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Simulation-based optimization and design of refinery hydrogen networks with hydrogen sulfide removal
international journal of hydrogen energy xxx (xxxx) xxx
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Simulation-based optimization and design of refinery hydrogen networks with hydrogen sulfide removal Minbo Yang, Xiao Feng* School of Chemical Engineering and Technology, Xi’an Jiaotong University, Xi’an, Shaanxi 710049, China
highlights Rigorous thermodynamic and process modeling of H2S removal unit in Aspen HYSYS. A simulation-based optimization model for synthesizing hydrogen network with H2S removal. A solution strategy to enhance the computational performance.
article info
abstract
Article history:
Hydrogen consumption in oil refineries increases sharply because of more and more heavy
Received 11 March 2019
and sour crude oil processing, which also makes hydrogen sulfide a considerable
Received in revised form
contaminant in off-gases of hydrotreaters. This work presents a simulation-based opti-
10 July 2019
mization model for synthesis of hydrogen networks with H2S removal. Aspen HYSYS is
Accepted 14 July 2019
employed for rigorous process and thermodynamic modeling of the H2S removal unit. The
Available online xxx
proposed model is solved using the genetic algorithm combined with the linprog and fmincon solvers in the Matlab platform. The optimal hydrogen sources and the recirculated
Keywords:
absorbent fed into the H2S removal unit as well as the optimal design of the hydrogen
Hydrogen network
network can be determined simultaneously. A case study is performed to illustrate the
Hydrogen sulfide removal
application and effectiveness of the proposed model. The result shows that the introduc-
Simulation-based optimization
tion of H2S removal can decrease the fresh hydrogen consumption by 43% and the total
Genetic algorithm
annualized cost by 17%.
Total annualized cost
© 2019 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.
Introduction Hydrogen is an essential utility and contributes to considerable proportion of the operating cost for oil refining. Due to the increase in heavy and sour crude oil processing, lower sulfur fuel specifications, and markets shifting towards lighter fuels, additional hydrotreating capacities are required in refineries [1,2]. Consequently, the hydrogen demand is significantly
increasing, which leads to the unbalance between hydrogen demand and supply as well as the increase in the operating cost for oil refining. Therefore, hydrogen management has been a critical issue, and how to use hydrogen efficiently is vital to oil refineries. The technique of hydrogen network integration is an effective tool for refinery hydrogen management and of great research interest. The main reason is that hydrogen network
* Corresponding author. E-mail address: [email protected] (X. Feng). https://doi.org/10.1016/j.ijhydene.2019.07.108 0360-3199/© 2019 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved. Please cite this article as: Yang M, Feng X, Simulation-based optimization and design of refinery hydrogen networks with hydrogen sulfide removal, International Journal of Hydrogen Energy, https://doi.org/10.1016/j.ijhydene.2019.07.108
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integration aims to maximize reuse of the existing hydrogen within the process to reduce the fresh hydrogen consumption. In this light, costs and environmental impacts associated with hydrogen production can be cut down. For these advantages, plenty of studies have been conducted. Both pinch-based methods and superstructure-based mathematical programming ones have been developed for hydrogen network integration [3]. Based on the pinch technology for heat exchanger network integration, Alves and Towler [4] employed the pinch conception for hydrogen network integration and proposed a graphical method to identify the minimum fresh hydrogen consumption as well as the pinch location. Afterwards, several graphical techniques were developed based on different coordinates, including impurity load versus flowrate [5], hydrogen load versus flowrate [6], impurity concentration versus flowrate [7,8], and impurity load versus hydrogen load [9]. Researchers also proposed different algebraic methods for targeting the minimum fresh hydrogen consumption [10e12]. Although some of these contributions have incorporated hydrogen purification units into hydrogen networks for more fresh hydrogen savings [7,11], all operating parameters of hydrogen purifiers are specified without optimization. Based on these pioneer studies, the pinch technology has been extended for optimal placement of hydrogen purifiers. Liu et al. [13] analyzed the maximum feed of hydrogen purifiers, and they proposed a method for targeting the optimal feed [14]. Zhang et al. [15] developed a graphical technique for integration of hydrogen network with hydrogen purifiers. Later, this technique was extended with consideration of purifiers’ performance [16,17] and to identify the optimal purification process [18]. The pinch-based methods are able to show insights for hydrogen network integration, even with hydrogen purifiers. However, the existing pinch-based methods often simplified hydrogen purifiers with specified hydrogen recovery ratio, product purity, or tail gas purity for easy solving. Mathematical programming approaches for hydrogen network integration usually consist of two steps: superstructure generation and mathematical model formulation. The first systematic mathematical-based approach for efficient use of hydrogen was proposed by Hallale and Liu [19]. They developed a superstructure that includes the placement of compressors and purifiers and formulated a mixed integer nonlinear programming (MINLP) model for the hydrogen distribution problem. Later, Liu and Zhang [20] developed a systematic methodology to select appropriate purifiers from pressure swing adsorption (PSA), membranes, or hybrid systems for hydrogen recovery. Liao et al. [21] presented a statespace superstructure incorporating more network structure possibilities for the placement of compressors and purifiers. Deng et al. [22] constructed a superstructure including all the feasible interconnections among hydrogen sources, hydrogen sinks, fuel system, compressors, and purifiers and compared different hydrogen network synthesis scenarios. Khajehpour et al. [23] used a superstructure reduced on the basis of engineering judgment to obtain the feasible results faster. To ensure the feasibility of solutions, Jia and Zhang [24] developed a modeling and optimization framework for hydrogen management taking into account multi-components. Wu
et al. [25] proposed a sequential optimization strategy to minimize the total exergy consumption and number of compressors of hydrogen networks. Later, synthesis of hydrogen networks with minimum compression costs was addressed [26]. Recently, mathematical programming approaches are developed for more complex hydrogen management problems. Ahmad et al. [27] proposed a methodology for design of multi-period hydrogen networks with varying operating conditions of hydrogen consuming processes. The operational flexibility and capacity redundancy of compressors were also taken into account for the design of multi-period hydrogen networks [28]. Lou et al. [29] introduced a robust optimization framework to optimize hydrogen network under uncertainties. Wei et al. [30] optimized the disturbance resistance ability of the hydrogen network under minimum fresh hydrogen consumption. Deng et al. [31] embedded hydrogen headers in the superstructure and proposed a methodology to synthesize hydrogen network with intermediate hydrogen header. In their later contribution, a superstructure for inter-plant hydrogen network integration with purifiers and corresponding mathematical programming models were proposed [32]. Lou et al. [33] proposed a two-step method combining the pinch insight with mathematical programming to optimize the inter-plant hydrogen network. The optimal design of inter-plant hydrogen network with intermediate headers of purity and pressure was addressed by Kang et al. [34]. In refineries, sulfur in crude oil is removed by hydrotreating in the form of H2S, which is a noteworthy impurity because it can corrode the equipment and damage catalysts. To reduce H2S contents in hydrogen streams, desulfurization units have been employed in some refineries. However, there are very limited publications focusing on optimal synthesis of hydrogen networks with H2S removal. Li et al. [35] developed a mathematical modeling and optimization approach to integrate H2S removal units into hydrogen networks. Wu et al. [36] constructed a superstructure of hydrogen networks with unified purification models for PSA, membrane, and desulfurization processes. Jia and Liu [37] developed a mathematical programming model for optimization of hydrogen network with multiple impurities and impurity removal. The H2S removal process depends on complex thermodynamic and fluid properties, which pose great challenges for process modeling and solving, especially for various hydrogen sources. Therefore, in the aforementioned studies, simplified H2S removal models were adopted to get the global optimum using deterministic global optimization algorithms. However, if such simplifications are too unrealistic, the obtained optimum may be suboptimal or even infeasible in the real world [38]. Therefore, more rigorous models for the H2S removal unit are required. In this work, a novel simulation-based optimization model is developed for synthesizing hydrogen network with H2S removal. The high-fidelity process simulation model for H2S removal is developed in Aspen HYSYS. To efficiently solve the simulation-based optimization model, a solution strategy that integrates genetic algorithm with the linprog and fmincon solvers is proposed based on the Matlab platform. The proposed model is solved to minimize the total annualized cost of the hydrogen network integrated with H2S removal,
Please cite this article as: Yang M, Feng X, Simulation-based optimization and design of refinery hydrogen networks with hydrogen sulfide removal, International Journal of Hydrogen Energy, https://doi.org/10.1016/j.ijhydene.2019.07.108
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determining the optimal hydrogen sources and recirculated absorbent fed into H2S absorbers and the optimal design of the hydrogen network. The contributions of this work can be summarized as below: A novel simulation-based optimization model for synthesizing hydrogen network with H2S removal; A solution strategy that integrates genetic algorithm with the linprog and fmincon solvers to enhance the computational performance. The rest of this paper is organized as follows. The investigated problem is stated in Section Problem statement. Section Model formulation introduces the simulation-based optimization model for hydrogen network integration with H2S removal. The solution strategy is presented in Section Solution strategy, and a case study is performed in Section Case study. Section Discussion discusses the proposed method, and the last section gives conclusion of this work.
Problem statement Fig. 1 shows a simplified diagram of a hydrotreating process [19]. The hydrogen stream mixed with a liquid feed is introduced into a hydrotreater, which can be considered as a hydrogen sink. In the hydrotreater, a part of the introduced hydrogen is consumed, and the rest leaves the reactor within the reaction effluent. Subsequently, hydrogen is separated from the reaction effluent in a separator. The resulting hydrogen stream, namely, hydrogen source, may be directly recycled to the hydro-treating reactor along with the makeup hydrogen or sent to the fuel system. The key aspects of the problem statement are shown in Fig. 2. There are a set of hydrogen sources, SOURCE ¼ fiji ¼ 1; 2…Ig as well as a set of hydrogen sinks, SINK ¼ fjjj ¼ 1; 2…Jg in an oil refinery. Fresh hydrogen as the supplementary source is purchased from hydrogen plants or specially produced. Each hydrogen source has a known flowrate Fi, a pressure Pi, and a hydrogen concentration yH2 i . Hydrogen sources also contain a set of impurities, IM ¼ fkjk ¼ 1; 2…Kg, with corresponding concentrations yki . Each hydrogen sink requires a feed with a flowrate Fj, a pressure Pj, and a minimum allowable hydrogen concentration yH2 j . Besides, the concentration of each impurity in the feed should not be higher than its maximum allowable value ykj . The problem of hydrogen network integration with H2S removal can be stated as below. A hydrogen sink can accept
Fig. 2 e Superstructure of a hydrogen distribution network. SR: hydrogen source; SK: hydrogen sink.
one hydrogen source or a mixture of several hydrogen sources if its requirements on flowrate, hydrogen concentration, and impurity concentrations can be fulfilled. A number of hydrogen compressors are placed to lift the pressure of hydrogen sources if necessary. For the desulfurization unit, methyldiethanolamine (MDEA) is chosen as the chemical absorbent, because it is widely for H2S removal [39]. Any hydrogen sources that contain H2S can be treated in the H2S absorbers, REMU ¼ frujru ¼ 1; 2…RUg. The resulting products can be allocated to any hydrogen sink to fulfill the requirements. The main objectives of this work include: (1) determine the optimal hydrogen sources and the optimal recirculated MDEA solution fed into H2S absorbers; (2) synthesize the hydrogen network with H2S removal to minimize the total annualized cost. For the simulation-based optimization problem, the given parameters include: Hydrogen sources: the number of hydrogen sources (I), the number of impurities (K), flowrate (Fi), pressure (Pi), hydrogen concentration (yH2 i ), and impurity concentrations (yki ). Hydrogen sinks: the number of hydrogen sink (J), the number of impurities (K), required flowrate (Fj), required pressure (Pj), minimum hydrogen concentration (yH2 j ), and maximum impurity concentrations (ykj ). H2S removal unit: the number of H2S absorbers (RU), and operating temperatures and pressures of all devices. Connections between hydrogen sources and H2S absorbers. All economic parameters. The decision variables include:
Fig. 1 e Simplified diagram of a hydro-treating process.
The flowrate of each hydrogen source fed into each H2S absorber (Fi,ru). The flowrate of MDEA solution fed into each H2S absorber (Famine,ru). The flowrate of each hydrogen source and treated product allocated to each hydrogen sink (Fi,j and Fru,j).
Please cite this article as: Yang M, Feng X, Simulation-based optimization and design of refinery hydrogen networks with hydrogen sulfide removal, International Journal of Hydrogen Energy, https://doi.org/10.1016/j.ijhydene.2019.07.108
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H2S removal unit
Model formulation Based on the superstructure in Fig. 2, the simulation-based optimization model proposed in this work consists of five sections: (1) availability of hydrogen sources, (2) requirements of hydrogen sinks, (3) H2S removal unit, (4) compression, and (5) objective function.
Availability of hydrogen sources As aforementioned, each hydrogen source has a known flowrate. Thus, the total flowrate of a hydrogen source i allocated to each hydrogen sink and the H2S removal unit should not exceed the given flowrate, as shown in Equation (1). J X
Fi;j þ
RU X
Fi;ru Fi
(1)
ru¼1
j¼1
where Fi,j is the flowrate of hydrogen source i allocated to hydrogen sink j; Fi,ru is the flow rate of hydrogen source i fed into the H2S absorber ru.
Requirements of hydrogen sinks In order to maintain the operation of all hydrogen consumers, their requirements on flow rate, hydrogen purity, and impurity concentrations should be fulfilled. These requirements correspond to Equations (2)e(4). I X i¼1
I X
Fi;j þ
RU X
Fi;j $yH2 i þ
i¼1
(2)
RU X
H2 Fru;j $yH2 ru Fj $yj
(3)
ru¼1
i¼1
I X
Fru;j ¼ Fj
ru¼1
Fi;j $yki þ
RU X
Fru;j $ykru Fj $ykj
(4)
ru¼1
where Fru,j is the flow rate of the purified product from the H2S absorber ru allocated to hydrogen source j; yH2 ru is the corresponding hydrogen purity of the purified product; ykru is the corresponding concentration of impurity k of the purified product.
The process flowsheet for H2S removal is illustrated in Fig. 3 [39]. The hydrogen source that contains H2S enters the bottom of the H2S absorber and contacts the lean MDEA solution from the top. The treated gas leaves from the top of the absorber is the purified product. The rich amine leaves the absorber bottom and passes through a valve to reduce the compressor to about 500 kPa. The dissolved gas is removed in a flash tank (V-101) and sent to the fuel system. After being heated to 80 C, the rich MDEA solution enters a solvent regenerator, where H2S in the rich MDEA solution is stripped off. The reboiler frequently uses low-pressure steam as the heating medium to prevent the amine from degradation. The lean MDEA solution exits the bottom of the regenerator and is pumped to the H2S absorber at the operating pressure along with the makeup water and MDEA. The gas that consists mostly of H2S and water exits the top of the regenerator and can be processed for sulfur recovery. This part is considered beyond the scope of the current study. In this work, Aspen HYSYS is used for H2S removal process and thermodynamic modeling. Consequently, the mass and energy balances associated with H2S removal can be determined. Besides, the following assumptions are considered for H2S removal. The operating pressure of an H2S absorber is the same to the pressure of the entering hydrogen source; Only hydrogen sources that have the same pressure are allowed to be treated in one H2S absorber. The resulting rich MDEA solution streams from all H2S absorbers are gathered and regenerated in a regenerator.
Compression The placement of compressors is determined according to the process requirement. When a hydrogen source has a higher pressure than a hydrogen sink or the flowrate between a hydrogen source and a hydrogen sink is zero, compression is not required, as described by Equations (5) and (6). Otherwise, compression is required to lift the pressure of the hydrogen source to meet that of the hydrogen sink. The power requirement for gas compression can be
Fig. 3 e Process flowsheet for H2S removal via MDEA treating. Please cite this article as: Yang M, Feng X, Simulation-based optimization and design of refinery hydrogen networks with hydrogen sulfide removal, International Journal of Hydrogen Energy, https://doi.org/10.1016/j.ijhydene.2019.07.108
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calculated by Equations (7)e(9) [40]. In this study, it is considered that electrical power is supplied for compression. Equations (7) and (8) convert the theoretical compression power to electrical power requirement by considering an efficiency h, which takes as 75% [41]. zcom i;j
¼
zcom ru;j ¼
8 <
0 if Pi Pj : 1 if Pi < Pj
(5)
where powerms,j is the power consumption; zms,i,j and zms,ru,j are binary variables that indicate whether the pressures of hydrogen source i and purified product from H2S absorber ru equals the pressure of the mixture hydrogen source Pms, respectively.
Objective function The objective is to minimize the total annualized cost of the hydrogen network, which is expressed as Equation (13).
8 <
0 if Pru Pj : 1 if Pru < Pj
(6)
tac ¼ c hydrogen þ c power þ c steam þ c water þ c mdea þ af $ðc com þ c hruÞ
2 3 g1 g$N 6 Pj 7 1 g com $Ni;j $R$Ti $6 17 poweri;j ¼ $ 4 Pi 5$Fi;j $zi;j h g1 2 3 g1 6 Pj g$N 7 1 g com $Nru;j $R$Tru $6 powerru;j ¼ $ 17 4 Pru 5$Fru;j $zru;j h g1 8 > > < 1 if cr 3 N ¼ 2 if 3 < cr 6 > > : 3 if 6 < cr 9
t
(7)
(8)
(9)
com where zcom i;j and zru;j are binary variables that indicate whether compression is required; poweri,j represents the compression power for lifting hydrogen source i to hydrogen sink j with a flowrate of Fi,j; powerru,j represents the compression power for lifting the purified product from the H2S absorber ru to hydrogen sink j with a flowrate of Fru,j; Pi, Pj, and Pr are the pressures of hydrogen source i, hydrogen sink j, and purified product from H2S absorber ru, respectively; g is the adiabatic index and taken as 1.40 for hydrogen streams [25]; N represents the number of compression stages and can be determined by the compression ratio cr [20]; R is the molar gas constant and equals 8.314 J mol1 K1; Ti and Tru are the inlet temperatures of the hydrogen source i and the purified product from the H2S absorber ru. To reduce the capital cost associated with compressors, we assume that hydrogen sources that have the same pressure and are allocated to a same hydrogen sink can be mixed before compression. According to the pressures of hydrogen sources and purified products, we can get a set of pressure levels of mixed hydrogen sources, PLMS ¼ fPms jms ¼ 1; 2…MSg. The power consumption for lifting the mixture of hydrogen sources to hydrogen sink j can be calculated as below:
powerms;j ¼
I X
poweri;j $zms;i;j þ
i¼1
RU X
powerru;j $zms;ru;j
(10)
ru¼1
8 <
zms;i;j
0 if Pi sPms ¼ : 1 if Pi ¼ Pms
zms;ru;j
af ¼
ir$ð1 þ irÞ
ð1 þ irÞ 1
where tac represents the total annualized cost; c_hydrogen, c_power, c_steam, c_water, and c_mdea are the cost related to consumptions of fresh hydrogen, power, steam, cooling water, and MDEA, respectively; af is the annualized factor that is given as Equation (14); c_com and c_hru are the capital costs of compressors and H2S removal units; ir is the interest rate; t is the number of years. The costs of fresh hydrogen, power, steam, and cooling water are calculated based on their annual consumptions and unit prices, as given by Equations (15)e(19). c hydrogen ¼ Fhydrogen $uchydrogen $aot 0 c power ¼ @
J X I X j¼1
poweri;j þ
i¼1
1
J ru X X
(15)
powerru;j
j¼1 ru¼1
þ powerremoval A$ucpower $aot
(16)
c steam ¼ steamremoval $ucsteam $aot
(17)
c water ¼ waterremoval $ucwater $aot
(18)
c mdea ¼ mdearemoval $ucmdea $aot
(19)
where Fhydrogen represents the fresh hydrogen consumption; powerremoval, steamremoval, waterremoval, and mdearemoval represent the consumptions of power, steam, cooling water, and MDEA in the H2S removal unit, respectively; uchydrogen, ucpower, ucsteam, ucwater, and ucmdea represent the unit prices of fresh hydrogen, power, steam, cooling water, and MDEA, respectively; aot is the annual operating time. The capital cost of a compressor can be estimated based on its power consumption by Equation (20) [41]. The total capital cost of all the compressors is given as Equation (21).
(11)
(12)
(14)
t
comms;j ¼ 5:375
8 <
0 if Pru sPms ¼ : 1 if Pru ¼ Pms
(13)
h power i CEPCI ms;j $exp 7:58 þ 0:80 ln CEPCI2006 746 (20)
c com ¼
J MS X X
comms;j
(21)
j¼1 ms¼1
Please cite this article as: Yang M, Feng X, Simulation-based optimization and design of refinery hydrogen networks with hydrogen sulfide removal, International Journal of Hydrogen Energy, https://doi.org/10.1016/j.ijhydene.2019.07.108
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where com represents the capital cost of a compressor; CEPCI and CEPCI2006 are the chemical economic plant cost indices in the studied year and 2006, respectively. The total capital cost associated with H2S removal is estimated by summing the cost of each equipment unit, as expressed by Equation (22) [41]. c hru ¼
sf EQ X Seq CEPCI $Ceq;base $ CEPCIbase Seq;base eq¼1
(22)
where CEPCIbase is the cost index in the base case; Ceq,base is the capital cost of equipment eq in the base case; Seq and Seq,base are capacities of equipment eq in the current case and base case, respectively; sf is the scaling factor.
Solution strategy In this work, the simulation-based optimization model is formulated and solved using Aspen HYSYS and Matlab. From the viewpoint of the formulated model, it contains a black box simulation model for H2S removal. Therefore, a metaheuristic method, Genetic Algorithms (GA), is employed to solve the model [42]. However, GA approaches encounter significant difficulties when solving problems containing complex constraints [43], which is a predominant feature of the hydrogen management problem. The use of penalty approaches as an attempted remedy causes a risk of spending most of the computational effort in handling invalid solutions [43]. To enhance the computational efficiency, a solution strategy is proposed as illustrated in Fig. 4. The calculation
starts in Matlab, where a set of process data for H2S removal are generated through GA. These data include the flow rate of a hydrogen source fed into an H2S absorber Fi,ru, and the corresponding recirculated MDEA solution Famine,ru. After receiving these process data, Aspen HYSYS solves the simulation model of H2S removal with rigorous thermodynamic calculations. The mass and energy balances associated with H2S removal can be determined. We can obtain data, including flow rate, hydrogen purity, and impurity concentrations, associated with each purified product. Meanwhile, the operating cost and capital cost related to H2S removal can be evaluated. All these data are then returned to Matlab, and a new hydrogen distribution problem is generated. The hydrogen sources in the new hydrogen network contains the purifed products and the rest of hydrogen sources in the original hydrogen network. The new hydrogen distribution problem is formulated as a nonlinear programming model as given in (P1), which can be solved by the fmincon solver in Matlab. In terms of the fmincon solver, it requires a feasible solution as the initial value [44]. Therefore, we formulate a linear programming model as (P2), which is solved by the linprog solver in Matlab and could generate a feasible initial value (i.e., a feasbile hydrogen network) for the fmincon solver in each iteration. In this way, we can obtain costs of fresh hydrogen, power, and compressors associated with hydrogen distribution. Next, the global objective function is evaluated based on the cost data related to H2S removal and hydrogen distribution. The solving process is iterated until the stopping criteria of GA are reached. Finally, Matlab provides the minimum total annualized cost, optimal values of hydrogen
Fig. 4 e Solution strategy for the simulation-based optimization model. Please cite this article as: Yang M, Feng X, Simulation-based optimization and design of refinery hydrogen networks with hydrogen sulfide removal, International Journal of Hydrogen Energy, https://doi.org/10.1016/j.ijhydene.2019.07.108
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sources and recirculated MDEA solutions fed into H2S absorbers, and the optimal design of the hydrogen network. P1: min. objp1 ¼ c hydrogen þ c power þ af $c com s.t. supply and demand constraints (23)e(26) energy balance constraints (5), (7), (9), (11) and (27), economic evaluation constraints (15), (20), (21) and (28), P2: min. objp2 ¼ c hydrogen s.t. supply and demand constraints (23)e(26) J X
Fi;j Fi
(23)
Fi;j ¼ Fj
(24)
Fi;j $yH2 Fj $yH2 i j
(25)
Fi;j $yki Fj $ykj
(26)
j¼1
I X i¼1
I X i¼1
I X i¼1
powerms;j ¼
I X
poweri;j $zms;i;j
c power ¼ @
J X I X j¼1
risk of spending computational effort in handling invalid solutions.
Case study In this section, a hydrogen network is investigated to illustrate the applicability of the proposed simulation-based optimization model. This hydrogen network is taken from reference [35]. There are five hydrogen consumers in the hydrogen network, namely, polyethylene unit (PE), gasoline hydrotreater (GHT), gasoline and diesel hydrotreater (G/DHT), polypropylene unit (PP), and diesel hydrotreater (DHT). The main hydrogen streams of this hydrogen network are presented in Fig. 5. The data of hydrogen sources and sinks can be determined from the makeup, purge, and recycle data according to Equations (29)e(32) [19]. Table A1 in the Appendix shows the data of hydrogen sources and sinks in this hydrogen network. Besides, the detailed parameters for evaluating the capital and operating costs are given in Tables A2 and A3 in the Appendix. Fsr ¼ Fpurge þ Frecycle
(29)
yksr ¼ ykpurge þ ykrecycle
(30)
Fsk ¼ Fmakeup þ Frecycle
(31)
(27)
i¼1
0
7
1 poweri;j A$ucpower $aot
(28)
i¼1
Based on the proposed solution strategy, it can be seen that the fmincon solver handles all linear and nonlinear constraints associated with hydrogen distribution. For an individual in each iteration, the fmincon solver generates a correspondingly optimal hydrogen network. In this way, the computational efficiency can be enhanced from two aspects. Firstly, the number of decision variables for the GA is reduced. As the fmincon solver determines decision variables of Fi,j and Fru,j, the GA with process simulation only handles decision variables of Fi,ru and Famine,ru. Secondly, the constrained optimization problem is converted to an unconstrained optimization problem in terms of the GA, which does not require remedial approaches (e.g., penalty algorithm) and avoids the
yksk ¼
Fmakeup $ykmakeup þ Frecycle $ykrecycle Fmakeup þ Frecycle
(32)
where subscripts sr, sk, purge, recycle, and makeup represent hydrogen source, hydrogen sink, purge stream, recycle stream, and makeup stream, respectively. The proposed simulation-based optimization model is applied to this hydrogen network, which is realized by combining Matlab R2018a and Aspen HYSYS V10. All computational studies were performed on an HP ProDesk 600 G3 MT desktop with four inter(R) Core(TM) i7-7700 CPUs @3.60 GHz and 16 GB RAM. The operating system is Windows 10 64-bit. As can be seen from Table A1, there are four hydrogen sources containing H2S, namely, high-pressure (HP) off-gases
Fig. 5 e Existing design of the hydrogen network [35]. Please cite this article as: Yang M, Feng X, Simulation-based optimization and design of refinery hydrogen networks with hydrogen sulfide removal, International Journal of Hydrogen Energy, https://doi.org/10.1016/j.ijhydene.2019.07.108
b
a
MDEA solution (kg/h) Purified product (mol/s) Product purity (mol%) H2S removal ratio
50 for 5 or fewer decision variables, otherwise 200 [45]. 100 decision variables for the GA [45].
4618 687.88 95.18 79.41%
1838 344.24 84.20 68.16%
4618 687.88 95.18 79.41%
1838 344.24 84.20 68.16%
0.000278 0 0.007 0.000276 64.44 71.27% 345.08 689.74 345.08
729.24 8.11 6614 735.09 95.09 87.63% Feed to absorbers (mol/s)
689.74
FDHT-HP,2, Famine,2 FG/DHT-HP,1, Famine,1, FG/DHT-HP,1, FDHT-HP,1, Famine,1 Decision variables for GA
50 400 50 14.4
absorber 1 50 300 50 24.5 Population sizea MaxGenerationsb MaxStallGenerations [45] Computational time (hr)
absorber 1
FG/DHT-HP,1, Famine,1,
FDHT-HP,2, Famine,2,
absorber 3 absorber 2 absorber 2
absorber 1
200 700 50 61.9
case 3 case 2 case 1 Items
and low-pressure (LP) off-gases of the G/DHT and DHT units. The two HP off-gases have notably larger flowrates, higher hydrogen purities, and higher pressures than the two LP offgases that are sent to the fuel system in the existing design. Since H2S absorbers should be preinstalled in the simulation model, three different cases are studied in this work. The cases 1 and 2 take the two HP off-gases as the avaliable feedstocks for H2S removal. As shown in Table A1, the two HP off-gases have the same pressure but notably different hydrogen purities, so they can be treated in an H2S aborber or two respective H2S absorbers. Both the two cases are investigated for thorough consideration. The two HP off-gases are cotreated in one H2S absorber in the case 1, while they are treated separately in the case 2. The case 3 considers all the four hydrogen sources containing H2S as avaliable feedstocks for H2S removal. Note that for each H2S absorber, neither the input hydrogen source nor the absorbent can be zero, otherwise Aspen HYSYS will report an error and stop. Thus, for each decision variable associated with an H2S absorber, a very small value is set as the lower bound. The case 1 considers the only two HP off-gases as the available feedstocks for the H2S removal unit, and they are cotreated in an H2S absorber. Table 1 shows the GA options and results associated with the H2S absorber. The optimal design of the hydrogen network is presented in Fig. 6. It can be seen that the fresh hydrogen consumption is reduced from 37.20 mol/s to 23.96 mol/s by 35.59%, while the waste gas decreases from 31.00 mol/s to 15.49 mol/s. Besides, the HP offgas from the G/DHT unit is all reused, but 3.09 mol/s of HP off-gas from the DHT is still discharged to the fuel system. In the case 2, the two HP off-gases are treated in two H2S absorbers. The GA options and results associated with the two H2S absorbers are listed in Table 1. The optimal design of the hydrogen network is given in Fig. 7. By introducing H2S removal, the fresh hydrogen consumption is reduced from 37.20 mol/s to 21.30 mol/s by 42.74%. The waste gas decreases from 31.00 mol/s to 12.40 mol/s contributed by only the two LP off-gases. The two HP off-gases are all reused with zero discharge. The case 3 is to study whether treating the two LP off-gases can further enhance the hydrogen recovery. As aforementioned, the two HP off-gases are all reused in the case 2 but not in the case 1. Therefore, the two HP off-gases are treated separately in case 3, and the optimal solution of the case 2 is taken as an initial population for the GA. In terms of the two LP off-gases, they have the same pressure, close hydrogen purities, and relatively small flowrates, so they are considered to be co-treated in an H2S absorber. Therefore, three H2S absorbers are preinstalled in the Aspen HYSYS platform. Table 1 shows the GA options and obtained results associated with the three absorbers. It can be seen that the total flow rate of the two LP off-gases fed into the absorber 3 is very small and can be ignored. Regarding to the other two H2S absorbers, they are the same to those in the case 2. In other words, the optimal design of the hydrogen network is the same to that in the case 2. It is noteworthy that the case 3 takes much more time than the cases 1 and 2 for solving the model, because of more decision variables and larger population size for the GA. The fresh hydrogen consumptions and waste gas discharge of the three cases are summarized and compared in Fig. 8. It
FG/DHT-LP,3, FDHT-LP,3, Famine,3
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Table 1 e GA options and results of the three cases.
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Please cite this article as: Yang M, Feng X, Simulation-based optimization and design of refinery hydrogen networks with hydrogen sulfide removal, International Journal of Hydrogen Energy, https://doi.org/10.1016/j.ijhydene.2019.07.108
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Fig. 6 e Optimal design of the hydrogen network for the case 1.
Fig. 7 e Optimal design of the hydrogen network for the case 2.
indicates that introducing H2S removal can notably improve the hydrogen network performance. The case 2 results in more fresh hydrogen saving than the case 1, showing better performance. The main reason is that the HP off-gas from the G/DHT unit has a notably higher hydrogen concentration than that from the DHT unit. Treating the two hydrogen sources in an H2S absorber will degrade the purified product as the ratio of FG/DHT-HP,1 to FDHT-HP,1 decreases, which causes negative impacts on the reuse of hydrogen. As can be seen from Table 1, the H2S removal unit in the case 1 takes less hydrogen sources as the feed and generates a product with a hydrogen purity between the two products in the case 2. The case 3
indicates that treating the two LP off-gases cannot improve the hydrogen network performance. This is because the two LP off-gases have low hydrogen purities, limiting the hydrogen reuse. Fig. 9 compares the total annualized costs of the existing design and the three cases. Note that the capital cost of compressors in the existing design is annualized for a fair comparison. Compared with the existing design, the introduction of H2S removal in the three cases decreases the fresh hydrogen cost by 35.60%, 42.74% and 42.74%, but increases the annualized capital cost by 67.39%, 70.33%, and 70.33%, respectively. Nevertheless, the three cases still result in lower
Please cite this article as: Yang M, Feng X, Simulation-based optimization and design of refinery hydrogen networks with hydrogen sulfide removal, International Journal of Hydrogen Energy, https://doi.org/10.1016/j.ijhydene.2019.07.108
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with any hydrogen sources, the simulation model will not be converged and no value will be generated and returned to the Matlab platform. Thirdly, a simulation model is less efficient than a mathematical model in terms of convergence. Thus, the simulation-based optimization model is more timeconsuming. Lastly, the use of GA cannot always guarantee the best possible solution in contrast to deterministic optimization methods [38].
Conclusion Fig. 8 e Fresh hydrogen consumptions and waste gas discharge.
Fig. 9 e Breakdowns of the total annualized costs.
total annualized cost than the existing design by 12.53%, 16.75%, and 16.75%, respectively. This is because the fresh hydrogen cost dominates the total annualized cost of the existing design. Besides, it can be seen that the case 2 results in slightly higher annualized capital costs and heating & cooling costs than the case 1, because the case 2 requires one more H2S absorber and treats more hydrogen sources. However, the case 2 results in lower total annualized cost than the case 1, meaning that treating the two HP off-gases in respective H2S absorbers is more cost-effective from the viewpoint of the whole hydrogen network.
In this work, a simulation-based optimization model was presented for synthesis of hydrogen networks with H2S removal. The proposed model employed rigorous simulation to formulate the H2S removal process in Aspen HYSYS. A solution strategy that integrates the genetic algorithm, linprog, and fmincon solvers was proposed for solving the model. The proposed model was illustrated using a refinery hydrogen network. The results showed that introducing H2S removal could improve the hydrogen utilization and economic performance of the hydrogen network. We also found that for hydrogen sources with considerably different hydrogen purities, it is better to treat them in respective H2S absorbers rather than co-treating them in one absorber. This could avoid the degradation of the purified products and promote the hydrogen reuse. Although the thermodynamic and fluid properties-based simulation model is more rigorous than simplified mathematical models, the proposed method suffers from several limitations. In the future work, the proposed simulationbased method can be extended to incorporate more issues into the superstructure and model, such as possible configurations of the H2S removal unit, more efficient modeling and solution strategies, influences of liquid feed on hydrogen sinks and sources, and uncertainties of hydrogen sources and sinks, cost factors, among others.
Acknowledgement This work was supported by the National Natural Science Foundation of China (No. 21736008) and the Fundamental Research Funds for the Central Universities (No. xzy012019031).
Discussion Nomenclature In the previous section, three different cases are studied via the proposed simulation-based optimization method. The incorporation of rigorous thermodynamic and fluid properties makes the H2S removal model accurate. However, the employment of rigorous process simulation also results in several limitations compared with mathematical programming methods for hydrogen network integration. Firstly, as can be seen in the three cases, the number of H2S absorbers needs to be specified beforehand rather than being considered as a decision variable, because equipment models can not be added to or removed from the Aspen HYSYS when the program is running. Secondly, the connections between hydrogen sources and H2S absorbers should be pre-specified as well. The reason is that if an H2S absorber is not allocated
Sets SOURCE SINK IM REMU PLMS EQ
set of hydrogen sources set of hydrogen sinks set of impurities set of H2S absorbers set of pressure levels set of equipment
Variables af annualized factor aot annual operating time c cost com capital cost of a compressor
Please cite this article as: Yang M, Feng X, Simulation-based optimization and design of refinery hydrogen networks with hydrogen sulfide removal, International Journal of Hydrogen Energy, https://doi.org/10.1016/j.ijhydene.2019.07.108
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F ir mdea P power s sf steam t tac uc water y
flow rate interest rate MDEA consumption pressure power consumption equipment capacity scaling factor steam consumption plant life span total annualized cost unit cost water consumption concentrations of impurity and hydrogen
Subscripts eq equipment hydrogen fresh hydrogen i hydrogen source i j hydrogen sink j makeup makeup stream
mdea ms purge removal recycle ru sk sr steam water
MDEA mixed hydrogen sources purge stream H2S removal unit recycle stream H2S absorber ru hydrogen sink hydrogen source steam cooling water
Superscripts com compressor k impurity k H2 hydrogen
Appendix. Data for the case study
Table A1 e Data of hydrogen sources and sinks. Flowrate (mol/s)
Pressure (kPa)
yH2 (mol%)
yH2S (mol%)
37.20 99.20 730.27 8.68 1076.79 3.72
2000 2000 6000 1000 6000 1000
99.0 90.0 95.0 64.0 84.0 60.0
0 0 0.476 1.79 0.476 2.21
6.20 3.72 784.83 3.72 1126.39
2000 8000 8000 2000 8000
99.0 99.0 94.9 90.0 84.3
0 0 0.439 0 0.453
Hydrogen sources Fresh H2 Reformed gas G/DHT (HP) off-gas G/DHT (LP) off-gas DHT (HP) off-gas DHT (LP) off-gas Hydrogen sinks PE GHT G/DHT PP DHT
Table A2 e Equipment capacities, costs, and scaling factors. (2017, RMB) Items
Benchmark
Seq,base
Units
sf
H2S absorber Flash tank Regenerator Pump Heat exchanger (E100) Heat exchanger (E101)
Solvent input Input Input Input Heat duty Heat duty
152.5 158.7 158.6 152.5 1.51 106 1.21 106
kg/h kg/h kg/h kg/h kJ/h kJ/h
0.67 0.67 0.67 0.35 0.42 0.42
Ceq,base 9.64 5.91 1.71 5.33 3.06 3.00
105 105 106 105 105 105
Note: Capital costs of equipment units associated with H2S removal are estimated using Aspen Process Economic Analyzer.
Table A3 e Parameters for economic analysis. (2017, RMB) Items Fresh H2 Power Steam Cooling water MDEA Interest rate Life span CEPCI2017
Values
Units
0.04 0.75 23.69 1.5 2.7 0.1 10 567.5
RMB/mol RMB/(kW$h) RMB/GJ RMB/GJ RMB/kg e year e
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Available online at www.sciencedirect.com
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Simulation-based optimization and design of refinery hydrogen networks with hydrogen sulfide removal Minbo Yang, Xiao Feng* School of Chemical Engineering and Technology, Xi’an Jiaotong University, Xi’an, Shaanxi 710049, China
highlights Rigorous thermodynamic and process modeling of H2S removal unit in Aspen HYSYS. A simulation-based optimization model for synthesizing hydrogen network with H2S removal. A solution strategy to enhance the computational performance.
article info
abstract
Article history:
Hydrogen consumption in oil refineries increases sharply because of more and more heavy
Received 11 March 2019
and sour crude oil processing, which also makes hydrogen sulfide a considerable
Received in revised form
contaminant in off-gases of hydrotreaters. This work presents a simulation-based opti-
10 July 2019
mization model for synthesis of hydrogen networks with H2S removal. Aspen HYSYS is
Accepted 14 July 2019
employed for rigorous process and thermodynamic modeling of the H2S removal unit. The
Available online xxx
proposed model is solved using the genetic algorithm combined with the linprog and fmincon solvers in the Matlab platform. The optimal hydrogen sources and the recirculated
Keywords:
absorbent fed into the H2S removal unit as well as the optimal design of the hydrogen
Hydrogen network
network can be determined simultaneously. A case study is performed to illustrate the
Hydrogen sulfide removal
application and effectiveness of the proposed model. The result shows that the introduc-
Simulation-based optimization
tion of H2S removal can decrease the fresh hydrogen consumption by 43% and the total
Genetic algorithm
annualized cost by 17%.
Total annualized cost
© 2019 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.
Introduction Hydrogen is an essential utility and contributes to considerable proportion of the operating cost for oil refining. Due to the increase in heavy and sour crude oil processing, lower sulfur fuel specifications, and markets shifting towards lighter fuels, additional hydrotreating capacities are required in refineries [1,2]. Consequently, the hydrogen demand is significantly
increasing, which leads to the unbalance between hydrogen demand and supply as well as the increase in the operating cost for oil refining. Therefore, hydrogen management has been a critical issue, and how to use hydrogen efficiently is vital to oil refineries. The technique of hydrogen network integration is an effective tool for refinery hydrogen management and of great research interest. The main reason is that hydrogen network
* Corresponding author. E-mail address: [email protected] (X. Feng). https://doi.org/10.1016/j.ijhydene.2019.07.108 0360-3199/© 2019 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved. Please cite this article as: Yang M, Feng X, Simulation-based optimization and design of refinery hydrogen networks with hydrogen sulfide removal, International Journal of Hydrogen Energy, https://doi.org/10.1016/j.ijhydene.2019.07.108
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integration aims to maximize reuse of the existing hydrogen within the process to reduce the fresh hydrogen consumption. In this light, costs and environmental impacts associated with hydrogen production can be cut down. For these advantages, plenty of studies have been conducted. Both pinch-based methods and superstructure-based mathematical programming ones have been developed for hydrogen network integration [3]. Based on the pinch technology for heat exchanger network integration, Alves and Towler [4] employed the pinch conception for hydrogen network integration and proposed a graphical method to identify the minimum fresh hydrogen consumption as well as the pinch location. Afterwards, several graphical techniques were developed based on different coordinates, including impurity load versus flowrate [5], hydrogen load versus flowrate [6], impurity concentration versus flowrate [7,8], and impurity load versus hydrogen load [9]. Researchers also proposed different algebraic methods for targeting the minimum fresh hydrogen consumption [10e12]. Although some of these contributions have incorporated hydrogen purification units into hydrogen networks for more fresh hydrogen savings [7,11], all operating parameters of hydrogen purifiers are specified without optimization. Based on these pioneer studies, the pinch technology has been extended for optimal placement of hydrogen purifiers. Liu et al. [13] analyzed the maximum feed of hydrogen purifiers, and they proposed a method for targeting the optimal feed [14]. Zhang et al. [15] developed a graphical technique for integration of hydrogen network with hydrogen purifiers. Later, this technique was extended with consideration of purifiers’ performance [16,17] and to identify the optimal purification process [18]. The pinch-based methods are able to show insights for hydrogen network integration, even with hydrogen purifiers. However, the existing pinch-based methods often simplified hydrogen purifiers with specified hydrogen recovery ratio, product purity, or tail gas purity for easy solving. Mathematical programming approaches for hydrogen network integration usually consist of two steps: superstructure generation and mathematical model formulation. The first systematic mathematical-based approach for efficient use of hydrogen was proposed by Hallale and Liu [19]. They developed a superstructure that includes the placement of compressors and purifiers and formulated a mixed integer nonlinear programming (MINLP) model for the hydrogen distribution problem. Later, Liu and Zhang [20] developed a systematic methodology to select appropriate purifiers from pressure swing adsorption (PSA), membranes, or hybrid systems for hydrogen recovery. Liao et al. [21] presented a statespace superstructure incorporating more network structure possibilities for the placement of compressors and purifiers. Deng et al. [22] constructed a superstructure including all the feasible interconnections among hydrogen sources, hydrogen sinks, fuel system, compressors, and purifiers and compared different hydrogen network synthesis scenarios. Khajehpour et al. [23] used a superstructure reduced on the basis of engineering judgment to obtain the feasible results faster. To ensure the feasibility of solutions, Jia and Zhang [24] developed a modeling and optimization framework for hydrogen management taking into account multi-components. Wu
et al. [25] proposed a sequential optimization strategy to minimize the total exergy consumption and number of compressors of hydrogen networks. Later, synthesis of hydrogen networks with minimum compression costs was addressed [26]. Recently, mathematical programming approaches are developed for more complex hydrogen management problems. Ahmad et al. [27] proposed a methodology for design of multi-period hydrogen networks with varying operating conditions of hydrogen consuming processes. The operational flexibility and capacity redundancy of compressors were also taken into account for the design of multi-period hydrogen networks [28]. Lou et al. [29] introduced a robust optimization framework to optimize hydrogen network under uncertainties. Wei et al. [30] optimized the disturbance resistance ability of the hydrogen network under minimum fresh hydrogen consumption. Deng et al. [31] embedded hydrogen headers in the superstructure and proposed a methodology to synthesize hydrogen network with intermediate hydrogen header. In their later contribution, a superstructure for inter-plant hydrogen network integration with purifiers and corresponding mathematical programming models were proposed [32]. Lou et al. [33] proposed a two-step method combining the pinch insight with mathematical programming to optimize the inter-plant hydrogen network. The optimal design of inter-plant hydrogen network with intermediate headers of purity and pressure was addressed by Kang et al. [34]. In refineries, sulfur in crude oil is removed by hydrotreating in the form of H2S, which is a noteworthy impurity because it can corrode the equipment and damage catalysts. To reduce H2S contents in hydrogen streams, desulfurization units have been employed in some refineries. However, there are very limited publications focusing on optimal synthesis of hydrogen networks with H2S removal. Li et al. [35] developed a mathematical modeling and optimization approach to integrate H2S removal units into hydrogen networks. Wu et al. [36] constructed a superstructure of hydrogen networks with unified purification models for PSA, membrane, and desulfurization processes. Jia and Liu [37] developed a mathematical programming model for optimization of hydrogen network with multiple impurities and impurity removal. The H2S removal process depends on complex thermodynamic and fluid properties, which pose great challenges for process modeling and solving, especially for various hydrogen sources. Therefore, in the aforementioned studies, simplified H2S removal models were adopted to get the global optimum using deterministic global optimization algorithms. However, if such simplifications are too unrealistic, the obtained optimum may be suboptimal or even infeasible in the real world [38]. Therefore, more rigorous models for the H2S removal unit are required. In this work, a novel simulation-based optimization model is developed for synthesizing hydrogen network with H2S removal. The high-fidelity process simulation model for H2S removal is developed in Aspen HYSYS. To efficiently solve the simulation-based optimization model, a solution strategy that integrates genetic algorithm with the linprog and fmincon solvers is proposed based on the Matlab platform. The proposed model is solved to minimize the total annualized cost of the hydrogen network integrated with H2S removal,
Please cite this article as: Yang M, Feng X, Simulation-based optimization and design of refinery hydrogen networks with hydrogen sulfide removal, International Journal of Hydrogen Energy, https://doi.org/10.1016/j.ijhydene.2019.07.108
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determining the optimal hydrogen sources and recirculated absorbent fed into H2S absorbers and the optimal design of the hydrogen network. The contributions of this work can be summarized as below: A novel simulation-based optimization model for synthesizing hydrogen network with H2S removal; A solution strategy that integrates genetic algorithm with the linprog and fmincon solvers to enhance the computational performance. The rest of this paper is organized as follows. The investigated problem is stated in Section Problem statement. Section Model formulation introduces the simulation-based optimization model for hydrogen network integration with H2S removal. The solution strategy is presented in Section Solution strategy, and a case study is performed in Section Case study. Section Discussion discusses the proposed method, and the last section gives conclusion of this work.
Problem statement Fig. 1 shows a simplified diagram of a hydrotreating process [19]. The hydrogen stream mixed with a liquid feed is introduced into a hydrotreater, which can be considered as a hydrogen sink. In the hydrotreater, a part of the introduced hydrogen is consumed, and the rest leaves the reactor within the reaction effluent. Subsequently, hydrogen is separated from the reaction effluent in a separator. The resulting hydrogen stream, namely, hydrogen source, may be directly recycled to the hydro-treating reactor along with the makeup hydrogen or sent to the fuel system. The key aspects of the problem statement are shown in Fig. 2. There are a set of hydrogen sources, SOURCE ¼ fiji ¼ 1; 2…Ig as well as a set of hydrogen sinks, SINK ¼ fjjj ¼ 1; 2…Jg in an oil refinery. Fresh hydrogen as the supplementary source is purchased from hydrogen plants or specially produced. Each hydrogen source has a known flowrate Fi, a pressure Pi, and a hydrogen concentration yH2 i . Hydrogen sources also contain a set of impurities, IM ¼ fkjk ¼ 1; 2…Kg, with corresponding concentrations yki . Each hydrogen sink requires a feed with a flowrate Fj, a pressure Pj, and a minimum allowable hydrogen concentration yH2 j . Besides, the concentration of each impurity in the feed should not be higher than its maximum allowable value ykj . The problem of hydrogen network integration with H2S removal can be stated as below. A hydrogen sink can accept
Fig. 2 e Superstructure of a hydrogen distribution network. SR: hydrogen source; SK: hydrogen sink.
one hydrogen source or a mixture of several hydrogen sources if its requirements on flowrate, hydrogen concentration, and impurity concentrations can be fulfilled. A number of hydrogen compressors are placed to lift the pressure of hydrogen sources if necessary. For the desulfurization unit, methyldiethanolamine (MDEA) is chosen as the chemical absorbent, because it is widely for H2S removal [39]. Any hydrogen sources that contain H2S can be treated in the H2S absorbers, REMU ¼ frujru ¼ 1; 2…RUg. The resulting products can be allocated to any hydrogen sink to fulfill the requirements. The main objectives of this work include: (1) determine the optimal hydrogen sources and the optimal recirculated MDEA solution fed into H2S absorbers; (2) synthesize the hydrogen network with H2S removal to minimize the total annualized cost. For the simulation-based optimization problem, the given parameters include: Hydrogen sources: the number of hydrogen sources (I), the number of impurities (K), flowrate (Fi), pressure (Pi), hydrogen concentration (yH2 i ), and impurity concentrations (yki ). Hydrogen sinks: the number of hydrogen sink (J), the number of impurities (K), required flowrate (Fj), required pressure (Pj), minimum hydrogen concentration (yH2 j ), and maximum impurity concentrations (ykj ). H2S removal unit: the number of H2S absorbers (RU), and operating temperatures and pressures of all devices. Connections between hydrogen sources and H2S absorbers. All economic parameters. The decision variables include:
Fig. 1 e Simplified diagram of a hydro-treating process.
The flowrate of each hydrogen source fed into each H2S absorber (Fi,ru). The flowrate of MDEA solution fed into each H2S absorber (Famine,ru). The flowrate of each hydrogen source and treated product allocated to each hydrogen sink (Fi,j and Fru,j).
Please cite this article as: Yang M, Feng X, Simulation-based optimization and design of refinery hydrogen networks with hydrogen sulfide removal, International Journal of Hydrogen Energy, https://doi.org/10.1016/j.ijhydene.2019.07.108
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H2S removal unit
Model formulation Based on the superstructure in Fig. 2, the simulation-based optimization model proposed in this work consists of five sections: (1) availability of hydrogen sources, (2) requirements of hydrogen sinks, (3) H2S removal unit, (4) compression, and (5) objective function.
Availability of hydrogen sources As aforementioned, each hydrogen source has a known flowrate. Thus, the total flowrate of a hydrogen source i allocated to each hydrogen sink and the H2S removal unit should not exceed the given flowrate, as shown in Equation (1). J X
Fi;j þ
RU X
Fi;ru Fi
(1)
ru¼1
j¼1
where Fi,j is the flowrate of hydrogen source i allocated to hydrogen sink j; Fi,ru is the flow rate of hydrogen source i fed into the H2S absorber ru.
Requirements of hydrogen sinks In order to maintain the operation of all hydrogen consumers, their requirements on flow rate, hydrogen purity, and impurity concentrations should be fulfilled. These requirements correspond to Equations (2)e(4). I X i¼1
I X
Fi;j þ
RU X
Fi;j $yH2 i þ
i¼1
(2)
RU X
H2 Fru;j $yH2 ru Fj $yj
(3)
ru¼1
i¼1
I X
Fru;j ¼ Fj
ru¼1
Fi;j $yki þ
RU X
Fru;j $ykru Fj $ykj
(4)
ru¼1
where Fru,j is the flow rate of the purified product from the H2S absorber ru allocated to hydrogen source j; yH2 ru is the corresponding hydrogen purity of the purified product; ykru is the corresponding concentration of impurity k of the purified product.
The process flowsheet for H2S removal is illustrated in Fig. 3 [39]. The hydrogen source that contains H2S enters the bottom of the H2S absorber and contacts the lean MDEA solution from the top. The treated gas leaves from the top of the absorber is the purified product. The rich amine leaves the absorber bottom and passes through a valve to reduce the compressor to about 500 kPa. The dissolved gas is removed in a flash tank (V-101) and sent to the fuel system. After being heated to 80 C, the rich MDEA solution enters a solvent regenerator, where H2S in the rich MDEA solution is stripped off. The reboiler frequently uses low-pressure steam as the heating medium to prevent the amine from degradation. The lean MDEA solution exits the bottom of the regenerator and is pumped to the H2S absorber at the operating pressure along with the makeup water and MDEA. The gas that consists mostly of H2S and water exits the top of the regenerator and can be processed for sulfur recovery. This part is considered beyond the scope of the current study. In this work, Aspen HYSYS is used for H2S removal process and thermodynamic modeling. Consequently, the mass and energy balances associated with H2S removal can be determined. Besides, the following assumptions are considered for H2S removal. The operating pressure of an H2S absorber is the same to the pressure of the entering hydrogen source; Only hydrogen sources that have the same pressure are allowed to be treated in one H2S absorber. The resulting rich MDEA solution streams from all H2S absorbers are gathered and regenerated in a regenerator.
Compression The placement of compressors is determined according to the process requirement. When a hydrogen source has a higher pressure than a hydrogen sink or the flowrate between a hydrogen source and a hydrogen sink is zero, compression is not required, as described by Equations (5) and (6). Otherwise, compression is required to lift the pressure of the hydrogen source to meet that of the hydrogen sink. The power requirement for gas compression can be
Fig. 3 e Process flowsheet for H2S removal via MDEA treating. Please cite this article as: Yang M, Feng X, Simulation-based optimization and design of refinery hydrogen networks with hydrogen sulfide removal, International Journal of Hydrogen Energy, https://doi.org/10.1016/j.ijhydene.2019.07.108
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calculated by Equations (7)e(9) [40]. In this study, it is considered that electrical power is supplied for compression. Equations (7) and (8) convert the theoretical compression power to electrical power requirement by considering an efficiency h, which takes as 75% [41]. zcom i;j
¼
zcom ru;j ¼
8 <
0 if Pi Pj : 1 if Pi < Pj
(5)
where powerms,j is the power consumption; zms,i,j and zms,ru,j are binary variables that indicate whether the pressures of hydrogen source i and purified product from H2S absorber ru equals the pressure of the mixture hydrogen source Pms, respectively.
Objective function The objective is to minimize the total annualized cost of the hydrogen network, which is expressed as Equation (13).
8 <
0 if Pru Pj : 1 if Pru < Pj
(6)
tac ¼ c hydrogen þ c power þ c steam þ c water þ c mdea þ af $ðc com þ c hruÞ
2 3 g1 g$N 6 Pj 7 1 g com $Ni;j $R$Ti $6 17 poweri;j ¼ $ 4 Pi 5$Fi;j $zi;j h g1 2 3 g1 6 Pj g$N 7 1 g com $Nru;j $R$Tru $6 powerru;j ¼ $ 17 4 Pru 5$Fru;j $zru;j h g1 8 > > < 1 if cr 3 N ¼ 2 if 3 < cr 6 > > : 3 if 6 < cr 9
t
(7)
(8)
(9)
com where zcom i;j and zru;j are binary variables that indicate whether compression is required; poweri,j represents the compression power for lifting hydrogen source i to hydrogen sink j with a flowrate of Fi,j; powerru,j represents the compression power for lifting the purified product from the H2S absorber ru to hydrogen sink j with a flowrate of Fru,j; Pi, Pj, and Pr are the pressures of hydrogen source i, hydrogen sink j, and purified product from H2S absorber ru, respectively; g is the adiabatic index and taken as 1.40 for hydrogen streams [25]; N represents the number of compression stages and can be determined by the compression ratio cr [20]; R is the molar gas constant and equals 8.314 J mol1 K1; Ti and Tru are the inlet temperatures of the hydrogen source i and the purified product from the H2S absorber ru. To reduce the capital cost associated with compressors, we assume that hydrogen sources that have the same pressure and are allocated to a same hydrogen sink can be mixed before compression. According to the pressures of hydrogen sources and purified products, we can get a set of pressure levels of mixed hydrogen sources, PLMS ¼ fPms jms ¼ 1; 2…MSg. The power consumption for lifting the mixture of hydrogen sources to hydrogen sink j can be calculated as below:
powerms;j ¼
I X
poweri;j $zms;i;j þ
i¼1
RU X
powerru;j $zms;ru;j
(10)
ru¼1
8 <
zms;i;j
0 if Pi sPms ¼ : 1 if Pi ¼ Pms
zms;ru;j
af ¼
ir$ð1 þ irÞ
ð1 þ irÞ 1
where tac represents the total annualized cost; c_hydrogen, c_power, c_steam, c_water, and c_mdea are the cost related to consumptions of fresh hydrogen, power, steam, cooling water, and MDEA, respectively; af is the annualized factor that is given as Equation (14); c_com and c_hru are the capital costs of compressors and H2S removal units; ir is the interest rate; t is the number of years. The costs of fresh hydrogen, power, steam, and cooling water are calculated based on their annual consumptions and unit prices, as given by Equations (15)e(19). c hydrogen ¼ Fhydrogen $uchydrogen $aot 0 c power ¼ @
J X I X j¼1
poweri;j þ
i¼1
1
J ru X X
(15)
powerru;j
j¼1 ru¼1
þ powerremoval A$ucpower $aot
(16)
c steam ¼ steamremoval $ucsteam $aot
(17)
c water ¼ waterremoval $ucwater $aot
(18)
c mdea ¼ mdearemoval $ucmdea $aot
(19)
where Fhydrogen represents the fresh hydrogen consumption; powerremoval, steamremoval, waterremoval, and mdearemoval represent the consumptions of power, steam, cooling water, and MDEA in the H2S removal unit, respectively; uchydrogen, ucpower, ucsteam, ucwater, and ucmdea represent the unit prices of fresh hydrogen, power, steam, cooling water, and MDEA, respectively; aot is the annual operating time. The capital cost of a compressor can be estimated based on its power consumption by Equation (20) [41]. The total capital cost of all the compressors is given as Equation (21).
(11)
(12)
(14)
t
comms;j ¼ 5:375
8 <
0 if Pru sPms ¼ : 1 if Pru ¼ Pms
(13)
h power i CEPCI ms;j $exp 7:58 þ 0:80 ln CEPCI2006 746 (20)
c com ¼
J MS X X
comms;j
(21)
j¼1 ms¼1
Please cite this article as: Yang M, Feng X, Simulation-based optimization and design of refinery hydrogen networks with hydrogen sulfide removal, International Journal of Hydrogen Energy, https://doi.org/10.1016/j.ijhydene.2019.07.108
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where com represents the capital cost of a compressor; CEPCI and CEPCI2006 are the chemical economic plant cost indices in the studied year and 2006, respectively. The total capital cost associated with H2S removal is estimated by summing the cost of each equipment unit, as expressed by Equation (22) [41]. c hru ¼
sf EQ X Seq CEPCI $Ceq;base $ CEPCIbase Seq;base eq¼1
(22)
where CEPCIbase is the cost index in the base case; Ceq,base is the capital cost of equipment eq in the base case; Seq and Seq,base are capacities of equipment eq in the current case and base case, respectively; sf is the scaling factor.
Solution strategy In this work, the simulation-based optimization model is formulated and solved using Aspen HYSYS and Matlab. From the viewpoint of the formulated model, it contains a black box simulation model for H2S removal. Therefore, a metaheuristic method, Genetic Algorithms (GA), is employed to solve the model [42]. However, GA approaches encounter significant difficulties when solving problems containing complex constraints [43], which is a predominant feature of the hydrogen management problem. The use of penalty approaches as an attempted remedy causes a risk of spending most of the computational effort in handling invalid solutions [43]. To enhance the computational efficiency, a solution strategy is proposed as illustrated in Fig. 4. The calculation
starts in Matlab, where a set of process data for H2S removal are generated through GA. These data include the flow rate of a hydrogen source fed into an H2S absorber Fi,ru, and the corresponding recirculated MDEA solution Famine,ru. After receiving these process data, Aspen HYSYS solves the simulation model of H2S removal with rigorous thermodynamic calculations. The mass and energy balances associated with H2S removal can be determined. We can obtain data, including flow rate, hydrogen purity, and impurity concentrations, associated with each purified product. Meanwhile, the operating cost and capital cost related to H2S removal can be evaluated. All these data are then returned to Matlab, and a new hydrogen distribution problem is generated. The hydrogen sources in the new hydrogen network contains the purifed products and the rest of hydrogen sources in the original hydrogen network. The new hydrogen distribution problem is formulated as a nonlinear programming model as given in (P1), which can be solved by the fmincon solver in Matlab. In terms of the fmincon solver, it requires a feasible solution as the initial value [44]. Therefore, we formulate a linear programming model as (P2), which is solved by the linprog solver in Matlab and could generate a feasible initial value (i.e., a feasbile hydrogen network) for the fmincon solver in each iteration. In this way, we can obtain costs of fresh hydrogen, power, and compressors associated with hydrogen distribution. Next, the global objective function is evaluated based on the cost data related to H2S removal and hydrogen distribution. The solving process is iterated until the stopping criteria of GA are reached. Finally, Matlab provides the minimum total annualized cost, optimal values of hydrogen
Fig. 4 e Solution strategy for the simulation-based optimization model. Please cite this article as: Yang M, Feng X, Simulation-based optimization and design of refinery hydrogen networks with hydrogen sulfide removal, International Journal of Hydrogen Energy, https://doi.org/10.1016/j.ijhydene.2019.07.108
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sources and recirculated MDEA solutions fed into H2S absorbers, and the optimal design of the hydrogen network. P1: min. objp1 ¼ c hydrogen þ c power þ af $c com s.t. supply and demand constraints (23)e(26) energy balance constraints (5), (7), (9), (11) and (27), economic evaluation constraints (15), (20), (21) and (28), P2: min. objp2 ¼ c hydrogen s.t. supply and demand constraints (23)e(26) J X
Fi;j Fi
(23)
Fi;j ¼ Fj
(24)
Fi;j $yH2 Fj $yH2 i j
(25)
Fi;j $yki Fj $ykj
(26)
j¼1
I X i¼1
I X i¼1
I X i¼1
powerms;j ¼
I X
poweri;j $zms;i;j
c power ¼ @
J X I X j¼1
risk of spending computational effort in handling invalid solutions.
Case study In this section, a hydrogen network is investigated to illustrate the applicability of the proposed simulation-based optimization model. This hydrogen network is taken from reference [35]. There are five hydrogen consumers in the hydrogen network, namely, polyethylene unit (PE), gasoline hydrotreater (GHT), gasoline and diesel hydrotreater (G/DHT), polypropylene unit (PP), and diesel hydrotreater (DHT). The main hydrogen streams of this hydrogen network are presented in Fig. 5. The data of hydrogen sources and sinks can be determined from the makeup, purge, and recycle data according to Equations (29)e(32) [19]. Table A1 in the Appendix shows the data of hydrogen sources and sinks in this hydrogen network. Besides, the detailed parameters for evaluating the capital and operating costs are given in Tables A2 and A3 in the Appendix. Fsr ¼ Fpurge þ Frecycle
(29)
yksr ¼ ykpurge þ ykrecycle
(30)
Fsk ¼ Fmakeup þ Frecycle
(31)
(27)
i¼1
0
7
1 poweri;j A$ucpower $aot
(28)
i¼1
Based on the proposed solution strategy, it can be seen that the fmincon solver handles all linear and nonlinear constraints associated with hydrogen distribution. For an individual in each iteration, the fmincon solver generates a correspondingly optimal hydrogen network. In this way, the computational efficiency can be enhanced from two aspects. Firstly, the number of decision variables for the GA is reduced. As the fmincon solver determines decision variables of Fi,j and Fru,j, the GA with process simulation only handles decision variables of Fi,ru and Famine,ru. Secondly, the constrained optimization problem is converted to an unconstrained optimization problem in terms of the GA, which does not require remedial approaches (e.g., penalty algorithm) and avoids the
yksk ¼
Fmakeup $ykmakeup þ Frecycle $ykrecycle Fmakeup þ Frecycle
(32)
where subscripts sr, sk, purge, recycle, and makeup represent hydrogen source, hydrogen sink, purge stream, recycle stream, and makeup stream, respectively. The proposed simulation-based optimization model is applied to this hydrogen network, which is realized by combining Matlab R2018a and Aspen HYSYS V10. All computational studies were performed on an HP ProDesk 600 G3 MT desktop with four inter(R) Core(TM) i7-7700 CPUs @3.60 GHz and 16 GB RAM. The operating system is Windows 10 64-bit. As can be seen from Table A1, there are four hydrogen sources containing H2S, namely, high-pressure (HP) off-gases
Fig. 5 e Existing design of the hydrogen network [35]. Please cite this article as: Yang M, Feng X, Simulation-based optimization and design of refinery hydrogen networks with hydrogen sulfide removal, International Journal of Hydrogen Energy, https://doi.org/10.1016/j.ijhydene.2019.07.108
b
a
MDEA solution (kg/h) Purified product (mol/s) Product purity (mol%) H2S removal ratio
50 for 5 or fewer decision variables, otherwise 200 [45]. 100 decision variables for the GA [45].
4618 687.88 95.18 79.41%
1838 344.24 84.20 68.16%
4618 687.88 95.18 79.41%
1838 344.24 84.20 68.16%
0.000278 0 0.007 0.000276 64.44 71.27% 345.08 689.74 345.08
729.24 8.11 6614 735.09 95.09 87.63% Feed to absorbers (mol/s)
689.74
FDHT-HP,2, Famine,2 FG/DHT-HP,1, Famine,1, FG/DHT-HP,1, FDHT-HP,1, Famine,1 Decision variables for GA
50 400 50 14.4
absorber 1 50 300 50 24.5 Population sizea MaxGenerationsb MaxStallGenerations [45] Computational time (hr)
absorber 1
FG/DHT-HP,1, Famine,1,
FDHT-HP,2, Famine,2,
absorber 3 absorber 2 absorber 2
absorber 1
200 700 50 61.9
case 3 case 2 case 1 Items
and low-pressure (LP) off-gases of the G/DHT and DHT units. The two HP off-gases have notably larger flowrates, higher hydrogen purities, and higher pressures than the two LP offgases that are sent to the fuel system in the existing design. Since H2S absorbers should be preinstalled in the simulation model, three different cases are studied in this work. The cases 1 and 2 take the two HP off-gases as the avaliable feedstocks for H2S removal. As shown in Table A1, the two HP off-gases have the same pressure but notably different hydrogen purities, so they can be treated in an H2S aborber or two respective H2S absorbers. Both the two cases are investigated for thorough consideration. The two HP off-gases are cotreated in one H2S absorber in the case 1, while they are treated separately in the case 2. The case 3 considers all the four hydrogen sources containing H2S as avaliable feedstocks for H2S removal. Note that for each H2S absorber, neither the input hydrogen source nor the absorbent can be zero, otherwise Aspen HYSYS will report an error and stop. Thus, for each decision variable associated with an H2S absorber, a very small value is set as the lower bound. The case 1 considers the only two HP off-gases as the available feedstocks for the H2S removal unit, and they are cotreated in an H2S absorber. Table 1 shows the GA options and results associated with the H2S absorber. The optimal design of the hydrogen network is presented in Fig. 6. It can be seen that the fresh hydrogen consumption is reduced from 37.20 mol/s to 23.96 mol/s by 35.59%, while the waste gas decreases from 31.00 mol/s to 15.49 mol/s. Besides, the HP offgas from the G/DHT unit is all reused, but 3.09 mol/s of HP off-gas from the DHT is still discharged to the fuel system. In the case 2, the two HP off-gases are treated in two H2S absorbers. The GA options and results associated with the two H2S absorbers are listed in Table 1. The optimal design of the hydrogen network is given in Fig. 7. By introducing H2S removal, the fresh hydrogen consumption is reduced from 37.20 mol/s to 21.30 mol/s by 42.74%. The waste gas decreases from 31.00 mol/s to 12.40 mol/s contributed by only the two LP off-gases. The two HP off-gases are all reused with zero discharge. The case 3 is to study whether treating the two LP off-gases can further enhance the hydrogen recovery. As aforementioned, the two HP off-gases are all reused in the case 2 but not in the case 1. Therefore, the two HP off-gases are treated separately in case 3, and the optimal solution of the case 2 is taken as an initial population for the GA. In terms of the two LP off-gases, they have the same pressure, close hydrogen purities, and relatively small flowrates, so they are considered to be co-treated in an H2S absorber. Therefore, three H2S absorbers are preinstalled in the Aspen HYSYS platform. Table 1 shows the GA options and obtained results associated with the three absorbers. It can be seen that the total flow rate of the two LP off-gases fed into the absorber 3 is very small and can be ignored. Regarding to the other two H2S absorbers, they are the same to those in the case 2. In other words, the optimal design of the hydrogen network is the same to that in the case 2. It is noteworthy that the case 3 takes much more time than the cases 1 and 2 for solving the model, because of more decision variables and larger population size for the GA. The fresh hydrogen consumptions and waste gas discharge of the three cases are summarized and compared in Fig. 8. It
FG/DHT-LP,3, FDHT-LP,3, Famine,3
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Table 1 e GA options and results of the three cases.
8
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Fig. 6 e Optimal design of the hydrogen network for the case 1.
Fig. 7 e Optimal design of the hydrogen network for the case 2.
indicates that introducing H2S removal can notably improve the hydrogen network performance. The case 2 results in more fresh hydrogen saving than the case 1, showing better performance. The main reason is that the HP off-gas from the G/DHT unit has a notably higher hydrogen concentration than that from the DHT unit. Treating the two hydrogen sources in an H2S absorber will degrade the purified product as the ratio of FG/DHT-HP,1 to FDHT-HP,1 decreases, which causes negative impacts on the reuse of hydrogen. As can be seen from Table 1, the H2S removal unit in the case 1 takes less hydrogen sources as the feed and generates a product with a hydrogen purity between the two products in the case 2. The case 3
indicates that treating the two LP off-gases cannot improve the hydrogen network performance. This is because the two LP off-gases have low hydrogen purities, limiting the hydrogen reuse. Fig. 9 compares the total annualized costs of the existing design and the three cases. Note that the capital cost of compressors in the existing design is annualized for a fair comparison. Compared with the existing design, the introduction of H2S removal in the three cases decreases the fresh hydrogen cost by 35.60%, 42.74% and 42.74%, but increases the annualized capital cost by 67.39%, 70.33%, and 70.33%, respectively. Nevertheless, the three cases still result in lower
Please cite this article as: Yang M, Feng X, Simulation-based optimization and design of refinery hydrogen networks with hydrogen sulfide removal, International Journal of Hydrogen Energy, https://doi.org/10.1016/j.ijhydene.2019.07.108
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with any hydrogen sources, the simulation model will not be converged and no value will be generated and returned to the Matlab platform. Thirdly, a simulation model is less efficient than a mathematical model in terms of convergence. Thus, the simulation-based optimization model is more timeconsuming. Lastly, the use of GA cannot always guarantee the best possible solution in contrast to deterministic optimization methods [38].
Conclusion Fig. 8 e Fresh hydrogen consumptions and waste gas discharge.
Fig. 9 e Breakdowns of the total annualized costs.
total annualized cost than the existing design by 12.53%, 16.75%, and 16.75%, respectively. This is because the fresh hydrogen cost dominates the total annualized cost of the existing design. Besides, it can be seen that the case 2 results in slightly higher annualized capital costs and heating & cooling costs than the case 1, because the case 2 requires one more H2S absorber and treats more hydrogen sources. However, the case 2 results in lower total annualized cost than the case 1, meaning that treating the two HP off-gases in respective H2S absorbers is more cost-effective from the viewpoint of the whole hydrogen network.
In this work, a simulation-based optimization model was presented for synthesis of hydrogen networks with H2S removal. The proposed model employed rigorous simulation to formulate the H2S removal process in Aspen HYSYS. A solution strategy that integrates the genetic algorithm, linprog, and fmincon solvers was proposed for solving the model. The proposed model was illustrated using a refinery hydrogen network. The results showed that introducing H2S removal could improve the hydrogen utilization and economic performance of the hydrogen network. We also found that for hydrogen sources with considerably different hydrogen purities, it is better to treat them in respective H2S absorbers rather than co-treating them in one absorber. This could avoid the degradation of the purified products and promote the hydrogen reuse. Although the thermodynamic and fluid properties-based simulation model is more rigorous than simplified mathematical models, the proposed method suffers from several limitations. In the future work, the proposed simulationbased method can be extended to incorporate more issues into the superstructure and model, such as possible configurations of the H2S removal unit, more efficient modeling and solution strategies, influences of liquid feed on hydrogen sinks and sources, and uncertainties of hydrogen sources and sinks, cost factors, among others.
Acknowledgement This work was supported by the National Natural Science Foundation of China (No. 21736008) and the Fundamental Research Funds for the Central Universities (No. xzy012019031).
Discussion Nomenclature In the previous section, three different cases are studied via the proposed simulation-based optimization method. The incorporation of rigorous thermodynamic and fluid properties makes the H2S removal model accurate. However, the employment of rigorous process simulation also results in several limitations compared with mathematical programming methods for hydrogen network integration. Firstly, as can be seen in the three cases, the number of H2S absorbers needs to be specified beforehand rather than being considered as a decision variable, because equipment models can not be added to or removed from the Aspen HYSYS when the program is running. Secondly, the connections between hydrogen sources and H2S absorbers should be pre-specified as well. The reason is that if an H2S absorber is not allocated
Sets SOURCE SINK IM REMU PLMS EQ
set of hydrogen sources set of hydrogen sinks set of impurities set of H2S absorbers set of pressure levels set of equipment
Variables af annualized factor aot annual operating time c cost com capital cost of a compressor
Please cite this article as: Yang M, Feng X, Simulation-based optimization and design of refinery hydrogen networks with hydrogen sulfide removal, International Journal of Hydrogen Energy, https://doi.org/10.1016/j.ijhydene.2019.07.108
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F ir mdea P power s sf steam t tac uc water y
flow rate interest rate MDEA consumption pressure power consumption equipment capacity scaling factor steam consumption plant life span total annualized cost unit cost water consumption concentrations of impurity and hydrogen
Subscripts eq equipment hydrogen fresh hydrogen i hydrogen source i j hydrogen sink j makeup makeup stream
mdea ms purge removal recycle ru sk sr steam water
MDEA mixed hydrogen sources purge stream H2S removal unit recycle stream H2S absorber ru hydrogen sink hydrogen source steam cooling water
Superscripts com compressor k impurity k H2 hydrogen
Appendix. Data for the case study
Table A1 e Data of hydrogen sources and sinks. Flowrate (mol/s)
Pressure (kPa)
yH2 (mol%)
yH2S (mol%)
37.20 99.20 730.27 8.68 1076.79 3.72
2000 2000 6000 1000 6000 1000
99.0 90.0 95.0 64.0 84.0 60.0
0 0 0.476 1.79 0.476 2.21
6.20 3.72 784.83 3.72 1126.39
2000 8000 8000 2000 8000
99.0 99.0 94.9 90.0 84.3
0 0 0.439 0 0.453
Hydrogen sources Fresh H2 Reformed gas G/DHT (HP) off-gas G/DHT (LP) off-gas DHT (HP) off-gas DHT (LP) off-gas Hydrogen sinks PE GHT G/DHT PP DHT
Table A2 e Equipment capacities, costs, and scaling factors. (2017, RMB) Items
Benchmark
Seq,base
Units
sf
H2S absorber Flash tank Regenerator Pump Heat exchanger (E100) Heat exchanger (E101)
Solvent input Input Input Input Heat duty Heat duty
152.5 158.7 158.6 152.5 1.51 106 1.21 106
kg/h kg/h kg/h kg/h kJ/h kJ/h
0.67 0.67 0.67 0.35 0.42 0.42
Ceq,base 9.64 5.91 1.71 5.33 3.06 3.00
105 105 106 105 105 105
Note: Capital costs of equipment units associated with H2S removal are estimated using Aspen Process Economic Analyzer.
Table A3 e Parameters for economic analysis. (2017, RMB) Items Fresh H2 Power Steam Cooling water MDEA Interest rate Life span CEPCI2017
Values
Units
0.04 0.75 23.69 1.5 2.7 0.1 10 567.5
RMB/mol RMB/(kW$h) RMB/GJ RMB/GJ RMB/kg e year e
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Please cite this article as: Yang M, Feng X, Simulation-based optimization and design of refinery hydrogen networks with hydrogen sulfide removal, International Journal of Hydrogen Energy, https://doi.org/10.1016/j.ijhydene.2019.07.108
international journal of hydrogen energy xxx (xxxx) xxx
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Please cite this article as: Yang M, Feng X, Simulation-based optimization and design of refinery hydrogen networks with hydrogen sulfide removal, International Journal of Hydrogen Energy, https://doi.org/10.1016/j.ijhydene.2019.07.108