ID:
publications-4834
Type:
Article
Year:
2024
Authors:
Wang H.; Guo Y.; Li L.; Li S.
Title:
Development of AI-based process controller of sour water treatment unit using deep reinforcement learning
Venue/Journal:
Journal of the Taiwan Institute of Chemical Engineers
DOI:
10.1016/j.jtice.2024.105407
Research type:
Water System:
Technical Focus:
Abstract:
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. Β© 2024 Taiwan Institute of Chemical Engineers
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