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Real‐time property prediction for an industrial rubber‐mixing process with probabilistic ensemble G aussian process regression models
Author(s) -
Liu Yi,
Gao Zengliang
Publication year - 2015
Publication title -
journal of applied polymer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.575
H-Index - 166
eISSN - 1097-4628
pISSN - 0021-8995
DOI - 10.1002/app.41432
Subject(s) - probabilistic logic , gaussian process , mixing (physics) , kriging , computer science , natural rubber , process (computing) , artificial intelligence , linear regression , machine learning , variance (accounting) , gaussian , data mining , materials science , physics , quantum mechanics , composite material , operating system , accounting , business
In internal rubber‐mixing processes, data‐driven soft sensors have become increasingly important for providing online measurements for the Mooney viscosity information. Nevertheless, the prediction uncertainty of the model has rarely been explored. Additionally, traditional viscosity prediction models are based on single models and, thus, may not be appropriate for complex processes with multiple recipes and shifting operating conditions. To address both problems simultaneously, we propose a new ensemble Gaussian process regression (EGPR)‐based modeling method. First, several local Gaussian process regression (GPR) models were built with the training samples in each subclass. Then, the prediction uncertainty was adopted to evaluate the probabilistic relationship between the new test sample and several local GPR models. Moreover, the prediction value and the prediction variance was generated automatically with Bayesian inference. The prediction results in an industrial rubber‐mixing process show the superiority of EGPR in terms of prediction accuracy and reliability. © 2014 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2015 , 132 , 41432.