Optimizing wastewater treatment plant operational efficiency through integrating machine learning predictive models and advanced control strategies

Abstract

This research optimizes wastewater treatment plant (WWTP) operational performance by integrating advanced control strategies and predictive modeling. Emphasizing the significance of machine learning (ML), feature extraction techniques (filter, wrapper, and embedded methods) were employed to develop robust prediction models. The random forest (RF) model was applied to predict target variables, effluent ammonia, and nitrogen concentrations. Integrating these predictive models into the WWTP’s control system is necessary for enhanced efficiency and pollution regulation. Benchmark Simulation Model 1 (BSM1) was used as the WWTP model. The two tested control strategies included a hybrid approach, combining feedforward and feedback control, resulting in an improved effluent quality index (EQI), a marginal increase in aeration energy (AE) and the operational cost index (OCI), and a significant decrease in effluent ammonia concentration. The second strategy utilized self-organizing fuzzy inference system (SOFIS) control, resulting in promising outcomes with improvements in EQI, ammonia, and nitrogen concentrations, with negligible increases in AE and OCI. The findings highlight the pivotal role of predicting effluent quality parameters and integrating the prediction into WWTP control systems. This integrated approach proves effective in optimizing pollutant regulation and overall system performance. The research provides insights into the practical implementation of ML-based control strategies in wastewater treatment. It offers future scope for exploring advanced ML algorithms and their real-time application in operational WWTPs. This research introduces a novel approach by integrating machine learning with the BSM1 weather dataset and sensor data for feature selection to predict effluent concentrations in a WWTP. Through the comparative analysis with the default proportional-integral (PI) control configuration, the research highlights the importance of integrating machine learning techniques into WWTP control systems.