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

被引：1

作者：

Arcano-Bea, Paula ^{[1
]}

Timiraos, Miriam ^{[1
,2
]}

Diaz-Longueira, Antonio ^{[1
,3
]}

Michelena, Alvaro ^{[1
,3
]}

Jove, Esteban ^{[1
,3
]}

Calvo-Rolle, Jose Luis ^{[1
,3
]}

机构：

[1] Univ A Coruna, CTC Res Grp, Calle Mendizabal S-N, Ferrol 15403, Spain

[2] Fdn Inst Tecnol Galicia, Dept Water Technol, Calle Canton Grande 9, La Coruna 15008, Spain

[3] Univ A Coruna, Ctr Invest Tecnol Informac & Comunicac CITIC, Campus Elvina S, La Coruna 15071, Spain

来源：

APPLIED SCIENCES-BASEL | 2024年 / 14卷 / 12期

关键词：

WWTP; one class; faul detection; supervision system; kmeans; autoencoder; Gaussian model; NCBoP; FAULT-DETECTION; DIAGNOSIS; POLLUTION;

D O I：

10.3390/app14125185

中图分类号：

O6 [化学];

学科分类号：

0703 ;

摘要：

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.

引用

页数：15

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