Empirical modeling of turbidity removal in a dissolved air flotation system: Application of artificial neural networks

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dc.contributor.authorOLIVEIRA E SOUZA, A. C.
dc.contributor.authorDA SILVA, F. V.
dc.contributor.authorFERREIRA, N. L.
dc.date.accessioned2022-01-16T18:58:53Z
dc.date.available2022-01-16T18:58:53Z
dc.date.issued2021-11-01
dc.description.abstract© 2021 IWA Publishing. All rights reserved.Dissolved air flotation (DAF) is a physical separation process that uses air microbubbles to remove suspended material dispersed in a liquid phase. Even though DAF is considered a well-established unit operation, modeling it is difficult due to the complexity of the phenomena involved, resulting in conceptual models with no practical application. Thereby, the objective of this work was to evaluate empirical modeling efficiency in predicting the turbidity removal dynamic using artificial neural networks applied to a DAF prototype. For the study of the neural network input variables, a two-level, full-factorial design was utilized to verify the statistical significance of the saturation pressure and the saturated water flow rate in relation to the turbidity removal. Using a time-delay recurrent neural network architecture, two empirical models were proposed to simulate the dynamic behavior of the turbidity removal promoted by the DAF prototype. The real-time model provided good predictions with R¼ 0.9717 and MSE¼1.0482, and the simulation model was also able to predict the process behavior presenting performance criteria equal to R¼ 0.9475 and MSE¼1.8640.
dc.description.firstpage3946
dc.description.issuenumber7
dc.description.lastpage3959
dc.description.volume21
dc.identifier.citationOLIVEIRA E SOUZA, A. C.; DA SILVA, F. V.; FERREIRA, N. L. Empirical modeling of turbidity removal in a dissolved air flotation system: Application of artificial neural networks. Water Supply, v. 21, n. 7, p. 3946-3959, November, 2021.
dc.identifier.doi10.2166/ws.2021.152
dc.identifier.issn1607-0798
dc.identifier.urihttps://repositorio.fei.edu.br/handle/FEI/4388
dc.relation.ispartofWater Supply
dc.rightsAcesso Aberto
dc.rights.licenseCreative Commons "Este é um artigo publicado em acesso aberto sob uma licença Creative commons (CC BY 4.0). Fonte: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85119204294&origin=inward. Acesso em: 23 agosto 2022.
dc.subject.otherlanguageArtificial neural networks
dc.subject.otherlanguageDissolved air flotation
dc.subject.otherlanguageProcess modeling
dc.subject.otherlanguageWater treatment
dc.titleEmpirical modeling of turbidity removal in a dissolved air flotation system: Application of artificial neural networks
dc.typeArtigo
fei.scopus.citations1
fei.scopus.eid2-s2.0-85119204294
fei.scopus.subjectAir flotation systems
fei.scopus.subjectDissolved air flotations
fei.scopus.subjectEmpirical model
fei.scopus.subjectMicrobubbles
fei.scopus.subjectPhysical separation
fei.scopus.subjectProcess-models
fei.scopus.subjectSeparation process
fei.scopus.subjectSuspended material
fei.scopus.subjectSystem applications
fei.scopus.subjectTurbidity removal
fei.scopus.updated2024-07-01
fei.scopus.urlhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85119204294&origin=inward
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