Explainable aeration prediction using deep learning with interpretability analysis

Abstract

The rapid development of urban areas and increasing industrial activity have escalated the significance of effective wastewater treatment to maintain ecological balance and support essential ecosystem services. However, traditional aeration control methods in wastewater treatment plants (WWTPs) often fall short due to their inability to adapt dynamically to varying operational conditions, leading to inefficiencies in energy usage and treatment outcomes. This study introduces a novel predictive model that leverages a Multi-Scale Convolutional Neural Network (MCNN) combined with Transformer technology to enhance the accuracy and control of aeration processes. The model's effectiveness was validated using a comprehensive dataset from a WWTP, covering a range of operational parameters influencing aeration demand. Results indicate that the MCNN-Transformer model significantly outperforms traditional methods by accurately predicting aeration needs, thereby optimizing energy consumption and reducing operational costs. The implications of this study are profound, offering a scalable solution that can be integrated into existing WWTP operations to enhance efficiency and sustainability while providing a methodological framework for future research in environmental management and engineering.