Chapter 8 - Application of mechanistic model-based nonlinear filtering and observation techniques for optimal state estimation in multicomponent reactive batch distillation with optimal sensor configuration

Abstract

Reactive batch distillation is an intrinsically dynamic process associated with the complexities caused due to interactions between reaction kinetics, mass transfer, and thermodynamics. Optimal measurement selection and composition estimation are important tasks for effective monitoring and control of reactive batch distillation process. In this chapter, an inferential model-based estimation scheme based on extended Kalman filter (EKF) with optimal selection of temperature measurements using principal component analysis is presented for composition estimation in multicomponent reactive batch distillation. The performance of the proposed scheme is evaluated by applying it for composition estimation on all trays, reboiler, reflux drum, and products of a reactive batch distillation column, in which ethyl acetate is produced through an esterification reaction between acetic acid and ethanol. The effectiveness of the estimator is investigated toward the influence of various factors which include changes in initial batch compositions, slop cut recycle, measurement sampling instants, observation noise, and filter design parameters. The results demonstrate the robust performance of EKF estimator with optimal sensor configuration for inferential estimation of compositions in multicomponent reactive batch distillation.