An influent responsive control strategy with machine learning: Q-learning based optimization method for a biological phosphorus removal system.
Chemosphere
; 234: 893-901, 2019 Nov.
Article
en En
| MEDLINE
| ID: mdl-31252361
ABSTRACT
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, Qt+1(st,st+1)=Qt(st,st+1)+k·[R(st,st+1)+γ·maxatQt(st,st+1)-Qt(st,st+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 Palabras clave
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Asunto principal:
Fósforo
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Aguas del Alcantarillado
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Algoritmos
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Aguas Residuales
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Aprendizaje Automático
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En
Revista:
Chemosphere
Año:
2019
Tipo del documento:
Article