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Articles

Artificial neural network–genetic algorithm-based optimization of biodiesel production from Simarouba glauca

ORCID Icon, , &
Pages 393-401 | Received 03 Jul 2017, Accepted 28 Dec 2017, Published online: 13 Feb 2018
 

ABSTRACT

A transesterification reaction was carried out employing an oil of paradise kernel (Simarouba glauca), a non-edible source for producing Simarouba glauca methyl ester (SGME) or biodiesel. In this study, the effects of three variables – reaction temperature, oil-to-alcohol ratio and reaction time – were studied and optimized using response surface methodology (RSM) and an artificial neural network (ANN) on the free fatty acid (FFA) level. Formation of methyl esters due to a reduction in FFA was observed in gas chromatography–mass spectroscopy (GC–MS) analysis. It was inferred that optimum conditions such as an oil-to-alcohol ratio of 1:6.22, temperature of 67.25 and duration of 20 h produce a better yield of biodiesel with FFA of 0.765 ± 0.92%. The fuel properties of paradise oil meet the requirements for biodiesel, by Indian standards. The results indicate that the model is in substantial agreement with current research, and simarouba oil can be considered a potential oil source for biodiesel production.

Acknowledgements

The authors are thankful to the management of Kumaraguru College of Technology, Coimbatore, India, for the providing the research facilities.

Disclosure statement

No potential conflict of interest was reported by the authors.

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