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dc.contributor.authorVIANA, J. D. da R.
dc.contributor.authorSOUZA, A. C. R. de
dc.contributor.authorRIBEIRO, P. R. V.
dc.contributor.authorSILVA, L. M. A. e
dc.contributor.authorCANUTO, K. M.
dc.contributor.authorREZZADORI, K.
dc.contributor.authorAREND, G. D.
dc.contributor.authorDIONISIO, A. P.
dc.contributor.authorPETRUS, J. C. C.
dc.date.accessioned2026-08-17T11:41:14Z-
dc.date.available2026-08-17T11:41:14Z-
dc.date.created2026-08-17
dc.date.issued2026
dc.identifier.citationMembranes, v. 16, n. 7, 221, July 2026.
dc.identifier.issn2077-0375
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1189220-
dc.descriptionAlthough coconut water is recognized for its desirable sensory appeal and nutritional composition, its broader industrial use is constrained by the rapid deterioration that occurs after extraction. In this study, crossflow microfiltration of coconut water with a silicon carbide membrane was optimized by investigating pressure and temperature through a face-centered design (FCD) and artificial neural network modeling coupled with a genetic algorithm (ANN–GA). Permeate flux and fouling index were used as process responses, and the optimized condition was further examined in terms of hydraulic resistance, fouling behavior, and retention of minerals and primary metabolites. Pressure and temperature affected the process differently: permeate flux showed marked nonlinear behavior, whereas fouling index was governed mainly by pressure. At the sample level, ANN described permeate flux more accurately than FCD (R2 = 0.99 vs. 0.96), whereas FCD showed better grouped cross-validated predictivity across unseen pressure–temperature conditions (Q2 = 0.85 vs. 0.57). For the fouling index, FCD outperformed ANN in both sample-level fit and grouped validation (R2 = 0.95 vs. 0.60; Q2 = 0.70 vs. 0.61). Both approaches converged on the same favorable operating window, and experimental validation at 60 kPa and 35 °C yielded 1085.23 ± 23.12 L h−1 m−2 and 83.56 ± 1.56%. During concentration mode, flux decline was severe but predominantly reversible, with high clean-water permeance recovery after chemical cleaning. Resistance partition and fouling modeling indicated that the main hydraulic limitation was associated with concentration polarization and external cake-layer buildup rather than irreversible membrane damage. The clarified fraction also preserved high transmission of major minerals and relevant primary metabolites, indicating that the selected condition combined high productivity, manageable fouling, and satisfactory nutritional retention.
dc.language.isoeng
dc.rightsopenAccess
dc.subjectCrossflow microfiltration
dc.subjectMembrane fouling
dc.subjectGenetic algorithms
dc.subjectPermeate flux
dc.subjectConcentration polarization
dc.subjectCake filtration
dc.titleCoconut water microfiltration optimization using response surface modeling, neural networks, and genetic algorithms: performance and nutritional retention.
dc.typeArtigo de periódico
dc.subject.thesagroCocos Nucifera
dc.subject.thesagroÁgua de Coco
dc.subject.thesagroFiltração
dc.subject.thesagroMembrana
dc.subject.thesagroMétodo de Otimização
dc.subject.thesagroAnálise Estatística
dc.subject.thesagroTecnologia de Alimento
dc.subject.thesagroClarificação
dc.subject.thesagroValor Nutritivo
dc.subject.thesagroNutriente Mineral
dc.subject.thesagroPressão
dc.subject.thesagroTemperatura
dc.subject.thesagroPermeabilidade
dc.subject.thesagroLimpeza
dc.subject.nalthesaurusCoconut water
dc.subject.nalthesaurusMicrofiltration
dc.subject.nalthesaurusCeramics
dc.subject.nalthesaurusSilicon carbide
dc.subject.nalthesaurusFood processing
dc.subject.nalthesaurusNonthermal processing
dc.subject.nalthesaurusSystem optimization
dc.subject.nalthesaurusResponse surface methodology
dc.subject.nalthesaurusNeural networks
dc.subject.nalthesaurusAlgorithms
dc.subject.nalthesaurusExperimental design
dc.subject.nalthesaurusFouling
dc.subject.nalthesaurusFilter cake
dc.subject.nalthesaurusPermeability
dc.subject.nalthesaurusCleaning
dc.subject.nalthesaurusPressure
dc.subject.nalthesaurusTemperature
dc.subject.nalthesaurusNutrient retention
dc.subject.nalthesaurusFood composition
dc.subject.nalthesaurusMineral content
dc.subject.nalthesaurusMetabolites
dc.subject.nalthesaurusNuclear magnetic resonance spectroscopy
dc.description.notesAutoria: Lorena Mara Alexandre Silva.
riaa.ainfo.id1189220
riaa.ainfo.lastupdate2026-08-17
dc.identifier.doihttps://doi.org/10.3390/membranes16070221
dc.contributor.institutionJOSÉ DIOGO DA ROCHA VIANA, UNIVERSIDADE FEDERAL DE SANTA CATARINA; ARTHUR CLAUDIO RODRIGUES DE SOUZA, CNPAT; PAULO RICELI VASCONCELOS RIBEIRO, CNPAT; LORENA MARA ALEXANDRE E SILVA, CNPAT; KIRLEY MARQUES CANUTO, CNPAT; KATIA REZZADORI, UNIVERSIDADE FEDERAL DE SANTA CATARINA; GIORDANA DEMAMAN AREND, UNIVERSIDADE FEDERAL DE SANTA CATARINA; ANA PAULA DIONISIO, CNPAT; JOSÉ CARLOS CUNHA PETRUS, UNIVERSIDADE FEDERAL DE SANTA CATARINA.
Aparece en las colecciones:Artigo em periódico indexado (CNPAT)

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