This article presents the design of an exponential state estimator for a distillation column. The proposed algorithm mainly estimates the imprecisely known parameters based on the available measurements. For the example distillation column, the nonlinear state estimator is formed with two component material balance equations; one is around the condenser-reflux drum system and the other one is around the reboiler-column base system. As a result, there is an excessive process/model mismatch. In this situation, the proposed scheme estimates the augmented states with sufficient accuracy. The simulation experiments have been performed to investigate the convergence ability of the estimator. The less computational requirements and simple design make the estimator attractive for online use.
The goal of this paper is to develop a nonlinear observer-based control strategy for a jacketed continuous stirred tank reactor (CSTR). The nonlinear adaptive state estimator/observer (ASE/ASO) is designed based on a model structure that mainly consists of an energy balance equation. In this observation approach, reactor concentration is considered as an imprecisely known extra state (augmented state) with no dynamics. Despite significant process/model discrepancy, the proposed state observer estimated adequately the states of the simulated reactor. Mainly due to the design simplicity, negligible computational effort and fast convergence, the observer is recommended for online implementation. The generic model controller (GMC) has also been synthesized for the example reactor. The nonlinear GMC scheme receives the required information about the reactor concentration from the ASE for calculating the controller responses. Simulation experiments have been carried out to investigate the superior performance provided by the proposed GMC-ASE algorithm compared to the conventional proportional integral (PI) controller.