文献类型：期刊文献

英文题名：Online Quality Prediction of Industrial Terephthalic Acid Hydropurification Process Using Modified Regularized Slow-Feature Analysis

作者：Zhong, Weimin[1];Jiang, Chao[1];Peng, Xin[1];Li, Zhi[1];Qian, Feng[1]

机构：[1]East China Univ Sci & Technol, Key Lab Adv Control & Optimizat Chem Proc, Minist Educ, Shanghai 200237, Peoples R China

年份：2018

卷号：57

期号：29

起止页码：9604

外文期刊名：INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH

收录：;EI(收录号：20182705403537);WOS:【SCI-EXPANDED(收录号:WOS:000440512700024)】；

基金：This work was supported by the National Natural Science Foundation of China (Key Program 61333010), the National Science Fund for Distinguished Young Scholars (61725301), the International (Regional) Cooperation and Exchange Project (61720106008), the Program of Introducing Talents of Discipline to Universities (the 111 Project) under Grant B17017, Fundamental Research Funds for the Central Universities (222201814041), and Shanghai Sailing Program (18YF1405200)

语种：英文

外文关键词：Forecasting - Textile industry - Learning systems - Quality control

摘要：Purified terephthalic acid (PTA) is an important product for the polyester and textile industry. In the industrial PTA-production process, 4-carboxybenzaldehyde (4-CBA) is a detrimental byproduct that can lower the polymerization rate and the average molecular weight of the polymer. Therefore, the content of 4-CBA in the final product can be used as a quality index to evaluate the current running status of the PTA-production process. However, because of the slow catalyst deactivation, this process is notable for its nonlinearity and dynamics. It is very difficult to obtain the 4-CBA-content values using traditional prediction methods from the process directly in real-time. For a better estimation of the status of the PTA-production process, a novel, online quality-prediction method based on modified regularized slow-feature analysis (ReSFA) is proposed in this paper for predicting the concentration of 4-CBA. The proposed method can handle the dynamics of the process better by exploring the temporal relationship of the input variables and incorporating the neighboring relationships of the input and output variables. Meanwhile, a modified just-in-time-learning method is introduced to deal with nonlinearity to improve online prediction performance. Finally, a case study is conducted with data sampled from a practical industrial terephthalic acid hydropurification process to demonstrate the effectiveness and superiority of the proposed method.