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Deep Study on Fouling Modelling of Ultrafiltration Membranes Used for OMW Treatment: Comparison Between Semi-empirical Models, Response Surface, and Artificial Neural Networks

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Deep Study on Fouling Modelling of Ultrafiltration Membranes Used for OMW Treatment: Comparison Between Semi-empirical Models, Response Surface, and Artificial Neural Networks

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dc.contributor.author Cifuentes-Cabezas, Magdalena es_ES
dc.contributor.author Bohórquez-Zurita, José Luis es_ES
dc.contributor.author Gil-Herrero, Sandra es_ES
dc.contributor.author Vincent Vela, Maria Cinta es_ES
dc.contributor.author Mendoza Roca, José Antonio es_ES
dc.contributor.author Alvarez Blanco, Silvia es_ES
dc.date.accessioned 2024-05-27T18:08:10Z
dc.date.available 2024-05-27T18:08:10Z
dc.date.issued 2023-10 es_ES
dc.identifier.issn 1935-5130 es_ES
dc.identifier.uri http://hdl.handle.net/10251/204442
dc.description.abstract [EN] Olive oil production generates a large amount of wastewater called olive mill wastewater. This paper presents the study of the effect of transmembrane pressure and cross flow velocity on the decrease in permeate flux of different ultrafiltration membranes (material and pore size) when treating a two-phase olive mill wastewater (olive oil washing wastewater). Both semi-empirical models (Hermia models adapted to tangential filtration, combined model, and series resistance model), as well as statistical and machine learning methods (response surface methodology and artificial neural networks), were studied. Regarding the Hermia model, despite the good fit, the main drawback is that it does not consider the possibility that these mechanisms occur simultaneously in the same process. According to the accuracy of the fit of the models, in terms of R-2 and SD, both the series resistance model and the combined model were able to represent the experimental data well. This indicates that both cake layer formation and pore blockage contributed to membrane fouling. The inorganic membranes showed a greater tendency to irreversible fouling, with higher values of the R-a/R-T (adsorption/total resistance) ratio. Response surface methodology ANOVA showed that both cross flow velocity and transmembrane pressure are significant variables with respect to permeate flux for all membranes studied. Regarding artificial neural networks, the tansig function presented better results than the selu function, all presenting high R-2, ranging from 0.96 to 0.99. However, the comparison of all the analyzed models showed that depending on the membrane, one model fits better than the others. Finally, through this work, it was possible to provide a better understanding of the data modelling of different ultrafiltration membranes used for the treatment of olive mill wastewater. es_ES
dc.description.sponsorship Funding Open Access funding provided thanks to the CRUE-CSIC (Universitat Politecnica de Valencia) agreement with Springer Nature. This research has been financed by the Ministry of Economy, Industry and Competitiveness of Spain through the project CTM2017-88645-R and the European Union through the Operational Program of the Social Fund (FSE) financing ACIF-2018. es_ES
dc.language Inglés es_ES
dc.publisher Springer-Verlag es_ES
dc.relation.ispartof Food and Bioprocess Technology es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject Artificial neural networks es_ES
dc.subject Fouling mechanisms es_ES
dc.subject Response surface es_ES
dc.subject Semi-empirical models es_ES
dc.subject Ultrafiltration es_ES
dc.subject.classification INGENIERIA QUIMICA es_ES
dc.title Deep Study on Fouling Modelling of Ultrafiltration Membranes Used for OMW Treatment: Comparison Between Semi-empirical Models, Response Surface, and Artificial Neural Networks es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1007/s11947-023-03033-0 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/CTM2017-88645-R/ES/IMPLEMENTACION DE TECNOLOGIA DE MEMBRANAS PARA LA VALORIZACION DE LOS COMPUESTOS FENOLICOS PRESENTES EN LAS AGUAS RESIDUALES DE LA INDUSTRIA DE PRODUCCION DE ACEITE DE OLIVA/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/ESF//ACIF-2018/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Escuela Técnica Superior de Ingenieros Industriales - Escola Tècnica Superior d'Enginyers Industrials es_ES
dc.description.bibliographicCitation Cifuentes-Cabezas, M.; Bohórquez-Zurita, JL.; Gil-Herrero, S.; Vincent Vela, MC.; Mendoza Roca, JA.; Alvarez Blanco, S. (2023). Deep Study on Fouling Modelling of Ultrafiltration Membranes Used for OMW Treatment: Comparison Between Semi-empirical Models, Response Surface, and Artificial Neural Networks. Food and Bioprocess Technology. 16(10):2126-2146. https://doi.org/10.1007/s11947-023-03033-0 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1007/s11947-023-03033-0 es_ES
dc.description.upvformatpinicio 2126 es_ES
dc.description.upvformatpfin 2146 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 16 es_ES
dc.description.issue 10 es_ES
dc.relation.pasarela S\487986 es_ES
dc.contributor.funder European Social Fund es_ES
dc.contributor.funder AGENCIA ESTATAL DE INVESTIGACION es_ES
dc.contributor.funder Universitat Politècnica de València es_ES


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