Hybrid neural network modeling and particle swarm optimization for improved ethanol production from cashew apple juice.

A hybrid neural model (HNM) and particle swarm optimization (PSO) was used to optimize ethanol production by a flocculating yeast, grown on cashew apple juice. HNM was obtained by combining artificial neural network (ANN), which predicted reaction specific rates, to mass balance equations for substrate (S), product and biomass (X) concentration, being an alternative method for predicting the behavior of complex systems. ANNs training was conducted using an experimental set of data of X and S, temperature and stirring speed. The HNM was statistically validated against a new dataset, being capable of representing the system behavior. The model was optimized based on a multiobjective function relating efficiency and productivity by applying the PSO. Optimal estimated conditions were: S0 = 127 g L-1, X0 = 5.8 g L-1, 35 °C and 111 rpm. In this condition, an efficiency of 91.5% with a productivity of 8.0 g L-1 h-1 was obtained at approximately 7 h of fermentation.

Mathematical modeling of the ethanol fermentation of cashew apple juice by a flocculent yeast: the effect of initial substrate concentration and temperature.

In this work, the effect of initial sugar concentration and temperature on the production of ethanol by Saccharomyces cerevisiae CCA008, a flocculent yeast, using cashew apple juice in a 1L-bioreactor was studied. The experimental results were used to develop a kinetic model relating biomass, ethanol production and total reducing sugar consumption. Monod, Andrews, Levenspiel and Ghose and Tyagi models were investigated to represent the specific growth rate without inhibition, with inhibition by substrate and with inhibition by product, respectively. Model validation was performed using a new set of experimental data obtained at 34 °C and using 100 g L-1 of initial substrate concentration. The model proposed by Ghose and Tyagi was able to accurately describe the dynamics of ethanol production by S. cerevisiae CCA008 growing on cashew apple juice, containing an initial reducing sugar concentration ranging from 70 to 170 g L-1 and temperature, from 26 to 42 °C. The model optimization was also accomplished based on the following parameters: percentage volume of ethanol per volume of solution (%V ethanol/V solution), efficiency and reaction productivity. The optimal operational conditions were determined using response surface graphs constructed with simulated data, reaching an efficiency and a productivity of 93.5% and 5.45 g L-1 h-1, respectively.

Evaluation of cashew apple juice for the production of fuel ethanol.

A commercial strain of Saccharomyces cerevisiae was used for the production of ethanol by fermentation of cashew apple juice. Growth kinetics and ethanol productivity were calculated for batch fermentation with different initial sugar (glucose + fructose) concentrations. Maximal ethanol, cell, and glycerol concentrations were obtained when 103.1 g L(-1) of initial sugar concentration was used. Cell yield (Y (X/S)) was calculated as 0.24 (g microorganism)/(g glucose + fructose) using cashew apple juice medium with 41.3 g L(-1) of initial sugar concentration. Glucose was exhausted first, followed by fructose. Furthermore, the initial concentration of sugars did not influence ethanol selectivity. These results indicate that cashew apple juice is a suitable substrate for yeast growth and ethanol production.