Bioethanol production from aqueous sugarcane bagasse extract through fermentation process optimization using Saccharomyces cerevisiae MTCC 180

Growing concerns over dependence on fossil fuels and the accumulation of agro-industrial residues have accelerated the search for sustainable biofuel alternatives. Sugarcane bagasse, produced in substantial quantities by sugar mills and local juice vendors, remains largely underexploited despite its fermentation potential. Unlike conventional bagasse-based bioethanol production, which commonly requires chemical or enzymatic pretreatment, the present study investigated aqueous sugarcane bagasse extract containing approximately 0.764 g/L reducing and 10 g/L total soluble sugar as a fermentation substrate for bioethanol production using Saccharomyces cerevisiae MTCC 180 under submerged fermentation conditions. Fermentation variables were initially screened using a one-variable-at-a-time strategy and subsequently optimized through response surface methodology based on a central composite design. The optimum fermentation conditions predicted by the model were 22.60 h and 30.20 °C, producing an experimentally validated ethanol concentration of 0.342 g/100 mL. The fitted quadratic model showed excellent statistical performance (R 2 = 0.9368, adjusted R 2 = 0.8916, predicted R 2 = 0.7385), while lack-of-fit was statistically non-significant ( p = 0.4299), confirming good model adequacy. Results demonstrated that the aqueous extract of untreated bagasse effectively supported ethanol fermentation, while the measured furfural concentration was below the reported inhibitory level for yeast fermentation. Process optimization substantially enhanced ethanol production (approximately 2.5-fold). Building upon our previous demonstration of ethanol production from aqueous sugarcane bagasse extract, the present study provides the first systematic statistical optimization and experimental validation of the fermentation process using Saccharomyces cerevisiae MTCC 180. By eliminating pretreatment requirements and integrating statistical optimization, this work proposes a simplified, potentially cost-reducing, waste valorization strategy for sustainable bioethanol production.