2019
DOI: 10.1016/j.btre.2019.e00329
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Bioethanol production from sugarcane leaf waste: Effect of various optimized pretreatments and fermentation conditions on process kinetics

Abstract: Highlights Bioethanol kinetics was investigated under SSA-F, SSA-U, MSA-F and MSA-U conditions. Monod, logistic and modified Gompertz models gave R 2 > 0.97. SSA-U pretreated SLW produced 25% more bioethanol than MSA-U. No difference was observed between filtered and unfiltered enzymatic hydrolysate. SLW residue showed a suitable protein and fat content for animal feed.

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Cited by 42 publications
(35 citation statements)
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References 46 publications
(58 reference statements)
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“…The free cells in suspension resulted in low productivity of 2.41 ± 0.088 g L −1 hr −1 with yield of 14.47 ± 0.53% which is attributed to the susceptibility of cells toward the fermentation inhibitors and feedback inhibition. However, the average efficiency of these strategies were found to be quite comparable to the ethanol production strategies with feedstock such as Taro waste (72–88%) (Wu et al, 2015), sugarcane leaf waste (74–92%) (Moodley & Kana, 2019), and wheat straw hydrolysate (65.6–78.7%) (Dhabhai, Chaurasia, Singh, & Dalai, 2013). Another important parameter that has much significant role in scale up is the product yield coefficient ( Y p/s ) that defines the amount of ethanol produced with the provided sugar solution at a given time where the hierarchy among the three strategies was found to be PBR (0.49 ± 0.02) > Immobilized cell is suspension (0.38 ± 0.017) > free cells in suspension (0.37 ± 0.003) (Table 3).…”
Section: Resultsmentioning
confidence: 93%
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“…The free cells in suspension resulted in low productivity of 2.41 ± 0.088 g L −1 hr −1 with yield of 14.47 ± 0.53% which is attributed to the susceptibility of cells toward the fermentation inhibitors and feedback inhibition. However, the average efficiency of these strategies were found to be quite comparable to the ethanol production strategies with feedstock such as Taro waste (72–88%) (Wu et al, 2015), sugarcane leaf waste (74–92%) (Moodley & Kana, 2019), and wheat straw hydrolysate (65.6–78.7%) (Dhabhai, Chaurasia, Singh, & Dalai, 2013). Another important parameter that has much significant role in scale up is the product yield coefficient ( Y p/s ) that defines the amount of ethanol produced with the provided sugar solution at a given time where the hierarchy among the three strategies was found to be PBR (0.49 ± 0.02) > Immobilized cell is suspension (0.38 ± 0.017) > free cells in suspension (0.37 ± 0.003) (Table 3).…”
Section: Resultsmentioning
confidence: 93%
“…This observation was in correlation with the ethanol production studies carried out with sugar beet juice by Dodic et al (2012). As explained by Moodley and Kana (2019), lag period is dependent on certain fermentation aspects like type and quantity of inoculum used, variations in reaction volume, substrate nature, and its corresponding concentration.…”
Section: Resultsmentioning
confidence: 99%
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“…The obtained productivities were 7.8-fold and 8.3-fold, respectively lower when compared to the current study. The reported variations observed in these bioethanol productivities can be attributed mainly to the presence of nano additives as well as the different potato waste feedstock, yeast strain, and the fermentation approach employed [ 48 ].…”
Section: Resultsmentioning
confidence: 99%
“…[2,3]. These renewable fuels are expected to offer many benefits including sustainability, low greenhouse gas emissions, regional development, social construction and agricultural development [4].…”
Section: Introductionmentioning
confidence: 99%