Soft Sensor Development Based on Quality-Relevant Slow Feature Analysis and Bayesian Regression with Application to Propylene Polymerization

Miao Zhang,Xiaoli Wu,Le Zhou,Jing Jie,Chengkuo Lee

doi:10.1155/2021/9985747

Miao Zhang, Xiaoli Wu + Show 3 more

Open Access

https://doi.org/10.1155/2021/9985747

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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.

Highlights

As modern process industries become larger scale and more integrated, pivotal key performance indices about product quality, process safety, and pollution reduction should be closely monitored [1,2,3,4]
To deal with the nonlinearity and dynamics in propylene polymerization (PP) processes, a soft sensor based on quality-relevant slow feature analysis and Bayesian regression is proposed in this paper
The comparison results prove that the soft sensor based on the QSFARVR model can achieve a good performance in the melt index (MI) prediction for the propylene polymerization process

Summary

Introduction

As modern process industries become larger scale and more integrated, pivotal key performance indices about product quality, process safety, and pollution reduction should be closely monitored [1,2,3,4]. Shang et al [37] proposed probabilistic slow feature analysis (PSFA) based a soft sensor model for quality prediction. To deal with the nonlinearity and dynamics in PP processes, a soft sensor based on quality-relevant slow feature analysis and Bayesian regression is proposed in this paper. Based on the selected QSFs, a Bayesian inference framework named relevance vector regression (RVR) is developed to predict the quality variable, i.e., MI for the polypropylene products. It takes advantages of a probability framework with iterative maximum likelihood techniques for parameter estimation and a sparse constraint for avoiding overfitting.

Methods

Results and Discussion

Conclusions