movagharnejad, K., Saffar, F. (2018). Predicting the coefficients of the Daubert and Danner correlation using a neural network model. Iranian Journal of Chemical Engineering(IJChE), 15(2), 78-90.

k. movagharnejad; F. Saffar. "Predicting the coefficients of the Daubert and Danner correlation using a neural network model". Iranian Journal of Chemical Engineering(IJChE), 15, 2, 2018, 78-90.

movagharnejad, K., Saffar, F. (2018). 'Predicting the coefficients of the Daubert and Danner correlation using a neural network model', Iranian Journal of Chemical Engineering(IJChE), 15(2), pp. 78-90.

movagharnejad, K., Saffar, F. Predicting the coefficients of the Daubert and Danner correlation using a neural network model. Iranian Journal of Chemical Engineering(IJChE), 2018; 15(2): 78-90.

Predicting the coefficients of the Daubert and Danner correlation using a neural network model

^{}Chemical Eng. Dept., Faculty of Eng., Shomal University

Abstract

In the present research, three different architectures were investigated to predict the coefficients of the Daubert and Danner equation for calculation of saturated liquid density. The first architecture with 4 network input parameters including critical temperature, critical pressure, critical volume and molecular weight, the second architecture with 6 network input parameters including the ones in the first architecture with acentric factor and compressibility factor. The third architecture contains 12 network input parameters including 6 input parameters of the second architecture and 6 structural functional groups of different hydrocarbons. The three different architectures were trained and tested with the 160 sets of Daubert and Danner coefficients gathered from the literature. The trained neural networks were also applied to 15 un-known hydrocarbons and the outputs (Daubert and Danner coefficients) were used to predict the saturated liquid densities. The calculated liquid densities were compared with the experimental values. The Results indicated that the coefficients obtained from the second architecture produced more precise values for the liquid densities of the 15 selected hydrocarbons.

[1] Rohani, A. A., Pazuki, G., Abedini, H., Seyfi, S. and Vossughi, M., “Comparison between the artificial neural network system and SAFT equation in obtaining vapor pressure and liquid density of pure alcohols”, ExpertSystemswithApplications, 38 (3), 1738(2011).

[2] Nguyen, V. D., Tan, R. R., Brondial, Y. and Fuchino, T., “Prediction of vapor-liquid equilibrium data for temary system using artificial neural networks”, J. Fluid Phase Equilibria, 254, 188 (2007).

[3] Lashkarbolooki, M., Shafipour, Z. S., Zeinolabedini Hezave, A. and Farmani, H., “Use of artificial neural networks for prediction of phase equilibria in the binary system containing carbon dioxide”, J. Supercritical Fluids, 75, 144 (2013).

[4] Ghaderi, F., Ghaderi, A. H., Najafi, B. and Ghaderi, N., “Viscosity prediction by computational method and artificial neural network approach: the case of six refrigerant”, J. Supercritical Fluids, 81, 67 (2013).

[5] Kuhne, R., Uweebert, R. and Schuurmann, G., “Estimation of vapor pressure for hydrocarbons and halogened hydrocarbons from chemical structure by a neural network”, Chemosphere, 34, 671 (1997).

[6] Moosavi, M. and Soltani, N., “Prediction of hydrocarbon densities using an artificial neural network-group contribution method up to high temperatures and pressures”, J. Thermochimica Acta, 556, 89 (2013).

[7] Moghadassi, A., Nikkholgh, M., Hosseini, S. M. and Parvizian, F., “Estimation of vapor pressures, compressed liquid, and supercritical densities for sulfur dioxide using artificial neural networks”, International J. Industrial Chemistry, 4, 1 (2013).

[8] Lazzus, J. A., “ -T-P prediction for ionic liquids using neural network”, J. Taiwan Inst. Chem. Eng., 40, 213 (2009).

[9] Yaffe, D. and Cohen, Y., “Neural network based temperature-dependent quantitative structure property relations (QSPRs) for predicting vapor pressure of hydrocarbons”, J.Chem.Inf.Comput.Sci., 41, 463 (2001).

[10] Espinoza, G., Yaffe, D., Arenas, A., Cohen, Y. and Giralt, F., “A fuzzy ARTMAP-based quantitative structure−property relationship (QSPR) for predictingphysical properties of organiccompounds”,J. Ind. Eng. Chem. Res., 40,2757 (2001).

[11] Espinoza, G., Yaffe, D., Cohen, Y., Arenas, A. and Giralt, F., “Neural network based quantitative structure property relations (QSPRs) for predicting boiling points of aliphatic hydrocarbons”, J. Chem. Inf. Comput. Sci., 40, 859 (2000).

[12] Lazzus, J. A., “Predition of solid vapor pressures for organic and inorganic compounds using a neural network” , J. Thermochimia Acta, 489, 53 (2009).

[13] Taskinen, J., and Yliruusi, J., “Prediction of physicochemical properties based on neural network modeling”, J. Adv. Drug Deliv. Rev., 55, 1163 (2003).

[14] Reid, R. C., Prausnitz, J. M. and Poling, B. E., The properties of gases and liquids, McGraw-Hill, New York, (1987).

[15] Kuan, C. M. and White, H. “Artificial neural networks: An econometric perspective”, Econometric Reviews, 13,1 (1994).

[16] Movagharnejad, K. and Nikzad, M. “Modeling of tomato drying using artificial neural network”, J. Computers and Electronics in Agriculture, 59, 78 (2007).

[17] Cybenco, G., “Approximation by superpositions of a sigmoidal function”, J. Math. Cont. Sig. Syst. (MCSS), 2, 303 (1989).

[18] Arai, Y., “Measurement of isothermal vapor-liquid equilibria for hydrocarbon + monocarboxylic acid binary systems by a flow-type apparatus”. J. Chem. Eng. Data, 45, 857 (2000).

[19] Zhang, G., Patuwo, B. E. and Hu, M. Y., “Forecasting with artificial neural network: The state of art”, International J. Forecasting, 14, 35 (1998).

[20] Rumelhart, D. E. Hinton, G. E. and Williams, R. J., “Learning representations by backpropagation errors”, Nature, 323, 533 (1986).

[21] Daubert, T. E. and Danner, R. P., Physical and thermodynamic properties of pure chemicals: Data complation, Taylor, New York, USA (1989).

[22] Perry, H. R. and Green. W. D., Perry’s chemical engineers handbook, McGraw Hill, (2014).

[23] David, R., CRC Handbook of chemistry and physics, Taylor & Francis, (2007).

[24] NIST Standard reference database 15, Reference fluid thermodynamic and transport properties 721 ed. National Institute of Standards and Technology, Gaithersburg, MD.

[26] Friend, D. G., Ely, J. F. and Ingham, H., “Thermophysical properties of methane”, J. Phys. Chem. Ref. Data, 18, 583 (1989).

[27] DIPPR Data compiliation of pure compound properties ASCLL Files, Institute of Science and Technology, Standard Refrence Data, Gaithersburg, MD, 1, 458 Chemicals, Extant, (1995).

[28] Vargaftik, N. B., Tables on the thermo-physical properties of liquids and gases in normal and dissociated states, 2^{nd} edition, Hemisphere, London (1975).

[29] Bejarano, A., Quezada, N. and de la Fuente, J. C., “Complementary vapor pressure data for 2-methyl-1-propanol and 3-methyl-1-butanol at a pressure range of (15 to 177) kPa”, J. Chem. Thermodynamics, 41, 1020 (2009).

[30] N'Guimbi, J., Berro, C., Mokbel, I., Rauzy, E. and Jose, J., “Experimental vapour pressure of 13 secondary and tertiary alcohols: correlation and prediction by a group contribution method”, J. Fluid Phase Equilibria, 162, 143 (1999).

[31] Karimi, H. and Ghaedi, M., “Simultaneous determination of thiocyanate and salycilate by a combined UV-spectrophotometric detection principal component artificial neural network”, J. Ann. Chem., 96, 657 (2006).

[32] Rackett, H. G., “Equation of state for saturated liquids”, J. Chemical Engineering Data, 15, 514 (1970).