18-22: Modeling and optimization of the effect of initial substrate concentration on ethanol fermentation by Saccharomyces cerevisiae using cashew apple juice as carbon source

Tuesday, May 1, 2012
Napoleon Ballroom C-D, 3rd fl (Sheraton New Orleans)
Álvaro Daniel Teles Pinheiro, Emanuel Meneses Barros, Maria Valderez Ponte Rocha and Luciana Rocha Barros Gonçalves, Chemical Engineering, Universidade Federal do Ceará, Fortaleza, Brazil
The most common renewable fuel today is ethanol produced by fermentation of sucrose in Brazil or corn glucose in the United States, however, these raw material bases will not be sufficient to satisfy the international demand. So, the optimization of alternative low-cost processes is imperative. In the Northeast Brazilian, the cashew agroindustry has an outstanding role in the local economy. However, only 12% of the total penduncle, is processed and it does not play an important role to the economy of the state. In this work, mathematical models for the effects of initial substrate concentration on ethanol fermentation of cashew apple juice by Saccharomyces cerevisiae at 30°C were proposed and their kinetic model parameters were estimated. Batch experimental observations obtained in bioreactor 1L at five initial concentration substrate (70, 90, 110, 130 and 170 g.L-1) were used to formulate the model discrimination problem. Were evaluated the models of Monod, Andrews, Levenspiel, Ghose and Thyagi and Tosetto. Parameter values were obtained by a technique of non-linear regression, using the software FORTRAN version 6.1. Subsequently, the mathematical models and estimated parameters adequacy was evaluated and the variances of model predictions were compared. The validation of the models chosen was done using statics analysis and data obtained from experiments and the initial substrate concentration was optimizated. The kinetic models of Levenspiel, Ghose and Thyagi and, Tosetto described satisfactorily the batch fermentation process as demonstrated by the experimental results and after was optimizated used the method Particle Swarm Optimization (PSO).
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