Contextual Mixture of Experts: Integrating Knowledge into Predictive Modeling

Abstract

This article proposes a new data-driven model devised to integrate process knowledge into its structure to increase the human-machine synergy in the process industry. The proposed contextual mixture of experts (cMoE) explicitly uses process knowledge along the model learning stage to mold the historical data to represent operators' context related to the process through possibility distributions. This model was evaluated in two real case studies for quality prediction, including a sulfur recovery unit and a polymerization process. The cMoEs was employed to represent different contexts in both experiments. The results indicate that integrating process knowledge has increased the predictive performance while improving interpretability by providing insights into the variables affecting the process's different regimes.