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Please use this identifier to cite or link to this item: http://hdl.handle.net/1843/47828
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dc.creatorFábio Coelho Sampaiopt_BR
dc.creatorJanaína Teles de Fariapt_BR
dc.creatorGabriel Dumond de Lima Silvapt_BR
dc.creatorRicardo Melo Gonçalvespt_BR
dc.creatorCristiano Grijó Pitanguipt_BR
dc.creatorAlessandro Alberto Casazzapt_BR
dc.creatorSaleh al Arnipt_BR
dc.creatorAttilio Convertipt_BR
dc.date.accessioned2022-12-07T18:59:52Z-
dc.date.available2022-12-07T18:59:52Z-
dc.date.issued2017-
dc.citation.volume40pt_BR
dc.citation.issue1pt_BR
dc.citation.spage122pt_BR
dc.citation.epage129pt_BR
dc.identifier.doihttps://doi.org/10.1002/ceat.201600066pt_BR
dc.identifier.issn0930-7516pt_BR
dc.identifier.urihttp://hdl.handle.net/1843/47828-
dc.description.resumoPrevious experimental data of xylose-to-xylitol bioconversion by Debaryomyces hansenii carried out according to a 33 full factorial design were used to model this process by two different artificial neural network (ANN) training methods. Models obtained for four responses were compared with those of response surface methodology (RSM). ANN models were shown to be superior to RSM in the predictive capacity, whereas the latter showed better performance in the generalization capability step. RSM with optimization using a genetic algorithm was revealed as a whole to be the best modeling option, which suggests that the comparative performances of RSM and ANN may be a highly problem-specific issue.pt_BR
dc.languageengpt_BR
dc.publisherUniversidade Federal de Minas Geraispt_BR
dc.publisher.countryBrasilpt_BR
dc.publisher.departmentICA - INSTITUTO DE CIÊNCIAS AGRÁRIASpt_BR
dc.publisher.initialsUFMGpt_BR
dc.relation.ispartofChemical Engineering & Technologypt_BR
dc.rightsAcesso Restritopt_BR
dc.subjectArtificial neural networkspt_BR
dc.subjectDebaryomyces hanseniipt_BR
dc.subjectResponse surface methodologypt_BR
dc.subjectXylitolpt_BR
dc.subjectYeastpt_BR
dc.subject.otherRedes neurais (Computação)pt_BR
dc.subject.otherLevedospt_BR
dc.titleComparison of response surface methodology and artificial neural network for modeling xylose-to-xylitol bioconversionpt_BR
dc.typeArtigo de Periódicopt_BR
dc.url.externahttps://onlinelibrary.wiley.com/doi/full/10.1002/ceat.201600066pt_BR
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