Multi-objective modeling and optimization for scheduling of cracking furnace systems

doi:10.1016/j.cjche.2017.03.040

›› 2017, Vol. 25 ›› Issue (8): 992-999.DOI: 10.1016/j.cjche.2017.03.040

Multi-objective modeling and optimization for scheduling of cracking furnace systems

Peng Jiang¹, Wenli Du^1,2

1 Key Laboratory of Advanced Control and Optimization for Chemical Process, Ministry of Education, Shanghai 200237, China;
2 School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, China

收稿日期:2016-09-24 修回日期:2016-12-16 出版日期:2017-08-28 发布日期:2017-09-11
通讯作者: Wenli Du
基金资助:
Supported by the National Natural Science Foundation of China (21276078), “Shu Guang” project of Shanghai Municipal Education Commission, 973 Program of China (2012CB720500) and the Shanghai Science and Technology Program (13QH1401200).

Multi-objective modeling and optimization for scheduling of cracking furnace systems

Peng Jiang¹, Wenli Du^1,2

1 Key Laboratory of Advanced Control and Optimization for Chemical Process, Ministry of Education, Shanghai 200237, China;
2 School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, China

Received:2016-09-24 Revised:2016-12-16 Online:2017-08-28 Published:2017-09-11
Supported by:
Supported by the National Natural Science Foundation of China (21276078), “Shu Guang” project of Shanghai Municipal Education Commission, 973 Program of China (2012CB720500) and the Shanghai Science and Technology Program (13QH1401200).

摘要/Abstract

摘要： Cracking furnace is the core device for ethylene production. In practice, multiple ethylene furnaces are usually run in parallel. The scheduling of the entire cracking furnace system has great significance when multiple feeds are simultaneously processed in multiple cracking furnaces with the changing of operating cost and yield of product. In this paper, given the requirements of both profit and energy saving in actual production process, a multi-objective optimization model contains two objectives, maximizing the average benefits and minimizing the average coking amount was proposed. The model can be abstracted as a multi-objective mixed integer nonlinear programming problem. Considering the mixed integer decision variables of this multi-objective problem, an improved hybrid encoding non-dominated sorting genetic algorithm with mixed discrete variables (MDNSGA-II) is used to solve the Pareto optimal front of this model, the algorithm adopted crossover and mutation strategy with multi-operators, which overcomes the deficiency that normal genetic algorithm cannot handle the optimization problem with mixed variables. Finally, using an ethylene plant with multiple cracking furnaces as an example to illustrate the effectiveness of the scheduling results by comparing the optimization results of multi-objective and single objective model.

关键词: Cracking furnace systems, Feed scheduling, Multi-objective mixed integer nonlinear, optimization, Genetic algorithm

Abstract: Cracking furnace is the core device for ethylene production. In practice, multiple ethylene furnaces are usually run in parallel. The scheduling of the entire cracking furnace system has great significance when multiple feeds are simultaneously processed in multiple cracking furnaces with the changing of operating cost and yield of product. In this paper, given the requirements of both profit and energy saving in actual production process, a multi-objective optimization model contains two objectives, maximizing the average benefits and minimizing the average coking amount was proposed. The model can be abstracted as a multi-objective mixed integer nonlinear programming problem. Considering the mixed integer decision variables of this multi-objective problem, an improved hybrid encoding non-dominated sorting genetic algorithm with mixed discrete variables (MDNSGA-II) is used to solve the Pareto optimal front of this model, the algorithm adopted crossover and mutation strategy with multi-operators, which overcomes the deficiency that normal genetic algorithm cannot handle the optimization problem with mixed variables. Finally, using an ethylene plant with multiple cracking furnaces as an example to illustrate the effectiveness of the scheduling results by comparing the optimization results of multi-objective and single objective model.

Key words: Cracking furnace systems, Feed scheduling, Multi-objective mixed integer nonlinear, optimization, Genetic algorithm

Peng Jiang, Wenli Du. Multi-objective modeling and optimization for scheduling of cracking furnace systems[J]. , 2017, 25(8): 992-999.

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Multi-objective modeling and optimization for scheduling of cracking furnace systems

Multi-objective modeling and optimization for scheduling of cracking furnace systems

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