Melt index prediction with a mixture of Gaussian process regression with embedded clustering and variable selections

In this study, a penalized mixture of the Gaussian process regression model was proposed for the prediction of melt index (MI) in industrial polymer production. MI plays an important role in detecting the grade of a product. It is difficult to measure directly and is characterized by a large number of variables and multigrades. Because of multigrade products, in the development of soft sensors for MI prediction, it is not valid to assume unimodal Gaussian distribution of the data. To this end, the proposed method is capable of the simultaneous identification of significant variables and determination of important clusters of multigrade products. It is based on the shrinkage methods that have been shown to provide stable models that are more interpretable. Case studies are presented to show the features of the proposed method and its applicability to industrial MI prediction. © 2017 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2017 , 134, 45237.