Adaptive real-time dosing strategy for advanced phosphorus removal using model predictive control and time-series...

Efficient and compliant phosphorus removal in wastewater treatment plants (WWTPs) remains a major challenge, as dynamic influent conditions often lead to suboptimal chemical dosing, resulting in excessive operating costs or failure to meet effluent standards. To address this issue, this study proposed an intelligent real-time dosing control framework that integrates advanced machine learning with process optimization and control algorithms, specifically applied to advanced chemical phosphorus removal processes. The core of this framework was a hybrid deep learning model combining a one-dimensional convolutional neural network with a long short-term memory network (1DCNN-LSTM), which accurately predicted the effluent total phosphorus (TP_eff) concentration by capturing complex nonlinear relationships and temporal dynamics from multi-source monitoring data. The optimal dosing amount was determined using a genetic algorithm (GA), minimizing the predicted TP_eff while adhering to operational constraints. Furthermore, a model predictive control (MPC) strategy enhanced with a Smith predictor was implemented to compensate for time delays, thereby enabling precise real-time adjustment of chemical injection. Simulation results based on 12 months of full-scale WWTP data demonstrated that the proposed intelligent dosing system can effectively maintain TP_eff below the stringent compliance limit of 0.5 mg/L, achieving an approximately 25% reduction in annual chemical consumption compared to traditional control strategies. These results indicate that the proposed framework provides a scalable and efficient solution for achieving stable phosphorus compliance and sustainable operation in WWTPs.