Fuzzy-Enhanced Modeling of Lignocellulosic Biomass Enzymatic Saccharification

Vitor B. Furlong, Luciano J. Corrêa, Roberto C. Giordano and Marcelo P. A. Ribeiro
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Vitor B. Furlong: Chemical Engineering Department, Federal University of São Carlos, P.O. Box 676, São Carlos 13565-905, SP, Brazil
Luciano J. Corrêa: Department of Engineering, Federal University of Lavras, P.O. Box 3037, Lavras 37200-000, MG, Brazil
Roberto C. Giordano: Chemical Engineering Department, Federal University of São Carlos, P.O. Box 676, São Carlos 13565-905, SP, Brazil
Marcelo P. A. Ribeiro: Chemical Engineering Department, Federal University of São Carlos, P.O. Box 676, São Carlos 13565-905, SP, Brazil

Energies, 2019, vol. 12, issue 11, 1-17

Abstract: 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.

Keywords: fed-batch; fuzzy modeling; high solids; lignocellulosic biomass hydrolysis (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
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