A One-Class-Based Supervision System to Detect Unexpected Events in Wastewater Treatment Plants
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A One-Class-Based Supervision System to Detect Unexpected Events in Wastewater Treatment PlantsAutor(es)
Data
2024Cita bibliográfica
Arcano-Bea, P.; Timiraos, M.; Díaz-Longueira, A.; Michelena, Á.; Jove, E.; Calvo-Rolle, J.L. A One-Class-Based Supervision System to Detect Unexpected Events in Wastewater Treatment Plants. Appl. Sci. 2024, 14, 5185. https://doi.org/10.3390/app14125185
Resumo
[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.
Palabras chave
WWTP
One class
Faul detection
Supervision system
kmeans
Autoencoder
Gaussian model
NCBoP
One class
Faul detection
Supervision system
kmeans
Autoencoder
Gaussian model
NCBoP
Versión do editor
Dereitos
Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/)
ISSN
2076-3417