Enzymatic hydrolysis of sugarcane bagasse for bioethanol production: determining optimal enzyme loading using neural networks

dc.creatorRivera, EC
dc.creatorRabelo, SC
dc.creatorGarcia, DD
dc.creatorMaciel, R
dc.creatorda Costa, AC
dc.date2010
dc.dateJUL
dc.date2014-07-30T17:19:53Z
dc.date2015-11-26T17:54:04Z
dc.date2014-07-30T17:19:53Z
dc.date2015-11-26T17:54:04Z
dc.date.accessioned2018-03-29T00:37:42Z
dc.date.available2018-03-29T00:37:42Z
dc.identifierJournal Of Chemical Technology And Biotechnology. Wiley-blackwell, v. 85, n. 7, n. 983, n. 992, 2010.
dc.identifier0268-2575
dc.identifierWOS:000279510900012
dc.identifier10.1002/jctb.2391
dc.identifierhttp://www.repositorio.unicamp.br/jspui/handle/REPOSIP/64941
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/64941
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1290696
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.descriptionBACKGROUND: The efficient production of a fermentable hydrolyzate is an immensely important requirement in the utilization of lignocellulosic biomass as a feedstock in bioethanol production processes. The identification of the optimal enzyme loading is of particular importance to maximize the amount of glucose produced from lignocellulosic materials while maintaining low costs. This requirement can only be achieved by incorporating reliable methodologies to properly address the optimization problem. RESULTS: In this work, a data-driven technique based on artificial neural networks and design of experiments have been integrated in order to identify the optimal enzyme combination. The enzymatic hydrolysis of sugarcane bagasse was used as a case study. This technique was used to build up a model of the combined effects of cellulase (FPU/L) and beta-glucosidase (CBU/L) loads on glucose yield (%) after enzymatic hydrolysis. The optimal glucose yield, above 99%, was achieved with cellulase and beta-glucosidase concentrations in the ranges of 460.0 to 580.0 FPU L(-1) (15.3-19.3 FPU g(-1) bagasse) and 750.0 to 1140.0 CBU L(-1) (2-38 CBU g(-1) bagasse), respectively. CONCLUSIONS: The dynamic model developed can be used not only to the prediction of glucose concentration profiles for different enzymatic loadings, but also to obtain the optimum enzymes loading that leads to high glucose yield. It can promote both a successful hydrolysis process control and a more effective employment of enzymes. (C) 2010 Society of Chemical Industry
dc.description85
dc.description7
dc.description983
dc.description992
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.languageen
dc.publisherWiley-blackwell
dc.publisherMalden
dc.publisherEUA
dc.relationJournal Of Chemical Technology And Biotechnology
dc.relationJ. Chem. Technol. Biotechnol.
dc.rightsfechado
dc.rightshttp://olabout.wiley.com/WileyCDA/Section/id-406071.html
dc.sourceWeb of Science
dc.subjectenzymatic hydrolysis
dc.subjectsugarcane bagasse
dc.subjectenzyme loading, optimization
dc.subjectmodeling
dc.subjectartificial intelligence
dc.subjectResponse-surface Methodology
dc.subjectFuel Ethanol-production
dc.subjectCorn Stover
dc.subjectOptimization
dc.subjectFermentation
dc.subjectPretreatment
dc.subjectDigestibility
dc.subjectIntegration
dc.subjectGlucose
dc.subjectBiomass
dc.titleEnzymatic hydrolysis of sugarcane bagasse for bioethanol production: determining optimal enzyme loading using neural networks
dc.typeArtículos de revistas


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