An autonomous operational trajectory searching system for an economic and environmental membrane bioreactor plant using deep reinforcement learning
Optimal operation of membrane bioreactor (MBR) plants is crucial to save operational costs while satisfying legal effluent discharge requirements. The aeration process of MBR plants tends to use excessive energy for supplying air to micro-organisms. In the present study, a novel optimal aeration sys...
| Veröffentlicht in: | Water science and technology : a journal of the International Association on Water Pollution Research. - 1986. - 81(2020), 8 vom: 09. Apr., Seite 1578-1587 |
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| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2020
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| Zugriff auf das übergeordnete Werk: | Water science and technology : a journal of the International Association on Water Pollution Research |
| Schlagworte: | Journal Article Membranes, Artificial |
| Zusammenfassung: | Optimal operation of membrane bioreactor (MBR) plants is crucial to save operational costs while satisfying legal effluent discharge requirements. The aeration process of MBR plants tends to use excessive energy for supplying air to micro-organisms. In the present study, a novel optimal aeration system is proposed for dynamic and robust optimization. Accordingly, a deep reinforcement learning (DRL)-based optimal operating system is proposed, so as to meet stringent discharge qualities while maximizing the system's energy efficiency. Additionally, it is compared with the manual system and conventional reinforcement learning (RL)-based systems. A deep Q-network (DQN) algorithm automatically learns how to operate the plant efficiently by finding an optimal trajectory to reduce the aeration energy without degrading the treated water quality. A full-scale MBR plant with the DQN-based autonomous aeration system can decrease the MBR's aeration energy consumption by 34% compared to other aeration systems while maintaining the treatment efficiency within effluent discharge limits |
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| Beschreibung: | Date Completed 13.07.2020 Date Revised 15.12.2020 published: Print Citation Status MEDLINE |
| ISSN: | 0273-1223 |
| DOI: | 10.2166/wst.2020.053 |