Ethanol competitivity can be enhanced when the total use of sugarcane portions is practicable, including bagasse and straw, through hydrolysis technology, in which the polysaccharides are processed to produce fermentable sugar and posteriorly ethanol. Among the problems of hydrolysate fermentation...

Ethanol competitivity can be enhanced when the total use of sugarcane portions is practicable, including bagasse and straw, through hydrolysis technology, in which the polysaccharides are processed to produce fermentable sugar and posteriorly ethanol. Among the problems of hydrolysate fermentation is the low sugar concentration in the medium (when hydrolysis is performed at low solids loading), which leads to low ethanol concentration, increasing the energy requirement in distillation. This can be solved through the concentration of hydrolysates with molasses. Kinetics of a mixture hydrolysates - molasses changes significantly from that of molasses, due to presence of other sugars and inhibitors. Thus, the kinetic models developed for molasses are not useful to predict fermentation data for hydrolysates. Considering this, in this work, a kinetic model for fermentation of a mixture of sugarcane bagasse hydrolysate and molasses was developed. For this purpose, data from batch fermentations at temperatures of 30, 32, 34, 36 e 38 degrees C were used. The model for hydrolysates was based on kinetic expressions previously developed for molasses fermentation, with addition of a term considering acetic acid inhibition on Saccharomyces cerevisiae growth. Also, to describe the data for fermentations with hydrolysate, a parameter re-estimation was necessary. Due to the large number of parameters in the model, a re-estimation methodology was proposed, in which the most sensitive parameters were adjusted and the less sensitive were kept fixed, making the re-estimation easier. A parametric sensitivity analysis through Plackett-Burman designs was performed, using the software Statistica, by varying the kinetic parameters and calcullating their influence on the profiles of cell, substrate and ethanol concentrations. The model consisted of 13 parameters, of which 5 were considered as relevant on fermentation profiles (mu(max), P-max. Y-x, Y-p/x and X-max) and chosen to be re-estimated. Through the use of this methodology, an accurate model for second generation bioethanol production was developed

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