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

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Sivamani, Selvaraju
Selvakumar, Selvaraj
Rajendran, Karthik
Muthusamy, Shanmugaprakash
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Taylor & Francis
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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.
Biodiesel , Optimization , Transesterification , Artificial neural network , Response surface methodology
Sivamani, S., Selvakumar, S., Rajendran, K. and Muthusamy, S. (2018) 'Artificial neural network–genetic algorithm-based optimization of biodiesel production from Simarouba glauca', Biofuels, In Press, doi:10.1080/17597269.2018.1432267
© 2018. This is an Accepted Manuscript of an article published by Taylor & Francis in Biofuels on 13 Feb 2018, available online: