Reinforcement Learning Control with Deep Deterministic Policy Gradient Algorithm for Multivariable pH Process

Abstract

The pH treatment unit is widely used in various processes, such as wastewater treatment, pharmaceutical manufacturing, and fermentation. It is essential to get the on-specifications product. Thus, controlling pH is key management for accomplishing the manufacturing objective. However, the highly nonlinear pH characteristics of acid–base titration make pH regulation difficult. Applications of artificial intelligence for process control have progressed and gained popularity recently. The development of reinforcement learning (RL) control with a deep deterministic policy gradient (DDPG) algorithm to handle coupled pH and liquid level control in a continuous stirred tank reactor with a strong acid–base reaction is presented in this study. To validate the RL model, the reward functions are created individually for the level and pH controls. The grid search technique is deployed to optimize the hyperparameters of the RL controller models, including the number of nodes in the hidden layers and the number of episodes. The control performance of the proposed RL control system was compared with that of the proportional-integral controller in a servo-regulatory test. The simulation results show that the proposed RL controllers outperform the proportional-integral controllers in approaching setpoints faster, with better performance and less oscillation.