Smart optimization reveals high-efficiency operational strategies for the anaerobic–oxic–anoxic process in wastewater treatment

This study addressed the challenge of optimizing the continuous-flow Anaerobic-Oxic-Anoxic (AOA) process for treating low carbon-to-nitrogen ratio municipal wastewater in China. An activated sludge model was first established, which confirmed that the AOA process can be modeled, and the model results matched experimental discoveries well. Therefore, by integrating the activated sludge model with a differential evolution algorithm and supercomputing, an automated optimization framework was developed to efficiently identify optimal operational parameters. The framework successfully determined key conditions, including a hydraulic retention time of approximately 8 h (reducible to 5-6.5 h with supplementary filtration), sludge retention times of 18-22 days, and balanced reactor volume distributions favoring anoxic zones. Dynamic modeling validated the robustness of these strategies, confirming compliance with stringent effluent standards. The results demonstrate that data-driven optimization can significantly enhance process efficiency, reduce footprint, and lower operational costs. This approach can be applied to other bioprocesses, such as AAO, offering a transformative pathway for sustainable wastewater treatment.