KF-based MPC For the cascaded headbox system in papermaking process

In papermaking process, headbox is a key component of the paper machine, whose output has big influences on the produced paper quality and operation safety. In this paper, the double air-cushioned headboxes structure is modelled in a cascaded way, in which we take the liquid level and air-cushioned pressure into consideration. To achieve stable and fast-responded control performance for the proposed cascaded model, a Kalman Filter (KF)-based model predictive control (MPC) method is designed. The KF is utilized to estimate the state of the system under disturbance near the headbox operating points. Optimal state estimation results obtained from the KF are then applied into the MPC algorithm to predict future liquid level and air-cushioned pressure of the headbox. Simulation results show that the KF-based MPC control strategy achieves satisfactory performance on suppressing system disturbance and reducing output overshoot.