  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.
