scholarly journals Statistical Optimization for Biobutanol Production by Clostridium acetobutylicum ATCC 824 from Oil Palm Frond (OPF) Juice Using Response Surface Methodology

2017 ◽  
Vol 111 ◽  
pp. 03001
Author(s):  
Nur Syazana Muhamad Nasrah ◽  
Mior Ahmad Khushairi Mohd Zahari ◽  
Nasratun Masngut ◽  
Hidayah Ariffin
BioResources ◽  
2013 ◽  
Vol 8 (2) ◽  
Author(s):  
Mohamad Nafis Abdul Razak ◽  
Mohamad Faizal Ibrahim ◽  
Phang Lai Yee ◽  
Mohd Ali Hassan ◽  
Suraini Abd-Aziz

2017 ◽  
Vol 19 ◽  
pp. 36
Author(s):  
Nur Syazana Muhd Nasrah ◽  
Mior Ahmad Khushairi Mohd Zahari ◽  
Nasratun Masngut ◽  
Hidayah Ariffin

<p>Biobutanol is an alternative energy that can be promising as the future energy source. It can be produced from natural and renewable agriculture wastes such as oil palm frond (OPF) juice by microbes. <em>Clostridium acetobutylicum </em>has the ability to ferment the sugars in OPF juice as carbon source into biobutanol. This research aimed to investigate the effect of independent and interaction factors; initial pH medium (5-7), inoculum size (1-20%), initial total sugars concentration (40-60 g/L), temperature (32-42<sup>°</sup>C) and yeast extract concentration (1-10 g/L) on the production of biobutanol from oil palm frond (OPF) juice by <em>C. acetobutylicum </em>ATCC 824 using a two level half factorial design which have been developed by the Design Expert Software Version 7.1. Based on the factorial analysis, it was observed that the most significant parameter was yeast extract concentration, which contributes 8.20%, followed by inoculum size and temperature, which were contribute 7.84% and 7.56%, respectively. The analysis showed the R<sup>2</sup> value for the model was 0.9805 and the interaction between inoculum size and temperature gave the highest influenced to the fermentation process with contribution up to 16.31%. From the validation experiments, the experimental values were reasonable close to the predicted values with only 5.87% and 10.09% of errors. It confirmed the validity and adequacy of the predicted models. Hence, the data analysis developed from the Design Expert Software could reliably predict biobutanol yields. This study indicated that each of the factors may affect the fermentation process of the biobutanol production.</p><p>Chemical Engineering Research Bulletin 19(2017) 36-42</p>


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