期刊
CHEMOSPHERE
卷 234, 期 -, 页码 893-901出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.chemosphere.2019.06.103
关键词
Biological phosphorus removal; Machine learning; Improved QL algorithm; Real-time control strategy; ASM2d; Fluctuant influent loads
资金
- National Nature Science Foundation of China [51708154]
- Open Project of State Key Laboratory of Urban Water Resource and Environment [QA201927, ES201906]
- Key Laboratory of Research center for Eco-Environmental Science, Chinese Academy of Sciences [kf2018002]
Biological phosphorus removal (BPR) is an economical and sustainable processes for the removal of phosphorus (P) from wastewater, achieved by recirculating activated sludge through anaerobic and aerobic (An/Ae) processes. However, few studies have systematically analyzed the optimal hydraulic retention times (HRTs) in anaerobic and aerobic reactions, or whether these are the most appropriate control strategies. In this study, a novel optimization methodology using an improved Q-learning (QL) algorithm was developed, to optimize An/Ae HRTs in a BPR system. A framework for QL-based BPR control strategies was established and the improved Q function, Q(t+1) (s(t), s(t+1)) = Q(t)(s(t), s(t+1)) + k [R(s(t), s(t+1)) + gamma maxQ(t) (s(t), s(t+1)) - Q(t) (s(t), s(t+1))] was derived. Based on the improved Q function and the state transition matrices obtained under different HRT step-lengths, the optimum combinations of HRTs in An/Ae processes in any BPR system could be obtained, in terms of the ordered pair combinations of the . Model verification was performed by applying six different influent chemical oxygen demand (COD) concentrations, varying from 150 to 600 mg L-1 and influent P concentrations, varying from 12 to 30 mg L-1. Superior and stable effluent qualities were observed with the optimal control strategies. This indicates that the proposed novel QL-based BPR model performed properly and the derived Q functions successfully realized real-time modelling, with stable optimal control strategies under fluctuant influent loads during wastewater treatment processes. (C) 2019 Elsevier Ltd. All rights reserved.
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