Multi-objective operation optimization of olefin separation process for MTO plant

doi:10.11949/0438-1157.20200698

Abstract

Abstract:

In modern coal processing industries, methanol-to-olefins (MTO) is an important equipment. Its olefin separation process is facing with problems such as the change of raw materials, the loss of olefin products and the high consumption of utilities. Operation optimization is required to achieve maximum benefits under the circumstance of quality assurance and requirements. This article takes the pre-depropanized olefin separation process of Lummus as the research object. And the optimization objectives are the total yield of ethylene and propylene as well as the total energy consumption. Modeling, simulation and multi-objective optimization of the process are conducted. Non-dominated sorting genetic algorithm (NSGA-II) is used to solve multi-objective optimization problem. The simultaneous optimization of 15 operational variables is achieved. Under the current yield, the optimal operation point is found by reducing the reflux ratio of low pressure depropanizer, deethanizer and 1^# propylene tower and so on. The results show that the optimal operating point can reduce energy consumption by 20 MW compared with the existing operating point. The optimization interval of each operation variable corresponding to different trade-off points is determined by the comprehensive analysis of decision variables. It is also found that distillation equipment can operate in different optimal operation intervals.

Key words: methanol-to-olefins, olefin separation, multi-objective optimization, olefin yield, energy conservation

摘要：

现代煤化工中，甲醇制烯烃（MTO）是一个非常重要的装置。其烯烃分离过程面临着原料变动大、烯烃产品损失以及较高的公用工程消耗等问题。这就需要在满足产品规格和需求的情况下，优化操作条件以实现最大效益。以Lummus前脱丙烷的烯烃分离工艺为研究对象，以增加乙烯与丙烯的总收率和降低总能耗为优化目标，对该工艺流程进行建模模拟与多目标优化。采用非支配排序遗传算法（NSGA-II）进行多目标优化的求解，实现了15个操作变量的同时优化。在维持产品收率不变的前提下，可通过降低脱丙烷塔、脱乙烷塔和1^#丙烯精馏塔的回流比等优化措施找到了当前最优操作点。结果表明，该最优操作点与现有操作点相比可降低20 MW能耗。通过对决策变量的综合分析，确定了不同目标权衡下对应的各个操作变量的优化区间，发现精馏塔可以在多个最佳操作区间内运行。

关键词: MTO过程, 烯烃分离, 多目标优化, 烯烃收率, 节能

CLC Number:

TQ 221.2

Lu YANG, Shuoshi LIU, Xiaoyan LUO, Siyu YANG, Yu QIAN. Multi-objective operation optimization of olefin separation process for MTO plant[J]. CIESC Journal, 2020, 71(10): 4720-4732.

杨路, 刘硕士, 罗小艳, 杨思宇, 钱宇. MTO烯烃分离过程的多目标操作优化[J]. 化工学报, 2020, 71(10): 4720-4732.

Figures/Tables 12

References 31

1	朱杰, 崔宇, 陈元君, 等. 甲醇制烯烃过程研究进展[J]. 化工学报, 2010, 61(7): 1674-1684.
	Zhu J, Cui Y, Chen Y J, et al. Recent researches on process from methanol to olefins[J]. CIESC Journal, 2010, 61(7): 1674-1684.
2	谭捷. 我国乙烯的供需现状及发展前景[J]. 精细与专用化学品, 2019, 27(10): 18-20.
	Tan J. Supply and demand situation and development prospect of ethylene in China[J]. Fine and Specialty Chemicals, 2019, 27(10):18-20.
3	谭捷. 我国丙烯的供需现状及发展前景[J]. 精细与专用化学品, 2019, 27(11):13-15.
	Tan J. Supply and demand situation and development prospect of propylene in China[J]. Fine and Specialty Chemicals, 2019, 27(11):13-15.
4	Chen J Q, Bozzano A, Glover B, et al. Recent advancements in ethylene and propylene production using the UOP/Hydro MTO process[J]. Catalysis Today, 2005, 106(1/2/3/4): 103-107.
5	Bandoni J A, Eliceche A M, Serrani A, et al. Optimal operation of ethylene plants[C]//Bussemaker H T, Iedema P D. 21st Symp. Comput. Appl. Chem. Eng.. The Hague, Netherlands: Elsevier, 1990: 77-82.
6	Petracci N, Eliceche A M, Bandoni A, et al. Optimal operation of an ethylene plant utility system[J]. Comput. Chem. Eng., 1993, 17: S147-S152.
7	Eliceche A M, Petracci N C, Hoch P, et al. Optimal operation of an ethylene plant at variable feed conditions[J]. Comput. Chem. Eng., 1995, 19: 223-228.
8	Petracci N C, Hoch P M, Eliceche A M. Flexibility analysis of an ethylene plant[J]. Comput. Chem. Eng., 1996, 20: S443-S448.
9	Yan M. Simulation and optimization of ethylene plant[D]. Lubbock: Texas Tech University, 2000.
10	李忠多, 王新华, 王健. MTO技术的研究进展[J]. 黑龙江科学, 2015, 6(9): 24-25.
	Li Z D, Wang X H, Wang J. Research progress of MTO technology[J]. Hei Long Jiang Science, 2015, 6(9): 24-25.
11	陈昇, 曹新波, 赵梦, 等. MTO前脱丙烷分离流程模拟及优化[J]. 化工进展, 2019, 38(7): 3473-3481.
	Chen S, Cao X B, Zhao M, et al. Simulation and optimization of MTO front-end depropanizer separation process[J]. Chemical Industry and Engineering Progress, 2019, 38(7): 3473-3481.
12	展宝瑞. 甲醇制烯烃产品分离的模拟与优化[D]. 上海: 华东理工大学, 2015.
	Zhan B R. Simulation and optimization of methanol to olefins products separation[D]. Shanghai: East China University of Science and Technology, 2015.
13	李立新, 杨林林, 李鑫钢. 乙烯装置前脱丙烷前加氢流程设计要点的分析[J]. 石油化工, 2009, 38(2): 174-178.
	Li L X, Yang L L, Li X G. Analysis of design key points for front-end depropanization and acetylene front-end hydrogenation process in ethylene plant[J]. Petrochemical Technology, 2009, 38(2): 174-178.
14	曲宝学, 赵久旭. 甲醇制烯烃工艺介绍及技术方案选择与比较[J]. 科技创新导报, 2018, 15(6): 107-108.
	Qu B X, Zhao J X. Introduction of methanol to olefin process and selection and comparison of technical schemes[J]. Science and Technology Innovation Herald, 2018, 15(6): 107-108.
15	董小云. 精馏塔乙烯损失率建模及智能优化控制策略[D]. 大连: 大连理工大学, 2017.
	Dong X Y. Modeling and intelligent optimal control strategy for ethylene loss rate of distillation column[D]. Dalian: Dalian University of Technology, 2017.
16	王延敏, 姚平经. 热偶精馏过程模拟优化方法的改进——人工神经网络-遗传算法[J]. 化工学报, 2003, 54(9): 1246-1250.
	Wang Y M, Yao P J. Advancement of simulation and optimization for thermally coupled distillation using neural network and genetic algorithm[J]. Journal of Chemical Industry and Engineering(China), 2003, 54(9): 1246-1250.
17	施辰斐. 甲醇四塔精馏系统能效优化控制的研究[D]. 上海: 上海交通大学, 2014.
	Shi C F. Research on energy-efficiency optimization control of a four-column methanol distillation system[D]. Shanghai: Shanghai Jiao Tong University, 2014.
18	Osuolale F N, Zhang J. Thermodynamic optimization of atmospheric distillation unit[J]. Comput. Chem. Eng., 2017, 103: 201-209.
19	Jiang P, Du W L. Multi-objective modeling and optimization for scheduling of cracking furnace systems[J]. Chin. J. Chem. Eng., 2017, 25(8): 992-999.
20	Inamdar S V. Multi-objective optimization of an industrial crude distillation unit using the elitist non-dominated sorting genetic algorithm[J]. Chemical Engineering, 2004, 82(5): 611-623.
21	Chen Q L, Yin Q H, Wang S P. Energy-use analysis and improvement for delayed coking units[J]. Energy, 2004, 29(12/13/14/15): 2225-2237.
22	刘涛, 苏成利. 气体分馏装置的流程模拟及多目标优化[J]. 石油化工高等学校学报, 2013, 26(1): 76-80.
	Liu T, Su C L. Simulation analysis and multi-objective optimization of gas fractionation unit[J]. Journal of Petrochemical Universities, 2013, 26(1): 76-80.
23	Liau C K, Yang C K, Tsai M T. Expert system of a crude oil distillation unit for process optimization using neural networks[J]. Expert Systems with Applications, 2004, 26(2): 247-255.
24	刘海燕. 多目标优化算法及其在化工中的应用研究[D]. 武汉: 武汉理工大学, 2015.
	Liu H Y. Multi-objective optimization algorithms and their applications in chemical engineering[D]. Wuhan: Wuhan University of Technology, 2015.
25	Soave G. Equilibrium constants for modified redlich-kwong equation-of-state[J]. Chem. Eng. Sci., 1972, 27: 1196-1203.
26	Listijorini E, Pratama N D, Vianda M A, et al. Optimization of depropanizer column quality product by changing controller set points of reflux flow and reboiler heat rate[C]// 2016 6th International Annual Engineering Seminar. Yogyakarta, Indonesia: IEEE, 2016: 116-120.
27	Nawaz M, Jobson M. Synthesis and optimization of demethanizer flowsheets for low temperature separation processes[J]. Nawaz M & Jobson M, 2010, 9: 79-84.
28	Luyben W L. Comparison of a conventional two‐column demethanizer/deethanizer configuration requiring refrigerated condensers with a nonconventional column/rectifier configuration[J]. J. Chem. Technol. Biotechnol., 2016, 91(6): 1688-1696.
29	Huang D, Luo X L. Process transition based on dynamic optimization with the case of a throughput-fluctuating ethylene column[J]. Ind. Eng. Chem. Res., 2018, 57(18): 6292-6302.
30	Ryzhikov V G, Alekhin S V, Sokolov E N, et al. Experience in modernizing a propane-propylene fraction separation plant[J]. Chem. Technol. Fuels Oils, 2011, 46(6): 375-377.
31	Jana A K. Advances in heat pump assisted distillation column: a review[J]. Energy Convers. Manage., 2014, 77: 287-297.

塔名称	压力/MPa(G)	塔顶/塔底温度/℃
DP1	1.85	15.8/80
DP2	0.77	14.4/79.2
DM	2.65	-11.1/13.8
DE	2.40	-20.4/62.7
ET	1.64	-34.3/-11.6
PT2	1.94	51.9/59.1
PT1	1.80	45.8/51.8
DB	0.37	46.8/92.6

塔名称	压力/MPa(G)	塔顶/塔底温度/℃
DP1	1.85	15.8/80
DP2	0.77	14.4/79.2
DM	2.65	-11.1/13.8
DE	2.40	-20.4/62.7
ET	1.64	-34.3/-11.6
PT2	1.94	51.9/59.1
PT1	1.80	45.8/51.8
DB	0.37	46.8/92.6

Stream	Temperature/ ℃	Pressure/ MPa(G)	Mass flow/ (t/h)	Mole flows/(kmol/h)	Mole fraction/%
Stream	Temperature/ ℃	Pressure/ MPa(G)	Mass flow/ (t/h)	Mole flows/(kmol/h)	H₂,N₂,CO	CH₄	C₂H₆	C₂H₄	C₃H₈	C₃H₆	C₄H₁₀	C₄H₈	C₄H₆	C5	CH₃OCH₃
feed 1^①	12.2	2.139	47.89	1190	0.35	0.41	0.71	33.27	3.02	45.12	0.54	12.57	0.31	3.64	0.05
feed 2^①	11.5	1.87	49.12	1638	5.85	6.26	1.04	60.8	1.47	22.77	0.08	1.55	0.04	0.13	0.02
1	-34.1	1.641	38.52	1373	trace	0.01	0.04	99.95	0	trace	0	0	0	0	0
2	46	1.803	38	903	0	trace	0.02	0	0.35	99.64	0	0	0	0	0
3	40.1	0.369	10.5	188	0	0	0	0	0.1	0.15	4.08	92.85	2.25	0.5	0.07
4	89.2	0.407	3.37	48	0	0	0	0	0	1×10^-12	0.01	0.62	0	99.37	0
1^①	-34.2	1.641	38.52	1373	0	0.01	0.04	99.95	0	0	0	0	0	0	0
2^①	45.8	1.803	38	903	0	0	0.02	0	0.35	99.63	0	0	0	0	0
3^①	39.6	0.369	10.5	188	0	0	0	0	0.1	0.15	4.09	92.87	2.24	0.5	0.07
4^①	92.6	0.407	3.37	48	0	0	0	0	0	0	0.01	0.62	0	99.37	0

Stream	Temperature/ ℃	Pressure/ MPa(G)	Mass flow/ (t/h)	Mole flows/(kmol/h)	Mole fraction/%
Stream	Temperature/ ℃	Pressure/ MPa(G)	Mass flow/ (t/h)	Mole flows/(kmol/h)	H₂,N₂,CO	CH₄	C₂H₆	C₂H₄	C₃H₈	C₃H₆	C₄H₁₀	C₄H₈	C₄H₆	C5	CH₃OCH₃
feed 1^①	12.2	2.139	47.89	1190	0.35	0.41	0.71	33.27	3.02	45.12	0.54	12.57	0.31	3.64	0.05
feed 2^①	11.5	1.87	49.12	1638	5.85	6.26	1.04	60.8	1.47	22.77	0.08	1.55	0.04	0.13	0.02
1	-34.1	1.641	38.52	1373	trace	0.01	0.04	99.95	0	trace	0	0	0	0	0
2	46	1.803	38	903	0	trace	0.02	0	0.35	99.64	0	0	0	0	0
3	40.1	0.369	10.5	188	0	0	0	0	0.1	0.15	4.08	92.85	2.25	0.5	0.07
4	89.2	0.407	3.37	48	0	0	0	0	0	1×10^-12	0.01	0.62	0	99.37	0
1^①	-34.2	1.641	38.52	1373	0	0.01	0.04	99.95	0	0	0	0	0	0	0
2^①	45.8	1.803	38	903	0	0	0.02	0	0.35	99.63	0	0	0	0	0
3^①	39.6	0.369	10.5	188	0	0	0	0	0.1	0.15	4.09	92.87	2.24	0.5	0.07
4^①	92.6	0.407	3.37	48	0	0	0	0	0	0	0.01	0.62	0	99.37	0

符号	变量	下限	上限
x₁	高压脱丙烷塔(DP1)塔顶采出量, kg/h	101000	105000
x₂	高压脱丙烷塔(DP1)回流比	1.18	1.23
x₃	低压脱丙烷塔(DP2)回流比	1.0	1.4
x₄	低压脱丙烷塔(DP2)塔顶采出量, kg/h	7600	8240
x₅	脱丁烷塔(DB)回流比	0.95	1.2
x₆	脱丁烷塔(DB)塔顶采出量, kg/h	10200	10700
x₇	脱甲烷塔(DM)塔顶采出量, kg/h	4150	4350
x₈	脱甲烷塔(DM)丙烷流量, kg/h	16200	17400
x₉	脱乙烷塔(DE)回流比	1.3	1.6
x₁₀	脱乙烷塔(DE)塔顶采出量, kg/h	40000	43000
x₁₁	乙烯塔(ET)塔顶采出量, kg/h	143000	147000
x₁₂	乙烯塔(ET)中间测线采出量, kg/h	37000	40000
x₁₃	2^#丙烯塔(PT2)塔顶采出量, kg/h	524000	536000
x₁₄	1^#丙烯塔(PT1)塔顶采出量, kg/h	37350	39000
x₁₅	1^#丙烯塔(PT1)回流比	14.0	18.0