Soft Sensor Development Based on Quality-Relevant Slow Feature Analysis and Bayesian Regression with Application to Propylene Polymerization. (30th December 2021)

• Record Type: Journal Article
• Authors: Zhang, Miao; Zhou, Le; Jie, Jing; Wu, Xiaoli
• Other Names: Lee Chengkuo Academic Editor.
• Is Part Of: Journal of sensors. Volume 2021(2021)
• Publication Date: 2021-12-30
• Subjects: Detectors -- Periodicals; 681.205
• DOI: 10.1155/2021/9985747
• Languages: English
• ISSNs: 1687-725X

Abstract

Abstract : Data-driven soft sensors are widely used to predict quality indices in propylene polymerization processes to improve the availability of measurements and efficiency. To deal with the nonlinearity and dynamics in propylene polymerization processes, a novel soft sensor based on quality-relevant slow feature analysis and Bayesian regression is proposed in this paper. The proposed method can handle the dynamics of the process better by extracting quality-relevant slow features, which present both the slowly varying characteristic and the correlations with quality indices. Meanwhile, a Bayesian inference model is developed to predict the quality indices, which takes advantages of a probability framework with iterative maximum likelihood techniques for parameter estimation and a sparse constraint for avoiding overfitting. Finally, a case study is conducted with data sampled from a practical industrial propylene polymerization process to demonstrate the effectiveness and superiority of the proposed method.