The enzymatic and bio-enzymatic saccharification of waste broken rice (79.8% of starch) was successfully carried out to produce a reducible sugar solution (160 g/L). Bioethanol of concentration 71.2 g/L (9.0% v/v) was prepared by fermentation of reducing sugar solution, using the commercially available waste brewer's yeast (Saccharomyces cerevisiae). The fermentation process parameters were optimized through response surface methodology (RSM) and hybrid artificial neural network-genetic algorithm (ANN-GA) for optimizing the ethanol concentration. The hybrid ANN-GA model predicted a maximum concentration of 71.9 g/L with a deviation of only 0.97% from the experimental value (71.2 g/L). Four different kinetic models were attempted to fit the experimental time evolution of concentrations with the kinetic parameters estimated by the Levenberg-Marquardt optimization technique. The 4th order Runge-Kutta algorithm was implemented through a C program module. The accuracy of each model was checked against coefficient of determination R², adjusted R², the absolute mean deviation (AMD), and root mean square deviation (RMSD). The Andrew-Levenspiel kinetics produced the best performance criteria at two initial substrate concentrations of 160 and 170 g/L. Finally, the FTIR analysis of 781.2 g/L (98.5% v/v) bioethanol (concentrated by two-stage vacuum distillation followed by treatment with 3A molecular sieve) showed a favorable blending possibility with the commercial gasoline (petrol) as a green fuel.

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Journal Article

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Mondal P, Sadhukhan AK, Ganguly A, Gupta P

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