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Please use this identifier to cite or link to this item: http://acervodigital.unesp.br/handle/11449/129569
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dc.contributor.authorVilla-Velez, Harvey A.-
dc.contributor.authorVaquiro, Henry A.-
dc.contributor.authorTelis-Romero, Javier-
dc.date.accessioned2015-10-21T21:23:11Z-
dc.date.accessioned2016-10-25T21:14:38Z-
dc.date.available2015-10-21T21:23:11Z-
dc.date.available2016-10-25T21:14:38Z-
dc.date.issued2015-04-01-
dc.identifier.citationIndustrial Crops And Products, v. 66, p. 52-61, 2015.-
dc.identifier.issn0926-6690-
dc.identifier.urihttp://hdl.handle.net/11449/129569-
dc.identifier.urihttp://acervodigital.unesp.br/handle/11449/129569-
dc.description.abstractVarious pretreatment techniques can change the physical and chemical structure of lignocellulosic biomass and improve the hydrolysis rates. High-intensity ultrasound could be a promising technique in the biomass pretreatment process. The objective of this work was to study the effect of biomass concentration, pH, ultrasonic power level and sonication time on the production yield in total sugars (S-T) and reducing sugars (S-R) during the pretreatment of banana flower-stalk biomass. A qualitative evaluation was carried out by scanning electron microscopy, showing a disruptive effect on the biomass structure at high ultrasonic power levels and low biomass concentrations. An experimental design with three-levels for the four-variables was used in order to set the conditions for the pretreatments. Stepwise regression (SRG) and an artificial neural network (ANN) were applied in order to establish mathematical models that could represent and be used to study the dependence of the factors on both the S-T and S-R yields. The statistical results indicated that the ANN approach provided a more accurate estimation than SRG. (C) 2014 Elsevier B.V. All rights reserved.en
dc.description.sponsorshipConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)-
dc.description.sponsorshipCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)-
dc.description.sponsorshipFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)-
dc.format.extent52-61-
dc.language.isoeng-
dc.publisherElsevier B.V.-
dc.sourceWeb of Science-
dc.subjectArtificial neural networken
dc.subjectFermentable sugarsen
dc.subjectLignocellulosic materialsen
dc.subjectOptimizationen
dc.subjectStepwise regressionen
dc.titleThe effect of power-ultrasound on the pretreatment of acidified aqueous solutions of banana flower-stalk: Structural, chemical and statistical analysisen
dc.typeoutro-
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)-
dc.contributor.institutionUniv Tolima-
dc.description.affiliationUniv Estadual Paulista, Dept Engn &Tecnol Alimentos, Sao Paulo, Brazil-
dc.description.affiliationUniv Tolima, Fac Ingn Agron, Ibague 546, Tolima, Colombia-
dc.description.affiliationUnespUniv Estadual Paulista, Dept Engn &Tecnol Alimentos, Sao Paulo, Brazil-
dc.description.sponsorshipIdCNPq: 402102/2012-6-
dc.description.sponsorshipIdFAPESP: 2013/17497-5-
dc.identifier.doihttp://dx.doi.org/10.1016/j.indcrop.2014.12.022-
dc.identifier.wosWOS:000350932900007-
dc.rights.accessRightsAcesso restrito-
dc.relation.ispartofIndustrial Crops And Products-
Appears in Collections:Artigos, TCCs, Teses e Dissertações da Unesp

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