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| Multi-objective Optimization of Papermaking Wastewater Treatment Process Based on Deep Reinforcement Learning |
| Received:October 25, 2022 |
| DOI：10.11980/j.issn.0254-508X.2023.03.003 |
| Key Words:optimization process control wastewater treatment deep reinforcement learning model |
| Fund Project:国家重点研发计划（2020YFE0201400）；国家自然科学基金（52000078）；广州市科技计划项目（202201010356）。 |
| Author Name | Affiliation | Postcode | | LU Zaohao | State Key Lab of Pulp and Paper Engineering, South China University of Technology, Guangzhou, Guangdong Province, 510640 | 510640 | | MAN Yi | State Key Lab of Pulp and Paper Engineering, South China University of Technology, Guangzhou, Guangdong Province, 510640 | 510640 | | LI Jigeng | State Key Lab of Pulp and Paper Engineering, South China University of Technology, Guangzhou, Guangdong Province, 510640 | 510640 | | HONG Mengna | State Key Lab of Pulp and Paper Engineering, South China University of Technology, Guangzhou, Guangdong Province, 510640
China-Singapore International Joint Research Institute, Guangzhou, Guangdong Province, 510555 | 510555 | | HE Zhenglei* | State Key Lab of Pulp and Paper Engineering, South China University of Technology, Guangzhou, Guangdong Province, 510640 | 510640 |
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| Abstract:In this study， a dynamic optimization method based on multi-intelligent deep reinforcement learning was proposed to realize the collaborative optimization， of the operation cost and energy consumption of papermaking wastewater treatment process. The BSM1 benchmark simulation model was used to simulate the biochemical reaction and precipitation process of papermaking wastewater treatment process， the reinforcement learning intelligences were trained， and the actual paper making wastewater data was used to verify the model system. The results showed that the wastewater treatment system based on multi-intelligent deep reinforcement learning system could guarantee the effluent quality， realized the multi-objective optimization control of cost and energy consumption， and its performance was better than the traditional methods. |
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