This study presents a statistically optimal approach for bioethanol production from sugarcane bagasse (SCB) using alkaline pretreatment, enzymatic hydrolysis, and fermentation processes. To maximize cellulose yield, four extraction parameters—solvent type, concentration, temperature, and time—were optimized using a Taguchi statistical model. The Taguchi-optimized extraction yields 11.3% increased cellulose recovery relative to cellulose recovered via non-optimized extraction methods (i.e., alkaline treatment with 6% NaOH at 90 °C for 3 hours). This yielded 45.20% cellulose (w/w), with 37.80% weight loss (predicted weight loss was 36.94%). Statistical analysis of extraction results verified solvent type (p = 0.0022) and temperature (p = 0.0007) to be significant extraction variables; shown model reliability (R2adj = 0.9147 and R2pred = 0.9801) were both statistically acceptable. Using Celluclast 1.5L and Novozyme 188 (20 FPU/g), pretreated SCB was enzymatically hydrolyzed to yield 500.74 mg/g reducing sugars. This is a significant increase (85%) over untreated sugarcane bagasse (USCB) (270.07 mg/g). Using Plackett-Burman design (PBD), fermentation conditions were optimized; significant variables influencing fermentation were found to be yeast extract (4 g/L), 30% aeration, and pH of 6.6 (ANOVA p < 0.05). The bioethanol yield from the optimized fermentation process was 202.3 mg/g (i.e., 20.23% (w/w)) which represent 4.58% of total SCB waste. The final outcomes were verified for accuracy between samples and model prediction with a confidence interval of 95% (±0.42%), therefore demonstrating that this optimization approach for bioethanol production using agricultural waste has achieved reliability and redundancy.
Hussein et al. (Thu,) studied this question.