Iterative Learning Control for Tricalcium Neutralization Process with Initial Disturbance

Abstract

AbstractThe tricalcium neutralization process (TNP) is a key recovery process in the citric acid production process. The amount of calcium carbonate added needs to be controlled to ensure the pH of the reactants within the target range at the terminal of every batch. But the random initial pH has a great influence on the stability of the terminal pH. In this work, an iterative learning control (ILC)‐based control strategy is proposed to optimize the addition of calcium carbonate. First, the terminal iterative learning control (TILC) is performed and the optimal input of the process can be obtained. Then, an initial disturbance compensation controller optimized by ILC is proposed. The results of the TNP control experiments demonstrate that the suggested control strategy can suppress the disturbances and achieve terminal pH control.