Arabian Journal for Science and Engineering

, Volume 43, Issue 11, pp 6119–6131 | Cite as

Comparison of Response Surface Methodology (RSM) and Artificial Neural Network (ANN) in Optimization of Aegle marmelos Oil Extraction for Biodiesel Production

  • S. Sindhanai Selvan
  • P. Saravana Pandian
  • A. Subathira
  • S. SaravananEmail author
Research Article - Chemical Engineering


Non-edible feedstock is attaining importance due to authentic concerns behind the utilization of food crops for fuel production. Aegle marmelos seed is one such feedstock with high oil content portraying a better entrant among other non-edible feedstock. In this study, optimization of oil extraction, and biodiesel production from Aegle marmelos seeds had been reported. Oil extraction performed with n-hexane was optimized by response surface methodology (RSM) and artificial neural network (ANN). The influence of five parameters on oil extraction, namely particle size, acid concentration, solvent-to-seed ratio, extraction time and temperature were investigated. A comparison of performance evaluation between RSM and ANN was executed. The lower value of coefficient of determination (\(R^{2} = 0.998\)), root mean square error (\({ RMSE} = 0.2784\)), standard error of prediction (\({ SEP} = 0.7068\)) and absolute average deviation (\({ AAD} = 0.3425\)) for ANN compared to those of \(R^{2}\) (0.9769), RMSE (0.5349), SEP (1.3326) and AAD (1.1072) for RSM showed the prediction competence of the ANN was much better than RSM. Among the process parameters studied, solvent-to-seed ratio was the most influential variable on oil yield. The maximum oil yield of 45.99 wt% was obtained at optimum conditions, with an acid value of 18.92 mg KOH \(\hbox {g}^{-1}\). Hence, a dual-stage acid-base transesterification was employed to produce biodiesel. It was followed by \(^{1}\)H NMR spectroscopy study and fuel properties analysis. The highest conversion of 98% was ascertained using \(^{1}\)H NMR spectroscopy, and the biodiesel fuel properties were found to comply with ASTM standards.


Aegle marmelos Artificial neural network Biodiesel Modeling Response surface methodology Oil extraction 


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Copyright information

© King Fahd University of Petroleum & Minerals 2018

Authors and Affiliations

  1. 1.Department of Chemical EngineeringNational Institute of TechnologyTiruchirappalliIndia

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