Data-driven differentiable model for dynamic prediction and control in wastewater treatment

Abstract

Wastewater treatment plants, while critical for environmental protection, face mounting challenges in operational efficiency and sustainability due to increasing urbanization and stricter environmental standards. In this study, we introduce an innovative continuous-time neural framework based on Neural Ordinary Differential Equations (Neural ODEs) to enhance the modeling of sewage treatment processes. Addressing the dual challenges of operational efficiency and sustainable development in urban wastewater treatment plants (WWTPs), our methodology marks a significant departure from traditional approaches by implementing a continuous-time neural framework that captures the inherent dynamics of wastewater treatment processes while reducing computational demands by 95 % compared to discrete-time models. We analyzed operational data from three full-scale WWTPs over a year, demonstrating that our model not only achieves superior prediction accuracy (R² > 0.95) with various input window sizes but also significantly reduces memory usage—from 111.88–12,484.59 MB to just 17.74–50.92 MB. Notably, our framework exhibits robust performance even with up to 30 % missing data, uncovering new process insights through interpretable feature attribution. Further integration with reinforcement learning has led to a 21.9 % reduction in aeration energy consumption compared to conventional open-loop control strategies while adhering to effluent quality standards. This research establishes a novel paradigm for intelligent wastewater management that optimizes operational efficiency and promotes environmental sustainability.