Optimization and simultaneous heat integration design of a coal-based ethylene glycol refining process by a parallel differential evolution algorithm

doi:10.1007/s11705-023-2301-4

Front. Chem. Sci. Eng.

2023, Vol. 17

Issue (9) : 1280-1288 https://doi.org/10.1007/s11705-023-2301-4

RESEARCH ARTICLE

Optimization and simultaneous heat integration design of a coal-based ethylene glycol refining process by a parallel differential evolution algorithm

Jiahao Wang¹, Hao Lyu¹, Daoyan Liu¹, Chengtian Cui², Jinsheng Sun¹(

)

¹. School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
². Institute of Intelligent Manufacturing, Nanjing Tech University, Nanjing 211816, China

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Abstract

Coal to ethylene glycol still lacks algorithm optimization achievements for distillation sequencing due to high-dimension and strong nonconvexity characteristics, although there are numerous reports on horizontal comparisons and process revamping. This scenario triggers the navigation in this paper into the simultaneous optimization of parameters and heat integration of the coal to ethylene glycol distillation scheme and double-effect superstructure by the self-adapting dynamic differential evolution algorithm. To mitigate the influence of the strong nonconvexity, a redistribution strategy is adopted that forcibly expands the population search domain by exerting external influence and then shrinks it again to judge the global optimal solution. After two redistributive operations under the parallel framework, the total annual cost and CO₂ emissions are 0.61%/1.85% better for the optimized process and 3.74%/14.84% better for the superstructure than the sequential optimization. However, the thermodynamic efficiency of sequential optimization is 11.63% and 10.34% higher than that of simultaneous optimization. This study discloses the unexpected great energy-saving potential for the coal to ethylene glycol process that has long been unknown, as well as the strong ability of the self-adapting dynamic differential evolution algorithm to optimize processes described by the high-dimensional mathematical model.

Keywords ethylene glycol redistribution heat integration optimization parallel framework

Corresponding Author(s): Jinsheng Sun

About author:

^* These authors contributed equally to this work.

Just Accepted Date: 04 April 2023 Online First Date: 26 May 2023 Issue Date: 29 August 2023

Cite this article:

Jiahao Wang,Hao Lyu,Daoyan Liu, et al. Optimization and simultaneous heat integration design of a coal-based ethylene glycol refining process by a parallel differential evolution algorithm[J]. Front. Chem. Sci. Eng., 2023, 17(9): 1280-1288.

URL:

https://academic.hep.com.cn/fcse/EN/10.1007/s11705-023-2301-4
https://academic.hep.com.cn/fcse/EN/Y2023/V17/I9/1280

Fig.1 Evolution curve.

Tab.1 Initial column parameters

Fig.2 Flowsheet of the CtEG core process.

Fig.3 Optimization and heat integration logic structure.

Fig.4 The redistribution structure.

Fig.5 CAPEX, OPEX, and TAC comparison before and after optimization.

Fig.6 Heat integration structure and parameters for SI optimization.

Fig.7 CAPEX, OPEX, and TAC comparison before and after superstructure optimization.

Fig.8 Superstructure optimization process flowsheet.

Fig.9 Comparison of CO₂ emissions.

Fig.10 Thermodynamic efficiency comparison.

1	R Masoudi, B Tohidi, R Anderson, R Burgass, J Yang. Experimental measurement and thermodynamic modelling of clathrate hydrate equilibria and salt solubility in aqueous ethylene glycol and electrolyte solutions. Fluid Phase Equilibria, 2004, 219(2): 157–163 https://doi.org/10.1016/j.fluid.2004.01.031
2	J Yang, J Lv, B Gao, L Zhang, D Yang, C Shi, J Guo, W Li, Y Feng. Modification of polycarbonateurethane surface with poly(ethylene glycol) monoacrylate and phosphorylcholine glyceraldehyde for anti-platelet adhesion. Frontiers of Chemical Science and Engineering, 2014, 8(2): 188–196 https://doi.org/10.1007/s11705-014-1414-1
3	S Heyuan, J Ronghua, K Meirong, C Jing. Progress in synthesis of ethylene glycol through C1 chemical industry routes. Chinese Journal of Catalysis, 2013, 34(6): 1035–1050 https://doi.org/10.1016/S1872-2067(12)60529-4
4	Q Yang, S Xu, J Zhang, C Liu, D Zhang, H Zhou, S Mei, M Gao, H Liu. Thermodynamic and techno-economic analyses of a novel integrated process of coal gasification and methane tri-reforming to ethylene glycol with low carbon emission and high efficiency. Energy, 2021, 229: 120713 https://doi.org/10.1016/j.energy.2021.120713
5	Q Yang, S Zhu, Q Yang, W Huang, P Yu, D Zhang, Z Wang. Comparative techno-economic analysis of oil-based and coal-based ethylene glycol processes. Energy Conversion and Management, 2019, 198: 111814 https://doi.org/10.1016/j.enconman.2019.111814
6	Q Yang, J Zhang, G Chu, H Zhou, D Zhang. Optimal design, thermodynamic and economic analysis of coal to ethylene glycol processes integrated with various methane reforming technologies for CO2 reduction. Energy Conversion and Management, 2021, 244: 114538 https://doi.org/10.1016/j.enconman.2021.114538
7	J Chen, Y Qian, S Yang. Conceptual design and techno-economic analysis of a coal to methanol and ethylene glycol cogeneration process with low carbon emission and high efficiency. ACS Sustainable Chemistry & Engineering, 2020, 8(13): 5229–5239 https://doi.org/10.1021/acssuschemeng.0c00043
8	R Wei, C Yan, A Yang, W Shen, J Li. Improved process design and optimization of 200 kt·a–1 ethylene glycol production using coal-based syngas. Chemical Engineering Research & Design, 2018, 132: 551–563 https://doi.org/10.1016/j.cherd.2018.02.006
9	B Yu, I Chien. Design and optimization of dimethyl oxalate (DMO) hydrogenation process to produce ethylene glycol (EG). Chemical Engineering Research & Design, 2017, 121: 173–190 https://doi.org/10.1016/j.cherd.2017.03.012
10	J Humphrey. Separation processes: playing a critical role. Chemical Engineering Progress, 1995, 91(10): 31–41
11	A Kiss. Distillation technology—still young and full of breakthrough opportunities. Journal of Chemical Technology and Biotechnology, 2014, 89(4): 479–498 https://doi.org/10.1002/jctb.4262
12	A Kiss. Advanced Distillation Technologies: Design, Control and Applications. New York: John Wiley & Sons Ltd., 2013, 12
13	B V Babu, M Khan. Optimization of reactive distillation processes using differential evolution strategies. Asia-Pacific Journal of Chemical Engineering, 2007, 2(4): 322–335 https://doi.org/10.1002/apj.89
14	M Vázquez-Ojeda, J Segovia-Hernández, S Hernández, A Hernández-Aguirre, A Kiss. Optimization of an ethanol dehydration process using differential evolution algorithm. Computer-Aided Chemical Engineering, 2013, 32: 217–222 https://doi.org/10.1016/B978-0-444-63234-0.50037-3
15	M H Khademi, S Angooraj Taghavi. Optimization of ethylene oxychlorination fluidized-bed reactor using differential evolution (DE) method. Scientia Iranica, 2017, 24(3): 1253–1263 https://doi.org/10.24200/sci.2017.4109
16	L Wu, Y Wang, S Zhou, X Yuan. Self-adapting control parameters modified differential evolution for trajectory planning of manipulators. Journal of Control Theory and Applications, 2007, 5(4): 365–373 https://doi.org/10.1007/s11768-006-6178-9
17	C Wang, J Gao. A differential evolution algorithm with cooperative coevolutionary selection operation for high-dimensional optimization. Optimization Letters, 2014, 8(2): 477–492 https://doi.org/10.1007/s11590-012-0592-3
18	E Dragoi, S Curteanu. The use of differential evolution algorithm for solving chemical engineering problems. Reviews in Chemical Engineering, 2016, 32(2): 149–180 https://doi.org/10.1515/revce-2015-0042
19	G Rangaiah. Stochastic Global Optimization: Techniques and Applications in Chemical Engineering (with CD-ROM). Singapore: World Scientific Ltd., 2010, 204
20	C Cui, X Zhang, J Sun. Design and optimization of energy-efficient liquid-only side-stream distillation configurations using a stochastic algorithm. Chemical Engineering Research & Design, 2019, 145: 48–52 https://doi.org/10.1016/j.cherd.2019.03.001
21	G Rangaiah, S Sharma, H Lin. Evaluation of two termination criteria in evolutionary algorithms for multi-objective optimization of complex chemical processes. Chemical Engineering Research & Design, 2017, 124: 58–65 https://doi.org/10.1016/j.cherd.2017.05.030
22	J Leboreiro, J Acevedo. Processes synthesis and design of distillation sequences using modular simulators: a genetic algorithm framework. Computers & Chemical Engineering, 2004, 28(8): 1223–1236 https://doi.org/10.1016/j.compchemeng.2003.06.003
23	H Lyu, C Cui, X Zhang, J Sun. Population-distributed stochastic optimization for distillation processes: implementation and distribution strategy. Chemical Engineering Research & Design, 2021, 168: 357–368 https://doi.org/10.1016/j.cherd.2021.02.023
24	X Yu, M Burkholder, S Karakalos, G Tate, J Monnier, B Gupton, C Williams. Hydrogenation of dimethyl oxalate to ethylene glycol over Cu/KIT-6 catalysts. Catalysis Science & Technology, 2021, 11(7): 2403–2413 https://doi.org/10.1039/D0CY02334E
25	Q Yang, D Zhang, H Zhou, C Zhang. Process simulation, analysis and optimization of a coal to ethylene glycol process. Energy, 2018, 155: 521–534 https://doi.org/10.1016/j.energy.2018.04.153
26	Q Yang, D Zhang, H Zhou, C Zhang. Efficient utilization of CO2 in a coal to ethylene glycol process integrated with dry/steam-mixed reforming: conceptual design and technoeconomic analysis. ACS Sustainable Chemistry & Engineering, 2019, 7(3): 3496–3510 https://doi.org/10.1021/acssuschemeng.8b05757
27	L Jin, X Zhang, C Cui, Z Xi, J Sun. Simultaneous process parameters and heat integration optimization for industrial organosilicon production. Separation and Purification Technology, 2021, 265: 118520 https://doi.org/10.1016/j.seppur.2021.118520
28	A Yang, Y Su, T Shi, J Ren, W Shen, T Zhou. Energy-efficient recovery of tetrahydrofuran and ethyl acetate by triple-column extractive distillation: entrainer design and process optimization. Frontiers of Chemical Science and Engineering, 2022, 16(2): 303–315 https://doi.org/10.1007/s11705-021-2044-z
29	C Cui, N Long, J Sun, M Lee. Electrical-driven self-heat recuperative pressure-swing azeotropic distillation to minimize process cost and CO2 emission: process electrification and simultaneous optimization. Energy, 2020, 195: 116998 https://doi.org/10.1016/j.energy.2020.116998
30	W Luyben. Principles and Case Studies of Simultaneous Design. Hoboken: John Wiley & Sons Ltd., 2012, 53–57
31	A Yang, Y Su, L Teng, S Jin, T Zhou, W Shen. Investigation of energy-efficient and sustainable reactive/pressure-swing distillation processes to recover tetrahydrofuran and ethanol from the industrial effluent. Separation and Purification Technology, 2020, 250: 117210 https://doi.org/10.1016/j.seppur.2020.117210
32	M Gadalla, Ž Olujić, Rijke A De, P Jansens. Reducing CO2 emissions of internally heat-integrated distillation columns for separation of close boiling mixtures. Energy, 2006, 31(13): 2409–2417 https://doi.org/10.1016/j.energy.2005.10.029
33	A Yang, L Lv, W Shen, L Dong, J Li, X Xiao. Optimal design and effective control of the tert-amyl methyl ether production process using an integrated reactive dividing wall and pressure swing columns. Industrial & Engineering Chemistry Research, 2017, 56(49): 14565–14581 https://doi.org/10.1021/acs.iecr.7b03459
34	X Liu, X Yang, M Yu, W Zhang, Y Wang, P Cui, Z Zhu, Y Ma, J Gao. Energy, exergy, economic and environmental (4E) analysis of an integrated process combining CO2 capture and storage, an organic Rankine cycle and an absorption refrigeration cycle. Energy Conversion and Management, 2020, 210: 112738 https://doi.org/10.1016/j.enconman.2020.112738
35	Q Zhao, X Chu, Y Li, M Yan, X Wang, Z Zhu, P Cui, Y Wang, C Wang. Economic, environmental, exergy (3E) analysis and multi-objective genetic algorithm optimization of isopropyl acetate production with hybrid reactive-extractive distillation. Separation and Purification Technology, 2022, 301: 121973 https://doi.org/10.1016/j.seppur.2022.121973

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