Enhanced data-driven monitoring of wastewater treatment plants using the Kolmogorov-Smirnov test

Abstract

Wastewater treatment plants (WWTPs) are indispensable facilities that play a pivotal role in safeguarding public health, protecting the environment, and supporting economic development by efficiently treating and managing wastewater. Accurate anomaly detection in WWTPs is crucial to ensure their continuous and efficient operation, safeguard the final treated water quality, and prevent shutdowns. This paper introduces a data-driven anomaly detection approach to monitor WWTPs by merging the capabilities of principal component analysis (PCA) for dimensionality reduction and feature extraction with the Kolmogorov-Smirnov (KS)-based scheme. No labeling is required when using this anomaly detection approach, and it utilizes the nonparametric KS test, making it a flexible and practical choice for monitoring WWTPs. Data from the COST benchmark simulation model (BSM1) is employed to validate the effectiveness of the investigated methods. Different sensor faults, including bias, intermittent, and aging faults, are considered in this study to evaluate the proposed fault detection scheme. Various types of faults, including bias, drift, intermittent, freezing, and precision degradation faults, have been simulated to assess the detection performance of the proposed approach. The results demonstrate that the proposed approach outperforms traditional PCA-based techniques.