Novel Intelligent Control Framework for WWTP Optimization to Achieve Stable and Sustainable Operation

Intelligent control is a promising approach to achieve stable and sustainable operation at municipal wastewater treatment plants (WWTPs). A desirable WWTP intelligent control system can be responsive to influent dynamics and adaptable for complex multi-objective optimization. In this study, we developed a novel intelligent control framework based on machine learning methods, which comprises a prediction module and control module. The stacking ensemble learning model (SELM) and Q-learning model (QLM) were used to capture influent dynamics and intelligently identify optimal parameters, respectively. This SELM-QLM framework was trained and validated with historical monitoring data archived at a full-scale WWTP to optimize the nitrogen removal process. The results showed that control parameters were frequently adjusted in response to influent variation and energy consumption of aeration, and the sludge returning process was effectively decreased while maintaining the stability of effluent total nitrogen (TN) (TN decreased by 19.53% and energy consumption decreased by 10.37%). Specifically, the SELM provided accurate predictions of TN concentration without increasing the data set scale, and the QLM showed superior ability in determining the optimal solution from nearly contradictory objectives. This study provides a framework with significant application values for improving WWTP management inspired by the objective of stable and sustainable operation.