Generalized predictive control applied to a pH neutralization process

Abstract

In this work, generalized predictive control (GPC) was applied to a pH neutralization process. The process consists of a continuous flow tubular reactor. The process stream is a water solution of acetic acid and the titrating stream is a water solution of sodium hydroxide. The aim of the control is to keep the pH value at a given set point value when the process is subjected to variations in feed flowrate. The use of polynomial autoregressive integral moving average with external input (ARIMAX) model related with pH and base flowrate was emphasized. The model parameters were determined by using Bierman and Genetic algorithms. The λ weighting was varied in order to achieve optimal GPC performance. The efficiency of the GPC was observed by calculating the integral of the square of the error (ISE) and the integral of the absolute value of the error (IAE) from experimental results.