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(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

Fuzzy-Enhanced Modeling of Lignocellulosic Biomass Enzymatic Saccharification

Texto completo
Autor(es):
Furlong, Vitor B. [1] ; Correa, Luciano J. [2] ; Giordano, Roberto C. [1] ; Ribeiro, Marcelo P. A. [1]
Número total de Autores: 4
Afiliação do(s) autor(es):
[1] Univ Fed Sao Carlos, Chem Engn Dept, BR-13565905 Sao Carlos, SP - Brazil
[2] Univ Fed Lavras, Dept Engn, POB 3037, BR-37200000 Lavras, MG - Brazil
Número total de Afiliações: 2
Tipo de documento: Artigo Científico
Fonte: ENERGIES; v. 12, n. 11 JUN 1 2019.
Citações Web of Science: 0
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. (AU)

Processo FAPESP: 16/10636-8 - Da fábrica celular à biorrefinaria integrada Biodiesel-Bioetanol: uma abordagem sistêmica aplicada a problemas complexos em micro e macroescalas
Beneficiário:Roberto de Campos Giordano
Linha de fomento: Auxílio à Pesquisa - Programa BIOEN - Temático