Industrial polyethylene melt index prediction using ensemble manifold learning–based local model

Abstract

ABSTRACTFor online melt index prediction in multiple‐grade polyethylene polymerization processes, using only a fixed model is insufficient. Additionally, without enough process knowledge, it is difficult to select suitable input variables to accurately construct prediction models. A novel manifold learning based local probabilistic modeling method named ensemble just‐in‐time Gaussian process regression (EJGPR) is developed. By utilizing output variables, an optimization framework is proposed to preserve the local structure of both input and output variables. Then the output information is integrated into construction of a JGPR‐based local model. Additionally, some new extracted variables in the projection space can be obtained. Moreover, using the probabilistic prediction information, the uncertainty of each JGPR‐based local candidate model can be simply described. Consequently, using an efficient ensemble strategy, a more accurate EJGPR prediction model can be constructed online. The melt index prediction results in an industrial polyethylene process show it has better performance than conventional methods. © 2017 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2017, 134, 45094.