文献类型：期刊文献

英文题名：Data-Driven Batch-End Quality Modeling and Monitoring Based on Optimized Sparse Partial Least Squares

作者：Jiang, Qingchao[1];Yan, Xuefeng[1];Yi, Hui[2];Gao, Furong[3]

机构：[1]East China Univ Sci & Technol, Minist Educ, Key Lab Adv Control & Optimizat Chem Proc, Shanghai 200237, Peoples R China;[2]Nanjing Univ Technol, Coll Elect Engn & Control Sci, Nanjing 211816, Peoples R China;[3]Hong Kong Univ Sci & Technol, Dept Chem & Biomol Engn, Hong Kong, Peoples R China

年份：2020

卷号：67

期号：5

起止页码：4098

外文期刊名：IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS

收录：;EI(收录号：20200708179151);WOS:【SCI-EXPANDED(收录号:WOS:000516608400073)】；

基金：This work was supported in part by the National Natural Science Foundation of China under Grant 61603138 and Grant 21878081, in part by the Shanghai Pujiang Program under Grant 17PJD009, in part by the Hong Kong Research Grant Council Project under Grant 16207717, and in part by the Programme of Introducing Talents of Discipline to Universities (the 111 Project) under Grant B17017.

语种：英文

外文关键词：Predictive models; Monitoring; Input variables; Batch production systems; Optimization; Mathematical model; Analytical models; Batch-end quality prediction; batch processes; optimized sparse partial least square (OSPLS); soft sensing; sparse modeling

摘要：Batch-end quality modeling is used to predict the quality by using batch measurements and generally involves a large number of predictor variables. However, not all of the variables are beneficial for the prediction. Conventional multiway partial least squares (PLS) may not function properly for batch-end quality modeling because of many irrelevant predictor variables. This paper proposes an optimized sparse PLS (OSPLS) modeling approach for simultaneous batch-end quality prediction and relevant-variable selection. The effect of irrelevant variables on the quality-prediction performance is analyzed, and the importance of the relevant-variable selection is emphasized. Then, an OSPLS batch-end quality modeling approach is developed by incorporating the variable resolution optimization and sparse PLS modeling. The quality-prediction accuracy and modeling interpretability are improved because only quality-relevant variables are selected, and quality-irrelevant variables are eliminated. Based on the selected quality-relevant variables, a statistic is established for monitoring the quality status. The proposed OSPLS-based modeling and monitoring approach is applied on a fed-batch penicillin fermentation process and an industrial injection molding process. The results are compared with the state-of-the-art methods to verify the effectiveness of the OSPLS approach.