Mostrar o rexistro simple do ítem

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

dc.contributor.authorArcano-Bea, Paula
dc.contributor.authorTimiraos, Míriam
dc.contributor.authorDíaz-Longueira, Antonio
dc.contributor.authorMichelena, Álvaro
dc.contributor.authorJove, Esteban
dc.contributor.authorCalvo-Rolle, José Luis
dc.date.accessioned2024-06-24T08:02:52Z
dc.date.available2024-06-24T08:02:52Z
dc.date.issued2024
dc.identifier.citationArcano-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/app14125185es_ES
dc.identifier.issn2076-3417
dc.identifier.urihttp://hdl.handle.net/2183/37321
dc.description.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.es_ES
dc.description.sponsorshipMíriam Timiraos’s research was supported by the Xunta de Galicia (Regional Government of Galicia) through grants to industrial Ph.D. (http://gain.xunta.gal (accessed on 12 June 2024)), under the Doutoramento Industrial 2022 grant with reference 04_IN606D_2022_2692965. Álvaro Michelena’s research was supported by the Spanish Ministry of Universities (https://www.universidades.gob.es/ (accessed on 12 June 2024)), under the “Formación de Profesorado Universitario” grant with reference FPU21/00932. Antonio Díaz-Longueira’s research was supported by the Xunta de Galicia (Regional Government of Galicia) through grants to Ph.D. (http://gain.xunta.gal (accessed on 12 June 2024)), under the “Axudas á etapa predoutoral” grant with reference ED481A-2023-072. This work was supported by Xunta de Galicia through Axencia Galega de Innovación (GAIN) by grant IN853C 2022/01, Centro Mixto de Investigación UDC-NAVANTIA “O estaleiro do futuro”, which is ongoing until the end of September 2025. The support was inherited from both the starting and consolidation stages of the same project throughout 2015–2018 and 2018–2021, respectively. This stage was also co-funded by ERDF funds from the EU in the framework of program FEDER Galicia 2021–2027. CITIC, as a center accredited for excellence within the Galician University System and a member of the CIGUS Network, receives subsidies from the Department of Education, Science, Universities, and Vocational Training of the Xunta de Galicia. Additionally, it is co-financed by the EU through the FEDER Galicia 2021-27 operational program (Ref. ED431G 2023/01). This research is the result of the Strategic Project “Critical infrastructures cybersecure through intelligent modeling of attacks, vulnerabilities and increased security of their IoT devices for the water supply sector” (C061/23), as a result of the collaboration agreement signed between the National Institute of Cybersecurity (INCIBE) and the University of A Coruña. This initiative is carried out within the framework of the funds of the Recovery Plan, Transformation and Resilience Plan funds, financed by the European Union (Next Generation).es_ES
dc.description.sponsorshipXunta de Galicia; 04_IN606D_2022_2692965es_ES
dc.description.sponsorshipXunta de Galicia; ED481A-2023-072es_ES
dc.description.sponsorshipAxencia Galega de Innovación; IN853C 2022/01es_ES
dc.description.sponsorshipXunta de Galicia; ED431G 2023/01es_ES
dc.description.sponsorshipInstituto Nacional de Ciberseguridad; C061/23es_ES
dc.language.isoenges_ES
dc.publisherMDPIes_ES
dc.relationinfo:eu-repo/grantAgreement/MUNI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/FPU21%2F00932/ESes_ES
dc.relation.urihttps://doi.org/10.3390/app14125185es_ES
dc.rightsCreative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/)es_ES
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subjectWWTPes_ES
dc.subjectOne classes_ES
dc.subjectFaul detectiones_ES
dc.subjectSupervision systemes_ES
dc.subjectkmeanses_ES
dc.subjectAutoencoderes_ES
dc.subjectGaussian modeles_ES
dc.subjectNCBoPes_ES
dc.titleA One-Class-Based Supervision System to Detect Unexpected Events in Wastewater Treatment Plantses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessinfo:eu-repo/semantics/openAccesses_ES
UDC.journalTitleApplied Scienceses_ES
UDC.volume14es_ES
UDC.issue12es_ES
UDC.startPage1es_ES
UDC.endPage15es_ES
dc.identifier.doihttps://doi.org/10.3390/app14125185


Ficheiros no ítem

Thumbnail
Nome:
Arcano-Bea_Paula_2024_A_One-Cl ...
Tamaño:
937.0Kb
Formato:
PDF
Ver/abrir
Thumbnail
Nome:
license_rdf
Tamaño:
1.337Kb
Formato:
application/rdf+xml
Ver/abrir

Este ítem aparece na(s) seguinte(s) colección(s)

Mostrar o rexistro simple do ítem