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Investigation of Artificial Neural Networks Performance to Predict Net Heating Value of Crude Oil by Its Properties

Authors: Mousavian, M. Moghimi Mofrad, M. H. Vakili, D. Ashouri, R. Alizadeh


The aim of this research is to use artificial neural networks computing technology for estimating the net heating value (NHV) of crude oil by its Properties. The approach is based on training the neural network simulator uses back-propagation as the learning algorithm for a predefined range of analytically generated well test response. The network with 8 neurons in one hidden layer was selected and prediction of this network has been good agreement with experimental data.

Keywords: Neural Network, Net Heating Value, Crude Oil, Experimental, Modeling.

Digital Object Identifier (DOI):

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[1] ASTM Standards: D 240 Standard test method for heat of combustion of liquid hydrocarbon fuel by bomb calorimeter, 1994
[2] ASTM Standards: D 4529 Standard test method for estimation of net heat of combustion of aviation fuels, 1994
[3] ASTM Standards: D 611 Standard test method for Aniline point and mixed aniline point of petroleum products and hydrocarbon solvent, 1994
[4] ASTM Standards: D 4052 Standard test method for density and relative density of liquid by digital density meter, 1994
[5] ASTM Standards: D 3120 Standard test method for trace quantity of sulfur in light liquid petroleum hydrocarbons by oxidative microcoulometry
[6] El Ouahed AK, Tiab D, Mazouzi A (2005) Application of artificial intelligence to characterize naturally fractured zones in Hassi Messaoud Oil Field, Algeria. J Pet Sci Eng 49:122-141
[7] D. M. Himmelblau, Korean J. Chem. Eng., 17(4), 373 (2000).
[8] E. A. Medina and J. I. P. Paredes, Math. Comput. Model., 49, 207 (2009).
[9] J. Michalopoulos, S. Papadokonstadakis, G. Arampatzis and A. Lygeros, Trans. IChemE, 79, 137 (2001).
[10] J. A. Blasco, N. Fueyo, J. C. Larroya, C. Dopazo and Y. J. Chen, Comput. Chem. Eng., 23, 1127 (1999).
[11] K. L. Priddy and P. E. Keller, Artificial neural networks: An introduction, The Soc. of Photo-Opt. Instrum. Eng. (SPIE) Publication, Washington (2005).
[12] S. K. Lahiri and K. C. Ghanta, Chem. Ind. Chem. Eng. Q., 15(2), 103 (2009).