Chapter 17 - Optimal state estimator-based inferential control of continuous reactive distillation column

Abstract

Optimal state estimation provides the estimates of the state variables defining a process based on 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 presents a composition estimator based on extended Kalman filter (EKF) for composition control of continuous reactive distillation column. The estimated product compositions by EKF serve as inferential composition measurements for the classical proportional-integral (PI) controllers and genetically tuned PI controllers to control the column’s desired top and bottom product compositions. The performance of the state estimator-based genetic algorithm (GA) tuned decentralized control scheme is evaluated by applying it to a metathesis reactive distillation column, and the results are compared with conventionally tuned PI controllers. The results demonstrate the better regulatory, and servo performance of the GA tuned inferential control scheme for composition control of reactive distillation column.