Fuzzy-enhanced modeling of lignocellulosic biomass enzymatic saccharification

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.creatorFurlong, Vitor B.-
Autor(es): dc.creatorCorrêa, Luciano J.-
Autor(es): dc.creatorGiordano, Roberto C.-
Autor(es): dc.creatorRibeiro, Marcelo P. A.-
Data de aceite: dc.date.accessioned2026-02-09T11:20:47Z-
Data de disponibilização: dc.date.available2026-02-09T11:20:47Z-
Data de envio: dc.date.issued2020-05-07-
Data de envio: dc.date.issued2020-05-07-
Data de envio: dc.date.issued2019-06-
Fonte completa do material: dc.identifierhttps://repositorio.ufla.br/handle/1/40689-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/capes/1138929-
Descrição: dc.descriptionThe 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.-
Formato: dc.formatapplication/pdf-
Idioma: dc.languageen-
Publicador: dc.publisherMDPI Journals-
Direitos: dc.rightsacesso aberto-
Direitos: dc.rightshttp://creativecommons.org/licenses/by/4.0/-
Direitos: dc.rightshttp://creativecommons.org/licenses/by/4.0/-
???dc.source???: dc.sourceEnergies-
Palavras-chave: dc.subjectFed-batch-
Palavras-chave: dc.subjectFuzzy modeling-
Palavras-chave: dc.subjectHigh solids-
Palavras-chave: dc.subjectLignocellulosic biomass hydrolysis-
Palavras-chave: dc.subjectModelagem fuzzy-
Palavras-chave: dc.subjectBiomassa lignocelulósica-
Palavras-chave: dc.subjectHidrólise enzimática-
Título: dc.titleFuzzy-enhanced modeling of lignocellulosic biomass enzymatic saccharification-
Tipo de arquivo: dc.typeArtigo-
Aparece nas coleções:Repositório Institucional da Universidade Federal de Lavras (RIUFLA)

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