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<p>This study proposes an inverse neural network (INN) framework to optimize aeration control in a sequencing batch reactor(SBR), addressing the dual challenges of energy efficiency and regulatory compliance in wastewater treatment. By integrating data-driven modeling with constrained optimization, the method dynamically adjusts aeration rates to maintain effluent ammonia concentrations below 5&#xa0;mg/L while minimizing energy consumption. A back-propagation neural network (BPNN) establishes input–output correlations between process parameters and effluent ammonia concentration, constructing the inverse mapping foundation for the INN to resolve constraint-driven aeration optimization. Experimental validation across 20 operational cycles demonstrated a 20.3% reduction in energy usage compared to conventional fixed-rate aeration, achieving 95% compliance with discharge standards. The framework's penalty-based optimization and gradient clipping mechanisms ensure stability in applications, overcoming limitations of traditional proportional-integral-derivative(PID) controllers and mechanistic models. This work advances intelligent control strategies for sustainable wastewater management, offering a template for constraint-aware optimization in environmental engineering systems.</p>

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Adaptive Aeration Control in SBR: An Inverse Neural Network Approach Under Ammonia Discharge Constraints and Energy Efficiency

  • 期刊论文

  • 发表日期：

    2025年12月12日

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  • Qiu Cheng,
  • Luo Yuxin,
  • Qiu Yang,
  • Luo Le,
  • Wang Xiuying,
  • Wu Juzhen,
  • Wang Mingxi,
  • Li Qianglin

摘要

This study proposes an inverse neural network (INN) framework to optimize aeration control in a sequencing batch reactor(SBR), addressing the dual challenges of energy efficiency and regulatory compliance in wastewater treatment. By integrating data-driven modeling with constrained optimization, the method dynamically adjusts aeration rates to maintain effluent ammonia concentrations below 5 mg/L while minimizing energy consumption. A back-propagation neural network (BPNN) establishes input–output correlations between process parameters and effluent ammonia concentration, constructing the inverse mapping foundation for the INN to resolve constraint-driven aeration optimization. Experimental validation across 20 operational cycles demonstrated a 20.3% reduction in energy usage compared to conventional fixed-rate aeration, achieving 95% compliance with discharge standards. The framework's penalty-based optimization and gradient clipping mechanisms ensure stability in applications, overcoming limitations of traditional proportional-integral-derivative(PID) controllers and mechanistic models. This work advances intelligent control strategies for sustainable wastewater management, offering a template for constraint-aware optimization in environmental engineering systems.