Conception and evaluation of anomaly detection models for monitoring analytical parameters in wastewater treatment plants

Abstract

The exponential growth of technology in recent decades has led to the emergence of some challenges inherent to this growth. One of these challenges is the enormous amount of data collected by the different sensors in our society, namely in management processes such as Wastewater Treatment Plants (WWTPs). These infrastructures comprise several processes to treat wastewater and discharge clean water in water courses. Therefore, the concentration of pollutants must be below the allowable emissions limits. In this work, anomaly detection models were conceived, tuned and evaluated to monitor essential parameters such as nitrate and ammonia concentrations and pH to improve WWTP management. Four Machine Learning models were considered, particularly Local Outlier Fraction, Isolation Forest, One-Class Support Vector Machines and Long Short-Term Memory-Autoencoders (LSTM-AE), to detect anomalies in the three parameters mentioned. Through the different experiments, it was possible to verify that, in terms of F1-Score, the best candidate model for the three analyzed parameters was LSTM-AE-based, with a value consistently higher than 97%.