Stepwise optimization of hydrogen network integrated sulfur compound removal kinetics and a fluid catalytic cracker

Chemical Engineering Research and Design 1 5 1 ( 2 0 1 9 ) 168–178

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Chemical Engineering Research and Design journal homepage: www.elsevier.com/locate/cherd

Stepwise optimization of hydrogen network integrated sulfur compound removal kinetics and a fluid catalytic cracker Le Wu a,b,∗ , Yuqi Wang a,b , Lan Zheng a,b , Xiaolong Han a,b a b

School of Chemical Engineering, Northwest University, Xi’an, 710069, China Shaanxi Provincial Institute of Energy Resource & Chemical Engineering, Xi’an, 710069, China

a r t i c l e

i n f o

a b s t r a c t

Article history:

The hydrogen consumption in refineries is rapidly growing because more sour and heavy

Received 2 August 2018

crude oils are being processed to produce more transportation fuels and meet increasing

Received in revised form 7 July 2019

market demands. A hydrogen network integration (HNI) strategy including two mathemat-

Accepted 8 September 2019

ical models (M1 and M2) is built to reduce the hydrogen consumption and the total cost

Available online 13 September 2019

based on the sulfur compound removal (SCR) kinetics and a fluid catalytic cracker (FCC). M1

Keywords:

hydrogen consumption by optimizing the degrees of impurity removal and operating condi-

Hydrogen network integration

tions of hydrotreating units. M2 is an HNI model to optimize the hydrogen network structure

Sulfur compound removal kinetics

based on the results from M1. The case study shows that the HNI without kinetics and FCC

is an operational optimization model integrated the SCR kinetics and an FCC to reduce the

Fluid catalytic cracker

only reduces by 19.1% and 32.6% in total annualized cost (TAC) and hydrogen consumption.

Stepwise solution strategy

While in the hydrogen network optimization with SCR kinetics and FCC, the reductions of the hydrogen consumption and TAC have reached to 47.9% and 37.1%, which are also more than the reductions of 44.5% and 34.4% in the optimization with lumped kinetics and FCC. Therefore, it is imperative to integrate the SCR kinetics and an FCC in a hydrogen network optimization. © 2019 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.

1.

Introduction

consumption in HDT units is challenging while meeting more stringent environmental regulations (Ogumerem et al., 2018).

The hydrogen consumption in refineries is rapidly growing because more sour and heavy crude oils are being processed to produce more transportation fuels and satisfy the increasing market demands

Hydrogen network integration (HNI) is an efficient way to reduce the hydrogen consumption by adjusting the connections between the hydrogen sinks (SK) and hydrogen sources (SR) in a refinery (Jagannath

(Sardashti Birjandi et al., 2017). Hydrogen is mainly consumed in a hydrotreating (HDT) unit to remove the impurities like sulfur, nitrogen aromatics, oxygen and metals and it is mainly controlled by the

et al., 2018; Marques et al., 2017). Deng et al. (2017) proposed an optimization model integrated hydrogen purification and reuse of an inter-plant hydrogen network design, the total cost was mini-

degrees of impurity removal, namely the difference between the impurity concentrations of the feed and the product. Reduction of hydrogen

mized by using the hydrogen-rich gases in a fertilizer plant and an ethylene plant. Jagannath and Almansoori (2017) presented a mathe-

Abbreviations: HDT, hydrotreating; HNI, hydrogen network integration; SK, hydrogen sink; SR, hydrogen source; VGO, vacuum gas oil; HDS, hydrodesulfurization; HDN, hydrodenitrogenation; HDA, hydrogenation of aromatics; FCC, fluid catalytic cracker; CD, cracked diesel; CG, cracked gasoline; SCR, sulfur compound removal; TAC, total annualized cost; MINLP, mixed-integer nonlinear programming; NArS, nonaromatic sulfides; T/BT, thiophenes and benzothiophenes; C1- DBT, C0 /C1 dibenzothiophenes; BNT, benzonapthothiophenes; C2+ DBT, C2+ dibenzothiophenes; PhT, phenathrothiophenes; ERSC, easy removal sulfur compounds; HRSC, hard removal sulfur compounds. ∗ Corresponding author at: School of Chemical Engineering, Northwest University, Xi’an, 710069, China. E-mail address: [email protected] (L. Wu). https://doi.org/10.1016/j.cherd.2019.09.012 0263-8762/© 2019 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.

Chemical Engineering Research and Design 1 5 1 ( 2 0 1 9 ) 168–178

Nomenclature Abbreviations BNT Benzonapthothiophenes CD Cracked diesel Cracked gasoline CG Cycle hydrogen CH DBT Dibenzothiophenes Easy removal sulfur compounds ERSC Fluid catalytic cracker FCC Hydrogenationof aromatics HDA Hydrodenitrogenation HDN HDS Hydrodesulfurization HRSC Hard removal sulfur compounds Hydrogen network integration HNI Liquid hourly space velocity LHSV NArS Nonaromatic sulfides PhT Phenathrothiophenes Pressure swing absorbers PSA Sulfur compound removal SCR TAC Total annualized cost Thiophenes and benzothiophenes T/BT VGO Vacuum gas oil Sets HS HU M N O SK SR

Hydrogen sources Hydrogen utilities Compressors PSA Pipelines Hydrogen sinks Hydrogen sources

Parameters a,b Coefficients of investment cost or FCC impurity distributions d Diameter of pipe, mm Dissolve coefficients, Nm3 m−3 dis e Coefficient of investment cost Activity energy, J mol−1 E Inhibition coefficient of nitrogen concentration KN in feed on HDS reaction KS Inhibition coefficient of sulfur concentration in feed on HDS reaction LHSV Liquid hourly space velocity, h−1 Number of compressors or purifiers No p Price of utilities, CNY mol−1 , CNY kWh−1 , CNY MJ−1 r Recovery ratio of purifier, % Gas mole constant, J mol−1 K−1 R v Volumetric feed flowrate, m3 h−1 y Hydrogen purity of cycle hydrogen, % Variables A Af C F k l M N

Aromatics content, % Annualized factor Cost, CNY·y−1 Flowrate of hydrogen, Nm3 h−1 or mol s−1 Reaction constant, h−1 Distance of the pipe line, m Ratio of non-aromatics and aromatics Nitrogen content, ␮g g−1

P S T TAC W X

169

Operating pressure, MPa Sulfur content, ␮g g−1 Operating temperature, o C Total annualized cost, CNY·y−1 Power of compressor, kW Conversion of aromatics, %

Superscripts Aromatics A Cycle hydrogen CH D Dissolve Forward reaction of HDA f Feed feed Hydrogen H2 in Inlet stream L Lower bound Nitrogen N Other O out Outlet steam Product prod resi Residue r Reverse reaction of HDA Sulfur S U Upper bound Subscripts CD Cracked diesel Cracked gasoline CG Methane CH4 Comp Compressors Electricity Elec FCC Fluid catalyst cracker Fuel gas Fuel H2 Hydrogen g Sulfur compounds Hydrogen sinks i j Hydrogen sources New compressors, PSA and pipelines new p, q Pipeline between two devices Pipeline pipe power Power demand of electricity Purifiers PSA VGO Vacuum gas oil Greek letter a Pressure dependence term Enthalpy value, MJ mol−1 H

matical model of a hydrogen network integrated a rigorous compressor model to discuss the relations among the compressor number, power and the total cost. Liu’s group (Kang et al., 2018; Liang et al., 2016) built a multi-period hydrogen network optimization considered the operational flexibility. Hwangbo et al. (2017) proposed a double stage stochastic programming for a hydrogen network to discuss the effects of uncertainties of hydrogen demands on hydrogen network structure. However, all the above-mentioned researches are based on one of the following assumptions: (1) the pressure of a hydrogen sink is unchanged, (2) the hydrogen demand of a sink is invariant. Hydrogen consumption can be calculated by the degrees of impurity removal which are mainly related with the operating conditions and reaction kinetics. It is necessary to integrate the kinetics to the optimization of a hydrogen network. Mao et al. (2015) used an empir-

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Chemical Engineering Research and Design 1 5 1 ( 2 0 1 9 ) 168–178

ical correlation to calculate the hydrogen consumption of a vacuum gas oil (VGO) HDT unit based on its degree of sulfur removal, and discussed the effect of sulfur removal on the hydrogen network structure. Considering the constraints on hydrogen sink pressures, Umana et al. (2014 and 2016) adopted the kinetic equation, proposed by Choudhary et al. (2008), to fit the hydrodesulfurization (HDS) kinetics of HDT units and the hydrocracking kinetics of a hydrocracking unit. Then these kinetics were integrated to an HNI model to discuss the superiority in achieving more promising results when these kinetics are considered. Based on this idea, Wu et al. (2017a) proposed a step-by-step HNI model with consideration of HDS kinetics, hydrodenitrogenation (HDN) kinetics and hydrogenation of aromatics (HDA) kinetics. The operating conditions were first optimized to minimize the total hydrogen consumption of hydrogen sinks by integrating these kinetics. Then the HNI model was solved to attain the optimal structure based on the minimized hydrogen consumption of the first step. However, all these researches used lump kinetics to represent the different kinds of impurities in feed oil. The feed oil contains thiophenes, benzothiophenes, phenathrothiophenes and nonaromatic sulfides etc. These sulfides have different removal difficulties due to their different reactivity (Choudhary et al., 2006). Considering the superiority in minimizing hydrogen consumption and optimizing operating conditions when the different removal difficulties of sulfides are utilized (Wu et al., 2017b), the kinetics of different sulfides should be considered in an HNI. Furthermore, hydrogen consumption is mainly controlled by the difference between the feed impurity concentrations which are determined by its upstream unit, and the product ones in an HDT unit. A fluid catalytic cracker (FCC) in a refinery is used to process the refined VGO and produce more light fuels, cracked diesel and cracked gasoline (CD and CG). The impurities in the refined VGO also cracked to small impurities and then entered to CD or CG according their boiling points (Wu et al., 2017b). The FCC affects the impurities in the feed oil of the downstream HDT units. In addition, to protect the FCC catalyst, the allowable impurity contents of the upstream HDT unit are determined by the FCC’s purity requirements. That is to say, an FCC affects the total hydrogen consumptions of its upstream HDT unit and downstream HDT units due to its impurity distributions. Therefore, the sulfur compound removal (SCR) kinetics and the impurity distributions of an FCC should be considered when reducing the hydrogen consumption and optimizing a hydrogen network. In this work, an HNI strategy is proposed to reduce the total hydrogen consumptions of HDT units based on SCR kinetics and impurity distributions of an FCC. Two mathematical models (M1 and M2) are included in the proposed HNI strategy and used to optimize the hydrogen network of a refinery. M1 is a non-linear programing (NLP) integrating the SCR kinetics and an FCC to minimize the hydrogen consumption by optimizing the degrees of impurity removal and operating conditions of HDT units. M2 is a mixed integer nonlinear programming (MINLP) for optimization of the hydrogen network structure according to related results from M1. The effects of SCR kinetics and FCC as well as the effects of sulfur content on HNI are also analyzed by solving the proposed integration strategy.

2.

HNI strategy

in Fig. 1, to protect the FCC catalyst, the FCC can affect the impurity contents of its upstream HDT unit, i.e. the VGO HDT unit, and decide the impurity contents of its downstream HDT units, the CD HDT unit and the CG HDT unit. Furthermore, better results can be obtained in optimizing the operating conditions when SCR kinetics are adopted. That is to say, it is imperative to integrate the SCR kinetics and impurity distributions of an FCC when reducing hydrogen cost of the proposed system. An HNI strategy integrating the SCR kinetics and an FCC is proposed to reduce total annualized cost (TAC) of a hydrogen network. An MINLP is formed as SCR kinetic equations contain nonlinear terms and an HNI model has many binary variables. In Fig. 2, a stepwise solution strategy is presented to reduce the solving difficulty and then obtain the feasible and optimal solutions. The minimized hydrogen consumption of the system can be attained by solving M1 which integrated the SCR kinetics and the FCC. Then the M2 is solved to obtain the optimal hydrogen network structure with minimum TAC based on the optimal parameters from M1.

3.

Mathematical models: M1 and M2

3.1.

M1: operational optimization model

3.1.1.

Objective function

According to Fig. 2, M1 is an operational optimization model for HDT units based on SCR kinetics and an FCC. It is used to minimize the total hydrogen consumption of HDT units (hydrogen sinks) by adjusting the operating conditions and degrees of impurity removal. min



(1)

Fi

i ∈ SK

where SK is the set of hydrogen sinks; Fi denotes the hydrogen consumption of the ith HDT unit (hydrogen sink), in Nm3 h−1 . Hydrogen is mainly used to for desulfurization, denitrogenation and dearomatization in an HDT unit. Furthermore, the impurities like olefins, oxygen and metals also can be removed by consuming hydrogen. In addition, the dissolved hydrogen cannot be ignored. Thus, the total amount of hydrogen consumption can be expressed by Eq. (2) (Wu et al., 2017a). Fi = FiS + FiN + FiA + FiD + FiO

where the superscripts S, N, A, D and O denote the hydrogen consumption for the removal of sulfur, nitrogen and aromatics, dissolution hydrogen and other hydrogen consumption. Each term in Eq. (2) can be calculated as below:



prod

FiS = 23vfeed Sfeed − Si i i For a system including an FCC and three HDT units shown in Fig. 1, the FCC is the bridge among the three HDT units. The objective of this work is to minimize the total hydrogen consumption of the proposed system. Hydrogen consumption in an HDT unit is mainly decided by the degrees of impurity removal, i.e. the difference between the impurity contents in the feed and the product. The feed impurity contents are determined by its upstream unit while the product ones are determined by the operating conditions, hydrogenation reaction kinetics and the environmental regulations on product quality. According to the proposed system

(2)



prod

Nifeed − Ni FiN = 62vfeed i



/10000

(3)



prod

FiA = 480vfeed Afeed − Ai i i FiD = disi vfeed i



/10000

(4)

 (5)

(6)

denotes the feed oil flowrate, in m3 h−1 ; The superwhere vfeed i script prod is product; S, N and A denote the sulfur content,

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Fig. 1 – Diagrammatic sketch of the proposed system. nitrogen content and aromatics content, respectively, in ␮g g−1 and %; ‘dis’ denotes the dissolution coefficient, in Nm3 m-3 . As for other hydrogen consumption, the term FiO , it is mainly the hydrogen consumption for oxygen and metal removal and olefin saturations. Because the oxygen and metal concentrations in feed oil are much less than other impurities and saturations of olefins are fast reactions, FiO can be assumed as a constant although these reactions are occurred under different operating conditions (Wu et al., 2017b). Thus, FiO can be computed by subtracting the amounts of HDS, HDN, HDA and dissolution hydrogen from the amount of total hydrogen consumption which can be attained from the actual operating data.

3.1.2.

Kinetic equations of SCR, HDN and HDA

The kinetic equations of SCR, HDN and HDA are integrated to M1 to reduce the hydrogen consumption by optimizing the operating conditions. The kinetic equations are listed in Eqs. (7)–(12) according to Wu et al.’s study (Wu et al., 2017b). 1. SCR kinetics

prod

Si

=



⎛ feed,SC

Si,g

g

Sfeed = i

 g

feed,SC

Si,g

exp ⎝

−kSi,g





S H2 ˛i,g

Pi

LHSVi KN Nifeed + KS Sfeed i

⎞ ⎠

(7)

(8)

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Chemical Engineering Research and Design 1 5 1 ( 2 0 1 9 ) 168–178

prod

feed NVGO = NFCC

(16)

feed NFCC,CD = NCD

(17)

feed NFCC,CG = NCG

(18)

prod

Fig. 2 – Solution strategy. 2. HDN kinetics

⎛ = Nifeed exp ⎝−kN i

prod

Ni



 ⎞ ⎠

N H2 ˛i

Pi

(9)

LHSVi

3. HDA kinetics

 ⎞⎞ ⎛  A ⎞⎛ ⎛   H ˛A 2 f i + kr H2 ˛i − k P f r i i i ki Pi − ki Mi ⎠ ⎝1 − exp ⎝ ⎠⎠(10) XiA = ⎝  A H2

kfi Pi



˛ i

+ kri



/Afeed Mi = 1 − Afeed i i

LHSVi

(11)

feed, SC

(12)

where A denotes the pre-exponential factor, in h−1 ; E represents the activation energy, in J mol−1 ; R is the gas mole constant, in J mol−1 K−1 ; T denotes the operating temperature, in o C.

3.1.3.

AFCC,CD = Afeed CD

(20)

AFCC,CG = Afeed CG

(21)

where the subscripts “FCC, CD” and “FCC, CG” denote the products, CD and CG, of an FCC, respectively. An FCC cracks hydrocarbons with large molecule weight to small ones under high pressure and temperature on the surface of FCC catalysts in a fuel-type refinery (Zhang et al., 2014). A sulfide in FCC feed oil is also cracked and usually generated a hydrocarbon and a same type sulfide with smaller molecular weight because the cracked bond of the sulfide is the bond linking its side chain and its functional group (Valla et al., 2007). For instance, a small molecular benzothiophene and a hydrocarbon are more likely to generate when benzothiophene is cracked. As for the distributions of nitrogen and aromatics in an FCC, they can be expressed by linear functions (Wu et al., 2017b). Thus, the distributions of sulfides, nitrogen and aromatics of an FCC can be written as below: SFCC,CD =



feed, SC

(22)

feed,SC

(23)

aSC CD,g SFCC,g

g

SFCC,CG =



aSC CG,g SFCC,g

feed FCC,CD NFCC = aN + bN FCC,CD N FCC,CD

(24)

feed FCC,CG NFCC = aN + bN FCC,CG N FCC,CG

(25)

A FCC,CD Afeed + bA FCC,CD FCC = aFCC,CD A

(26)

A FCC,CG Afeed + bA FCC,CG FCC = aFCC,CG A

(27)

where a and b denote calculation coefficients.

3.1.4. Constraints for operating conditions and impurity contents The adjustments of operating conditions and impurity contents should be within the ranges of refinery regulations when reducing hydrogen consumption based on SCR, HDN and HDA kinetics. 1. Operating temperature TiL ≤ T i ≤ TiU

Impurity distributions of an FCC

According to Fig. 1, the impurities from the refined VGO pass an FCC and then distribute to FCC products, CD and CG. The relations for the impurities in these products are shown in Eqs. (13)–(21). prod

(19)

g

where Sg denotes the content of sulfur compounds in gth −1 group, in ␮g g ; k is rate constant, in h−1 ; KN and KS represent the inhibition constants of nitrogen and sulfur concentrations; LHSV denotes the abbreviation of liquid hourly space velocity, in h−1 ; PH2 denotes the hydrogen partial pressure, in MPa; ˛ is pressure dependence term; X denotes the conversion of HDA reaction, in %; superscripts f and r represent the forward reaction and reverse reaction of HDA, respectively; M is the ratio of non-aromatics and aromatics contents in feed oil. k = A exp (−E/R (T + 273.15))

AVGO = Afeed FCC

SVGO = Sfeed FCC

(13)

SFCC,CD = Sfeed CD

(14)

SFCC,CG = Sfeed CG

(15)

(28)

where the superscripts L and U are the lower bound and upper bound, respectively. 2. Operating pressure PLi ≤ Pi ≤ PU i

(29)

2 = Pi yCH PH i i

(30)

denotes where P represents the operating pressure, in MPa; yCH i the hydrogen mole fraction of cycle hydrogen, in %.

Chemical Engineering Research and Design 1 5 1 ( 2 0 1 9 ) 168–178

3. Regulations on the contents of sulfur, nitrogen and aromatics prod

prod,U

≤ Si

Si

prod

≤ Ni

prod

≤ Ai

Ni Ai

(31)

prod,U

(32)

prod,U

(33)

where HS is the set for hydrogen sources; H represents the combustion heat, in kJ mol−1 ; the subscript CH4 is methane.

3.2.2.

Constraints for HNI model

Constraints can be classified to mass and hydrogen balances and inequality constraints for each device in the HNI model. The mass and hydrogen balances are shown as below: 1. The hydrogen sources and the hydrogen sinks



4. Restrictions on sulfur content of feed oil

173

Fi,j = Fi , i ∈ SK

(42)

j ∈ SR feed,U

Sfeed ≤ Si i

(34)

3.2.

M2: HNI model

3.2.1.

Objective function

Af(



Ccomp +

m ∈ Mnew



CPSA +

n ∈ Nnew



(35)

Cpipe ) − CFG

(p,q) ∈ Onew

(44)

s s d d ym = Fm ym Fm

(45)

where superscripts s and d are the suction stream and the discharge stream of a compressor, respectively. 3. PSA prod

Fnfeed = Fn



Fi,j − FjU ≤ 0, j ∈ SR

yLi − yi ≤ 0, i ∈ SK

zm ecomp (acomp + bcomp Wm )



zn ePSA aPSA + bPSA Fnfeed



(38)



(39)

pipe



zp,q ep,q lp,q apipe + bpipe d2p,q



(40)

where l denotes the length of a pipeline, in meter; d is the diameter, in millimeter.

j ∈ HS

Zm ≤ No

comp

(51)







zj Fj,FG HH2 yj + HCH4 1 − yj

Zn ≤ No PSA

(52)

n∈N

(p,q) ∈ Onew



(50)

m∈M

where e, a and b are the coefficients of investment cost.

CFG = pFG

(49)

3. Quantity (No) of compressors and purifiers



n ∈ Nnew

Cpipe =

(48)

2. Concentrations

m ∈ Mnew



(47)

where superscript resi is residual gas of a PSA; r denotes hydrogen recovery percentage. The inequality constraints are shown as below: 1. Demands

where M is the set of compressors.

CPSA =

+ Fnresi yresi n

prod prod yn

(37)

zm Wm

m∈M



(46)

i ∈ SK

where p is prices of utilities, in CNY per unit utility; HU denotes the set of hydrogen utilities; z denotes the binary variable.

Ccomp =

+ Fnresi

r = Fn Fnfeed yfeed n

(36)

j ∈ HU

Celec = pelec

(43)

s d = Fm Fm

prod prod yn

zj Fj



Fi,j yj = Fi yi , i ∈ SK

j ∈ HS

= Fn Fnfeed yfeed n

where C denotes utility costs and capital costs, in Subscripts H2 , elec, comp, PSA, pipe and FG are hydrogen, electricity, compressor, PSA, pipeline and fuel gas, respectively; Af is the abbreviation of annualized factor; Mnew is the set of new installed compressors; Nnew denotes the set of new installed PSA; Onew represents the set of new installed pipelines. Each term in Eq. (35) is expressed below:





2. Compressors

CNY·y−1 .

CH2 = pH2 ,j

Fi,j yj +

j ∈ HU

In the HNI model, TAC is minimized based on the data of hydrogen sources (SR) and hydrogen sinks (SK). The costs of hydrogen and electricity consumptions, investments of new installed compressors, pressure swing absorbers (PSA) and pipelines are mainly considered to calculate TAC (Liu and Zhang, 2004). In addition, the fuel gas revenue is taken out from TAC because it is utilized as another hydrogen source. TAC = CH2 + Celec +





(41)

4.

Case study

4.1.

Original parameters of a hydrogen network

4.1.1.

Original data of HDT units

In a refinery with process capability of 8 × 106 metric tons per year, the processing scheme including a VGO HDT unit, an FCC and CD and CG HDT units is adopt to elucidate the proposed HNI strategy. The feed oil properties and the operating conditions of these units are shown in Table S1 and the related refinery regulations are listed in Table S2 of Supplemental Materials.

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Table 1 – Sulfur compounds of each HDT unit. HDT units Feed Product Feed Product Feed Product

VGO CD CG a

NArSa

T/BT

C1- DBT

BNT

C2+ DBT

PhT

Total

172

1946

3081 676 578 29

1029

6

7674 834 1064

13902 1510 2038 38 171 4

42

249

141 9

129 4

All units are in ␮g g−1 .

Fig. 3 – Original structure of the hydrogen network. Based on Choudhary and Wu et al.’s studies (Choudhary et al., 2006, 2008; Wu et al., 2017b), six groups of sulfides can be classified in distillates of a refinery: (1) Nonaromatic sulfides (NArS), (2) Thiophenes and Benzothiophenes (T/BT), (3) C0 /C1 Dibenzothiophenes (C1- DBT), (4) Benzonapthothiophenes (BNT), (5) C2+ Dibenzothiophenes (C2+ DBT), and (6) Phenathrothiophenes (PhT). The sulfur compound contents of the HDT units are shown in Table 1.

4.1.2.

Original hydrogen network data

The original parameters of the hydrogen network are presented in Table S3 of Supplemental Materials. The hydrogen flowrate of 379.8 mol s−1 from the hydrogen source SR1 is utilized to meet all the hydrogen demands of SK1, SK2 and SK3 in the original structure. Other hydrogen sources like SR2 and SR3 are entered into a fuel system. As shown in Fig. 3, two compressors are existed in the original structure.

4.2.

M1: Optimization of hydrogen sink data

4.2.1. Optimal operating conditions and degrees of impurity removal Based on the stepwise solving strategy shown in Fig. 2, the operating conditions and impurity removal of HDT units are

optimized by solving M1 in GAMS with solver KNITRO according to the related parameters in Table S1, Table S2 and Table 1. There are 46 blocks of equations, 103 single equations, 33 blocks of variables and 117 single variables in M1. The CPU time for solving M1 is less than 1 s. The optimal operating conditions are presented in Table 2 and optimal degrees of impurity removal are shown in Table S10. According to Table 2, the adjusting trends of operating conditions are prefer to increase the pressure and decrease the temperature when the SCR kinetics and an FCC are integrated by comparing with the original data, results based on lumped kinetics and FCC, and optimal results in this work. The main reason for these adjustments maybe the HDA reaction is a reversible reaction and high temperature is favored to the forward reaction. Then the hydrogen consumption would be reduced when decreasing temperature while the regulations of impurity contents would be satisfied by increasing pressure. In conclusion, under the premise of satisfying the refinery regulations on operating conditions and impurity contents, increasing pressure and decreasing temperature are an efficient way to lower the hydrogen consumption of an HDT unit.

4.2.2.

Optimal sulfur compound removal data

The comparisons of sulfides in the feed and the product between the original and optimal data are shown in Figs. 4–6. Two basic rules (Wu et al., 2017b) are introduced first to discuss the superiority of SCR kinetics when these kinetics are considered: (1) Two groups of sulfur compounds can be classified based on their reactivity, i.e. the easy removal sulfur compounds (ERSC) which contain NArS, T/BT, C1- DBT and BNT, and the hard removal sulfur compounds (HRSC) which are PhT and C2+ DBT. (2) A sulfide in FCC feed oil is also cracked and usually generated a hydrocarbon and a same type sulfide with smaller molecular weight because the cracked bond of the sulfide is the bond linking its side chain and its functional group. In Fig. 4, the total sulfur content increases by 32.5%, the ERSC (T/BT) increases by 34.1% while the HRSC (C2+ DBT) only raises by 30.5%, which means more ERSC and less HRSC are entered into FCC. According to the second rule mentioned in the above paragraph, the products of the FCC (CD and CG)

Table 2 – Optimal operation conditions. T/o C

HDT units

VGO CD CG

P/MPa

Original data

Results based on lumped kinetics and FCC Wu et al. (2018)

This work

Original data

Results based on lumped kinetics and FCC

This work

370 313 258

370 313 258

360 302 240

10.8 7.05 2.1

10.4 7.02 2.06

11.0 7.72 2.18

Chemical Engineering Research and Design 1 5 1 ( 2 0 1 9 ) 168–178

4.2.3.

175

Optimization of hydrogen sink data

The optimal hydrogen sink data from M1 integrated the SCR kinetics and FCC are shown in Table 3. The total hydrogen demand of all the hydrogen sinks declines to 318.3 mol s−1 with a 16.2% reduction according to Fig. 3 and Table 3. According to the comparison between results based on lumped kinetics and FCC and results in this work, the hydrogen consumption is further reduced if SCR kinetics are integrated. Therefore, the SCR kinetics and an FCC should be considered to reduce the hydrogen consumption.

4.3.

Fig. 4 – Sulfide contents in the VGO HDT unit.

Fig. 5 – Sulfide contents in the CD HDT unit.

Fig. 6 – Sulfide contents in the CG HDT unit. would have more ERSC and less HRSC, which can be verified by Figs. 5 and 6. As it is shown in Fig. 5, the ERSC (NArS, T/BT and C1- DBT) raises by 33.1% while the HRSC (C2+ DBT and PhT) only raises by 30.9%. The ERSC in Fig. 6 also increases by 33.9%. Based on the above analysis, the VGO HDT unit which has higher operating pressure and operating temperature can remove more HRSC while the CD HDT unit and the CG HDT unit with lower pressures and temperatures can remove more ERSC. That is to say, the operating conditions can be further optimized by integrating the SCR kinetics compared with the results with lump kinetics, then hydrogen consumption can be minimized in a more efficient way. Therefore, the SCR kinetics should be considered to reduce hydrogen consumption and optimize the operating conditions.

M2: Optimization of the hydrogen network

According to the related data in Table S3 and Table 3, M2 is solved in GAMS with solver LINDOGlobal to obtain the optimal operating data and the structure of the hydrogen network. There are 49 blocks of equations, 183 single equations, 40 blocks of variables and 148 single variables in M2. The CPU time for solving M2 is less than 18,000 s. Four scenarios are proposed to make easy understand of this work, they are (1) Original data, (2) HNI without kinetics and FCC, (3) HNI with lumped kinetics and FCC, and (4) This work i.e. HNI with SCR kinetics and FCC. The comparison of results are shown in Table 4. According to Table 4, the annual cost of hydrogen reduces to 1.196 × 108 CNY with a 47.9% reduction when the SCR kinetics and impurity distributions of the FCC are integrated. This is much more than the 32.6% reduction when solving the HNI model with the original data in Table S3, also more than the 44.5% reduction of the results which the lumped kinetics and FCC are integrated. The revenue of fuel gas declines by 3.038 × 107 CNY·y−1 due to the utilization of the off-gases in VGO, CD and CG HDT units. The electricity cost increases to 1.514 × 106 CNY·y−1 because of the utilization of the offgas with lower pressure. In addition, the capital cost of the new installed pipeline is 9.861 × 105 CNY·y−1 and cost for the new installed PSA is 1.480 × 107 CNY·y−1 . Whereas the TAC directly reduces to 1.229 × 108 CNY·y−1 with a 37.1% reduction. It is more than the TAC reduction of 34.4% in the HNI based on lumped kinetics and FCC, and the 19.1% reduction of HNI results without kinetics and FCC. Thus, TAC can be declined by using the HNI method, it can be further declined by considering the SCR kinetics and impurity distributions of an FCC. The optimal hydrogen network structure is shown in Fig. 7 and the related flowrates are listed in Table 5. The hydrogen utility from SR1, the hydrogen source from SR2 and the purified hydrogen from the new installed PSA are all entered into the compressors C1 and C2. The compressed hydrogen with 244.7 mol s−1 and 90% hydrogen concentration from C1 is used to satisfy all the demand of SK1 with 213.6 mol s−1 and 31.1 mol s−1 hydrogen demand of SK2. The hydrogen with 73.6 mol s−1 and 85% hydrogen concentration from C2 is to satisfy the partial hydrogen demand of SK2 with 46.6 mol s−1 and all the demand of SK3 with 27.0 mol s−1 . Thus, the hydrogen concentrations of SK1, SK2 and SK3 are 90%, 87% and 85%, respectively, which are the exactly concentration demands of these hydrogen sinks. The remain hydrogen of 75.3 mol s−1 and 7.3 mol s−1 from SR2 and SR3 respectively are both fed into the PSA to obtain the high concentrate hydrogen with 53.5 mol s−1 and the residue gas with 29.1 mol s−1 which is then entered into the fuel system. In the optimal hydrogen network structure, a PSA is installed to reduce the consumption of hydrogen utility from

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Table 3 – Optimal hydrogen flowrates of hydrogen sinks. SK

Parameters

−1

Results based on lumped kinetics and FCC Wu et al. (2018)

Flowrate/mol s Reduction/% Pressure/MPa Flowrate/mol s−1 Reduction/% Pressure/MPa

This work

1

2

3

219.2 −11.1 12.6 213.6 −13.4 12.2

81.4 −13.8 8.67 77.7 −17.7 9.56

31.7 −18.3 3.46 27.0 −30.4 3.58

Table 4 – Cost composites in different scenarios. H2 a Original data HNI without kinetics and FCC HNI with lumped kinetics and FCC(Wu et al. (2018) This work a

Electricity

Pipeline

PSA

Comp

TAC

8

Fuel gas

2.297 × 10 1.548 × 108

7

4.552 × 10 1.345 × 107

7

1.132 × 10 1.349 × 107

/ 1.085 × 106

/ 1.348 × 107

/ 0

1.955 × 108 1.582 × 108

1.275 × 108

1.469 × 107

1.176 × 107

9.861 × 105

1.444 × 107

0

1.282 × 108

1.196 × 108

1.514 × 107

1.480 × 107

9.649 × 105

1.480 × 107

0

1.228 × 108

All units are in CNY·y−1 .

Fig. 7 – Optimal hydrogen network structure.

SR1 by purifying the hydrogen-poor streams from SR2 and SR3. The investment of the PSA is only 1.480 × 107 CNY·y−1 , way less than the reduction of hydrogen utility cost of 1.101 × 108 CNY·y−1 which is obtained according to the hydrogen utility costs of the original case and the optimal results of this work in Table 4. The demands of the hydrogen flowrate and the hydrogen concentration of the hydrogen sinks are both exactly satisfied to lower the total hydrogen consumption because the hydrogen cost is the largest cost in a hydrogen network. Thus, several pipelines are added in the optimal hydrogen network structure. As the investment of a compressor is usually relatively higher, there is no new compressor added in the hydrogen network structure. The existing compressors, C1 and C2, are used to satisfy all the hydrogen demands of the three hydrogen sinks, SK1, SK2 and SK3. In this case, there is indeed a little waste of the exceed pressure from C1 with discharge pressure of 12.2 MPa to SK2 with pressure of 9.6 MPa and C2 with discharge pressure of 9.6 MPa to SK3 with pres-

Fig. 8 – Effect of sulfur content on results of M1.

sure of 3.6 MPa. But the electricity cost is much less than the investment of a new compressor.

4.4.

Effect of sulfur content on results of M1 and M2

Five scenarios are proposed to investigate the effect of the sulfur content on the hydrogen consumption of M1 and hydrogen network structure of M2, of which the content of each sulfur compound is +10%, +5%, +0%, −5% and −10% compared to the original contents. The optimal results of M1 for the five scenarios are shown in Fig. 8. The results of M2 are listed in Table 6 and Table S11 of Supplementary Material.

Table 5 – Hydrogen flowrate of the optimal hydrogen network structure. Flowrate/mol s−1

SR1

SR2

SR3

SK1

SK2

SK3

P1

C1 C2 P1

191.6 6.2 /

31.2 35.8 75.3

/ / 7.3

213.6 / /

31.1 46.6 /

/ 27.0 /

21.9 31.6 /

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Table 6 – Cost composites in different scenarios. Scenario

H2 a

+10% +5% 0% −5% −10%

1.260 × 10 1.239 × 108 1.196 × 108 1.193 × 108 1.129 × 108

a

Fuel gas 8

1.500 × 10 1.511 × 107 1.514 × 107 1.633 × 107 1.529 × 107 7

Electricity

Pipeline

1.618 × 10 1.600 × 107 1.480 × 107 1.536 × 107 1.424 × 107

1.150 × 10 1.127 × 106 9.649 × 105 9.037 × 105 9.460 × 105

7

1.469 × 10 1.477 × 107 1.480 × 107 1.400 × 107 1.492 × 107 7

Comp

TAC

0 0 0 0 0

1.309 × 108 1.285 × 108 1.228 × 108 1.218 × 108 1.155 × 108

All units are in CNY·y−1 .

According to Fig. 8, the increase of the content of each sulfur compound causes the increase of the total hydrogen consumption of the VGO, CD and CG HDT units, especially for the consumption of VGO HDT unit which increases from 201.7 mol s−1 to 225.1 mol s−1 when the sulfur content varies from −10% to +10% of the content in the original case. While for the CD and CG HDT units, their hydrogen consumptions are slightly changed due to the optimal sulfur contents of refined VGO for all five scenarios are the same with 2000 ␮g g−1 , then the sulfur contents of the FCC products, CD and CG are barely affected. The sulfur content increase also affects the hydrogen consumption and TAC in M2, the hydrogen consumption increases from 1.129 × 108 CNY·y−1 to 1.260 × 108 CNY·y−1 and the TAC raises from 1.155 × 108 CNY·y−1 to 1.309 × 108 CNY·y−1 , respectively. There is no regularity for other costs, like fuel gas revenue, electricity and investments for pipeline and PSA based on Table 6.

5.

PSA 6

Conclusions

An integration strategy consisting of two mathematical models (M1 and M2) is proposed to minimize the hydrogen consumption and TAC of a hydrogen network based on the SCR kinetics and impurity distributions of an FCC unit. A stepby-step method is used to obtain the optimal solutions of the proposed strategy, i.e. M1 is first solved to attain the optimal hydrogen sink data according the SCR kinetics and FCC unit, then the optimal operating conditions and structure of the hydrogen network are obtained by solving M2 based on the optimal hydrogen sink data from M1. A system including the VGO, FCC, CD and CG units in a refinery is used to illustrate the proposed strategy. The results show that the total hydrogen consumption of these HDT units is reduced to 318.3 mol s−1 with a 16.2% reduction by considering the FCC and SCR kinetics in M1. The consumption of hydrogen utility can be further reduced to 197.8 mol s−1 by solving M2, and the annual hydrogen cost decline to 1.101 × 108 CNY with a 47.9% reduction. The TAC also drop to 1.228 × 108 CNY with a 37.1% reduction more than the 34.4% reduction in hydrogen network optimization in which the lumped kinetics are considered. Reducing hydrogen consumption in HDT units is increasingly important as more heavy and sour crude oils are being processed in refineries. The hydrogen network optimization integrated impurity distributions of an FCC unit and SCR kinetics is an effective way to further reduce the hydrogen consumption in refineries.

Acknowledgments The authors gratefully acknowledge funding by the project (No. 21808183) sponsored by National Science Foundation of China (NSFC) and the project from (Group) Co. Ltd. (No. YJSYZX18SKF0003). This work is also supported by Yound Tal-

ent Fund of University Association for Science and Technology in Shaanxi, China (No. 20190602).

Appendix A. Supplementary data Supplementary material related to cle can be found, in the online doi:https://doi.org/10.1016/j.cherd.2019.09.012.

this artiversion, at

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