A Real-Time Model Predictive Control Framework for Enhanced Process Monitoring in Industrial Chemical Reactors

In modern industrial environments, efficient process control and monitoring are critical to ensuring optimal performance, safety, and product quality. As chemical and manufacturing processes become increasingly complex and nonlinear, traditional control techniques such as Proportional-Integral-Derivative (PID) controllers exhibit limited adaptability and robustness in the face of dynamic operating conditions, disturbances, and system uncertainties. This paper proposes a real-time integrated control and monitoring framework based on Model Predictive Control (MPC) and an Extended Kalman Filter (EKF) for fault-tolerant regulation of chemical reactors. The MPC component predicts future system behavior over a finite horizon and optimizes control inputs while respecting operational constraints. In parallel, the EKF provides accurate state estimation and residual analysis for anomaly detection and real-time health monitoring. The proposed methodology is applied to a benchmark nonlinear system—a continuously stirred tank reactor (CSTR)—to evaluate its effectiveness. Simulation results demonstrate superior performance of the MPC-EKF framework in terms of setpoint tracking, disturbance rejection, fault detection speed, and energy efficiency, compared to conventional PID control. Notably, the system can detect and respond to abrupt faults, such as flow disruptions, within a few seconds, thus minimizing process deviations and operational risks. The findings highlight the potential of combining predictive control with model-based monitoring for next-generation smart process systems and industrial digitalization initiatives.