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http://repositorio.ufla.br/jspui/handle/1/40689
Título: | Fuzzy-enhanced modeling of lignocellulosic biomass enzymatic saccharification |
Palavras-chave: | Fed-batch Fuzzy modeling High solids Lignocellulosic biomass hydrolysis Modelagem fuzzy Biomassa lignocelulósica Hidrólise enzimática |
Data do documento: | Jun-2019 |
Editor: | MDPI Journals |
Citação: | FURLONG, V. B. et al. Fuzzy-enhanced modeling of lignocellulosic biomass enzymatic saccharification. Energies, Basel, v. 12, n. 11, Jun. 2019. doi:10.3390/en12112110. |
Resumo: | The 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. |
URI: | http://repositorio.ufla.br/jspui/handle/1/40689 |
Aparece nas coleções: | DEG - Artigos publicados em periódicos |
Arquivos associados a este item:
Arquivo | Descrição | Tamanho | Formato | |
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ARTIGO_Fuzzy-Enhanced Modeling of Lignocellulosic Biomass Enzymatic Saccharification.pdf | 2,54 MB | Adobe PDF | Visualizar/Abrir |
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