2018
DOI: 10.5937/ror1801053k
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Optimization of biodiesel production from corn oil by methanolysis catalyzed by corn cob ash

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Cited by 5 publications
(4 citation statements)
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“…The ANOVA showed that at a confidence level of 95 % only the catalyst loading (B), the reaction time (D), and their interaction (BxD), as well as the squared reaction temperature, catalyst loading, and reaction time (A 2 , B 2 , and D 2 ) were significant model terms (Table 5). Having the highest F-value of 162.37, the reaction time was the most important variable affecting the FAME content, followed by catalyst loading, which agreed with the optimization results of the methanolysis of corn germ oil using corn cobs ash as a catalyst [27]. On the other hand, Kostić et al [20] showed for methanolysis of sunflower oil over palm kernel shell biochar that the reaction temperature and the methanol-to-oil molar ratio were statistically significant for the FAME content while the effect of catalyst loading was statistically insignificant.…”
Section: Statistical Modeling and Optimizationsupporting
confidence: 82%
See 1 more Smart Citation
“…The ANOVA showed that at a confidence level of 95 % only the catalyst loading (B), the reaction time (D), and their interaction (BxD), as well as the squared reaction temperature, catalyst loading, and reaction time (A 2 , B 2 , and D 2 ) were significant model terms (Table 5). Having the highest F-value of 162.37, the reaction time was the most important variable affecting the FAME content, followed by catalyst loading, which agreed with the optimization results of the methanolysis of corn germ oil using corn cobs ash as a catalyst [27]. On the other hand, Kostić et al [20] showed for methanolysis of sunflower oil over palm kernel shell biochar that the reaction temperature and the methanol-to-oil molar ratio were statistically significant for the FAME content while the effect of catalyst loading was statistically insignificant.…”
Section: Statistical Modeling and Optimizationsupporting
confidence: 82%
“…Moreover, methanolysis of various oils was successfully carried out in the presence of husk ashes obtained from different biomass wastes, such as cocoa pod [8,16], coconut [17], and rice [18], walnut kernel [19] and palm kernel [20] shells, mango [21], and tucuma [22] peels ashes. Furthermore, ashes from bark [23], leaves [24,25], whole plants [26], and cobs [27] were used to catalyze biodiesel production. Some ashes were improved before the use as catalysts.…”
Section: Introductionmentioning
confidence: 99%
“…In the case of the methanolysis of Bauhinia monandra oil catalyzed by banana peel ash, a positive effect of the methanol-to-oil molar ratio, catalyst amounts, and two-parameter interactions of all factors on FAME content was observed, while the reaction time and the square of reaction time were not statistically significant [20]. Along with the catalyst amount and methanol-tooil molar ratio, the reaction temperature had a significant effect on FAME yield in the methanolysis of Calophyllum inophyllum oil over sugarcane leaf ash as a solid catalyst while the influence of the reaction time was not investigated [15].…”
Section: Analysis Of Variance (Anova)mentioning
confidence: 89%
“…The statistical methods are widely used for modeling and optimizing biodiesel production processes and assessing the statistical significance of the influential process factors (like initial methanol-to-oil molar ratio, catalyst amount, reaction temperature, and time) on the desired response (ester content or yield). The response surface methodology (RSM), combined with the full 3 3 factorial design [19,20], central composite design (CCD) [21], or Box-Behnken design (BBD) [22], has been most frequently applied procedure for the statistical analysis of batch biodiesel production processes. The second-order polynomial equation is commonly used to relate ester content to the process factors.…”
Section: Introductionmentioning
confidence: 99%