响应面法耦合NSGA-Ⅱ算法的隔壁塔结构优化

doi:10.16085/j.issn.1000-6613.2019-1716

化工进展 ›› 2020, Vol. 39 ›› Issue (8): 2962-2971.DOI: 10.16085/j.issn.1000-6613.2019-1716

响应面法耦合NSGA-Ⅱ算法的隔壁塔结构优化

谢江维¹(), 李春利¹^,²(), 黄国明³

^1.河北工业大学化工学院，天津 300130
^2.化工节能过程集成与资源利用国家地方联合工程实验室，天津 300130
^3.华北制药集团先泰药业有限公司，河北石家庄 052165

出版日期:2020-08-01 发布日期:2020-08-12
通讯作者: 李春利
作者简介:谢江维（1988—），男，博士研究生，研究方向为分离与纯化。E-mail：ctstxjw@163.com。
基金资助:
国家自然科学基金(21878066);中央引导地方科技发展专项资金项目(19944507G);天津市重点研发计划科技支撑重点项目(17YFZCGX00590)

Structural optimization of dividing wall column using response surface methodology coupled with NSGA-Ⅱ algorithm

Jiangwei XIE¹(), Chunli LI¹^,²(), Guoming HUANG³

^1.School of Chemical Engineering, Hebei University of Technology, Tianjin 300130, China
^2.National-Local Joint Engineering Laboratory for Energy Conservation of Chemical Process Integration and Resources Utilization, Tianjin 300130, China
^3.North China Pharmaceutical Group Xiantai Pharmaceutical Co. , Ltd. , Shijiazhuang 052165, Hebei, China

Online:2020-08-01 Published:2020-08-12
Contact: Chunli LI

摘要/Abstract

摘要：

隔壁塔的优化设计涉及多个变量且变量之间存在复杂的相互作用关系，这不仅提高了隔壁塔的设计难度，而且制约了其在工业应用方面的潜力。为了解决这一问题，本文提出了一种利用响应面法（RSM）耦合非支配遗传算法（NSGA-Ⅱ）对隔壁塔进行多目标优化设计的方法。首先，确定设计变量并利用单因素分析确定各变量水平，采用BBD（Box-Behnken）方法进行实验设计，并通过数值模拟计算年度总费用（TAC）和再沸器热负荷（Q）的目标函数；然后利用方差分析（ANOVA）评估各回归模型的统计重要性,并用二次多项式形式表示；最后通过NSGA-Ⅱ算法对响应面模型进行优化，计算Pareto前沿获得一系列优化方案。研究表明，相比于传统流程，采用该方法对隔壁塔进行分析和优化，能在降低TAC的同时有效降低Q,为隔壁塔的优化设计提供一种新思路。

关键词: 隔壁塔, 多目标优化, 响应面法, NSGA-Ⅱ, 年度总费用

Abstract:

The optimal design of the dividing wall column (DWC) involves multiple variables. The complex interactions among the variables are complicated. This not only increases the design difficulty of the DWC but also limits its potential for industrial applications. In order to solve this problem, a multi-objective optimization using the response surface methodology (RSM) coupled non-dominated sorting genetic algorithm (NSGA-Ⅱ) was proposed for DWC design and optimization. Firstly, the design variables were determined and their levels were determined by using single-factor analysis. The experimental design were carried out by the BBD (Box-Behnken) method, and the objective functions of total annual cost (TAC) and reboiler duty (Q) were calculated by numerical simulation. Secondly, the statistical significance of each regression model was then evaluated by analysis of variance (ANOVA) and expressed as a quadratic polynomial. Finally, the response surface models were optimized by NSGA-Ⅱ algorithm, and a series of optimization schemes was obtained by calculating the Pareto front. Compared with the traditional process, the research showed that the analysis and optimization of the DWC by this new method can effectively reduce the Q while reducing the TAC, and provide a new idea for the optimization design of the DWC.

Key words: dividing wall column, multi-objective optimization, response surface methodology（RSM）, NSGA-Ⅱ, total annual cost（TAC）

中图分类号:

TQ028.3

谢江维, 李春利, 黄国明. 响应面法耦合NSGA-Ⅱ算法的隔壁塔结构优化[J]. 化工进展, 2020, 39(8): 2962-2971.

Jiangwei XIE, Chunli LI, Guoming HUANG. Structural optimization of dividing wall column using response surface methodology coupled with NSGA-Ⅱ algorithm[J]. Chemical Industry and Engineering Progress, 2020, 39(8): 2962-2971.

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响应面法耦合NSGA-Ⅱ算法的隔壁塔结构优化

Structural optimization of dividing wall column using response surface methodology coupled with NSGA-Ⅱ algorithm

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