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Quality prediction via semisupervised Bayesian regression with application to propylene polymerization
Author(s) -
Sun Yuanmeng,
Liu Xinggao,
Zhang Zeyin
Publication year - 2018
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.3052
Subject(s) - overfitting , bayesian probability , regression , computer science , regression analysis , artificial intelligence , sampling (signal processing) , bayesian inference , quality (philosophy) , machine learning , statistics , mathematics , artificial neural network , philosophy , filter (signal processing) , epistemology , computer vision
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.