In the current study, coconut husk, a lignocellulosic biomass, was employed as the feedstock for production of bioethanol. The powderised coconut husks were subjected to thermal pretreatment, chemical pretreatment and microwave-assisted-alkaline (MAA) pretreatment prior to enzymatic and hydrolysis process. The composition profile of coconut husks was significantly altered upon the MAA pretreatment as compared to the untreated sample, with the cellulose content increasing from 18-21% to 38-39% while lignin content decreased from 46-53% to 31-33%. Enzymatic hydrolysis of MAA-pretreated coconut husk also achieved the highest yield of fermentable sugars (measured as glucose) with 0.279 g sugar/g coconut husk. Scanning electron microscopy (SEM) imaging also proved the obvious and significant disruption of coconut husks’ structure. The results demonstrated that the combination of microwave radiation with alkaline solution was effective in altering the physical structures of coconut husks. Hence, MAA-
pretreated coconut husk was chosen as the substrate for subsequent hydrolysis and fermentation process.For the optimization of simultaneous saccharification and bioethanol fermentation process, the critical variables that affected bioethanol production were identified by using Plackett-Burman design and tested using the analysis of variance (ANOVA). The factors with p-value less than 0.05 in this test were coconut husk loading (p = 0.0087) and pectinase loading (p = 0.0198). These two significant factors were further optimized using a Central Composite Design (CCD). The maximum response predicted from the model would yield 0.0525 g ethanol per g coconut husk daily under the optimal conditions of 3.06 g MAA-pretreated coconut husks, 0.58 mL cellulase, 0.38 mL pectinase and 1 g yeast extract in 100 mL of medium (pH 6) incubated at 30oC. The experimental result gave bioethanol productivity of approximately 0.0593 g ethanol per g coconut husks daily, which was 13% higher than the estimated value (0.0525 g ethanol per g coconut husk). The results of validation experiments proved the usefulness and effectiveness of CCD as an optimization tool in enhancement of bioethanol production from indigenous renewable resources.
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