversion models for sulphur, conradson carbon residue, asphaltenes and vacuum residue have been developed based on the feed quality, catalyst properties and process operating conditions. A five-lump yield model is derived incorporating a feedstock characteristic index and true boiling points. The results of the proposed (1)

model show reasonable accuracy with experimental data.

A simultaneous optimization of

hydrogen consumer models and the hydrogen network model is executed using the CONOPT solver in General Algebraic Modelling System (GAMS). Sensitivity analysis is carried out on the integrated framework to demonstrate the influence of varying operating conditions on product yields. As expected, the outcomes validate attainable trends in the industry.

Highlights

Proposed a five-lumped model for predicting conversion and yields in VRDS process The model is based on feedstock characteristic index, TBP, CCR conversion, and VR conversion Integration of models in hydrogen networks to exploit economic solutions for hydrogen use A case study is carried out to demonstrate the developed methodology

* Corresponding author: [email protected] Keywords: Vacuum Residue Desulphurization, Vacuum Residue Hydrocracking, Hydrogen Utilization, Hydrogen Network Optimization

1 ACS Paragon Plus Environment


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List of Tables Table 1 Influence of process parameters on vacuum residue conversion ................................................... 6 Table 2 Feed, Operating data and Parameters obtained from CCR model ................................................. 8 Table 3 Feed, Operating conditions, and Parameters obtained from CCR model (Varying Temperature) .. 9 Table 4 Feed, Operating data and Parameters obtained from Asphaltene model ..................................... 11 Table 5 Experimental data for SFEF fractions of SQVR ............................................................................. 16 Table 6 Products obtained from hydroprocessing of SFEF residue fractions ............................................ 16 Table 7 Feed and operating data in the refinery ......................................................................................... 18 Table 8 Yield comparison of Industrial data and Model Predictions ........................................................... 19 Table 9 Effect of increasing hydrogen pressure on conversion and product yield distribution in VRDS process ........................................................................................................................................................ 26 Table 10 Effect of decreasing temperature on conversion on product yield distribution ............................ 27 Table 11 Effect of variations in hydrogen partial pressure on profit in VRDS process ............................... 30 Table 12 Effect of sequential variation in H2 pressure and temperature on profit in VRDS ....................... 31 Table 13 Effect of Fixed and Varying inlet H2 conditions on the overall network ....................................... 36 Table 14 Effect of a further limitation of H2 supply on the overall hydrogen network ................................. 37 Table 15 Sulphur distribution among products in VGO and VRDS hydroprocessors ................................ 38

List of Figures Figure 1 Simplified flow diagram of an ebullated bed process ..................................................................... 4 Figure 2 CCR model fit with experimental data (varying PH2) ...................................................................... 8 Figure 3 CCR model fit with experimental data (varying Temperature) ....................................................... 9 Figure 4 Asphaltenes model fit with experimental data (varying CCR in product) ..................................... 11 Figure 5 Asphaltenes model fit with experimental data (varying sulphur in product) ................................. 12 Figure 6 VR conversion model fit with experimental data (increasing PH2) ............................................... 14 Figure 7 VR conversion model fit with experimental data (increasing Temperature) ................................. 15 Figure 8 Comparison of Industrial and Predicted Yields ............................................................................. 19 Figure 9 Methodology for integration of VRDS unit in a refinery hydrogen network .................................. 24 Figure 10 Effect of hydrogen pressure on product yield distribution in VRDS process .............................. 27 Figure 11 Effect of temperature on product yield distribution in VRDS ...................................................... 29 Figure 12 Integrated hydrogen networks under fixed operating conditions ................................................ 33 Figure 13 Integrated hydrogen networks under varying H2 inlet conditions ............................................... 34

1.

Introduction

Hydrodesulphurization is a term used to describe processes by which molecules in petroleum feedstocks are split or saturated with hydrogen gas. It includes hydrotreating, hydrocracking and hydrogenation of petroleum hydrocarbons. Hydrocracking is a thermal and catalytic hydrogenation process that converts high molecular weight feedstocks to lower molecular weight products in the presence of a bifunctional catalyst. The catalyst consists of a metallic part, which provides hydrogenation, and an acid part that promotes cracking. Cracking will break bonds, and the resulting unsaturated products hydrogenate into


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stable compounds. Residue conversion processes (fixed, ebullated and moving bed) use supported palletized catalysts of the bifunctional composition. The fixed bed system is used for lighter and cleaner feedstocks: naphtha, middle distillate, atmospheric gas oils, vacuum gas oils and atmospheric residue treatment. With increasing level of complexity in the feed composition and density, the ebullated bed reactor systems are well suited to process heavy feed streams, particularly feeds with high metal, sulphur, asphaltenes and CCR content. Hydrocracking of heavy oils and residua have become increasingly important due to the increased global production of heavy and extra heavy crude oils coupled with increased demand worldwide for low sulphur middle distillates and residual fuel oils. These trends emphasize the importance of refinery processes that are capable of converting heavy petroleum fractions, such as vacuum residues, into lighter, valuable and cleaner products. This increased reliance on VR upgrading for clean middle distillate fuel has also led to a rise in hydrogen consumption, thus stretching the existing hydrogen production capacity, and creating an imbalance between the cost of hydrogen required and value of products. Anticipated future trends and regulations are expected to increase further hydrogen consumption. Consequently, it becomes imperative to optimize hydrogen use in a refinery hydrogen distribution system. The development of hydrogen consumer 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 heteroatom conversion models and steady-state lumped yield models that are robust and sufficiently detailed to capture the behaviour of the process with changes in operating conditions. 2. Integration of VR hydrodesulphurization (VRDS) models and hydrogen network models to assess the effects of process performance on the hydrogen distribution network. The resulting superstructure would facilitate the efficient utilization of hydrogen for improved process operation. A detailed review of hydrogen network optimisation is presented. 2.

(2)

Integrated design of VRDS processes and hydrogen networks

Residue hydrodesulphurization can be classified into two major routes: non-catalytic and catalytic processes. Non- catalytic residue process can be categorized into solvent deasphalting and thermal or carbon rejection processes. Catalytic processes are subdivided into residue fluid catalytic cracking (RFCC) and residue hydroprocessing. Hydroprocessing is the combination of hydrotreating and hydrocracking processes, in which residue feedstock is treated at low temperatures and high hydrogen partial pressure, usually in the presence of a catalyst. The increasing demand for middle distillates has intensified the need for hydrocracking. Ebullating bed reactors are capable of performing both hydrotreating and hydrocracking functions, thus referred to as dual purpose reactors. The process scheme of a typical ebullated bed system is as shown in Figure 1.



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H2 purge

Catalyst addition

HP

Gas H2 make -up

Naphtha FRACTIONATOR Gas oil Vacuum gas oil

Vacuum residue

Catalyst

Vacuum residue

Figure 1 Simplified flow diagram of an ebullated bed process

In ebullated bed hydroprocessing, the catalyst within the reactor is not fixed.

(3)

The hydrocarbon feed

stream enters at the bottom and flows upwards through the catalyst. In this process, oil and catalyst are separated at the top of the reactor and catalyst is recirculated to the bottom of the bed to mix with the new feed. Fresh catalyst is added on top of the reactor and spent catalyst is withdrawn from the bottom of the (3)

reactor.

The liquid is sent to a high-pressure (HP) flash and routed to a fractionator for separation into

hydrocracked products. A major advantage of this type of reactor is its stirred reactor type operation with a fluidized catalyst. Its intrinsic ability to handle exothermic reactions, solid containing feedstock and a flexible operation while changing feedstocks or operating objectives makes it suitable to operate over a wide range of conversion levels producing high liquid yields. The quantity and quality of hydrocracker yields obtained are determined by the combination of feed, operating conditions and catalyst properties that characterize the process. The interactions between these process conditions, feed quality, catalyst properties, product yields and product quality may not be adequately represented without the use of robust process models.


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The present methodology addresses the development of VRDS process models and their subsequent integration in the hydrogen network. This work proposes a generic representation of conversion and VRDS models embedded into a hydrogen network model to yield an integrated superstructure of hydrogen distribution-consumer system. There are four key steps in the development of this methodology: (i) Development of residue hydrotreating models that are sufficiently detailed to capture the dynamic interactions between operating conditions in hydrogen consumer and product quality; (ii) Development of VRDS yield models that are dependent on conradson carbon residue and vacuum residue conversion levels; (iii) Integration of conversion and yield models in the hydrogen network model to assess the consequence of interactions between processes and the distribution system on the overall network objective; (iv) Scenario optimization of integrated hydrogen networks. 2.1

Model development and validation

There are two fundamental aspects of VRDS methods studied: residue hydrotreating and residue hydrocracking. Residue hydrotreating (RHT) improves quality for product blending or additional processing – demetallation, desulphurization, deasphaltenization, conradson carbon conversion and saturation; Residue hydrocracking (RHC) increases liquid yields – 1000+ F conversion. The overall conversion of vacuum residue is constant in practice. 2.1.1

Residue hydrotreating models

2.1.1.1 Desulfurization By far the most common heteroatom is sulphur whose concentration can reach 6-8 % by weight. Sulphur concentration in products increases with increasing boiling points, and are predominantly present as thiophenic sulphur in condensed structures (such as benzo, dibenzo, and naphtobenzo), but also as aliphatic sulphur in sulphide and disulphide type functional groups. These functionalities are often used to (2)

create links between hydrocarbon clusters. The desulfurization model

has been modified in this work as

shown in Eq. (1). Another variable, catalyst concentration was introduced to the model to describe the influence of catalyst characteristics on sulphur conversion in vacuum residue hydroprocessing. The physical contact of hydrogen with the catalyst ensures adequate conversion and impurities removal while minimizing carbon deposition. Increasing the hydrogen partial pressure reduces the reactor start of run temperature as well as the rate of catalyst deactivation. Eq. (1) shows the relationship between process variables and product quality in a vacuum residue hydrodesulphurization process. = ×

(×)

×

×∝ × !"

#

(1)

Where = sulphur content in product, ppmw; = sulphur content in feed, ppmw; $ = rate

constant of HDS reaction, h ; % = 3+ ring aromatic inhibition constant, 3 + ( = 3+ ring core aromatic -1

content in feed, ppmw; )*+ = recycle hydrogen partial pressure, bar; , = pressure dependent term; -./0 = -1

(4)

catalyst concentration (-); 1*2 = liquid hourly space velocity, h . Morawski and Mosio-Mosiewski



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reported the dependence of process parameters: temperature, catalyst content, hydrogen pressure, and LHSV on sulphur, asphaltenes, conradson carbon residue (CCR), and vacuum residue (VR) conversion in the experimental data in Table 1. Table 1 Influence of process parameters on vacuum residue conversion Process parameters Temperature (0C)

LHSV (h-1)

Conversion, wt %

Pressure (MPa)

Catalyst content (%)

VR

Sulphur

CCR

Asphaltenes

19.9

13.8

Effect of reaction temperature 410

0.5

16

1

27.8

51.8

420

0.5

16

1

45.1

61.2

32

28.5

430

0.5

16

1

61.6

70.5

45.9

44.1

440

0.5

16

1

77.5

80.7

64.2

63.5

450

0.5

16

1

92.7

91

84.2

83.5

Effect of liquid hourly space velocity 430

0.25

16

1

88.7

84.8

82.5

81.1

430

0.38

16

1

74.7

77.4

63.1

61.3

430

0.5

16

1

61.6

70.5

45

44.1

430

0.63

16

1

50.9

65.4

31.4

30.2

430

0.75

16

1

40.9

60.9

18.6

17.2

Effect of hydrogen pressure 430

0.5

12

1

62.4

69.1

39.9

38.9

430

0.5

14

1

62.1

69.8

42.8

41.5

430

0.5

16

1

61.6

70.5

45.9

44.1

430

0.5

18

1

60.4

71.2

47.5

46.8

430

0.5

20

1

59

71.9

49.2

49.4

44.1

Effect of catalyst concentration 430

0.5

16

1

61.6

70.5

45.9

430

0.5

16

5

62.1

80.8

47.3

45.8

430

0.5

16

10

62.9

88.4

49.6

48.1

Table 1 shows the effect of parameters on the conversion levels obtained within the following operating

ranges: reaction temperature – 410-450 C, hydrogen pressure – 12-20 MPa, 1*2 – 0.25-0.75h , and 0

-1

-./0 – 1-10 wt %. For purposes of brevity, the correlation of sulphur model is presented in Table S1 and

Fig S1 in the supporting information file. A simultaneous increase in sulphur conversion is obtained with a decrease of sulphur in the product. The modified sulphur model shows good agreement with the experimental data. The average absolute error between experimental and calculated concentrations was (4)

0.045 %. The model is validated on another set of data

for changes in temperature as shown in Table

S2 and Figure S2 in the supporting information file. The result from the model for a different case of


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operating conditions shows good agreement with the experimental data in Table 1. The average absolute error obtained is 2.6 %. 2.1.1.2 Conradson Carbon Residue (CCR) conversion Several studies have shown that an important variable in determining coke yield is CCR in the feed. CCR conversion depends on the content of coke forming precursors in the feed. Kirchen et al.

(5)

found a linear

relationship between the amount of coke formed and micro-carbon residue (MCR). The relationship (6)

between MCR / CCR and different parameters has been studied. Sanford

(7)

and Gray et al.

reported a

linear correlation between MCR content of the residue fractions and the aromatic carbon content. Trasobares et al.

(8)

0

obtained a similar relationship between CCR and aromatic carbon contents at 415 C.

The removal of compounds which contribute to CCR is thought to be due to aromatics saturation and is (9)

an indirect way of studying aromatic saturation. Beaton and Bertolacini

indicated the effect of aromatic

saturation on CCR conversion. CCR reduction comprises of the catalytic hydrogenation of aromatic rings and thermal cracking of the naphthenic rings produced by hydrogenation. The reaction is approximately first order with respect to hydrogen partial pressure. Eq. (2) shows the rate equation assuming constant density. 3445 6785

=

9

!"

(2)

where --( = initial concentration of CCR in feed, ppmw; --( = outlet concentration of CCR in product, ppmw; 1*2 = liquid hourly space velocity and : = rate of reaction. CCR removal rate can be expressed as follows: −: = %)*+< - =

(3)

The influence of temperature has been assumed to follow Arrhenius equation: % = %> AB

?@

(4) (9)

Beaton and Bertolacini

found that the reaction of Ramsbottom carbon conversion is roughly first order

with respect to hydrogen partial pressure for hydroprocessing of a typical vacuum residue. In this work, CCR conversion is assumed to fit first order kinetics. 3445 6785 6785

=

!"

(5)

The overall equation for predicting the amount of CCR in product is given as: --( =

C

3445

D9

H EFG L IGJK

(6)



Where M = parameter that indicates the aromaticity of the feed. Table 4 shows the parameters obtained from Eq. (6). A chart of CCR products in the experiment and model is plotted against hydrogen pressure in Figure 2. Table 2 Feed, Operating data and Parameters obtained from CCR model Feed properties CCR in feed (ppmw)

158,000

Operating conditions Temperature (K)

703

LHSV (h-1)

0.5

Parameters γ (indicates the aromaticity of the feed)

1.29

µ (indicates the order dependence on H2 pressure)

0.34

96 94 CCRprod * 103 (ppmw)

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92 CCR prod (model)

90

CCRprod (expt)

88 86 84 82 80 78 11

13

15

17

19

21

PH2 (MPa)

Figure 2 CCR model fit with experimental data (varying PH2)

The model prediction shows good agreement with the experimental data. The average absolute error between experimental and calculated concentrations is 0.33 %. The model in Eq. (6) was validated using another data with varying temperature and constant H2 pressure and LHSV. The results are described in


Page 9 of 41

Table S3 and Figure S3 of the supporting information file. Table 3 shows the parameters obtained from Eq. (6). The plot of CCR model fit with experimental data at varying temperature is as shown in Figure 3. Table 3 Feed, Operating conditions, and Parameters obtained from CCR model (Varying Temperature)

CCR in feed (ppmw)

158,000

Operating conditions H2 Pressure (MPa)

16

-1

0.5

LHSV (h ) Parameters γ (indicates the aromaticity of the feed)

0.99

µ (indicates the order dependence on H2 pressure)

10.56

140,000 CCRprod (ppm) (model)

120,000

CCRprod (ppm) (expt) CCRprod (ppm)

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100,000 80,000 60,000 40,000 20,000 0 680

690

700

710

720

730

Temp (K)

Figure 3 CCR model fit with experimental data (varying Temperature)

2.1.1.3 Deasphaltenization Asphaltenes are the major precursors to sludge and sediments. They are very large polyaromatic compounds with a molecular weight ranging from 1,000 to 20,000 and possessing a boiling point above 0

538 C. High boiling point fractions contain the so-called resins and asphaltenes fractions, generally defined with high polarity and aromaticity, combined with large contents of heteroatoms such as sulphur (S) and nitrogen (N), metals such as vanadium (V) and nickel (Ni) and functional groups. Some metal



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Page 10 of 41

compounds, for example, are known to be included in complex structures as porphyrins.

(9)

(10)

Marafi et al.

showed that only a limited HDM of a residue could be achieved unless a desirable rate of HDAs is maintained. Most of the metals (V and Ni) which have to be removed are associated with asphaltenes entities. Therefore, a high rate of HDAs is a prerequisite for achieving high HDMs. Similarly, sulphur is also distributed primarily in the resins and asphaltenes. Asphaltenes is reported in the literature to consist (11)

of a two-dimensional structure of naphthenic, aromatic linkage by aliphatic chains and sulphur bridges.

It has been shown that large polynuclear aromatics that predominate in asphaltenes limit the conversion of residue feedstocks due to the formation of coke and asphaltenic sediments downstream. The linear 0

relationship between CCR, asphaltenes and 350 C fraction indicates that coke precursors reside in the asphaltenes and high boiling fractions.

(8)

The conversion of asphaltenes into valuable hydrocarbons

would require severe operating conditions at a high temperature and hydrogen partial pressure while using a hydrogenation catalyst with low acidic support to avoid high coke formation. Schabron and Speight

(12)

developed a correlation in their paper relating asphaltenes content, molecular weight and (13)

heteroatom content with CCR and MCR of whole residua. Ancheyta et al.

reported a first order kinetic

model for two types of asphaltenes: hard-to-react and easy-to-react asphaltenes. −:N = O9 $9 -N - + (1 − O9 )$+ -N -

(7)

where O9 = fraction of the heavy hydrocarbon that reacts slowly; O+ = less refractory fraction that reacts

more quickly; -N = asphaltene concentration; - = hydrogen concentration; Q = reaction order for hydrogen. Since O9 , O+ , $9 , and $+ are constants, Eq. (7) can be rearranged and grouped to obtain:

−:N = SO9 $9 + (1 − O9 )$+ T-N -

(8)

−:N = $> -N -

(9)

where $> = O9 $9 + (1 − O9 )$+ .

Assuming reaction order with respect to hydrogen concentration is one, a relationship between asphaltenes in product and CCR and sulphur heteroatom contents in feed and products can be developed, as in Eq. 10. The possible relationship between CCR content and asphaltenes content was studied, and a linear relationship was observed. The CCR content decreases as the asphaltenes content decreases. Some authors have indicated the presence of thiophenic sulphur type in asphaltenes. Page et al.

(11)

(13)

have reported the existence of sulphur bridges in asphaltenic structures. Ancheyta et al.

Le (13)

stated that the content of sulphur in asphaltenes is in the range of 6 to 8 wt. %, which is higher than in maltenes (3 to 5 wt.%). OUℎ = WOUℎ × O9 X + (OUℎ × O+ ) × Y

3445

6785

× Z[ × \] Y

!3445

!6785

[

where OUℎ = asphaltenes in feed, ppmw; OUℎ = asphaltenes in product, ppmw.


(10)

Page 11 of 41

Table 4 shows the feed, operating data and parameters obtained from Eq. 10. Table 4 Feed, Operating data and Parameters obtained from Asphaltene model

Feed properties Asphaltenes in feed (ppmw)

52,400


703

LHSV (h-1)

0.5

Parameters A1 (refractory fraction)

0.18

A2 (less refractory fraction)

0.82

μ (exponential coefficient)

0.11

The model shows good prediction of the experimental data when correlated with CCR in product and sulphur in product obtained from the model as in Figure 4 and Figure 5.

33 32 Ashprod * 103 (ppmw)

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31 30

Asphprod (ppm) (model) Asphprod (ppm) (expt)

29 28 27 26 78

80

82

84

86

88

CCRprod *

103

90

92

94

(ppmw)

Figure 4 Asphaltenes model fit with experimental data (varying CCR in product)


96


33.0 32.0 Ashprod * 103 (ppmw)

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Page 12 of 41

31.0 30.0 Asphprod (ppmw) (model)

29.0

Asphprod (ppmw) (expt) 28.0 27.0 26.0 7.1

7.2

7.3

7.4

7.5

7.6

7.7

7.8

7.9

Sprod * 103 (ppmw) model

Figure 5 Asphaltenes model fit with experimental data (varying sulphur in product)

The values for O9 and O+ are 0.17 and 0.83 respectively. The sum of these values is 1 as reported in literature.

(13)

The average absolute error between the experimental and calculated results of asphaltenes

in the product is 0.7 %. Note that this equation only fits data with varying H2 pressure at fixed reactor temperature, WHSV and catalyst content.

When correlated with CCR and sulphur data at varying

temperature, it predicts temperatures from 683 K to 713 K. Higher values of temperature are poorly predicted by the model. 2.1.2

Residue hydrocracking models

2.1.2.1 Vacuum Residue (VR) conversion In the characterization of thermal conversion of vacuum residues, few assumptions have been made: 1. Thermal reactions are considered to be irreversible because cracked fragments are saturated immediately with hydrogen; 2. the feedstock consists of several pseudo or lumped components that react (13)

in the first order.

A first-order VR conversion model derived from steady state reaction kinetics has

been developed to describe quantitatively the interactions that exist between asphaltenes conversion, feed and catalyst properties, hydrogen partial pressure and operating conditions. ^KA

9 ^KA

= $_

(11)


Page 13 of 41

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The VR conversion also increases with the cracking rate constant, $; _ = residence time in s. The rate

constant ($) is highly dependent on the characteristics of VR feed, product properties and operating conditions in the reactor. In the upgrading of heavy oil, some properties such as heteroatom contents are also very important. Therefore, their incorporation into the conversion model may yield better predictions of VR conversion in hydrodesulphurization processes. fghihjklimnkmjn op qr plls e* tuv wx\ yℎt:tyz :xUtzxw] atyzw: (% ) c c OUℎt\z ] U yw]z ]z x] a { c c |hk}il op ~ios}jk c c wx\x]z wx]z t:t z : ( ) $ = a OUℎt\z ] U yw]z ]z x] :w{yz d rlhjkmo josmkmon c c c c :tz: () c xℎz ℎw:\v Uty u \wyxzv (*2) c b (14)

Yang and Wang

developed a feedstock characteristic index based on supercritical fluid extraction and

fractionation (SFEF) characterization of residue fractions. % = 10 ×

/

.

(12)

where is the average molecular weight, is the density at 20 C (g/ml) and H/C is the atomic hydrogen0

to-carbon ratio. This index is used to correlate properties such as carbon residue and compositional features, such as Saturates, Aromatics, Resins and Asphaltenes (SARA). According to Wang et al.

(15)

,a

vacuum residue with low H/C atomic ratio and high carbon residue has a high propensity to produce large amounts of coke. The author reported an increase in coke yield when % = 6 − 8; the increasing rate of

coke yield increases gradually with decreasing the feed % value and increases more when % < 6. Shi grouped % values according to their processability:

(16)

et al.

% > 7.5 (adaptable to secondary

processing); 6.5 < % < 7.5 (intermediate); % < 6.5 (difficult in secondary processing).

The temperature effect of the specific reaction rate could be correlated with the Arrhenius equation: $ = $>

(13)

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

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 of subfractions up to vacuum gas oil (VGO). It is assumed that the simulated distillation data can easily be obtained for these products. ^KA

(9^KA

= )

× ¡6¢ £

G ×B × × = 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. §®+>> © =

^KA × !¯ ¬ 67E

(17)

Where )®+>> = selectivity to >°> © (−) = −§®+>> W+>>°> © ± X + §®+>> W−+>>°> © X + §®+>> WZ+>>°> +>>°> © + X + §®+>> WZ®+>> ®+>> © X − M+>>°> +>>°> ©

(18)

§®+>> WZ°>°>> °>°>> © X + M°>°>>> °>°>> ©

(19)

§°>°>> © (−) = §®+>> W°>°>> © ± X − §®+>> W−°>°>> © X − §®+>> WZ°>°>> °>°>> © + X −

where §®+>> represent the sum of the yields for < 200> -. ®+>> , +>>°> , °>°>> are the ratios of the

pseudocomponent boiling points and the heaviest pseudocomponent boiling point of feed for < 200> -, 200 − 350> -, and 350 − 500> - pseudocomponent range respectively. Another yield equation was

developed for coke yield based on the amount of carbon residue in the product, selectivity to coke fraction and % . . §. © =

6785 × !8³4 G¬ 67ª

(20)

Where ). = selectivity to the formation of coke; t:t- = heavy oil characterization parameter for

coke fraction; % © = heavy feed characterization for each fraction in the coke range. The reduction of

conradson carbon residue minimizes the amount of petroleum coke produced in a refinery. Similar studies have shown that the amount of coke formed in the coking step is a function of the amount of CCR in the hydrocracked product.

(6)



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

A

maximum

of

eleven

Page 18 of 41

parameters

(O, )¨N! , )®+>> , ). , t:t, t:t%, t:t-, Z®+>> , Z+>>°> , Z°>°>>,

M®+>> , M+>>°> , M°>°>> , ¦)

are obtained for the conversion and yield models for five product lumps. As parameter ‘O’ affects the shape of the yield curve, it varies to some extent with different feedstocks. In order to determine the parameters, the developed model is implemented in a software package for nonlinear regression, based

on the least squares method. Parameters )¨N! , )®+>> , t]{ ). represent the selectivity to gas,

naphtha and coke yield respectively. Parameters Z and M represent coefficients in the yield model for < 200> -, 200 − 350> -, and 350 − 500> - products. Parameter ¦ is associated with asphaltene

conversion. Table 7 shows the feed, operating data and the resulting parameters for the vacuum residua (SQVR) in Table 5. Table 7 Feed and operating data in the refinery

Feed properties Final pseudocomponent boiling point of feed (0C)

710

Product properties Final pseudocomponent boiling point of product (0C)

460


699

Hydrogen Pressure (MPa)

16

-1

LHSV (h )

0.25

Parameters SPgas (selectivity parameter to Gas yield)

0.45

SP>,¶ , ´+>>°>,¶ , ´°>°>>,¶ , ´¿" are flowrates of gas, < 200> -, 200 − 350> -, 350 − 500> - and unconverted residues fractions respectively.

The individual flowrates of gas, naphtha(
-), gasoil (200 − 350> -) and VGO (350 − 500> -) are functions of 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. ´ ,¶ + ´µ·,¶ + ´,¶ = ´¨N!,¶ + ´®+>>,¶ + ´+>>°>,¶ + ´°>°>>,¶ + ´·.Æ,¶ ∀¹ = ℎv{:wy:ty$ :U (42) 2.2.5

Hydrogen network model

The interactions existing between hydrogen producers and consumers can be represented with the following mass balance: ∑ ´,,¶ = ´µ·,¶

∑ ´,,¶ + ∑¶9 ´,¶9,¶ = ´µ©> + W´,"¨À + ´+>>°>," X × Ñ + W´ÄÅ,"¨À × ÑÄÅ X + W´°>°>>," × Ñ°>°>> X − W(´, × Ñ+ XÒ

(51)

´¨N!,"¨À , ´¨N!," , ´®+>>," , ´Ç©È,Ã , ´Ç©È,Ã , ´=/Ó,"¨À , ´,"¨À , ´+>>°>," , ´ÄÅ,"¨À , ´°>°>>," are flowrates of gas from VGO hydrocracking unit, gas from VRDS unit, naphtha from VRDS unit, naphtha from NHT, cracked naphtha from CNHT, total naphtha from VGO hydrocracking unit, kerosene from VGO hydrocracking unit, gas oil from VRDS unit, diesel from VGO hydrocracking unit, and VGO from VRDS unit respectively. Ñ¨N! , Ñ®+>>, Ñ , ÑÄÅ , Ñ°>°>> are unit prices of gas, < 200 w: ]tℎzℎt, kerosene, diesel, and 350 − 500 w: 2ÔÍ respectively. The additional process constraints proposed in the

formulation of this methodology are expected to give more realistic solutions as demonstrated in the case study.



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

3.

Page 24 of 41

Integrated optimization framework for hydrogen networks

An extended methodology framework in Figure 9 has been proposed to illustrate the integration of hydrotreater and hydrocracker models in the optimization of hydrogen networks. The optimization methodology describes the effect of changing process variables, such as H2 partial pressure and temperature on sulphur, CCR, asphaltenes, VR conversion and product yields in a VRDS unit.

Data collection

VRDS Process model regression and validation

Overall network modelling

Overall network optimization

Figure 9 Methodology for integration of VRDS unit in a refinery hydrogen network

The methodology can be summarised into three major steps: 3.1

Process model development, regression and validation

The non-linear process models developed from first principles steady state kinetics in section 2.1 have been successfully used to predict VR feed conversion and five-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 conversion models are regressed on an experimental data

(4)

(1)

and the yield models are regressed using experimental data

in

Table 5. The resulting trend from each fit shows that the process model is robust enough to define the performance of a VRDS.


Page 25 of 41

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


3.2

Overall network modelling

The non-linear conversion and yield models are integrated into the hydrogen network model resulting in an integrated superstructure of process and network models. The overall network is modelled in the GAMs environment. The inlet hydrogen flow to VRDS is allowed to vary to accommodate the effects of changing operating conditions on VRDS performance. 3.3

Overall network optimization

The process models are integrated in a hydrogen network model to exploit the interactions between changing operating conditions and hydrocracker 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 seen in the hydrogen consumption levels, product distribution from hydrocrackers and overall hydrogen requirements in the network.

4.

Case study

4.1

Base case

The Base case hydrogen network

(2)

is modified in this work to include a VRDS unit. The hydrogen

network consists of two hydrogen producers: Hydrogen plant, H2Plant; catalytic reformer, CCR; three hydrotreaters: naphtha hydrotreater, NHT; cracked naphtha hydrotreater, CNHT; diesel hydrotreater, DHT; two hydrocrackers: vacuum gas oil hydrocracker, VGOHC; and VRDS. The detailed feed stream data for the base case and operating conditions in the hydroprocessing units is as shown in Table S3 and Table S4 in the supporting information file. Non-linear VRDS process models developed in section 2.1 and hydrotreater models are integrated into the hydrogen network under fixed and varying operating conditions for the objective of maximum profit. The prices for VGO feedstock, hydrogen, butane, naphtha, kerosene and diesel are £562.91/t

(20)

(21)

, £3000/t

(22)

, £385.95/t

(22)

, £594.81/t

(22)

, £675.95/t

, and £593.3/t

(22)

respectively. The objective is to minimize hydrogen at fixed operating conditions across hydroprocessing units. The hydrogen production flowrate is 21.44 t/h. By integrating hydrogen consuming processes in hydrogen networks, the interactions between hydrogen distribution and use in hydroprocessors can be exploited. First, we will consider the outcomes of manipulating operating variables in the VRDS. Then, we shall consider the simultaneous effect of different decision variables on the overall hydrogen network profitability. 4.2

Optimization with varying hydrogen partial pressure in VRDS

The recycle stream is used to maintain the H2 partial pressure and the physical contact of hydrogen with the catalyst to ensure adequate conversion and impurities removal while minimizing carbon deposition.



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 26 of 41

Increasing the hydrogen partial pressure reduces the reactor start of run temperature as well as the rate of catalyst deactivation. In Table 9, H2 partial pressure has been varied at constant temperature and LHSV to study its influence on product distribution and chemical hydrogen consumption. Table 9 Effect of increasing hydrogen pressure on conversion and product yield distribution in VRDS process

H2 Pressure (bars)

150

160

170

Chemical H2 consumed (t/h)

7.56

7.89

8.13

Asphaltene conversion (wt %)

31.65

33.34

35.07

CCR conversion (wt %)

39.27

40.62

41.92

Sulphur conversion (wt %)

65.08

66.60

68.06

VR conversion (wt %) / Yields (wt%)

52.75

53.33

53.93

Gas

1.95

2.01

2.05

Naphtha

7.39

7.41

7.44

Gas oil

34.22

35.83

37.46

VGO

9.19

8.08

6.97

VR

47.25

46.67

46.07

As expected, increasing hydrogen pressure increases vacuum residue conversion, decreases unconverted vacuum residue and VGO yields, while increasing the amount of gas, naphtha, and gasoil yields. Other authors have reported an increase in yield of light fractions with increasing vacuum residue conversion.

(23)

(24)

Gillis et al.

mentioned that a hydrogen-rich environment would facilitate very high

conversion of residue to liquid products, particularly distillate boiling range components, contrary to (4)

results reported in Morawski and Mosio-Mosiewski . The latter obtained an increase in UCVR, VGO, gasoline yields and a corresponding decrease in gas and gas oil yields. The authors attributed the behaviour of the system to secondary reactions (polymerization, alkylation, and hydrogenation) of (4)

cracking products with increasing hydrogen pressure. Morawski and Mosio-Mosiewski

explained that

this effect was due to the excess amount of hydrogen present in the reactor. Figure 12 describes the influence of hydrogen pressure on product yield distribution.


Page 27 of 41

120

100

80

Yield (wt %)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Gas Naphtha

60

Gas oil VGO VR

40

20

0 150

160

170

H2 Pressure (bar)

Figure 10 Effect of hydrogen pressure on product yield distribution in VRDS process

The correlations obtained for asphaltene, CCR, sulphur and VR conversion have been based on experimental data.

(4)

The chemical hydrogen consumed in the VRDS is obtained from the combination of

hydrogen consumed due to VR hydrocracking reactions, HDS reactions and light hydrocarbons formation.

4.3

Optimization with varying temperature in VRDS process

Table 10 describes the effects of varying temperature at constant hydrogen partial pressure in VRDS on product yield pattern.

Table 10 Effect of decreasing temperature on conversion on product yield distribution



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Temeperature (K)

699

696

693

Chemical H2 consumed (t/h)

7.892

7.329

6.626


33.34

29.24

25.98


40.62

35.66

31.11


66.60

63.55

60.62


53.33

48.02

43.00

Gas

2.01

1.89

1.79

Naphtha

7.41

7.15

6.99

Gas oil

35.83

20.21

3.34

VGO

8.08

18.77

30.89

VR

46.67

51.98

57.00

Page 28 of 41

The result in Table 10 shows the effect of decreasing temperature on product yield distribution at H2 -1

pressure of 160 bars and LHSV of 0.5 h . The results obtained for gas, naphtha, gas oil, VGO and VR (4)

are similar to the outcomes of Morawski and Mosio-Mosiewski

experiment. The authors reported a

decrease in VGO at 683 K. This work shows that a decrease in temperature necessitates a corresponding increase in VR and VGO as expected, and a decrease in the light fractions. Conversely, an increase in temperature results in a corresponding increase in the yield of light fractions and a decrease in heavier fractions and unconverted vacuum residue. The ratio of decrease in weight fractions for decreasing temperature is slight in gas and naphtha compared to gas oil and VGO fractions. Morawski (4)

and Mosio-Mosiewski

have noted the importance of VGO in the production of low sulphur fractions of (1)

motor fuels. The result shows that the data

is very sensitive to small changes in temperature. Although,

slight changes in the lighter fraction yield distribution are reported, it is important to note that the resulting trend is plausible. Figure 11 describes the results graphically.


Page 29 of 41

120

100

80

Yields (wt %)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Gas Naphtha

60

Gas oil VGO VR

40

20

0 699

696

693

Temperature (K)

Figure 11 Effect of temperature on product yield distribution in VRDS

In Figure 11, profiles of sectional areas for gas, naphtha, gas oil, VGO yields and unconverted VR are presented to show the effect of decreasing temperature. The yield distribution for heavier fractions is very sensitive to temperature compared to the effects of hydrogen pressure in Figure 11. Beaton and (9)

Bertolacini

also predicted similar effects of temperature on product yield distribution. The yield of light

gases, naphtha, and gas oil increased with increasing residue conversion as temperature increased. (1)

Although the model showed reasonable accuracy in predicting the industrial data , the extent of conversion is limited by how much change in temperature is accommodated by the data. A feasible set of solutions can be obtained between 693 K and 699 K. The simultaneous effects of increases/decreases in conversion are clearly seen in the chemical hydrogen consumed and the overall hydrogen production requirements of the network as shown in Table S5 in the supporting information file. .



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

4.4

Page 30 of 41

Optimization of maximum profit at varying inlet H2 conditions in VRDS process

Table 11 describes the corresponding effect on profit with changes in hydrogen partial pressure. 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 Temperature (K)

NHT

CNHT

DHT

VGOHC

Fixed

Fixed

Fixed

Fixed

Fixed

Vary

Vary

623

653

633

672

699

699

699

150

160

170

1.726

2.881

3.059

3.131

66.88

52.75

53.33

53.93

Hydrogen partial pressure in VR (bars) H2S formed (t/h)

0.329

0.538

5.430

Calculated conversion for VGOHC & VR (wt%)

VR

Makeup hydrogen (t/h)

1.334

0.649

2.494

8.393

8.175

8.338

8.416

Chemical hydrogen consumed (t/h)

0.344

0.426

1.845

7.070

7.565

7.892

8.180

Dissolved hydrogen (t/h)

0.990

0.224

0.651

1.324

0.611

0.445

0.236

Products formed from VGO hydrocrack ing reactions (t/h) Flowrate of light gases

6.34

Flowrate of naphtha

66.89

Flowrate of kerosene

90.13

Flowrate of Diesel

44.58

Products formed from VR hydrocrack ing reactions (t/h) Flowrate of light gases

3.61

3.71

3.80

Flowrate of naphtha

13.64

13.70

13.76

Flowrate of gasoil

63.20

66.23

69.26

Flowrate of VGO

16.97

14.94

12.89

H2 production flowrate (t/h)

21.44

21.58

21.63

Overall Profit (£B/y)

3.69

3.70

3.71

The result shows a slight relative increase of 0.6 % in H2 production flowrate from H2 pressures at 150 bars and 160 bars. From the results, it could be inferred that the product yields is not very sensitive to variations in hydrogen pressure, and thus, differences in annual profit are minimal, similar to results (4)

obtained in Morawski and Mosio-Mosiewski . Under such scenarios of limited sensitivity, the operator may be able to save hydrogen depending on how much profit giveaways can be accommodated. In Table 11, the gains in profit outweigh the increase in hydrogen flowrate with increasing hydrogen partial pressure.

4.5

Optimum Profitability in VRDS Process (4)

Morawski and Mosio-Mosiewski

reported the significant effects of reaction temperature on the

hydrocracking of VR and CCR and asphaltenes content in products. In Table 12, the effect of temperature


Page 31 of 41

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


and hydrogen partial pressure on hydrogen production flowrate, profit and the amount of CCR left in the product is presented. Table 12 Effect of sequential variation in H2 pressure and temperature on profit in VRDS Hydrogen consumer

VR

Temperature (K)

699

699

696

696

693

Hydrogen partial pressure in VR (bars)

150

160

160

170

170


31.65

33.34

29.24

30.62

27.07


39.27

40.62

35.66

36.86

32.19


65.08

66.60

63.55

64.95

61.95


52.75

53.33

48.02

48.50

43.38


7.56

7.89

6.99

7.19

6.47

Flowrate of light gases

3.61

3.71

3.48

3.57

3.39

Flowrate of naphtha

13.64

13.70

13.19

13.22

12.88

Flowrate of gasoil

63.20

66.23

37.29

39.98

8.63

Flowrate of VGO

16.97

14.94

34.63

32.69

55.07

H 2 production flowrate (t/h)

21.44

21.58

21.24

21.19

21.15

Overall Profit (£B/y)

3.695

3.700

3.636

3.643

3.573

Yccr (wt %)

9.60

9.38

10.17

9.98

10.71

Products formed from VR hydrocrack ing reactions(t/h)

As shown in Table 12, a balance between temperature and hydrogen partial pressure requirements for the VRDS process can be obtained, while maintaining the profitability of the process. For example, maximum profit is obtained at 699 K and 160 bars; the amount of CCR produced is least at 699 K and (10)

160 bars compared to other operating cases. Marafi et al.

have indicated that the content of CCR

depends on the content of coke forming precursors in the feed. In this regard, an attempt to correlate CCR content in products with coke formed has been demonstrated in Eq. (20). An increase in temperature results in a corresponding increase in CCR conversion, thus a decrease of CCR in products and coke formed. Other authors have established a temperature limit before the rapid formation of coke is reached. Font et al.

(25)

0

reported a rapid decrease in conversion beyond temperatures of 417 C, especially 0

0

highly negative conversions at 450 C and 470 C. The authors indicated that the decrease in conversion was attributed to the gradual influence of both recombination and coking reactions, as a result of hydrogen deficit induced by the strong consumption of this element while temperature increases. As (4)

described by Morawski and Mosio-Mosiewski , the primary function is to maintain the concentration and reactivity of hydrogen donors in the asphaltenes during high-temperature hydroconversion. This function prevents the growth of polynuclear aromatics and makes them less likely to come out of solution as either coke or downstream asphaltenic fouls, even at increased conversion. This work describes the effect of temperature between an allowable range of 693 K and 699 K on hydrogen production requirements and overall profit. Beyond these temperatures, the result is a negative conversion as reflected in the (1)

experimental yield data of Yang et al. . From the simulations in Table 12, it is considered productive to



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 32 of 41

operate at high temperatures of 699 K and high hydrogen partial pressures of 160 bars, while less amount of CCR, a precursor to coke formation is produced. Consequently, the effect on catalyst deactivation could be measured as minimal, as a result of reduced coking activity. 4.6

Overall synthesis of integrated networks under varying H2 inlet conditions

Figure 12 and Figure 13 describes the integrated hydrogen network under fixed and varying operating conditions.

7.29 t/h 86.64 %

0.000 t/h 86.64 %

1.42 t/h 92.56 %

NHT 10.78 t/h 83.38 %

CNHT 46.81 t/h 84.76 %

0.88 t/h 0.000 t/h 83.38 %

0.21 t/h

0.000 t/h 84.76 %

2.695 t/h 92.56 % 21.44 t/h 92.56 %

DHT

H2 Plant

15.47 t/h 8.44 %

0.61 t/h 0.000 t/h 8.44 %

9.05 t/h 92.56 %

VGOHC 1.48 t/h 5.29 t/h 80.00 %

8.26 t/h 92.56 %

0.000 t/h 80.00 %

VR 0.33 t/h


To external recycle

Page 33 of 41

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Figure 12 Integrated hydrogen networks under fixed operating conditions

Figure 13 describes the integrated hydrogen network under varying conditions when hydrogen availability is 50 t/h.



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

7.29 t/h 86.64 %

Page 34 of 41

0.000 t/h 86.64 %

1.24 t/h 92.56 %

0.20 t/h 38.03 %

NHT 0.88 t/h 10.78 t/h 83.38 %

CNHT 46.81 t/h 84.76 %

0.000 t/h 83.38 %

0.214 t/h

0.000 t/h 84.76 %

2.65 t/h 92.56 % 22.78 t/h 92.56 %

DHT

H2 Plant

15.47 t/h 8.44 %

0.613 t/h 0.000 t/h 8.44 %

10.66 t/h 92.56 %

VGOHC 1.477 t/h 5.29 t/h 85.00 %

8.22 t/h 92.56 %

0.000 t/h 80.00 %

1.28 t/h 38.03 %

VR 0.332 t/h

Figure 13 Integrated hydrogen networks under varying H2 inlet conditions

Table 13 shows the effect of variations of H2 inlet conditions in hydroprocessors on the overall network profitability. When hydrogen consumed is increased in the VGOHC and VRDS unit, light fractions increase and heavy fractions decrease resulting in an overall increase of 6 % and 2 % in hydrogen production flowrate and network profitability respectively. Where only limited H2 is available, an overall increase in profit of 0.3 % is obtained from the base case at fixed inlet H2 conditions. Although, the increment in profit is small, the decrease in hydrogen production requirements is approximately 1.3 %.


To external recycle

Page 35 of 41

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Consequently, the refiners can break even in the operation of hydroprocessing units across the refinery. Table 14 shows the effect of accommodating a further variation in H2 inlet conditions on the overall network.



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48

Page 36 of 41

Table 13 Effect of Fixed and Varying inlet H2 conditions on the overall network Effects of Fixed and Varying inlet H2 on product yields

Cases Hydrogen consumers

NHT Fixed

Maximum Hydrogen Limit for all consumers (t/h) Temperature (K)

Vary 50

623

CNHT Vary

Fixed 50

21.15 623

623

Vary

653

653

DHT Vary 21.15

Fixed

653

633

Vary 50 633

VGOHC Vary 21.15

Fixed

633

672

Vary 50 672

VR Vary 21.15

Fixed

672

Hydrogen partial pressure (bar) Calculated conversion for VGOHC & VR (wt%)

Vary

Vary 21.15

699

699

699

150

160

160

50

66.93

69.94

66.82

52.75

53.33

53.33

H2S formed (t/h)

0.329

0.329

0.329

0.568

0.568

0.568

5.448

5.448

5.448

1.726

1.726

1.954

2.984

3.062

3.062


1.350

1.222

1.334

0.649

0.639

0.639

2.494

2.457

2.457

8.414

9.872

8.365

8.138

8.098

7.986


0.344

0.344

0.344

0.426

0.426

0.426

1.845

1.845

1.845

7.088

8.395

7.046

7.545

7.765

7.705


1.007

0.879

0.991

0.224

0.214

0.214

0.651

0.613

0.613

1.324

1.477

1.321

0.593

0.332

0.281

C1 formed

0.0207

0.0207

0.0207

0.0214

0.0214

0.0214

0.0196

0.0196

0.0196

0.0061 0.0068

0.0068

0.0244

0.0250

0.0250

C2 formed

0.0362

0.0362

0.0362

0.0376

0.0376

0.0376

0.0343

0.0343

0.0343

0.0107 0.0120

0.0120

0.0437

0.0448

0.0448

C3 formed

0.1611

0.1611

0.1611

0.1669

0.1669

0.1669

0.1527

0.1527

0.1527

0.0477 0.0532

0.0532

0.1749

0.1790

0.1790

C4 formed

0.1525

0.1525

0.1525

0.1624

0.1624

0.1624

0.1445

0.1445

0.1445

0.0452 0.0504

0.0504

0.1677

0.1717

0.1717

C5 formed

0.0816

0.0816

0.0816

0.0846

0.0846

0.0846

0.0773

0.0773

0.0773

0.0242 0.0270

0.0270

0.0949

0.0972

0.0972

Light hydrocarbons formed (t/h)

Products formed from VGO hydrocrack ing reactions (t/h) Pure hydrogen inlet flowrate

9.72

11.18

Flowrate of light gases formed

6.34

6.53

9.67 6.33

Flowrate of light naphtha

25.35

26.13

25.32

Flowrate of heavy naphtha

41.10

47.63

40.86

Flowrate of total naphtha

66.45

73.77

66.18


90.66

124.88

89.39

Flowrate of diesel

44.08

12.81

45.25

Products formed from VR hydrocrack ing reactions (t/h) Flowrate of light gases

3.61

3.70

3.70

Flowrate of naphtha

13.64

13.68

13.68

Flowrate of gasoil

63.20

66.12

66.12

Flowrate of VGO

16.97

14.92

14.92

H2 production flowrate (t/h) - Fixed

21.44

H2 production flowrate (t/h) - Vary

22.78

H2 production flowrate (t/h) - Vary when H2 is limited

21.15

Overall Profit (£B/yr) - Fixed

3.696

Overall Profit (£B/yr) - Vary

3.773

Overall Profit (£B/yr) - Vary when H2 is limited

3.706

36

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Table 14 Effect of a further limitation of H2 supply on the overall hydrogen network Cases Hydrogen consumers

Effect of further limited H2 supply on network requirements and profitability NHT

CNHT

Vary

Vary

Maximum Hydrogen Limit for all consumers (t/h)

21.15

Temperature (K)

623


DHT

VGOHC

VR

20.95

Vary 21.15

Vary 20.95

Vary 21.15

Vary 20.95

Vary 21.15

Vary 20.95

Vary 21.15

Vary 20.95

623

653

653

633

633

672

672

699

699

66.82

66.55

53.33

53.28

1.334

1.327

0.639

0.636

2.457

2.445

8.365

8.251

7.986

7.937


0.344

0.344

0.426

0.426

1.845

1.845

7.046

6.945

7.705

7.679


0.991

0.984

0.214

0.210

0.613

0.599

1.321

1.308

0.281

0.258

Calculated conversion for VGOHC & VR (wt%)

Products formed from VGO hydrocrack ing reactions(t/h) Pure hydrogen inlet flowrate

9.67

9.56

Flowrate of light gases formed

6.33

6.31

Flowrate of light naphtha

25.32

25.26

Flowrate of heavy naphtha

40.86

40.28

Flowrate of total naphtha

66.18

65.54


89.39

86.37

Flowrate of diesel

45.25

48.03

Products formed from VR hydrocrack ing reactions(t/h) Flowrate of light gases

3.70

3.70

Flowrate of naphtha

13.68

13.68

Flowrate of gasoil

66.12

66.10

Flowrate of VGO

14.91

14.92

H2 prod. flowrate (t/h) - Vary when H2 is limited

21.15

H2 prod. flowrate (t/h) - Vary when H2 is further limited

20.95

Overall Profit (£B/yr) - Vary when H2 is limited

3.71

Overall Profit (£B/yr) - Vary when H2 is further limited

3.70

37

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Table 14 shows that a further limitation on H2 supply to 20.95 t/h, while expanding the variations in H2 inlet conditions results in a decrease in the amount of H2 available to the VGO hydrocracker, and hence an increase in flowrate of diesel and decrease in lighter end hydrocarbons. The VRDS is constrained on the maximum allowable changes in H2 pressure. The overall network is in deficit of a 0.11 % change in profit. A profit loss of approximately £4.2M is incurred compared to a savings in hydrogen of approximately £4.8M. Overall, the savings in hydrogen outweighs the loss in profit. Based on the inferences from Table 13 and Table 14, an optimum amount of hydrogen can be realized with a corresponding growth in profit. A hydrogen supply of 20.95 t/h is considered ideal in the profit maximization scenarios. Note also that the products obtained from VGO and VRDS at a hydrogen supply of 20.95 t/h of hydrogen constitutes some amount of sulphur depending on their boiling range and yield distribution, as shown in Table 15. Table 15 Sulphur distribution among products in VGO and VRDS hydroprocessors VGO S f eed, wt %

2.00

VR S f eed, wt %

2.54

VGO S prod, wt %

1.42

VR S prod, wt %

0.85 GAS

LN

HN

KER

DIE

UCO

Sulphur, wt %

0.000

0.000

0.000

0.002

0.006

1.409

Products from VRDS unit

GAS

NAPHTHA

GASOIL

VGO

UVR

Sulphur, wt %

0.000

0.000

0.001

0.001

0.846

Products from VGO unit

As expected, sulphur concentrates in the highest boiling range, in this case, the unconverted VGO and VR fractions. The small amount of sulphur in the lighter fractions suggests that the easy-to-react sulphur compounds are dispersed across the lower boiling range.

5.

Conclusions

Representation of hydrogen consumers with models that define the process chemistry is fundamental to optimizing the use of hydrogen in refineries. In this work, process models have been developed for VRDS process to accurately predict product formation based on significant characteristic variables and parameters. These hydrogen consumer models have been integrated in the hydrogen network model to exploit interactions between hydrogen consumers and the hydrogen distribution network. Of particular interest is the similarity in the behaviour of the models with existing optimization trends in the refining industry. An increase in hydrogen partial pressure by approximately 7 % improves profit by only 0.03 %, in contrast with temperature changes. A decrease in temperature enhances the production of heavier hydrocarbons and decreases the formation of light ends. The effect of this decrease or increase in yields in VGOHC and VRDS and changes in sulphur conversion is seen in the process hydrogen requirements and overall hydrogen production flowrate of the network. A sensitivity analysis has also been carried out to understand the effects of limited hydrogen availability


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on the overall network profitability for different case scenarios. Hydrogen savings realized from a decrease in hydrogen requirements counterbalances the loss in network profitability. By allowing simultaneous consideration of hydroprocessor integration, hydrogen network optimization, and varying operating conditions, an actual and effective hydrogen optimization methodology can be implemented. Supporting Information Statement Table S1: Feed, Operating Data and Parameters obtained from Sulphur model Figure S1: Effect of hydrogen pressure on sulphur in the product Table S2: Feed, Operating Data and Parameters (varying Temperature) Figure S2: Effect of Temperature on Sulphur in the product Table S3: Detailed Feed data for modified base case Table S4: Operating conditions in hydroprocessors Table S5: Effect of varying H2 partial pressure in VRDS process on hydrogen production This information is available free of charge via the internet at http://pubs.acs.org/. References (1) Yang, C.; Zhang, J.; Xu, C.; Lin, S. Hydroconversion Characteristics on Narrow Fractions of Residua. Journal of Fuel Chemistry and Technology. 1998, 5. (2) Umana, B.; Shoaib, A.; Zhang, N.; Smith, R. Integrating Hydroprocessors in Refinery Hydrogen Network Optimization. Applied Energy. 2014, 133, 169-182. (3) Rana, M. S.; Samano, V.; Ancheyta, J.; Diaz, J. A. I. A review of recent advances on process technologies for upgrading of heavy oils and residua. Fuel. 2007, 1216-1231. (4) Morawski, I.; Mosio-Mosiewski, J. Effects of parameters in Ni-Mo catalysed hydrocracking of vacuum residue on composition and quality of obtained products. Fuel Processing Technology. 2006, 87(7), 659-669. (5) Kirchen, R. P.; Sanford, E. C.; Gray, M. R.; George, Z. M. Coking of Athabasca Bitumen Derived Feedstock. AOSTRA J. Res. 1989, 5, 225. (6) Sanford, E. C. Conradson Carbon Residue Conversion during Hydrocracking of Athabasca Bitumen: Catalyst Mechanism and Deactivation. Energy & Fuels. 1995, 9, 549-559. (7) Gray, M. R.; Jokuty, P.; Yeniova, H.; Nazarewycz, L.; Wanke, S. E.; Achia, U.; Sanford, E. C.; Sy, O. K. Y. The Relationship between Chemical Structure and Reactivity of Alberta Bitumens 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 Residue Conversion 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 Modelling of Petroleum Residues Hydroprocessing. Catalysis Reviews: Science and Engineering. 2010, 52(2), 204-324.



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(11) Le Page, J. F.; Morel, F.; Trassard, A. M.; Bousquet, J. Thermal Cracking under Hydrogen Pressure: Preliminary Step to the Conversion of Heavy Oils and Residues. American Chemical Society, Division of Petroleum Chemistry, Preprints. 1987, 32, no. CONF-8704349. (12) Schabron, J. F.; Speight, J. G. Correlation between Carbon Residue and Molecular Weight. Am. Chem. Soc., Div. Fuel Chem. 1997, 42(2), 386-389. (13) Ancheyta, J.; Trejo, F.; Rana, M. S. Asphaltenes: Chemical Transformation during Hydroprocessing of Heavy Oils; CRC Press: Florida, 2010. (14) Yang, G. H.; Wang, R. A. The Supercritical Fluid Extractive Fractionation and the Characterization of Heavy Oil and Petroleum Residua. Journal of Petroleum Science and Engineering. 1999, 22, 47-52. (15) Wang, Z. X.; Guo, A. J.; Que, G. H. Coke Formation and Characterization during Thermal Treatment and Hydrocracking of Liaohe Vacuum Residuum. 1998. China University of Petroleum (online article). (16) Shi, T.; Xu, Z.; Cheng, M.; Hu, Y.; Wang, R. Characterization Index for Vacuum Residua and their Subfractions. Energy & Fuels. 1999, 13, 871-876. (17) Sadighi, S; Ahmad, A; Reza Seif Mohaddecy, S. 6-Lump Kinetic Model for a Commercial Vacuum Gas Oil Hydrocracker. International Journal of Chemical Reactor Engineering. 2010, 8, Article A1. (18) Gao, H; Wang, G; Xu, C; Gao, J. Eight-Lump Kinetic Modelling of Vacuum Residue Catalytic Cracking in an Independent Fluid Bed Reactor. Energy & Fuels. 2014, 28, 6554-6562. (19) Stangeland, B. E. Kinetic model for prediction of hydrocracker yields. Ind. Eng. Chem., Proc. Des. Dev. 1974, 13(1), 71-76. (20) US Energy Information Administration 2014. Retrieved from www.eia.gov (21) Blenco G. Hydrogen car revolution. (November 2009). Retrieved from www.h2carblog.com (22) Platts 2013. www.platts.com (23) Fukuyama, H; Terai, S. Kinetic Study on the Hydrocracking Reaction of Vacuum Residue Using a Lumping Model. Petroleum Science and Technology. 2007, 25, 277-287. (24) Gillis, D.; Wees, M. V.; Zimmerman, P. Upgrading Residues to Maximize Distillate Yields. UOP LLC. 2009. (25) Font, J.; Moros, A.; Fabregat, A.; Salvado, J.; Giralt, F. Influence of Fe and FeMo high loading supported catalysts on the coprocessing of two Spanish lignites with a vacuum residue. Fuel Processing Technology. 1994, 37, 163-173.


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