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Authordc.contributor.authorQuezada, Carolina
Authordc.contributor.authorEstay Cuenca, Humberto Antonio
Authordc.contributor.authorCassano, Alfredo
Authordc.contributor.authorTroncoso, Elizabeth
Authordc.contributor.authorRuby Figueroa, René
Admission datedc.date.accessioned2021-11-23T11:26:33Z
Available datedc.date.available2021-11-23T11:26:33Z
Publication datedc.date.issued2021
Cita de ítemdc.identifier.citationMembranes 2021, 11, 368.es_ES
Identifierdc.identifier.other10.3390/membranes11050368
Identifierdc.identifier.urihttps://repositorio.uchile.cl/handle/2250/182815
Abstractdc.description.abstractIn any membrane filtration, the prediction of permeate flux is critical to calculate the membrane surface required, which is an essential parameter for scaling-up, equipment sizing, and cost determination. For this reason, several models based on phenomenological or theoretical derivation (such as gel-polarization, osmotic pressure, resistance-in-series, and fouling models) and non-phenomenological models have been developed and widely used to describe the limiting phenomena as well as to predict the permeate flux. In general, the development of models or their modifications is done for a particular synthetic model solution and membrane system that shows a good capacity of prediction. However, in more complex matrices, such as fruit juices, those models might not have the same performance. In this context, the present work shows a review of different phenomenological and non-phenomenological models for permeate flux prediction in UF, and a comparison, between selected models, of the permeate flux predictive capacity. Selected models were tested with data from our previous work reported for three fruit juices (bergamot, kiwi, and pomegranate) processed in a cross-flow system for 10 h. The validation of each selected model's capacity of prediction was performed through a robust statistical examination, including a residual analysis. The results obtained, within the statistically validated models, showed that phenomenological models present a high variability of prediction (values of R-square in the range of 75.91-99.78%), Mean Absolute Percentage Error (MAPE) in the range of 3.14-51.69, and Root Mean Square Error (RMSE) in the range of 0.22-2.01 among the investigated juices. The non-phenomenological models showed a great capacity to predict permeate flux with R-squares higher than 97% and lower MAPE (0.25-2.03) and RMSE (3.74-28.91). Even though the estimated parameters have no physical meaning and do not shed light into the fundamental mechanistic principles that govern these processes, these results suggest that non-phenomenological models are a useful tool from a practical point of view to predict the permeate flux, under defined operating conditions, in membrane separation processes. However, the phenomenological models are still a proper tool for scaling-up and for an understanding the UF process.es_ES
Patrocinadordc.description.sponsorshipComision Nacional de Investigacion Cientifica y Tecnologica (CONICYT) Comision Nacional de Investigacion Cientifica y Tecnologica (CONICYT) CONICYT FONDECYT 11160131 CONICYT-PIA Project AFB180004es_ES
Lenguagedc.language.isoenes_ES
Publisherdc.publisherMDPIes_ES
Type of licensedc.rightsAttribution-NonCommercial-NoDerivs 3.0 United States*
Link to Licensedc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/us/*
Sourcedc.sourceMembraneses_ES
Keywordsdc.subjectUltrafiltrationes_ES
Keywordsdc.subjectPhenomenological modelses_ES
Keywordsdc.subjectNon-phenomenological modelses_ES
Keywordsdc.subjectPermeate flux predictiones_ES
Títulodc.titlePrediction of permeate flux in ultrafiltration processes: a review of modeling approacheses_ES
Document typedc.typeArtículo de revistaes_ES
dc.description.versiondc.description.versionVersión publicada - versión final del editores_ES
dcterms.accessRightsdcterms.accessRightsAcceso abiertoes_ES
Catalogueruchile.catalogadorapces_ES
Indexationuchile.indexArtículo de publícación WoSes_ES


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Attribution-NonCommercial-NoDerivs 3.0 United States
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 United States