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dc.contributor.authorCarreres Prieto, Daniel 
dc.contributor.authorGarcía Bermejo, Juan Tomás 
dc.contributor.authorCerdán Cartagena, José Fernando 
dc.contributor.authorSuardíaz Muro, Juan 
dc.date.accessioned2021-06-21T06:11:32Z
dc.date.available2021-06-21T06:11:32Z
dc.date.issued2020
dc.identifier.citationCarreres-Prieto, D.; García, J.T.; Cerdán-Cartagena, F.; Suardiaz-Muro, J. Wastewater Quality Estimation through Spectrophotometry-Based Statistical Models. Sensors 2020, 20, 5631. https://doi.org/10.3390/s20195631es_ES
dc.identifier.issn1424-8220
dc.description.abstractLocal administrations are increasingly demanding real-time continuous monitoring of pollution in the sanitation system to improve and optimize its operation, to comply with EU environmental policies and to reach European Green Deal targets. The present work shows a full-scale Wastewater Treatment Plant field-sampling campaign to estimate COD, BOD5, TSS, P, TN and NO3-N in both influent and effluent, in the absence of pre-treatment or chemicals addition to the samples, resulting in a reduction of the duration and cost of analysis. Different regression models were developed to estimate the pollution load of sewage systems from the spectral response of wastewater samples measured at 380-700 nm through multivariate linear regressions and machine learning genetic algorithms. The tests carried out concluded that the models calculated by means of genetic algorithms can estimate the levels of five of the pollutants under study (COD, BOD5, TSS, TN and NO3-N), including both raw and treated wastewater, with an error rate below 4%. In the case of the multilinear regression models, these are limited to raw water and the estimate is limited to COD and TSS, with less than a 0.5% error ratees_ES
dc.description.sponsorshipThe authors wish to thank the financial support received from the Seneca Foundation of the Región de Murcia (Spain) through the program devoted to training novel researchers in areas of specific interest for the industry and with a high capacity to transfer the results of the research generated, entitled: “Subprograma Regional de Contratos de Formación de Personal Investigador en Universidades y OPIs” (Mod. B, Ref. 20320/FPI/17).es_ES
dc.formatapplication/pdfes_ES
dc.language.isoenges_ES
dc.publisherMDPIes_ES
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.titleWastewater quality estimation through spectrophotometry-based statistical modelses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.subjectLED spectrophotometeres_ES
dc.subjectWastewater pollutant characterizationes_ES
dc.subjectOrganic matteres_ES
dc.subjectSuspended solidses_ES
dc.subjectNutrientses_ES
dc.subject.otherTecnología Electrónicaes_ES
dc.identifier.urihttp://hdl.handle.net/10317/9458
dc.identifier.doi10.3390/s20195631
dc.identifier.urlhttps://www.mdpi.com/1424-8220/20/19/5631
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.type.versioninfo:eu-repo/semantics/publishedVersiones_ES
dc.relation.projectID20320/FPI/17es_ES
dc.subject.unesco3206.08 Nutrienteses_ES
dc.subject.unesco3308.10 Tecnología de Aguas Residualeses_ES
dc.contributor.funderFundación Sénecaes_ES


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Except where otherwise noted, this item's license is described as Atribución-NoComercial-SinDerivadas 3.0 España