期刊
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
卷 67, 期 5, 页码 4098-4107出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIE.2019.2922941
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
资金
- National Natural Science Foundation of China [61603138, 21878081]
- Shanghai Pujiang Program [17PJD009]
- Hong Kong Research Grant Council Project [16207717]
- Programme of Introducing Talents of Discipline to Universities (the 111 Project) [B17017]
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.
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