A Novel Kinetic Modeling of Enzymatic Hydrolysis of Sugarcane Bagasse Pretreated by Hydrothermal and Organosolv Processes

Author:

Moreira Neto João1,Costa Josiel Martins2ORCID,Bonomi Antonio3,Costa Aline Carvalho4

Affiliation:

1. Department of Engineering, Federal University of Lavras, Lavras 37200-000, MG, Brazil

2. School of Food Engineering, University of Campinas, Campinas 13083-862, SP, Brazil

3. Brazilian Biorenewables National Laboratory (LNBR), Brazilian Center for Research in Energy and Materials (CNPEM), Campinas 13083-100, SP, Brazil

4. Laboratory of Fermentative and Enzymatic Process Engineering, School of Chemical Engineering, University of Campinas, Campinas 13083-852, SP, Brazil

Abstract

Lignocellulosic biomasses have a complex and compact structure, requiring physical and/or chemical pretreatments to produce glucose before hydrolysis. Mathematical modeling of enzymatic hydrolysis highlights the interactions between cellulases and cellulose, evaluating the factors contributing to reactor scale-up and conversion rates. Furthermore, this study evaluated the influence of two pretreatments (hydrothermal and organosolv) on the kinetics of enzymatic hydrolysis of sugarcane bagasse. The kinetic parameters of the model were estimated using the Pikaia genetic algorithm with data from the experimental profiles of cellulose, cellobiose, glucose, and xylose. The model considered the phenomenon of non-productive adsorption of cellulase on lignin and inhibition of cellulase by xylose. Moreover, it included the behavior of cellulase adsorption on the substrate throughout hydrolysis and kinetic equations for obtaining xylose from xylanase-catalyzed hydrolysis of xylan. The model for both pretreatments was experimentally validated with bagasse concentration at 10% w/v. The Plackett–Burman design identified 17 kinetic parameters as significant in the behavior of process variables. In this way, the modeling and parameter estimation methodology obtained a good fit from the experimental data and a more comprehensive model.

Funder

Fundação de Amparo à Pesquisa do Estado de São Paulo

Publisher

MDPI AG

Subject

Chemistry (miscellaneous),Analytical Chemistry,Organic Chemistry,Physical and Theoretical Chemistry,Molecular Medicine,Drug Discovery,Pharmaceutical Science

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