Industrial Mooney viscosity prediction using fast semi-supervised empirical model

Abstract

In industrial rubber mixing processes, the quality index (i.e., Mooney viscosity) cannot be online measured directly. Traditional data-driven empirical models for online prediction of the Mooney viscosity have not utilized the information hidden in lots of unlabeled data (e.g., process input variables during each mixing batch). A simple semi-supervised nonlinear soft sensor method for the Mooney viscosity prediction is developed. It integrates extreme learning machine (ELM) and the graph Laplacian regularization into a unified modeling framework. The useful information in unlabeled data can be explored and introduced into the prediction model. Furthermore, a bagging-based ensemble strategy is combined into semi-supervised ELM (SELM) to obtain more accurate predictions. The Mooney viscosity prediction in an industrial internal mixer exhibits its promising prediction performance of the proposed method by incorporating the information in unlabeled data efficiently.