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  • Title: Optimization of process parameters for bio-enzymatic and enzymatic saccharification of waste broken rice for ethanol production using response surface methodology and artificial neural network-genetic algorithm.
    Author: Mondal P, Sadhukhan AK, Ganguly A, Gupta P.
    Journal: 3 Biotech; 2021 Jan; 11(1):28. PubMed ID: 33442526.
    Abstract:
    Reducible sugar solution has been produced from waste broken rice by a novel saccharification process using a combination of bio-enzyme (bakhar) and commercial enzyme (α-amylase). The reducible sugar solution thus produced is a promising raw material for the production of bioethanol using the fermentation process. Response surface methodology (RSM) and Artificial neural network-genetic algorithm (ANN-GA) have been used separately to optimize the multivariable process parameters for maximum yield of the total reducing sugar (TRS) in saccharification process. The maximum yield (0.704 g/g) of TRS is predicted by the ANN-GA model at a temperature of 93 °C, saccharification time of 250 min, 6.5 pH and 1.25 mL/kg of enzyme dosages, while the RSM predicts the maximum yield of 0.7025 g/g at a little different process conditions. The fresh experimental validation of the said model predictions by ANN-GA and RSM is found to be satisfactory with the relative mean error of 2.4% and 3.8% and coefficients of determination of 0.997 and 0.996.
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