集成轻烃回收单元代理模型的氢气网络多目标优化

doi:10.11949/0438-1157.20211567

摘要/Abstract

摘要：

当前炼油企业氢气需求持续增长，导致炼厂成本及生产过程温室气体排放增加，炼油企业通过增设轻烃回收单元对氢气和轻烃组分进行回收利用，能有效缓解这一现状。因此，在氢气网络优化中有必要考虑轻烃回收单元。本研究提出了一种集成轻烃回收单元的氢气网络多目标数学规划模型，对轻烃回收单元采用代理模型建模方法，解决了直接嵌入严格机理模型可能导致的高计算成本问题，以总年度费用最小为优化目标，同时将系统的环境影响也纳入优化目标。实例计算表明，所提出的方法能够有效降低氢气网络的年度费用及温室气体排放，并揭示了集成轻烃回收单元的氢气网络经济性能与环境影响之间的权衡关系，为工业应用提供了一定的理论基础。

关键词: 过程系统, 氢气网络, 集成, 数学规划, 多目标, 优化设计

Abstract:

Global warming has become increasingly serious over the last decade. As one of the greenhouse gas (GHG) emission contributors, petroleum refineries are now encountering high GHG emissions and annual costs due to the increase of hydrogen demand. Light hydrocarbons recovery(LHR) unit can effectively reduce GHG emissions and improve resource utilization through recovering hydrogen and hydrocarbons components. Therefore, it is necessary to consider the light hydrocarbon recovery unit in the optimization of the hydrogen network. To avoid the high computational cost of rigorous process, this paper proposed surrogate models as approximations to the rigorous LHR process. Meanwhile, the environmental impact was added to the optimization goal. Finally, a hydrogen network multi-objective mathematical programming model was established. The proposed approach was applied to a case study taken from a real refinery. The results showed that the proposed method can effectively reduce the annual cost and GHG emissions of the hydrogen network, and reveal the trade-off relationship between the economic performance and the environmental impact of the hydrogen network integrated LHR unit. Hence it can provide a certain theoretical basis for further industrial application.

Key words: process system, hydrogen network, integration, mathematical programming, multi-objective, optimal design

中图分类号:

TQ 021.8

张淑君, 王诗慧, 张欣, 吉旭, 戴一阳, 党亚固, 周利. 集成轻烃回收单元代理模型的氢气网络多目标优化[J]. 化工学报, 2022, 73(4): 1658-1672.

Shujun ZHANG, Shihui WANG, Xin ZHANG, Xu JI, Yiyang DAI, Yagu DANG, Li ZHOU. Surrogate-assisted multi-objective optimization of hydrogen networks with light hydrocarbon recovery unit[J]. CIESC Journal, 2022, 73(4): 1658-1672.

图/表 16

图1 氢气网络的状态空间超结构

Fig.1 State space superstructure of hydrogen network

图2 轻烃回收单元流程图

Fig.2 Flow chart of light hydrocarbon recovery unit

图3 轻烃回收单元代理模型的构建步骤

Fig.3 Steps for constructing the surrogate model of light hydrocarbon recovery unit

图4 代理模型的输入与输出变量

Fig.4 Input and output variables of the surrogate model

图5 当前氢气网络的结构示意图

Fig.5 Schematic diagram of the current hydrogen network structure

表1 氢气网络中的相关流股的详细信息

Table 1 Detailed information of related streams in the hydrogen network

氢源供氢
单元	流量/(mol/s)	组成/%(mol)							压力/ MPa
单元	流量/(mol/s)	H₂	C1	C2	C3	C4	C5	H₂S	压力/ MPa
DHT-1	44.53	95.57	1.49	1.26	0.87	0.75	0.05	0	2
DHT-2	274.43	97.38	0.88	0.74	0.52	0.45	0.03	0	2
GHT	163.27	97.65	0.80	0.67	0.47	0.40	0.02	0	2
KHT-1	37.62	95.30	1.58	1.34	0.93	0.80	0.05	0	2
KHT-2	60.32	99.01	0.34	0.28	0.19	0.05	0.01	0	2
氢阱进口
单元	流量/(mol/s)	组成/%(mol)							压力/ MPa
单元	流量/(mol/s)	H₂	C1	C2	C3	C4	C5	H₂S	压力/ MPa
DHT-1	990.53	87.60	6.59	3.41	1.61	0.50	0.24	0.05	6.72
DHT-2	1004.5	90.00	5.21	2.75	1.34	0.47	0.19	0.04	7.00
GHT	811.8	89.32	5.62	2.94	1.41	0.46	0.21	0.04	2.70
KHT-1	60.70	92.23	3.58	2.17	1.20	0.68	0.12	0.02	3.83
KHT-2	83.43	95.75	2.14	1.18	0.60	0.25	0.07	0.01	5.45
高分气
单元	流量/(mol/s)	组成/%(mol)							压力 /MPa
单元	流量/(mol/s)	H₂	C1	C2	C3	C4	C5	H₂S	压力 /MPa
DHT-1	797.50	86.00	8.28	3.92	1.25	0	0	0.55	5.00
DHT-2	753.00	91.00	5.14	2.30	0.66	0.35	0.14	0.41	6.40
GHT	725.00	83.00	7.20	4.40	3.10	1.10	0.60	0.60	2.00
KHT-1	45.50	91.70	1.84	2.86	1.99	0.70	0.55	0.36	3.00
KHT-2	61.30	87.80	6.69	2.31	1.26	0.94	0.60	0.40	4.80
低分气
单元	流量/(mol/s)	组成/%(mol)							压力/ MPa
单元	流量/(mol/s)	H₂	C1	C2	C3	C4	C5	H₂S	压力/ MPa
DHT-1	36.00	52.30	8.08	13.52	10.80	8.22	5.98	1.10	1.1
DHT-2	43.40	50.31	16.71	11.51	8.71	7.28	3.78	1.70	1.2
GHT	19.80	42.32	20.51	15.02	9.21	8.56	2.58	1.80	1.0
KHT-1	5.20	35.21	32.36	8.64	9.73	7.90	4.66	1.50	1.0
KHT-2	5.60	67.60	10.65	8.36	5.98	4.67	0.94	1.80	1.0

表2 案例中各单元之间的管道距离

Table 2 The pipe distance between the units in the case

单元	间距/m
单元	DHT-1	DHT-2	GHT	KHT-1	KHT-2
CCR	500	680	1000	1150	1280
H₂ Plant	250	430	1250	1400	1150
DHT-1	0	180	890	1000	850
DHT-2	180	0	700	820	700
GHT	890	700	0	250	400
KHT-1	1000	820	250	0	150
KHT-2	850	700	400	150	0
PSA	480	300	510	760	910

表3 氢阱入口流股的浓度约束

Table 3 Concentration constraint of the inlet stream of the hydrogen sink

单元	$Y H 2 M i n$ /%(mol)	$Y H 2 S M a x$ /%(mol)
DHT-1	87.60	0.15
DHT-2	90.00	0.15
GHT	86.83	0.15
KHT-1	92.23	0.15
KHT-2	89.50	0.15

表3 氢阱入口流股的浓度约束

Table 3 Concentration constraint of the inlet stream of the hydrogen sink

单元	$Y H 2 M i n$ /%(mol)	$Y H 2 S M a x$ /%(mol)
DHT-1	87.60	0.15
DHT-2	90.00	0.15
GHT	86.83	0.15
KHT-1	92.23	0.15
KHT-2	89.50	0.15

表4 轻烃回收单元吸收塔输入变量范围

Table 4 Input variable range of absorption tower of light hydrocarbon recovery unit

输入变量	下限	上限
$F H 2 i n$ /(kmol/h)	157.48	236.22
$F H 2 S i n$ /(kmol/h)	0.62	0.94
$F C 1 i n$ /(kmol/h)	47.49	71.24
$F C 2 i n$ /(kmol/h)	39.68	59.52
$F C 3 i n$ /(kmol/h)	29.68	44.53
$F C 4 i n$ /(kmol/h)	24.37	36.56
$F C 5 i n$ /(kmol/h)	13.12	19.68
$F o i l i n$ /(kmol/h)	100.00	250.00

表4 轻烃回收单元吸收塔输入变量范围

Table 4 Input variable range of absorption tower of light hydrocarbon recovery unit

输入变量	下限	上限
$F H 2 i n$ /(kmol/h)	157.48	236.22
$F H 2 S i n$ /(kmol/h)	0.62	0.94
$F C 1 i n$ /(kmol/h)	47.49	71.24
$F C 2 i n$ /(kmol/h)	39.68	59.52
$F C 3 i n$ /(kmol/h)	29.68	44.53
$F C 4 i n$ /(kmol/h)	24.37	36.56
$F C 5 i n$ /(kmol/h)	13.12	19.68
$F o i l i n$ /(kmol/h)	100.00	250.00

表5 轻烃回收单元模型的验证结果

Table 5 Validation results of light hydrocarbon recovery unit model

装置	输出变量	RMSE	R²
吸收塔	$F H 2 o u t$ /(kmol/h)	5.40×10^-3	0.99
	$F H 2 S o u t$ /(kmol/h)	8.60×10^-3
	$F C 1 o u t$ /(kmol/h)	1.28×10^-2
	$F C 2 o u t$ /(kmol/h)	1.39×10^-1
	$F C 3 o u t$ /(kmol/h)	2.01×10^-1
脱乙烷塔	$F H 2 y o u t$ /(kmol/h)	3.07×10^-9	0.99
	$F H 2 S y o u t$ /(kmol/h)	2.66×10^-4
	$F C 1 y o u t$ /(kmol/h)	1.29×10^-6
	$F C 2 y o u t$ /(kmol/h)	3.01×10^-8
	$F C 3 y o u t$ /(kmol/h)	2.94×10^-9
	$Q c o o l y$ /kW	1.21×10^-1
	$???????? Q r e d y$ /kW	4.50×10^-1
脱丁烷塔	$F H 2 S d o u t$ /(kmol/h)	4.27×10^-8	0.99
	$F C 2 d o u t$ /(kmol/h)	1.82×10^-8
	$F C 3 d o u t$ /(kmol/h)	1.44×10^-5
	$F C 4 d o u t$ /(kmol/h)	3.32×10^-8
	$F C 5 d o u t$ /(kmol/h)	4.29×10^-9
	$??????? Q c o o l d$ /kW	1.18×10^-0
	$??????? Q r e d d$ /kW	3.31×10^-0

表5 轻烃回收单元模型的验证结果

Table 5 Validation results of light hydrocarbon recovery unit model

装置	输出变量	RMSE	R²
吸收塔	$F H 2 o u t$ /(kmol/h)	5.40×10^-3	0.99
	$F H 2 S o u t$ /(kmol/h)	8.60×10^-3
	$F C 1 o u t$ /(kmol/h)	1.28×10^-2
	$F C 2 o u t$ /(kmol/h)	1.39×10^-1
	$F C 3 o u t$ /(kmol/h)	2.01×10^-1
脱乙烷塔	$F H 2 y o u t$ /(kmol/h)	3.07×10^-9	0.99
	$F H 2 S y o u t$ /(kmol/h)	2.66×10^-4
	$F C 1 y o u t$ /(kmol/h)	1.29×10^-6
	$F C 2 y o u t$ /(kmol/h)	3.01×10^-8
	$F C 3 y o u t$ /(kmol/h)	2.94×10^-9
	$Q c o o l y$ /kW	1.21×10^-1
	$???????? Q r e d y$ /kW	4.50×10^-1
脱丁烷塔	$F H 2 S d o u t$ /(kmol/h)	4.27×10^-8	0.99
	$F C 2 d o u t$ /(kmol/h)	1.82×10^-8
	$F C 3 d o u t$ /(kmol/h)	1.44×10^-5
	$F C 4 d o u t$ /(kmol/h)	3.32×10^-8
	$F C 5 d o u t$ /(kmol/h)	4.29×10^-9
	$??????? Q c o o l d$ /kW	1.18×10^-0
	$??????? Q r e d d$ /kW	3.31×10^-0

图6 轻烃回收单元代理模型的残差图

Fig.6 Residual plot of the surrogate model of the light hydrocarbon recovery unit

图7 多目标优化后得到的Pareto曲线

Fig.7 Pareto curve obtained by multi-objective optimization

图8 最小总年度费用的氢气网络结构图

Fig.8 Hydrogen network structure diagram with minimum total annual cost

图9 最小总年度CO2排放的氢气网络结构图

Fig.9 Hydrogen network structure diagram with minimum total annual CO2 emissions

图10 原氢气网络与两种优化后氢气网络的TAC和TCE对比

Fig.10 Comparison of TAC and TCE between the original hydrogen network and the two optimized hydrogen networks

图11 不同Pareto最优解的CO2排放组成

Fig.11 CO2 emission composition of different Pareto optimal solutions

参考文献 36

1	周逸江. 国际组织自主性与全球气候治理中的联合国: 聚焦2019年联合国气候行动峰会[J]. 国际论坛, 2020, 22(5): 76-96, 158.
	Zhou Y J. The autonomy of international organizations and the united nations in global climate governance: the case of the 2019 UN global climate action summit[J]. International Forum, 2020, 22(5): 76-96, 158.
2	陈卫东. 碳中和是一场没有硝烟的全球博弈[J]. 中国石油和化工产业观察, 2021(8): 60-61.
	Chen W D. Carbon neutrality is a global game without gunpowder smoke [J]. China Petrochemical Industry Observer, 2021(8): 60-61.
3	周利. 炼油企业资源网络的集成优化方法研究[D]. 杭州: 浙江大学, 2015.
	Zhou L. Integrated optimization of resource networks in refinery[D]. Hangzhou: Zhejiang University, 2015.
4	魏莉莉. 炼油厂氢气系统稳定性的研究[D]. 杭州: 浙江大学, 2018.
	Wei L L. Stability optimization of hydrogen networks in refinery[D]. Hangzhou: Zhejiang University, 2018.
5	Zhou L, Liao Z W, Wang J D, et al. Optimal design of sustainable hydrogen networks[J]. International Journal of Hydrogen Energy, 2013, 38(7): 2937-2950.
6	Alves J J, Towler G P. Analysis of refinery hydrogen distribution systems[J]. Industrial & Engineering Chemistry Research, 2002, 41(23): 5759-5769.
7	Foo D C Y, Manan Z A. Setting the minimum utility gas flowrate targets using cascade analysis technique[J]. Industrial & Engineering Chemistry Research, 2006, 45(17): 5986-5995.
8	Zhao Z H, Liu G L, Feng X. New graphical method for the integration of hydrogen distribution systems[J]. Industrial & Engineering Chemistry Research, 2006, 45(19): 6512-6517.
9	Hallale N, Liu F. Refinery hydrogen management for clean fuels production[J]. Advances in Environmental Research, 2001, 6(1): 81-98.
10	Liao Z W, Wang J D, Yang Y R, et al. Integrating purifiers in refinery hydrogen networks: a retrofit case study[J]. Journal of Cleaner Production, 2010, 18(3): 233-241.
11	Liao Z W, Tu G N, Lou J Y, et al. The influence of purifier models on hydrogen network optimization: insights from a case study[J]. International Journal of Hydrogen Energy, 2016, 41(10): 5243-5249.
12	梁肖强, 刘永忠, 张亮. 集中式提纯器的设置与具有中间等级氢气网络的优化[J]. 化工进展, 2014, 33(3): 577-582.
	Liang X Q, Liu Y Z, Zhang L. Installation of a centralized purifier and optimization of hydrogen network with intermediate levels[J]. Chemical Industry and Engineering Progress, 2014, 33(3): 577-582.
13	Zhou L, Liao Z W, Wang J D, et al. Hydrogen sulfide removal process embedded optimization of hydrogen network[J]. International Journal of Hydrogen Energy, 2012, 37(23): 18163-18174.
14	Xia Z P, Wang S H, Zhou L, et al. Surrogate-assisted optimization of refinery hydrogen networks with hydrogen sulfide removal[J]. Journal of Cleaner Production, 2021, 310: 127477.
15	Jia X X, Liu G L. Optimization of hydrogen networks with multiple impurities and impurity removal[J]. Chinese Journal of Chemical Engineering, 2016, 24(9): 1236-1242.
16	焦云强, 苏宏业, 侯卫锋. 炼油厂氢气网络柔性优化[J]. 化工学报, 2012, 63(9): 2739-2748.
	Jiao Y Q, Su H Y, Hou W F. Flexible optimization of refinery hydrogen network[J]. CIESC Journal, 2012, 63(9): 2739-2748.
17	Lou J Y, Liao Z W, Jiang B B, et al. Robust optimization of hydrogen network[J]. International Journal of Hydrogen Energy, 2014, 39(3): 1210-1219.
18	Deng C, Lu X T, Zhang Q X, et al. Fuzzy optimization design of multicomponent refinery hydrogen network[J]. Chinese Journal of Chemical Engineering, 2021,DOI:10.1016/j.cjche.2021.04.014 . DOI
19	蒋迎花, 韩儒松, 康丽霞, 等. 厂际氢气网络多周期集成的分步优化方法[J]. 化工学报, 2021, 72(9): 4816-4829.
	Jiang Y H, Han R S, Kang L X, et al. Step-wise approach to integrate inter-plant hydrogen networks under multi-period operations[J]. CIESC Journal, 2021, 72(9): 4816-4829.
20	朱美倩. 炼厂气组合分离工艺设计与氢气系统优化[D]. 北京: 中国石油大学(北京), 2019.
	Zhu M Q. Design of hybrid separation process for refinery-off gas and optimization of hydrogen system[D]. Beijing: China University of Petroleum (Beijing), 2019.
21	李雪静, 乔明, 郑轶丹. 世界炼油工业二氧化碳减排进展[J]. 中外能源, 2010, 15(5): 64-70.
	Li X J, Qiao M, Zheng Y D. Advances in CO₂ emission reduction in global refining industry[J]. Sino-Global Energy, 2010, 15(5): 64-70.
22	Deng C, Zhu M Q, Liu J, et al. Systematic retrofit method for refinery hydrogen network with light hydrocarbons recovery[J]. International Journal of Hydrogen Energy, 2020, 45(38): 19391-19404.
23	Yang M B, Feng X, Zhao L. Coupling pinch analysis and rigorous process simulation for hydrogen networks with light hydrocarbon recovery[J]. Chinese Journal of Chemical Engineering, 2021, 40: 141-148.
24	Bhosekar A, Ierapetritou M. Advances in surrogate based modeling, feasibility analysis, and optimization: a review[J]. Computers & Chemical Engineering, 2018, 108: 250-267.
25	Wang S H, Zhou L, Ji X, et al. A surrogate-assisted approach for the optimal synthesis of refinery hydrogen networks[J]. Industrial & Engineering Chemistry Research, 2019, 58(36): 16798-16812.
26	Li H R, Liao Z W, Sun J Y, et al. Simultaneous design of hydrogen allocation networks and PSA inside refineries[J]. Industrial & Engineering Chemistry Research, 2020, 59(10): 4712-4720.
27	王红光, 王立国. 炼厂干气回收轻烃技术评述[J]. 炼油技术与工程, 2009, 39(12): 8-11.
	Wang H G, Wang L G. Recovery of light olefins from refinery dry-gas[J]. Petroleum Refinery Engineering, 2009, 39(12): 8-11.
28	Jin R, Chen W, Simpson T W. Comparative studies of metamodelling techniques under multiple modelling criteria[J]. Structural and Multidisciplinary Optimization, 2001, 23(1): 1-13.
29	Garud S S, Karimi I A, Kraft M. Design of computer experiments: a review[J]. Computers & Chemical Engineering, 2017, 106: 71-95.
30	Elkamel A, Alhajri I, Almansoori A, et al. Integration of hydrogen management in refinery planning with rigorous process models and product quality specifications[J]. International Journal of Process Systems Engineering, 2011, 1(3/4): 302.
31	牛亚群, 董康银, 姜洪殿, 等. 炼油企业碳排放估算模型及应用[J]. 环境工程, 2017, 35(3): 163-167.
	Niu Y Q, Dong K Y, Jiang H D, et al. Integrated carbon dioxide emissions evaluation method of Chinese petroleum refining enterprises[J]. Environmental Engineering, 2017, 35(3): 163-167.
32	Geoffrion A M. Proper efficiency and the theory of vector maximization[J]. Journal of Mathematical Analysis and Applications, 1968, 22(3): 618-630.
33	Davisson W I. Public investment criteria[J]. Land Economics, 1964, 40(2): 153.
34	Rosenberg R S. Simulation of genetic populations with biochemical properties(II):Selection of crossover probabilities[J]. Mathematical Biosciences, 1970, 8(1/2): 1-37.
35	Kim I Y, de Weck O L. Adaptive weighted-sum method for bi-objective optimization: Pareto front generation[J]. Structural and Multidisciplinary Optimization, 2005, 29(2): 149-158.
36	Brook A, Kendrick D, GAMS Meeraus A., user's guide a [J]. ACM SIGNUM Newsletter, 1988, 23(3/4): 10-11.

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