Prediction of palm oil-based methyl ester biodiesel density using artificial neural networks

Baroutian, S. and Aroua, M.K. and Abdul Raman, A.A. and Sulaiman, N.M.N. (2008) Prediction of palm oil-based methyl ester biodiesel density using artificial neural networks. Journal of Applied Sciences, 8 (10). pp. 1938-1943. ISSN 18125654 (ISSN)

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Abstract

In this study, a new approach based on Artificial Neural Networks (ANNs) has been designed to estimate the density of pure palm oil-based methyl ester biodiesel. The experimental density data measured at various temperatures from 14 to 90°C at 1°C intervals were used to train the networks. The present research, applied a three layer back propagation neural network with seven neurons in the hidden layer. The results from the network are in good agreement with the measured data and the average absolute percent deviation is 0.29. The results of ANNs have also been compared with the results of empirical and theoretical estimations. © 2008 Asian Network for Scientific Information.

Item Type: Article
Additional Information: Cited By (since 1996): 3 Export Date: 10 January 2011 Source: Scopus Language of Original Document: English Correspondence Address: Raman, A.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 (2007) J. Chem. Eng. Data, 532 (12). , In Press; Clements, L.D., Blending Rules for Formulating Biodiesel Fuel (1996) Liquid Fuels and Industrial Products from Renewable Resources. Proceedings of the 3rd Liquid Fuel Conference, pp. 44-53. , Nashville, TN, September 15-17, pp; Duran, A., Lapuerta, M., Rodriguez-Fernandez, J., Neural networks estimation of diesel particulate matter composition from transesterified waste oils blends (2005) J. Fuel, 84 (16), pp. 2080-2085; Hetch-Nielsen, R., Kolmogorov's apping neural networks existence theorem (1987) Proceedings of the 1st IEEE International. Conference on Neural Networks, 3, p. 11. , San Diego, CA; Knapp, H., Doring, R., Oellrich, L., Plocker, U., Prausnitz, J.M., (1982) Vapor-liquid equilibria for mixtures of low boiling substances. Chemistry Data Series, 6. , DECHEMA: Frankfurt am Main; Kumar, J., Bansal, A., Selection of best neural network for estimating properties of diesel-biodiesel blends (2007) Proceedings of the 6th WSEAS International Conference on Artificial Intelligence, , Knowledge Engineering and Data Bases, Corfu Island, Greece; Lau, C., (1991) Neural Networks: Theoretical Foundation and Analysis, , IEEE Press. Piscataway, NJ, USA; Liew, K.Y., Seng, C.E., Oh, L.L., Viscosities and densities of the methyl esters of some n-alkanoic acids (1992) JAOCS, 69 (2), pp. 155-158; Noureddini, H., Teoh, B.C., Clements, L.D., Densities of vegetable oils and fatty acids (1992) JAOCS, 69 (12), pp. 1184-1188; Parker, D.B., Learning Logic (1985), Technical Report TR-47. Center for Computational Research in Economics and Management Science. MassachuSetts. Institute of Technology, Cambridge, MAPlocker, U., Knapp, H., Prausnitz, Calculation of high-pressure vapor-liquid equilibria from a corresponding states correlation with emphasis on asymetric mixtures (1978) J. Ind. Eng Chem. Process Des. Dev, 17 (3), pp. 324-332; Ramadhas, A.S., Jayaraj, S., Muraleedharan, C., Padmakumari, K., Artificial neural networks used for the prediction of the cetane number of biodiesel (2006) J. Renewable Energy, 31 (15), pp. 2524-2533; Rumelhart, D.E., Hinton, G.E., Williams, R.J., Learning representations by back propagating errors (1986) J. Nat, 323 (6088), pp. 533-536; Spencer, C.F., Danner, R.P., Improved equation for prediction of saturated liquid density (1972) J. Chem. Eng. Data, 17 (2), pp. 236-241; Spretcher, D.A., On the Structure of continuous functions of several variables (1965) Transfer Am. Math. Soc, 115 (3), p. 340; 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 (7-8), pp. 1004-1009; Werbos, P., (1974) Beyond Regression: New tools for prediction and analysis in behavioral sciences, , Ph.D Thesis, Harvard University
Uncontrolled Keywords: Biodiesel Density 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 01:30
Last Modified: 13 Sep 2017 08:20
URI: http://eprints.um.edu.my/id/eprint/4519

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