Wastewater quality estimation through spectrophotometry-based statistical models

Local 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 Pl...

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Autores: Carreres Prieto, Daniel, García Bermejo, Juan Tomás, Cerdán Cartagena, José Fernando, Suardiaz Muro, Juan
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2020
País:España
Institución:Universidad Politécnica de Cartagena(UPCT)
Repositorio:Repositorio Digital UPCT
OAI Identifier:oai:repositorio.upct.es:10317/9458
Acceso en línea:http://hdl.handle.net/10317/9458
https://www.mdpi.com/1424-8220/20/19/5631
Access Level:acceso abierto
Palabra clave:LED spectrophotometer
Wastewater pollutant characterization
Organic matter
Suspended solids
Nutrients
Tecnología Electrónica
3206.08 Nutrientes
3308.10 Tecnología de Aguas Residuales
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spelling Wastewater quality estimation through spectrophotometry-based statistical modelsCarreres Prieto, DanielGarcía Bermejo, Juan TomásCerdán Cartagena, José FernandoSuardiaz Muro, JuanLED spectrophotometerWastewater pollutant characterizationOrganic matterSuspended solidsNutrientsTecnología Electrónica3206.08 Nutrientes3308.10 Tecnología de Aguas ResidualesLocal 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 rateThe 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).MDPIFundación Séneca202120212020info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttp://hdl.handle.net/10317/9458https://www.mdpi.com/1424-8220/20/19/5631reponame:Repositorio Digital UPCTinstname:Universidad Politécnica de Cartagena(UPCT)Inglés20320/FPI/17Atribución-NoComercial-SinDerivadas 3.0 Españahttp://creativecommons.org/licenses/by-nc-nd/3.0/es/info:eu-repo/semantics/openAccessoai:repositorio.upct.es:10317/94582026-05-15T06:39:02Z
dc.title.none.fl_str_mv Wastewater quality estimation through spectrophotometry-based statistical models
title Wastewater quality estimation through spectrophotometry-based statistical models
spellingShingle Wastewater quality estimation through spectrophotometry-based statistical models
Carreres Prieto, Daniel
LED spectrophotometer
Wastewater pollutant characterization
Organic matter
Suspended solids
Nutrients
Tecnología Electrónica
3206.08 Nutrientes
3308.10 Tecnología de Aguas Residuales
title_short Wastewater quality estimation through spectrophotometry-based statistical models
title_full Wastewater quality estimation through spectrophotometry-based statistical models
title_fullStr Wastewater quality estimation through spectrophotometry-based statistical models
title_full_unstemmed Wastewater quality estimation through spectrophotometry-based statistical models
title_sort Wastewater quality estimation through spectrophotometry-based statistical models
dc.creator.none.fl_str_mv Carreres Prieto, Daniel
García Bermejo, Juan Tomás
Cerdán Cartagena, José Fernando
Suardiaz Muro, Juan
author Carreres Prieto, Daniel
author_facet Carreres Prieto, Daniel
García Bermejo, Juan Tomás
Cerdán Cartagena, José Fernando
Suardiaz Muro, Juan
author_role author
author2 García Bermejo, Juan Tomás
Cerdán Cartagena, José Fernando
Suardiaz Muro, Juan
author2_role author
author
author
dc.contributor.none.fl_str_mv Fundación Séneca
dc.subject.none.fl_str_mv LED spectrophotometer
Wastewater pollutant characterization
Organic matter
Suspended solids
Nutrients
Tecnología Electrónica
3206.08 Nutrientes
3308.10 Tecnología de Aguas Residuales
topic LED spectrophotometer
Wastewater pollutant characterization
Organic matter
Suspended solids
Nutrients
Tecnología Electrónica
3206.08 Nutrientes
3308.10 Tecnología de Aguas Residuales
description Local 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 rate
publishDate 2020
dc.date.none.fl_str_mv 2020
2021
2021
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10317/9458
https://www.mdpi.com/1424-8220/20/19/5631
url http://hdl.handle.net/10317/9458
https://www.mdpi.com/1424-8220/20/19/5631
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv 20320/FPI/17
dc.rights.none.fl_str_mv Atribución-NoComercial-SinDerivadas 3.0 España
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Atribución-NoComercial-SinDerivadas 3.0 España
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:Repositorio Digital UPCT
instname:Universidad Politécnica de Cartagena(UPCT)
instname_str Universidad Politécnica de Cartagena(UPCT)
reponame_str Repositorio Digital UPCT
collection Repositorio Digital UPCT
repository.name.fl_str_mv
repository.mail.fl_str_mv
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