Kinetic study of the enzymatic hydrolysis of sugarcane bagasse

Brazilian Journal of Chemical Engineering

Volumn 30, Issue 3, 2013, Pages 437-447

  Carvalho, M L ^a   Sousa Jr , R ^a   Rodriguez Zuniga U F ^a   Suarez, C A G ^a   Rodrigues, D S ^b   Giordano, R C ^a   Giordano, R L C ^a  

^a FEDERAL UNIVERSITY OF SÃO CARLOS   (Brazil)

^b Parque Estação Biológica ^*  (Brazil)

Author keywords

Cellulose enzymatic hydrolysis; Kinetic study; Sugarcane bagasse

Indexed keywords

CELLULOSIC SUBSTRATES; KINETIC STUDY; MICHAELIS-MENTEN MODEL; MODEL PARAMETERS; SODIUM CITRATE; SUBSTRATE CONCENTRATIONS; SUBSTRATE MODEL; SUGAR-CANE BAGASSE;

CELLULOSE; ENZYMATIC HYDROLYSIS; KINETIC THEORY; KINETICS; SUBSTRATES;

BAGASSE;

BAGASSE; CELLULOSE; ENZYMOLYSIS; SUBSTRATES; SUGAR CANE;

EID: 84876952908     PISSN: 01046632     EISSN: None     Source Type: Journal    
DOI: 10.1590/S0104-66322013000300002     Document Type: Article

References (26)

1. Adney, B. and Baker, J., Measurement of cellulase activities: chemical analysis and testing task. Laboratory analytical procedure (1996).
2. Annema, A., Feed-forward Neural Networks: Vector Decomposition Analysis, Modeling and Analog Implementation. Kluwer Academic Publishers, Boston (1995).
3. Bezerra, R. M. F. and Dias, A. A., Discrimination among eight modified Michaelis-Menten kinetics models of cellulose hydrolysis with a large range of substrate/enzyme ratios. Appl. Biochem. Biotech., 112, 173 (2004).
4. Carrillo, F., Lis, M. J., Colom, X., Lopez-Mesas, M., Valldeperas, J., Effect of alkali pretreatment oncellulase hydrolysis of wheat straw: Kinetic study. Process Biochem., 40, 3360 (2005).
5. Chrastil, J. Enzymic product formation curves with the normal or diffusion limited reaction mechanism and in the presence of substrate receptors. Int. J. Biochem., 20, No. 7, 683 (1988).
6. Chrastil, J., Determination of the first order consecutive reaction rate constants from final products. Comput. Chem., 12, No. 4, 289 (1988b).
7. Ghose, T. K., Measurement of cellulase activity. Pure & Appl. Chem., 59, 257 (1987).
8. Gouveia, E. R., Nascimento, R. T., Souto-Maior, A. M., Rocha, G. J. M., Validação de metodologia para a caracterização química de bagaço de canade- açúcar. Química Nova, 32, 1500 (2009). (In Portuguese).
9. Granda, C. B., O'Dwyer, J. P., Zhu, L., Holtzapple, M. T., Enzymatic hydrolysis of lime-pretreated corn stover and investigation of the HCH-1 Model: Inhibition pattern, degree of inhibition, validity of simplified HCH-1 model. Bioresource Technology, 98, 2969 (2007).
10. Hagan, M., Demuth, H., Beale, M., Neural Network Design. PWS Publishing, Boston (1996).
11. Ingesson, H., Zacchi, G., Yang, B., Esteghlalian, A. R., Saddler, J. N., The effect of shaking regime on the rate and extend of enzymatic hydrolysis of cellulose. J. Biotech., 88, 177 (2001).
12. Kirkpatrick, S., Gelatt, C. D., Vecchi, M. P., Optimization by simulated annealing. Science, 220, No. 4598, 671 (1983).
13. Lee, Y. H. and Fan, L. T., Kinetic studies of enzymatic hydrolysis of insoluble cellulose: (II) analysis of extended hydrolysis times. Biotechnol. Bioeng., 25, 939 (1983).
14. Lynd, L. R., Weimer, P. J., van Zyl, W. H., Pretorius, I. S., Microbial cellulose utilization: Fundamentals and biotechnology. Microbiol. Mol. Biol. Rev., 66, 506 (2002).
15. Mansfield, S. D., Mooney, C., Saddler, J. N., Substrates and enzyme characteristics that limit cellulose hydrolysis. Biotechnol. Prog., 15, 804 (1999).
16. Miller, G. L., Use of dinitrosalicylic acid reagent for determination of reducing sugar. Anal. Chem., 31, 426 (1959).
17. Rivera, E. C., Rabelo, S. C., Garcia, D. D., Maciel, R., da Costa, A. C., Enzymatic hydrolysis of sugarcane bagasse for bioethanol production: Determining optimal enzyme loading using neural networks. J. Chem. Technol. Biotechnol., 85, 983 (2010).
18. Rocha, G. J. M., Silva, F. T., Araújo, G. T., Curvelo, A. A. S., A fast and accurate method for determination of cellulose and polyoses by HPLC. In: Proceedings of the V Brazilian Symposium on the Chemistry of Lignin and Other Wood Components, 5, 113 (1997).
19. Rothschild, W. G., Fractals in Chemistry. Wiley Interscience, New York (1998).
20. Schwaab, M., Biscaia, E. C., Monteiro, J. L., Pinto, J. C., Nonlinear parameter estimation through particle swarm optimization. Chem. Eng. Sci., 63, 1542 (2008).
21. Sousa, Jr. R., Carvalho, M. L., Giordano, R. L. C., Giordano, R. C., Recent trends in the modeling of cellulose hydrolysis. Braz. J. Chem. Eng., 28, No. 4, 545 (2011).
22. Walker, L. P., Wilson, D. B., Enzymatic hydrolysis of cellulose: An overview. Biores. Technol., 1991, 3 (1991).
23. Willams, J. C., In: Gartner, N. H., Messer, C. J., Rathi, A., (Eds.), Monograph on Traffic Flow Theory. Transportation Research Board Special Report, National Research Council, Washington, DC, 6-1 (2005). http://www.tfhrc.gov/its/ tft/chap9.pdf.
24. Woodward, J., Synergism in cellulase systems. Biores. Technol., 36, 67 (1991).
25. Zhang, Y., Xu, J. L., Yuan, Z. H., Modeling and prediction in the enzymatic hydrolysis of cellulose using artificial neural network. Fifth Intern. Conf. Natural. Comput., Tianjin, China (2009).
26. Zhang, Y. H. P. and Lynd, L. R., Toward an aggregated understanding of enzymatic hydrolysis of cellulose: Noncomplexed cellulase systems. Biotechnol. Bioeng., 88, No. 7, 797 (2004).