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

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.

Weighted target feature regression neural networks based soft sensing for industrial process

“…[4] For traditional manual inspection methods, cost-effective online measurements are lacking to produce high quality products, and therefore traditional mechanistic models are difficult to accurately describe real industrial processes. [5,6] These problems pose a great challenge for accurate online measurement of key variables in process industries. [7] Based on the above problems, many scholars have searched for effective modelling and prediction methods for industrial processes from the perspective of the complex background of process industries.…”