文献类型：期刊文献

英文题名：Data-driven worst case model predictive control algorithm for propylene distillation column under uncertainty of top composition

作者：He, Renchu[1,2];Ju, Keshuai[1];Zhao, Liang[1];Long, Jian[1];Yang, Minglei[1]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai, Peoples R China;[2]East China Univ Sci & Technol, Engn Res Ctr Proc Syst Engn, Minist Educ, Shanghai, Peoples R China

年份：2023

卷号：124

起止页码：199

外文期刊名：JOURNAL OF PROCESS CONTROL

收录：;EI(收录号：20231313809898);WOS:【SCI-EXPANDED(收录号:WOS:000970230800001)】；

基金：This work was supported by National Natural Science Foundation of China (Basic Science Center Program: 61988101) , National Natural Science Foundation of China (62293501, 62073142, 61973124) and Fundamental Research Funds for the Central Universities.

语种：英文

外文关键词：Propylene distillation column; Optimized control; Robust model predictive control; Data -driven robust optimization; Uncertainty

摘要：Composition soft sensors have wide application in the distillation process. In this study, considering the limitation of the prediction ability of the composition soft sensor, a data-driven worst case model predictive control of propylene distillation column is proposed to hedge against the uncertainty of top composition. Firstly, based on the compartmental method and the dynamic mechanism model, a linear state space model of the distillation column is constructed. Aiming at dealing with the uncertainty of top propylene content caused by composition soft sensor, the data-driven uncertainty set is constructed by the combination of principal component analysis and kernel density estimation based on the historical data. Then, the certainty equivalent, traditional worst case, data-driven worst case, set-point tracking and offset-free model predictive control algorithm are designed. Finally, a case study of composition control in a propylene distillation column is carried out. Compared with other strategies, the proposed algorithm ensures the quality of the product while achieving small quality surplus and low operating cost.(c) 2023 Elsevier Ltd. All rights reserved.