Digital Twin-Based Operational Optimization of Activated Sludge Process in Large-Scale Sewage Treatment

Abstract

The activated sludge process is widely employed in large-scale sewage treatment plants for the removal of organic pollutants and nutrient contaminants from municipal wastewater. However, maintaining optimal operational efficiency under fluctuating influent and environmental conditions remains a significant challenge in modern wastewater treatment systems. This study presents a digital twin-based operational optimization framework for enhancing the performance of activated sludge processes in large-scale sewage treatment facilities. The proposed system integrates real-time process monitoring, dynamic simulation modeling, and intelligent data analytics to create a virtual representation of the treatment plant for continuous operational assessment and optimization. Key operational parameters, including dissolved oxygen concentration, sludge retention time, aeration rate, biomass concentration, nutrient loading, and hydraulic flow conditions, were continuously analyzed using sensor-driven data acquisition and predictive modeling techniques. The digital twin framework enabled accurate simulation of biological treatment dynamics and facilitated adaptive process control under varying wastewater characteristics. Performance evaluation demonstrated significant improvements in pollutant removal efficiency, aeration energy optimization, sludge management, and overall treatment stability compared to conventional operational approaches. The integration of predictive analytics and machine learning algorithms further enhanced process forecasting, anomaly detection, and preventive maintenance capability within the sewage treatment infrastructure. Comparative analysis indicated reduced operational costs, improved effluent quality, and enhanced environmental compliance through intelligent real-time decision-making and automated process adjustments.