Quality prediction via semisupervised Bayesian regression with application to propylene polymerization

Abstract

Statistical learning techniques are widely used for quality prediction in polymerization processes during the last decades. However, compared to operation variables, quality variables of polypropylene process are usually difficult to acquire resulting from the absence of measuring units. A semisupervised Bayesian regression method is therefore presented to improve the prediction accuracy by sufficient usage of unlabeled sampling data for melt index prediction in polypropylene processes. The developed model consists of Bayesian inference to predict quality variables and neighborhood kernel density estimation for finding relationships between unlabeled data and labeled samples, which takes advantages of a probability framework with iterative maximum likelihood techniques for parameter estimation and a sparse constraint for avoiding overfitting. The quality prediction regression method is compared with published models by applying to a real dataset of industrial propylene polymerization. The experiment results demonstrate the effectiveness of the proposed semisupervised Bayesian method.