Neural-Network Assisted MPC for flow reactors including reactions

This paper presents a Neural-Network Assisted Model Predictive Control (NN-A-MPC) framework for robust control of complex systems such as flow reactors. The approach combines an iterative neural-network-based design-space exploitation (DSE) with physics-based model (PBM) optimization. The DSE provides a promising warm start and reduces the search space for the PBM optimization in which relevant input regions are refined to ensure accuracy and physical feasibility. An integral compensation state enhances robustness against model mismatch and disturbances. Experimental validation on a Paal-Knorr flow reactor demonstrates accurate tracking while satisfying real-time constraints.