A One-Class-Based Supervision System to Detect Unexpected Events in Wastewater Treatment Plants
Source abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Water Quality Monitoring Technologies · Water Quality Monitoring and Analysis · Data Stream Mining Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
CTC Research Group, University of A Coruña, Calle Mendizábal s/n, 15403 Ferrol, Spain
Fundación Instituto Tecnológico de Galicia, Department of Water Technologies, Calle Cantón Grande, 9, 15008 A Coruña, Spain
Centro de Investigación en Tecnologías de la Información y las Comunicaciones (CITIC), University of A Coruña, Campus de Elviña s/, 15071 A Coruña, Spain
Funding
Xunta de Galicia (Regional Government of Galicia) · Doutoramento Industrial 2022 · Spanish Ministry of Universities · Xunta de Galicia · EU · Recovery Plan, Transformation and Resilience Plan funds
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