scholarly journals A new search for thermotolerant yeasts, its characterization and optimization using response surface methodology for ethanol production

2015 ◽  
Vol 6 ◽  
Author(s):  
Richa Arora ◽  
Shuvashish Behera ◽  
Nilesh K. Sharma ◽  
Sachin Kumar
Keyword(s):  
Response Surface Methodology ◽  
Ethanol Production ◽  
Response Surface ◽  
Thermotolerant Yeasts

scholarly journals Acid hydrolysis optimization of cocoa pod shell using response surface methodology approach toward ethanol production

2018 ◽  
Vol 52 (6) ◽  
pp. 581-587
Author(s):  
Vinayaka B. Shet ◽  
Nisha sanil ◽  
Manasa Bhat ◽  
Manasa Naik ◽  
Leah Natasha Mascarenhas ◽  
...  
Keyword(s):  
Response Surface Methodology ◽  
Ethanol Production ◽  
Acid Hydrolysis ◽  
Response Surface

scholarly journals Response Surface ‎Methodology for Optimization of Ethanol ‎Production from Cocoa Waste

2020 ◽  
Vol 1 (1) ◽  
pp. 7-12
Keyword(s):  
Response Surface Methodology ◽  
Ethanol Production ◽  
Response Surface ◽  
Simultaneous Saccharification And Fermentation ◽  
Inoculum Size ◽  
Fermented Food ◽  
Optimum Conditions ◽  
Anova Analysis ◽  
Full Factorial ◽  
Simultaneous Saccharification

Cocoa waste (CW) is an inexpensive agro-industrial by-product that is available in large quantities in tropical countries such as Malaysia. The rate of ethanol production can be affected by different parameters involved during fermentation. In this study, response surface methodology (RSM) with the full factorial design was used to obtain optimum conditions for bioethanol production using CW as the substrate. The simultaneous saccharification and fermentation (SSF) performed with an isolated microorganism from locally fermented food tapai ubi and tapai pulut. The effect of four independent variables temperature, CW concentration, inoculum size, and pH was investigated. In the optimized condition temperature of 31.7°C, pH 6.0, inoculum size 10.5%, and CW concentration 0.3 g/L, the highest ethanol production was 9.5 ± 1.1. ANOVA analysis revealed that temperature and CW concentration had the most significant effects on ethanol production. In addition, ethanol production was increased in the highest level of pH and inoculum size. Therefore it can be concluded that ethanol production increased from 6.2 ± 0.8 g/L to 9.5 ± 1.1 g/L after optimization.

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