Optimization of Ethanol Production Process from Sugarcane Bagasse using Genetic Algorithm

The search for new technologies for converting plant biomass into alternative biofuels is leveraged by many social and environmental problems associated with the use of fossil fuels and their exploration. Bioethanol has emerged as a potential alternative for fossil fuels. Bioethanol production from lignocellulosic biomass obtained from sugarcane bagasse using Saccharomyces cerevisiae comprises the hydrolysis of cellulose and sugar fermentation. This research was focused on the optimization of ethanol production from sugarcane bagasse using genetic algorithm (GA). The relationship between the process variables were modelled by Response Surface Methodology (RSM) and found that the maximum ethanol concentration to be 17.44 g/l. GA was employed to find the optimal values for process variables that gives the maximum ethanol concentration. It was found that the optimal values of the variables are 20.14g/l for substrate concentration, 4.5 for pH and 24hours for fermentation time and 0.15 g/l for Na2HPO4. These optimal variables lead to an improved ethanol concentration of 47.85g/l which is 174% increase from the predicted concentration obtained by empirical testing.