Abstract
Copolymerization reactors are crucial for polymer synthesis but remain tough to regulate due to nonlinear kinetics, heat sensitivity, and disturbances such as inhibitor impurities. Traditional approaches like Proportional-Integral-Derivative (PID) controllers and linear Model Predictive Control (MPC) sometimes struggle with computational efficiency and accuracy trade-offs. This study presents a hybrid framework incorporating MPC, Neural Network (NN)-based gradient approximation, and adaptive Particle Swarm Optimization (PSO). The NN accelerates gradient computation to bypass iterative quadratic programming, while PSO enhances global search capabilities under actuator constraints. Validated on a nonlinear copolymerization reactor model, the hybrid method displays enhanced setpoint tracking and disturbance rejection compared to standard MPC and standalone PSO, with maintained computational feasibility. Robustness is emphasized by effective management of unmeasured inhibitor perturbations, achieving stable input adjustments and precise output regulation.
DOI: 10.61416/ceai.v27i4.9519