Optimization of biodiesel production from a calcium methoxide catalyst using a statistical model

Warakom Suwanthai; Vittaya Punsuvon; Pilanee Vaithanomsat

Korean Journal of Chemical Engineering, Vol.33, No.1, 90-98, January, 2016

DOI10.1007/s11814-015-0096-9 Export Citation

Optimization of biodiesel production from a calcium methoxide catalyst using a statistical model

Warakom Suwanthai¹, Vittaya Punsuvon^{1, 2, †}, and Pilanee Vaithanomsat³

¹Department of Chemistry, Faculty of Science, Kasetsart University, Bangkok 10900, Thailand
²Center of Excellence-Oil Palm, Kasetsart University, Bangkok 10900, Thailand
³***Kasetsart Agricultural and Agro-Industrial Product Improvement Institute, Kasetsart University, Bangkok 10900, Thailand

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Calcium methoxide catalyst was synthesized from quick lime and methanol, and further characterized using scanning electron microscopy (SEM), X-ray diffraction (XRD), attenuated total reflection Fourier transform infrared spectroscopy (ATR-FTIR), and energy dispersive X-ray spectroscopy (EDX). Response surface methodology (RSM) with a 5-level-3-factor central composite was applied for the calcium methoxide catalyzed transesterification of refined palm oil to investigate the effect of experimental factors on the methyl ester yield. A quadratic model with an analysis of variance (ANOVA) obtained from RSM is suggested for the prediction of methyl ester yield, and reveals that 95.99% of the observed variation is explained by the model. The optimum conditions obtained from RSM were 2.71 wt% of catalyst concentration, 11.5 : 1 methanol-to-oil molar ratio, and 175 min of reaction time. Under these conditions, the produced biodiesel met the standard requirements for methyl ester yield.

Keywords:Biodiesel;Calcium Methoxide;Transesterification;Response Surface Methodology;Refined Palm Oil

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[1] Kastner JR, Miller J, Geller DP, Locklin J, Keith LH, Johnson T, Catal. Today, 190(1), 122 (2012)

[2] Lam MK, Lee KT, Mohamed AR, Biotechnol. Adv., 28, 500 (2010)

[3] Gryglewicz S, Bioresour. Technol., 70(3), 249 (1999)

[4] Liu XJ, Piao XL, Wang YJ, Zhu SL, He HY, Fuel, 87(7), 1076 (2008)

[5] Masood H, Yunus R, Choong TSY, Rashid U, Yap YT, Appl. Catal. A: Gen., 425-426, 184 (2012)

[6] Park JY, Lee JS, Wang ZM, Kim DK, Korean J. Chem. Eng., 27(6), 1791 (2010)

[7] Malilas W, Kang SW, Kim SB, Yoo HY, Chulalaksananukul W, Kim SW, Korean J. Chem. Eng., 30(2), 405 (2013)

[8] Gandhi BS, Kumaran DS, Int. J. Green Energy, 11, 1084 (2014)

[9] Kawashima A, Matsubara K, Honda K, Bioresour. Technol., 100(2), 696 (2009)

[10] Lutz HD, Muller H, Schmidt M, J. Mol. Struct., 328, 121 (1994)

[11] Boey PL, Maniam GP, Hamid SA, Ali DMH, Fuel, 90(7), 2353 (2011)

[12] Mason RL, Gunst RF, Hess JL, Statistical design and analysis of experiments: With applications to engineering and science, John Wiley and Sons, New York (1989).

[13] Lee KT, Mohtar AM, Zainudin NF, Bhatia S, Mohamed AR, Fuel, 84(2-3), 143 (2005)

[14] Khuri AI, Cornell JA, Response Surfaces: Designs and Analyses, Marcel Dekker, New York (1987).

[15] Montgomery DC, Design and Analysis of Experiments 5th Ed., Wiley, New York (2001).

[16] Seber GAF, Linear Regression Analysis, John Wiley and Sons, New York (1977).

[17] Yuan X, Liu J, Zeng G, Shi J, Tong J, Huang GH, Renew. Energy, 33(7), 1678 (2008)

[18] Arzamendi G, Campo I, Arguinarena E, Sanchez M, Montes M, Gandia LM, Chem. Eng. J., 134(1-3), 123 (2007)

[19] Wan Z, Hameed BH, Bioresour. Technol., 102(3), 2659 (2011)

[20] Samart C, Sreetongkittikul R, Sookman C, Fuel Process. Technol., 90(7-8), 922 (2009)