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An influent responsive control strategy with machine learning: Q-learning based optimization method for a biological

Ji-Wei Pang1, Shan-Shan Yang1, Lei He1

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This study introduces an improved Q-learning algorithm to optimize hydraulic retention times for biological phosphorus removal (BPR) in wastewater treatment. The novel method achieves superior and stable effluent quality under varying conditions.

Keywords:
ASM2dBiological phosphorus removalFluctuant influent loadsImproved QL algorithmMachine learningReal-time control strategy

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Area of Science:

  • Environmental Engineering
  • Wastewater Treatment Technologies
  • Artificial Intelligence in Environmental Science

Background:

  • Biological phosphorus removal (BPR) is a sustainable wastewater treatment method.
  • Optimal hydraulic retention times (HRTs) for anaerobic/aerobic (An/Ae) processes in BPR are not well-established.
  • Current control strategies for BPR systems require systematic analysis.

Purpose of the Study:

  • To develop and validate a novel optimization methodology for An/Ae HRTs in BPR systems using an improved Q-learning (QL) algorithm.
  • To establish a framework for QL-based BPR control strategies.
  • To investigate the effectiveness of the proposed model in achieving stable effluent quality under fluctuating influent loads.

Main Methods:

  • Development of an improved Q-learning algorithm and derivation of its 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)].
  • Establishment of a QL-based BPR control strategy framework.
  • Model verification using diverse influent conditions (COD: 150-600 mg/L, P: 12-30 mg/L).

Main Results:

  • The improved QL algorithm successfully optimized An/Ae HRTs for BPR systems.
  • The derived Q functions enabled real-time modeling and stable control strategies.
  • Optimal control strategies resulted in superior and stable effluent qualities across tested influent loads.
  • The model demonstrated robust performance under fluctuating chemical oxygen demand (COD) and phosphorus (P) concentrations.

Conclusions:

  • The proposed QL-based BPR model is effective for optimizing wastewater treatment processes.
  • The novel methodology provides stable and optimal control strategies for BPR systems.
  • This approach offers a promising solution for enhancing the efficiency and sustainability of biological phosphorus removal.