Optimization for operating conditions of ethylbenzene dehydrogenation based on non-dominated sorting genetic algorithm

doi:10.3969/j.issn.0438-1157.2012.09.015

CIESC Journal ›› 2012, Vol. 63 ›› Issue (9): 2771-2776.DOI: 10.3969/j.issn.0438-1157.2012.09.015

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Optimization for operating conditions of ethylbenzene dehydrogenation based on non-dominated sorting genetic algorithm

YU Hui, WANG Chao, LI Lijuan, ZHANG Shi

College of Automation and Electrical Engineering, Nanjing University of Technology, Nanjing 211816, Jiangsu, China

Received:2012-06-12 Revised:2012-06-22 Online:2012-09-05 Published:2012-09-05
Supported by:
supported by the Natural Science Fund for Colleges and Universities in Jiangsu Province(09KJB510003).

基于非支配排序遗传算法的乙苯脱氢工艺条件优化

俞辉, 王超, 李丽娟, 张湜

南京工业大学自动化与电气工程学院, 江苏南京 211816

通讯作者: 李丽娟
作者简介:俞辉(1975-),男,硕士,讲师。
基金资助:
江苏省高校自然科学基金项目(09KJB510003)。

Abstract

Abstract: In order to improve the productivity and energy saving level of styrene in the dehydrogenation of ethylbenzene,optimization is an effective technological mean.The application of improved non-dominated sorting genetic algorithm is studied in optimization for operating conditions of dehydrogenation of ethylbenzene.Conversion and selectivity of the process of dehydrogenation of ethylbenzene to styrene are considered as the two objectives,and the kinetic model and process conditions are the constraints of the problems.NSGA-Ⅱ(non-dominated sorting genetic algorithm)is used to solve the optimization question of above dehydrogenation of ethylbenzene process.According to the obtained Pareto optimal solution set,the influence of operating conditions on conversion and selectivity of dehydrogenation of ethylbenzene is analyzed.Fuzzy comprehensive evaluation method is studied to satisfy specified demanding,supplying referenced optimal operating conditions.The results demonstrated good performance of NSGA-Ⅱ for achieving global optimal.With this algorithm,a satisfactory solution in different operating constraints can be obtained.

Key words: NSGA-Ⅱ, multi-objective optimization, dehydrogenation of ethylbenzene, fuzzy comprehensive evaluation

摘要： 为提高现有乙苯脱氢制苯乙烯生产装置的生产率和节能水平,优化技术是一种有效的技术手段。基于改进的非支配排序遗传算法(NSGA-Ⅱ)研究了乙苯脱氢工艺条件的优化问题。把乙苯脱氢反应过程的转化率、选择性作为优化目标,动力学模型以及实际生产状况作为约束条件,构造乙苯脱氢过程的多目标优化问题。基于NSGA-Ⅱ算法求解得到的优化问题的Pareto最优集,分析了各个操作条件对乙苯脱氢生产过程转化率和选择性的影响,最后利用模糊综合评价法,为合理决策提供了有效的依据。结果表明NSGA-Ⅱ具有良好的全局优化性能,运用该算法可在不同的操作约束条件下,求解得到相应的满意解。

关键词: NSGA-Ⅱ, 多目标优化, 乙苯脱氢, 模糊综合评价

CLC Number:

TP181

YU Hui, WANG Chao, LI Lijuan, ZHANG Shi. Optimization for operating conditions of ethylbenzene dehydrogenation based on non-dominated sorting genetic algorithm[J]. CIESC Journal, 2012, 63(9): 2771-2776.

俞辉, 王超, 李丽娟, 张湜. 基于非支配排序遗传算法的乙苯脱氢工艺条件优化[J]. 化工学报, 2012, 63(9): 2771-2776.

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Optimization for operating conditions of ethylbenzene dehydrogenation based on non-dominated sorting genetic algorithm

基于非支配排序遗传算法的乙苯脱氢工艺条件优化

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