MPC-PI cascade control for the Kaibel dividing wall column integrated with data-driven soft sensor model

Abstract This paper proposes the MPC-PI cascade control for the Kaibel dividing wall column (Kaibel DWC) integrated with a data-driven soft sensor model. The PI control in the inner layer is able to stabilize the Kaibel DWC fast, while the model predictive control (MPC) in the outer layer can reduce the overshoot, and improve the control performances. The prediction model employs the nonlinear autoregressive with exogenous inputs (NARX) neural network model, which is suitable and effective for the nonlinear systems. The extended Kalman filter (EKF) is used for error corrections of the detected and the predicted compositions. The feasibility of the proposed scheme using the NARX model and the EKF is verified, and the results have been compared with those adopting the linear State Space model and the Kalman filter (KF). Results show that the steady-state deviations adopting the NARX model are far less than those employing the State Space model.