Reinforcement Learning-Based Control of Dissolved Oxygen Levels in Activated Sludge Wastewater Systems

Abstract

Maintaining optimal dissolved oxygen (DO) concentration is essential for ensuring stable biological activity, efficient pollutant removal, and energy-efficient operation in activated sludge wastewater treatment systems. Conventional control strategies often struggle to adapt to dynamic influent conditions and fluctuating biological treatment demands, resulting in excessive aeration energy consumption and unstable treatment performance. This study presents a reinforcement learning-based control framework for regulating dissolved oxygen levels in activated sludge wastewater systems under varying operational conditions. The proposed approach integrates real-time sensor monitoring, intelligent control algorithms, and adaptive learning mechanisms to optimize aeration control and improve treatment efficiency. Key operational parameters, including dissolved oxygen concentration, influent organic load, biomass concentration, airflow rate, hydraulic retention time, and nutrient levels, were continuously monitored and analyzed to support autonomous decision-making and dynamic process optimization. The reinforcement learning model was trained to identify optimal aeration strategies by balancing treatment efficiency, microbial activity stability, and energy consumption minimization. Experimental and simulation analyses demonstrated significant improvements in DO regulation accuracy, pollutant removal performance, and aeration energy efficiency compared to conventional proportional-integral-derivative and rule-based control methods. The intelligent control system also exhibited rapid adaptation to sudden influent variations and operational disturbances, thereby enhancing process stability and treatment reliability. Comparative evaluation revealed reductions in operational costs, energy demand, and treatment variability through optimized aeration management and real-time control adjustments. Furthermore, the integration of predictive analytics and automated control strategies supported sustainable wastewater treatment operations and improved environmental compliance.