Machine learning aided model predictive control with multi-objective optimization and multi-criteria decision making

Abstract

Model predictive control (MPC) is a well-established control methodology in chemical engineering, but the increasing complexity of chemical processes necessitates the consideration of multiple objectives in the MPC optimization step. To address this research gap, this work proposes a comprehensive machine learning (ML) aided MPC with multi-objective optimization (MOO) and multi-criteria decision making (MCDM) methodology (abbreviated as ML-aided MPC-MOO-MCDM) for chemical process control. The proposed methodology is evaluated on a continuous stirred tank reactor (CSTR), and the results demonstrate its capability to achieve intended optimization considering multiple objectives in MPC without compromising the closed-loop stability of the controlled system. The present work also reinforces the viability of using ML models as surrogates for first-principles models in process control and optimization. Overall, this work exhibits the effectiveness of the proposed ML-aided MPC-MOO-MCDM methodology and its applicability to complex chemical processes.