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Optimizing wastewater treatment plants with advanced feature selection and sensor technologies
dc.contributor.author | Timiraos, Míriam | |
dc.contributor.author | Fernández Águila, Jesús | |
dc.contributor.author | Arce Fariña, Elena | |
dc.contributor.author | García Núñez, Moisés Alberto | |
dc.contributor.author | Zayas-Gato, Francisco | |
dc.contributor.author | Quintián, Héctor | |
dc.date.accessioned | 2024-09-10T11:11:31Z | |
dc.date.available | 2024-09-10T11:11:31Z | |
dc.date.issued | 2024 | |
dc.identifier.citation | Míriam Timiraos, Jesús F Águila, Elena Arce, Moisés Alberto GarcÍa Núñez, Francisco Zayas-Gato, Héctor Quintián, Optimizing wastewater treatment plants with advanced feature selection and sensor technologies, Logic Journal of the IGPL, 2024; jzae108, https://doi.org/10.1093/jigpal/jzae108 | es_ES |
dc.identifier.issn | 1368-9894 | |
dc.identifier.uri | http://hdl.handle.net/2183/38944 | |
dc.description | Funding for open access charge: Universidade da Coruña/CISUG. | es_ES |
dc.description.abstract | [Abstract] This research establishes a foundational framework for the development of virtual sensors and provides significant preliminary results. Our study specifically focuses on identifying the key factors essential for accurately predicting total nitrogen in the effluent of wastewater treatment plants. This contribution enhances the predictive capabilities and operational efficiency of these plants, demonstrating the practical benefits of integrating advanced feature selection methods and innovative sensor technologies. These findings provide crucial insights and pave the way for future advancements in the field. In this study, four different feature selection methods are employed to comprehensively explore the variables influencing total nitrogen predictions. The effectiveness of these methods is then evaluated by applying three regression techniques. The findings indicate acceptable levels of accuracy in all applied cases, with one method demonstrating particularly promising results, applicable to several wastewater treatment plants. This validation of the selected variables not only underlines their effectiveness, but also lays the foundation for future virtual sensor applications. The integration of such sensors promises to improve the accuracy and reliability of predictions, marking a significant advance in wastewater treatment plant instrumentation. | es_ES |
dc.description.sponsorship | Míriam Timiraos’ research was supported by the ‘Xunta de Galicia’ through grants to industrial PhD (http://gain.xunta.gal/), under the ‘Doutoramento Industrial 2022’ grant with reference: 04_IN606D_2022_2692965. 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). Grant PID2022-137152NB-I00 funded by MICIU/AEI/10.13039/501100011033 and by ERDF/EU. | es_ES |
dc.description.sponsorship | Xunta de Galicia; 04_IN606D_2022_2692965 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431G 2023/01 | es_ES |
dc.description.sponsorship | Instituto Nacional de Ciberseguridad; C061/23 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Oxford University Press | es_ES |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2022-137152NB-I00 | es_ES |
dc.relation.uri | https://doi.org/10.1093/jigpal/jzae108 | es_ES |
dc.rights | Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/ | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ | * |
dc.subject | Feature selection | es_ES |
dc.subject | Wastewater treatment plant | es_ES |
dc.subject | Regression techniques | es_ES |
dc.subject | Prediction | es_ES |
dc.subject | Total nitrogen | es_ES |
dc.title | Optimizing wastewater treatment plants with advanced feature selection and sensor technologies | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | Logic Journal of the IGPL | es_ES |
UDC.startPage | 1 | es_ES |
UDC.endPage | 22 | es_ES |
dc.identifier.doi | https://doi.org/10.1093/jigpal/jzae108 |
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