State estimation for batch distillation operations with a novel extended Kalman filter approach

The composition and parameter estimation for batch distillation operations is addressed using a novel extended Kalman filter with unknown inputs without direct feedthrough (EKF-UI-WDF) approach. The major advantage of this approach lies in its capability of estimating states and unknown inputs (e.g. arbitrary deterministic disturbances) simultaneously, whereas the traditional nonlinear filter approaches cannot deal with this problem. As a result, this EKF-UI-WDF approach is able to provide on-line estimation of column compositions, flow rates and other parameters such as the tray efficiency in presence of unknown disturbances and noises. The restrictions of the EKF-UI-WDF are also remarked. Simulation results demonstrate the efficiency of this novel EKF approach comparing with other traditional nonlinear filters and indicate its potential of applications to other complex systems.