Chapter 7 - Application of mechanistic model-based nonlinear filtering and observation techniques for optimal state estimation in multicomponent batch distillation

Abstract

The purpose of on-line state estimation is delivery of reliable, real-time estimates for the state variables defining a process on the basis of the available process knowledge including a dynamic process model and the incoming data from process measurement sensors. Stochastic filters address the problem of estimating process states in the presence of random process disturbances and measurement errors. This chapter evaluates the application of mechanistic model-based nonlinear filtering techniques for online estimation of compositions in multicomponent batch distillation. Various methods based on extended Kalman filter (EKF), adaptive fading extended Kalman filter and steady state Kalman filter are presented for inferential estimation of compositions using the data of temperature measurements in multiple fraction multicomponent batch distillation. This dynamic model-based composition estimators incorporate component balance equations together with thermodynamic relations that include bubble point temperature computation. A performance criterion with multiple performance indices is used to assess the composition estimators. The sensitivity of the estimators is studied with respect to the effect of number of measurements, measurement noise, and filter design parameters. The results of the state estimation methods are evaluated by applying them for composition estimation on all trays, reboiler, condenser, and products of a ternary hydrocarbon system. The results show the better suitability of the method of EKF for composition estimation in multicomponent batch distillation.