Contextual Mixture of Experts: Integrating Knowledge into Predictive Modeling

Abstract

This work 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 contextual mixture of experts was employed to represent different contexts in both experiments. The results indicate that integrating process knowledge has increased predictive performance while improving interpretability by providing insights into the variables affecting the process's different regimes.

Process Expert Knowledge is Essential in Creating Value from Data-Driven Industrial Soft Sensors

“…Next to improving the modelling accuracy, including expert knowledge can also increase the acceptance of data-driven technologies at the production facility, which is key to their practical use. This is also reflected in the current Industry 5.0 trend, which aims to (re)centre automation around humans operators (European Commission, 2024), and has sprouted technologies such as contextual mixture of experts-modelling (Souza et al, 2022).…”