Mathematical modeling and process parameters optimization studies by artificial neural network and response surface methodology: A case of non-edible neem (Azadirachta indica) seed oil biodiesel synthesis

Eriola Betiku, Oluwasesan Ropo Omilakin, Sheriff Olalekan Ajala, Adebisi Aminat Okeleye, Abiola Ezekiel Taiwo and Bamidele Ogbe Solomon

Energy, 2014, vol. 72, issue C, 266-273

Abstract: This study aimed at using a non-edible NO (neem oil) for biodiesel production by modeling and optimizing the two-step process involved. A significant quadratic regression model (p < 0.05) with R2 = 0.813 was obtained for the reduction of the acid value of the NO with high FFA to 1.22 mgKOH/g under the condition of methanol–oil ratio of 0.55, H2SO4 of 0.45%, time of 36 min and temperature of 60 °C using RSM (response surface methodology). For biodiesel synthesis, ANN (artificial neural networks) coupled with rotation inherit optimization established a better model than RSM. The condition established by ANN was temperature of 48.15 °C, KOH of 1.01%, methanol–oil ratio of 0.200, time of 42.9 min with actual NOB (neem oil biodiesel) yield of 98.7% while RSM quadratic model gave the condition as temperature of 59.91 °C, KOH of 1.01%, methanol–oil ratio of 0.164, time of 45.60 min with actual NOB yield of 99.1%. R2 and absolute average deviations of the models from ANN and RSM were 0.991, 0.983, and 0.288, 0.334%, respectively. The results demonstrated that the models developed adequately represented the processes they described. Properties of NOB produced were within the ASTM D6751 and DIN EN 14214 biodiesel specifications.

Keywords: Neem oil; Biodiesel; Artificial neural network; Response surface methodology; Optimization (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (21)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:72:y:2014:i:c:p:266-273

DOI: 10.1016/j.energy.2014.05.033

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