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A One-Class-Based Supervision System to Detect Unexpected Events in Wastewater Treatment Plants

Authors :
Paula Arcano-Bea
Míriam Timiraos
Antonio Díaz-Longueira
Álvaro Michelena
Esteban Jove
José Luis Calvo-Rolle
Source :
Applied Sciences, Vol 14, Iss 12, p 5185 (2024)
Publication Year :
2024
Publisher :
MDPI AG, 2024.

Abstract

The increasing importance of water quality has led to optimizing the operation of Wastewater Treatment Plants. This implies the monitoring of many parameters that measure aspects such as solid suspension, conductivity, or chemical components, among others. This paper proposes the use of one-class algorithms to learn the normal behavior of a Wastewater Treatment Plants and detect situations in which the crucial parameters of Chemical Oxygen Demand, Ammonia, and Kjeldahl Nitrogen present unexpected deviations. The classifiers are tested using different deviations, achieving successful results. The final supervision systems are capable of detecting critical situation, contributing to decision-making and maintenance effectiveness.

Details

Language :
English
ISSN :
20763417
Volume :
14
Issue :
12
Database :
Directory of Open Access Journals
Journal :
Applied Sciences
Publication Type :
Academic Journal
Accession number :
edsdoj.06ce86b0be394c568046a36e83855d0e
Document Type :
article
Full Text :
https://doi.org/10.3390/app14125185