Domain adaptation through active learning strategies for anomaly classification in wastewater treatment plants.

The increasing use of intermittent aeration controllers in wastewater treatment plants (WWTPs) aims to reduce aeration costs via continuous ammonia and oxygen measurements but faces challenges in detecting sensor and process anomalies. Applying machine learning to this unbalanced, multivariate, multiclass classification challenge requires much data, difficult to obtain from a new plant. This study develops a machine learning algorithm to identify anomalies in intermittent aeration WWTPs, adaptable to new plants with limited data.

Machine learning methods for anomaly classification in wastewater treatment plants.

Modern wastewater treatment plants base their biological processes on advanced control systems which ensure compliance with discharge limits and minimize energy consumption responding to information from on-line probes. The correct readings of probes are particularly crucial for intermittent aeration controllers, which rely on real-time measurements of ammonia and oxygen in biological tanks. These data are also an important resource for developing artificial intelligence algorithms that can identify process or sensor anomalies, thus guiding the choices of plant operators and automatic process controllers.