Refrigeration system synthesis based on de-redundant model by particle swarm optimization algorithm

doi:10.1016/j.cjche.2022.06.007

中国化学工程学报 ›› 2022, Vol. 50 ›› Issue (10): 412-422.DOI: 10.1016/j.cjche.2022.06.007

• Process Systems Engineering and Process Safety • 上一篇下一篇

Refrigeration system synthesis based on de-redundant model by particle swarm optimization algorithm

Danlei Chen¹, Yiqing Luo¹, Xigang Yuan^1,2

1 School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China;
2 State Key Laboratory of Chemical Engineering, Tianjin University, Tianjin 300072, China

收稿日期:2022-03-28 修回日期:2022-06-06 出版日期:2022-10-28 发布日期:2023-01-04
通讯作者: Yiqing Luo,E-mail:luoyq@tju.edu.cn
基金资助:
This research was supported by the National Natural Science Foundation of China (21978203) and the Natural Science Foundation of Tianjin (19JCYBJC20300).

Refrigeration system synthesis based on de-redundant model by particle swarm optimization algorithm

Danlei Chen¹, Yiqing Luo¹, Xigang Yuan^1,2

1 School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China;
2 State Key Laboratory of Chemical Engineering, Tianjin University, Tianjin 300072, China

Received:2022-03-28 Revised:2022-06-06 Online:2022-10-28 Published:2023-01-04
Contact: Yiqing Luo,E-mail:luoyq@tju.edu.cn
Supported by:
This research was supported by the National Natural Science Foundation of China (21978203) and the Natural Science Foundation of Tianjin (19JCYBJC20300).

摘要/Abstract

摘要： Simultaneous optimization of refrigeration system (RS) and its heat exchanger network (HEN) leads to a large-scale non-convex mixed-integer non-linear programming (MINLP) problem. Conventionally, researchers usually adopted simplifications to confine problem scale from being too large at the cost of reducing solution space. This study established an optimization framework for the simultaneous optimization of RS and HEN. Firstly, A more comprehensive and compact model was developed to guarantee a relatively complete solution space while reducing model scale as well as its solving difficulty. In this model, a tandem arrangement of connecting sub-coolers and expansion valves was considered in the superstructure; and the pressure/temperature levels were optimized as continuous variables. On this basis, we proposed a “two-step transformation method” to equivalently transform the cross-level structure into a non-cross-level structure, and the de-redundant superstructure was established with ensuring comprehensiveness and rigor. Furthermore, the MINLP model was developed and solved by Particle Swarm Optimization algorithm. Finally, our methodology was validated to get better optimal results with less CPU time in two case studies, an ethylene RS in an existing plant and a reported propylene RS.

关键词: Refrigeration system, Optimal design, Process systems, Particle swarm optimization, Mathematical modeling

Abstract: Simultaneous optimization of refrigeration system (RS) and its heat exchanger network (HEN) leads to a large-scale non-convex mixed-integer non-linear programming (MINLP) problem. Conventionally, researchers usually adopted simplifications to confine problem scale from being too large at the cost of reducing solution space. This study established an optimization framework for the simultaneous optimization of RS and HEN. Firstly, A more comprehensive and compact model was developed to guarantee a relatively complete solution space while reducing model scale as well as its solving difficulty. In this model, a tandem arrangement of connecting sub-coolers and expansion valves was considered in the superstructure; and the pressure/temperature levels were optimized as continuous variables. On this basis, we proposed a “two-step transformation method” to equivalently transform the cross-level structure into a non-cross-level structure, and the de-redundant superstructure was established with ensuring comprehensiveness and rigor. Furthermore, the MINLP model was developed and solved by Particle Swarm Optimization algorithm. Finally, our methodology was validated to get better optimal results with less CPU time in two case studies, an ethylene RS in an existing plant and a reported propylene RS.

Key words: Refrigeration system, Optimal design, Process systems, Particle swarm optimization, Mathematical modeling

Danlei Chen, Yiqing Luo, Xigang Yuan. Refrigeration system synthesis based on de-redundant model by particle swarm optimization algorithm[J]. 中国化学工程学报, 2022, 50(10): 412-422.

Danlei Chen, Yiqing Luo, Xigang Yuan. Refrigeration system synthesis based on de-redundant model by particle swarm optimization algorithm[J]. Chinese Journal of Chemical Engineering, 2022, 50(10): 412-422.

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Refrigeration system synthesis based on de-redundant model by particle swarm optimization algorithm

Refrigeration system synthesis based on de-redundant model by particle swarm optimization algorithm

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