Development of AI-based process controller of sour water treatment unit using deep reinforcement learning

Background: Due to the variability in the feedstock conditions and the nonlinearity of the sour water stripping process, determining the optimal operating conditions for Sour Water Treatment Unit (SWTU) is a huge challenge. Methods: In this study, we propose an AI-Based Process Controller (AIPC) for optimizing the SWTU, combining deep reinforcement learning (DRL) and expert knowledge. A surrogate model of an industrial SWTU digital twin was developed to serve as the environment for DRL. A reward function was designed and compared with others for evaluation. A method for seamless switching was devised to guarantee uninterrupted device operation by preventing any interference from the policy network. Significant Findings: In contrast to the alternative control schemes, the AIPC not only demonstrates superior performance in mitigating overshooting and enhancing setpoint tracking precision but achieves a reduction in stripping steam usage. The proposed method has great potential in the field of real -time optimization.