Issue 6, 2024

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.

Graphical abstract: Enhanced data-driven monitoring of wastewater treatment plants using the Kolmogorov–Smirnov test

Article information

Article type
Paper
Submitted
11 nov 2023
Accepted
14 apr 2024
First published
23 apr 2024
This article is Open Access
Creative Commons BY-NC license

Environ. Sci.: Water Res. Technol., 2024,10, 1464-1480

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

K. R. Kini, F. Harrou, M. Madakyaru and Y. Sun, Environ. Sci.: Water Res. Technol., 2024, 10, 1464 DOI: 10.1039/D3EW00829K

This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence. You can use material from this article in other publications, without requesting further permission from the RSC, provided that the correct acknowledgement is given and it is not used for commercial purposes.

To request permission to reproduce material from this article in a commercial publication, please go to the Copyright Clearance Center request page.

If you are an author contributing to an RSC publication, you do not need to request permission provided correct acknowledgement is given.

If you are the author of this article, you do not need to request permission to reproduce figures and diagrams provided correct acknowledgement is given. If you want to reproduce the whole article in a third-party commercial publication (excluding your thesis/dissertation for which permission is not required) please go to the Copyright Clearance Center request page.

Read more about how to correctly acknowledge RSC content.

Social activity

Spotlight

Advertisements