Your browser doesn't support javascript.
loading
Wastewater Quality Estimation Through Spectrophotometry-Based Statistical Models.
Carreres-Prieto, Daniel; García, Juan T; Cerdán-Cartagena, Fernando; Suardiaz-Muro, Juan.
Afiliación
  • Carreres-Prieto D; Department of Mining and Civil Engineering, Universidad Politécnica de Cartagena, 30202 Cartagena, Spain.
  • García JT; Department of Mining and Civil Engineering, Universidad Politécnica de Cartagena, 30202 Cartagena, Spain.
  • Cerdán-Cartagena F; Department of Information and Communications Technologies, Universidad Politécnica de Cartagena, 30202 Cartagena, Spain.
  • Suardiaz-Muro J; Department of Electronic Technology, Universidad Politécnica de Cartagena, 30202 Cartagena, Spain.
Sensors (Basel) ; 20(19)2020 Oct 01.
Article en En | MEDLINE | ID: mdl-33019750
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.
Palabras clave

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Risk_factors_studies Idioma: En Revista: Sensors (Basel) Año: 2020 Tipo del documento: Article País de afiliación: España Pais de publicación: Suiza

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Risk_factors_studies Idioma: En Revista: Sensors (Basel) Año: 2020 Tipo del documento: Article País de afiliación: España Pais de publicación: Suiza