Identification and Model Predictive Control of a pH Neutralization Process Based on Linear and Wiener Models

Abstract

In this paper, a comparison between linear model-based and Wiener model-based Identification and Predictive Control of a pH neutralization process is presented. Input-Output data from a nonlinear first principles simulation model of the pH neutralization process are used for subspace-based identification of black-box linear and Wiener-type models. The proposed nonlinear subspace method has the advantage that it delivers a Wiener model in a formal which is suitable for its use in a standard linear model-based predictive control scheme. The identitied models are used as the internal models in a model predictive controller which is used to control the nonlinear white-box simulation model. Simulation results show that the Wiener-based model predictive controller outperforms the one based on the linear model.