文献类型：期刊文献

中文题名：Multi-objective modeling and optimization for scheduling of cracking furnace systems

英文题名：Multi-objective modeling and optimization for scheduling of cracking furnace systems

作者：Peng Jiang[1];Wenli Du[1,2]

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

年份：2017

卷号：25

期号：8

起止页码：992

中文期刊名：Chinese Journal of Chemical Engineering

外文期刊名：中国化学工程学报（英文版）

收录：CSTPCD;;Scopus;CSCD:【CSCD2017_2018】；

基金：Supported by the National Natural Science Foundation of China(21276078);"Shu Guang"project of Shanghai Municipal Education Commission,973 Program of China(2012CB720500);the Shanghai Science and Technology Program(13QH1401200)

语种：英文

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

外文关键词：多目标优化模型;裂解炉;非支配排序遗传算法;目标建模;调度;系统;非线性规划问题;混合离散变量

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 non- linear 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 muta- tion 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.
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.