2008
DOI: 10.1007/s12155-008-9018-6
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Optimization of Process Conditions Using Response Surface Methodology (RSM) for Ethanol Production from Pretreated Sugarcane Bagasse: Kinetics and Modeling

Abstract: Based on a five level central composite design (CCD) involving the variables substrate concentration (C), pH (P), incubation temperature (T) and fermentation time (H), a response surface methodology (RSM) for the production of ethanol from pretreated sugarcane bagasse by cellulase and yeast Kluyveromyces fragilis was standardized. The design contains a total of 31 experimental trials in which the first 24 organized in a factorial design and from 25 to 31 involving the replications of the central points. Data o… Show more

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Cited by 56 publications
(24 citation statements)
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References 33 publications
(27 reference statements)
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“…There are several reports on the production of bioethanol by utilizing yeast [38,39]. Sasikumar and Viruthagiri [40] stated Kluyveromyces fragilis-NCIM 0557 preferable capacity to convert hexose to ethanol by central composite design. Response surface methodology (RSM) is another statistical process to improve enzyme yield by using designing minimum number of experiments for giant number of motives [41].…”
Section: Discussionmentioning
confidence: 99%
“…There are several reports on the production of bioethanol by utilizing yeast [38,39]. Sasikumar and Viruthagiri [40] stated Kluyveromyces fragilis-NCIM 0557 preferable capacity to convert hexose to ethanol by central composite design. Response surface methodology (RSM) is another statistical process to improve enzyme yield by using designing minimum number of experiments for giant number of motives [41].…”
Section: Discussionmentioning
confidence: 99%
“…Experiments were conducted under specified conditions. The independent variables selected were pretreatment solution (wt.%), temperature (°C), and reaction time (min) (Sasikumar and Viruthagiri 2008).…”
Section: Response Surface Methodology (Rsm)mentioning
confidence: 99%
“…The model was statistically validated with respect to all responses within the design space (Sasikumar and Viruthagiri, 2008). The conditions optimized from the model were further confirmed for accuracy by using the first order polynomial equation Y= β0 + Σ βi Xi (i = 1, ….…”
Section: Validation Of the Experimental Modelmentioning
confidence: 96%