Multi-objective operation optimization of ethylene cracking furnace based on AMOPSO algorithm

被引：35
作者：
Geng, Zhiqiang [1 ,2 ]
Wang, Zun [1 ,2 ]
Zhu, Qunxiong [1 ,2 ]
Han, Yongming [1 ,2 ]
机构：
[1] Beijing Univ Chem Technol, Coll Informat Sci & Technol, Beijing 100029, Peoples R China
[2] Minist Educ China, Engn Res Ctr Intelligent PSE, Beijing 100029, Peoples R China
来源：
CHEMICAL ENGINEERING SCIENCE | 2016年 / 153卷
基金：
中国国家自然科学基金;
关键词：
Multi-objective optimization; Ethylene cracking furnace; PSO; Fuzzy consistent matrix; AHP; PARTICLE SWARM OPTIMIZATION; THERMAL-CRACKING; GENETIC ALGORITHM; HYBRID ALGORITHM;
D O I：
10.1016/j.ces.2016.07.009
中图分类号：
TQ [化学工业];
学科分类号：
0817 ;
摘要：
The objective of this article is to research and design a multi-objective operation optimization strategy and comprehensive evaluation method of solutions, to efficiently solve the multi-objective operation optimization problem of ethylene cracking furnace. An adaptive multi-objective particle swarm optimization (AMOPSO) algorithm is proposed and developed based on dynamic analytic hierarchy process (AHP). The algorithm adopts fuzzy consistent matrix to select the global best solution, which ensures the right direction of particle evolution. Furthermore, the evolution state is measured to adjust the weight and learning coefficients adaptively. The proposed method is applied to the operation optimization of ethylene cracking furnace. Two cases are studied including the fixed cracking cycle with four objectives and the non-fixed cracking cycle with five objectives. According to the preferences, decision makers can select the appropriate operation optimization conditions from alternative Pareto optimal solutions by the results of fuzzy evaluation. A feasible solution is provided for the multi-objective operation optimization of ethylene cracking furnace. (C) 2016 Elsevier Ltd. All rights reserved.
引用
收藏
页码：21 / 33
页数：13
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