AI-Optimised aeration control in SBR systems: an inverse SVM framework toward carbon-neutral wastewater treatment

This study proposes an inverse support vector machine (ISVM) framework to optimise aeration control in a sequencing batch reactor(SBR), addressing the balancing of energy efficiency and regulatory compliance in wastewater treatment. By integrating data-driven modelling with constrained optimisation, the method dynamically adjusts aeration rate to maintain effluent NH₃-N concentrations below 5 mg/L while minimising energy consumption. A support vector machine (SVM) establishes input-output correlations between process parameters (influent NH₃-N, ORP, conductivity, aeration rate) and effluent NH₃-N concentration, enabling the ISVM to resolve constraint-driven aeration rate optimisation. 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 optimisation and gradient clipping mechanisms ensure stability in applications, overcoming limitations of traditional PID controllers and mechanistic models. This work advances intelligent control strategies for sustainable wastewater management, providing a constraint-aware optimisation template for environmental engineering systems.