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dc.creatorFurlong, Vitor B.-
dc.creatorCorrêa, Luciano J.-
dc.creatorGiordano, Roberto C.-
dc.creatorRibeiro, Marcelo P. A.-
dc.date.accessioned2020-05-07T18:35:14Z-
dc.date.available2020-05-07T18:35:14Z-
dc.date.issued2019-06-
dc.identifier.citationFURLONG, V. B. et al. Fuzzy-enhanced modeling of lignocellulosic biomass enzymatic saccharification. Energies, Basel, v. 12, n. 11, Jun. 2019. doi:10.3390/en12112110.pt_BR
dc.identifier.urihttp://repositorio.ufla.br/jspui/handle/1/40689-
dc.description.abstractThe enzymatic hydrolysis of lignocellulosic biomass incorporates many physico-chemical phenomena, in a heterogeneous and complex media. In order to make the modeling task feasible, many simplifications must be assumed. Hence, different simplified models, such as Michaelis-Menten and Langmuir-based ones, have been used to describe batch processes. However, these simple models have difficulties in predicting fed-batch operations with different feeding policies. To overcome this problem and avoid an increase in the complexity of the model by incorporating other phenomenological terms, a Takagi-Sugeno Fuzzy approach has been proposed, which manages a consortium of different simple models for this process. Pretreated sugar cane bagasse was used as biomass in this case study. The fuzzy rule combines two Michaelis-Menten-based models, each responsible for describing the reaction path for a distinct range of solids concentrations in the reactor. The fuzzy model improved fitting and increased prediction in a validation data set.pt_BR
dc.languageenpt_BR
dc.publisherMDPI Journalspt_BR
dc.rightsacesso abertopt_BR
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.sourceEnergiespt_BR
dc.subjectFed-batchpt_BR
dc.subjectFuzzy modelingpt_BR
dc.subjectHigh solidspt_BR
dc.subjectLignocellulosic biomass hydrolysispt_BR
dc.subjectModelagem fuzzypt_BR
dc.subjectBiomassa lignocelulósicapt_BR
dc.subjectHidrólise enzimáticapt_BR
dc.titleFuzzy-enhanced modeling of lignocellulosic biomass enzymatic saccharificationpt_BR
dc.typeArtigopt_BR
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