Estimation of biomass enzymatic hydrolysis state in stirred tank reactor through moving horizon algorithms with fixed and dynamic Fuzzy weights

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Autor(es): dc.creatorFurlong, Vitor B.-
Autor(es): dc.creatorCorrêa, Luciano J.-
Autor(es): dc.creatorLima, Fernando V.-
Autor(es): dc.creatorGiordano, Roberto C.-
Autor(es): dc.creatorRibeiro, Marcelo P. A.-
Data de aceite: dc.date.accessioned2026-02-09T11:31:06Z-
Data de disponibilização: dc.date.available2026-02-09T11:31:06Z-
Data de envio: dc.date.issued2020-09-04-
Data de envio: dc.date.issued2020-09-04-
Data de envio: dc.date.issued2019-
Fonte completa do material: dc.identifierhttps://repositorio.ufla.br/handle/1/42863-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/capes/1142019-
Descrição: dc.descriptionSecond generation ethanol faces challenges before profitable implementation. Biomass hydrolysis is one of the bottlenecks, especially when this process occurs at high solids loading and with enzymatic catalysts. Under this setting, kinetic modeling and reaction monitoring are hindered due to the conditions of the medium, while increasing the mixing power. An algorithm that addresses these challenges might improve the reactor performance. In this work, a soft sensor that is based on agitation power measurements that uses an Artificial Neural Network (ANN) as an internal model is proposed in order to predict free carbohydrates concentrations. The developed soft sensor is used in a Moving Horizon Estimator (MHE) algorithm to improve the prediction of state variables during biomass hydrolysis. The algorithm is developed and used for batch and fed-batch hydrolysis experimental runs. An alteration of the classical MHE is proposed for improving prediction, using a novel fuzzy rule to alter the filter weights online. This alteration improved the prediction when compared to the original MHE in both training data sets (tracking error decreased 13%) and in test data sets, where the error reduction obtained is 44%.-
Formato: dc.formatapplication/pdf-
Idioma: dc.languageen-
Publicador: dc.publisherMDPI-
Direitos: dc.rightsAttribution 4.0 International-
Direitos: dc.rightsAttribution 4.0 International-
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.sourceProcesses-
Palavras-chave: dc.subjectArtificial neural network-
Palavras-chave: dc.subjectBiomass enzymatic hydrolysis-
Palavras-chave: dc.subjectFuzzy logic-
Palavras-chave: dc.subjectLocal linear model tree-
Palavras-chave: dc.subjectMoving horizon estimation-
Palavras-chave: dc.subjectProcess monitoring-
Palavras-chave: dc.subjectSoft sensing-
Palavras-chave: dc.subjectRede neural artificial-
Palavras-chave: dc.subjectHidrólise enzimática de biomassa-
Palavras-chave: dc.subjectLógica Fuzzy-
Palavras-chave: dc.subjectÁrvore modelo linear local-
Palavras-chave: dc.subjectEstimativa de horizonte móvel-
Palavras-chave: dc.subjectMonitoramento de processos-
Título: dc.titleEstimation of biomass enzymatic hydrolysis state in stirred tank reactor through moving horizon algorithms with fixed and dynamic Fuzzy weights-
Tipo de arquivo: dc.typeArtigo-
Aparece nas coleções:Repositório Institucional da Universidade Federal de Lavras (RIUFLA)

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