AI-driven Early Warning of Process Anomalies in Wastewater Treatment Plants Using Real-time Monitoring Data

Reliable early warning is crucial for the operational stability of urban wastewater treatment infrastructure in response to emergencies that might threaten the environment and human health. In sewage treatment works (STWs), process anomalies can be driven by extreme influent loads, episodic operational interventions, or sensor faults. Therefore, under these multivariate, non-stationary conditions, it is challenging to ensure timely and robust practical response based on manual supervision alone. This study presents an AI-driven analytical framework for real-time early warning of process anomalies using multi-source sensor monitoring data. The framework offers a pipeline to transform continuous, real-time monitored sensor data into fixed-length time-series windows suitable for deep learning and real-time inference. A deep unsupervised learning model is innovatively introduced to learn multivariate dynamics and cross-variable dependencies of normal operation modes, and then to generate window-level scoring and map it to early warning alerts. To improve the interpretability, window-level alerts are aligned with timestamped, manually recorded event management logs to distinguish between sensor malfunctions and process disturbances. The framework is demonstrated on a multi-year real-world urban STW’s dataset, and evaluated based on detection timeliness, false alarm behaviour, and consistency with logged operational events. Results indicated that the proposed framework is a feasible approach to integrate contextual evidence with AI-driven early warning alarms. It also offers a promising powerful tool to support real-time anomaly diagnosis and decision-making for STWs’ operators.