State estimation of batch distillation columns using an extended Kalman filter

Composition monitoring and control play an essential role during a batch di stillation cycle, but on-line composition analyzers are expensive, difficul t to maintain and give delayed responses. Considering the need and lack of a stochastic estimator for batch distillation columns, a discrete extended Kalman filter (EKF) for binary and multicomponent systems has been develope d and tested. The aim of the EKF was to provide reliable and real-time colu mn composition profiles from few temperature measurements and easily availa ble information. Accurate composition estimates and fast convergence were o btained, and the EKF has confirmed its ability to incorporate the effects o f noise (from both measurement and modeling). The number of sensors and the observation frequency have shown to be important variables in the design o f the EKF, especially for systems with fast dynamics. (C) 2000 Elsevier Sci ence Ltd. All rights reserved.