Estimation of vegetable oil-based Ethyl esters biodiesel densities using artificial neural networks

Baroutian, S. and Aroua, M.K. and Abdul Raman, Abdul Aziz and Nik Sulaiman, N.M. (2008) Estimation of vegetable oil-based Ethyl esters biodiesel densities using artificial neural networks. Journal of Applied Sciences, 8 (17). pp. 3005-3011. ISSN 18125654 (ISSN),

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In this study a new approach based on Artificial Neural Networks (ANNs) has been designed to predict the density of various vegetable oil-based ethyl esters biodiesel. The experimental densities data measured at various temperatures from 15 to 90°C at 1 °C interval were used to train the networks. The present work, applied a three layer back propagation neural network with nine neurons in the hidden layer. The results from the network are in good agreement with the measured data and the average absolute percent deviation are 0.35, 0.72, 0.54, 0.68 and 0.72 for the ethyl esters of palm, canola, corn and ricebran oil, respectively. The results of ANNs have also been compared with the results of theoretical estimations. © 2008 Asian Network for Scientific Information.

Item Type: Article
Additional Information: Export Date: 10 January 2011 Source: Scopus Language of Original Document: English Correspondence Address: Abdul Raman, A. A.; Department of Chemical Engineering, Faculty of Engineering, University Malaya, 50603 Kuala Lumpur, Malaysia References: Baroutian, S., Aroua, M.K., Raman, A.A., Sulaiman, N.M., Density of palm oil-based methyl ester (2008) J. Chem. Eng. Data, 53, pp. 877-880; Baroutian, S., Aroua, M.K., Raman, A.A., Sulaiman, N.M., Prediction of palm oil-based methyl ester biodiesel density using artificial neural networks (2008) J. Applied Sci. Data, 8, pp. 1938-1943; Durán, A., Lapuerta, M., Rodríguez-Fernández, J., Neural networks estimation of diesel particulate matter composition from transesterified waste oils blends (2005) J. Fuel., 84, pp. 2080-2085; Grossberg, S., Competitive learning: From interactive activation to adaptive resonance (1987) J. Cognitive Sci., 11, pp. 23-63; Liew, K.Y., Seng, C.E., Oh, L.L., Viscosities and densities of the Methyl Esters of some n-alkanoic acids (1992) J. Am. Oil Chem. Soc., 69, pp. 155-158; Mitchell, T.M., (1997) Machine Learning, pp. 96-97. , 1stEdn., WCB-McGraw-Hill, Boston, ISBN: 0070428077; Noureddim, H., Teoh, B.C., Clements, L.D., Densities of vegetable oils and fatty acids (1992) J. Am. Oil Chem. Soc., 69, pp. 1184-1188; Plocker, U., Knapp, H., Prausnitz, J., Calculation of high-pressure vapor-liquid equilibria from a corresponding states correlation with emphasis on asymmetric mixtures (1978) J. Ind. Eng Chem. Process Design Dev., 17, pp. 324-332; Poling, B.E., Prausnitz, J.M., O'conell, J.P., (2000) The Properties of Gases and Liquids, pp. 14-15. , 5th Edn, McGraw-Hill, New York, ISBN: 0070116822/9780070116825; Ramadhas, A.S., Jayaraj, S., Muraleedharan, C., Padmakumari, K., Artificial neural networks used for the prediction of the octane number of biodiesel (2006) J. Renewable Energy, 31, pp. 2524-2533; Spencer, C.F., Danner, R.P., Improved equation for prediction of saturated liquid density (1972) J. Chem. Eng. Data, 17, pp. 236-241; Tate, R.A., Watts, K.C., Allen, C.A.W., Wilkie, K.I., The densities of three biodiesel fuels at temperatures up to 300°C (2006) J. Fuel, 85, pp. 1004-1009
Uncontrolled Keywords: Biodiesel Density Ethyl ester Neural networks
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Divisions: Faculty of Engineering
Depositing User: Mr. Mohammed Salim Abd Rahman
Date Deposited: 16 Jan 2013 02:16
Last Modified: 06 Dec 2019 07:45

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