文献类型：期刊文献

英文题名：Development of AI-based process controller of sour water treatment unit using deep reinforcement learning

作者：Wang, Hai[1];Guo, Yeshuang[1];Li, Long[1];Li, Shaojun[1]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China

年份：2024

卷号：157

外文期刊名：JOURNAL OF THE TAIWAN INSTITUTE OF CHEMICAL ENGINEERS

收录：;EI(收录号：20240915650765);WOS:【SCI-EXPANDED(收录号:WOS:001197519000001)】；

语种：英文

外文关键词：Deep reinforcement learning; Real-time optimization; Sour water stripping; Digital Twin; DDPG; Deep learning

摘要：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.