Correlating thermal conductivity of pure hydrocarbons and aromatics via perceptron artificial neural network (PANN) method

doi:10.1016/j.cjche.2016.08.025

摘要/Abstract

摘要： Accurate estimation of liquid thermal conductivity is highly necessary to appropriately design equipments in different industries. Respect to this necessity, in the current investigation a feed-forward artificial neural network (ANN) model is examined to correlate the liquid thermal conductivity of normal and aromatic hydrocarbons at the temperatures range of 257-338 K and atmospheric pressure. For this purpose, 956 experimental thermal conductivities for normal and aromatic hydrocarbons are collected from different previously published literature. During the modeling stage, to discriminate different substances, critical temperature (T_c), critical pressure (P^c) and acentric factor (ω) are utilized as the network inputs besides the temperature. During the examination, effects of different transfer functions and number of neurons in hidden layer are investigated to find the optimum network architecture. Besides, statistical error analysis considering the results obtained from available correlations and group contribution methods and proposed neural network is performed to reliably check the feasibility and accuracy of the proposed method. Respect to the obtained results, it can be concluded that the proposed neural network consisted of three layers namely, input, hidden and output layers with 22 neurons in hidden layer was the optimum ANN model. Generally, the proposed model enables to correlate the thermal conductivity of normal and aromatic hydrocarbons with absolute average relative deviation percent (AARD), mean square error (MSE), and correlation coefficient (R²) of lower than 0.2%, 1.05×10^-7 and 0.9994, respectively.

关键词: Thermal conductivity, Artificial neural network, Critical properties, Hydrocarbons, Aromatics

Abstract: Accurate estimation of liquid thermal conductivity is highly necessary to appropriately design equipments in different industries. Respect to this necessity, in the current investigation a feed-forward artificial neural network (ANN) model is examined to correlate the liquid thermal conductivity of normal and aromatic hydrocarbons at the temperatures range of 257-338 K and atmospheric pressure. For this purpose, 956 experimental thermal conductivities for normal and aromatic hydrocarbons are collected from different previously published literature. During the modeling stage, to discriminate different substances, critical temperature (T_c), critical pressure (P^c) and acentric factor (ω) are utilized as the network inputs besides the temperature. During the examination, effects of different transfer functions and number of neurons in hidden layer are investigated to find the optimum network architecture. Besides, statistical error analysis considering the results obtained from available correlations and group contribution methods and proposed neural network is performed to reliably check the feasibility and accuracy of the proposed method. Respect to the obtained results, it can be concluded that the proposed neural network consisted of three layers namely, input, hidden and output layers with 22 neurons in hidden layer was the optimum ANN model. Generally, the proposed model enables to correlate the thermal conductivity of normal and aromatic hydrocarbons with absolute average relative deviation percent (AARD), mean square error (MSE), and correlation coefficient (R²) of lower than 0.2%, 1.05×10^-7 and 0.9994, respectively.

Key words: Thermal conductivity, Artificial neural network, Critical properties, Hydrocarbons, Aromatics

Mostafa Lashkarbolooki, Ali Zeinolabedini Hezave, Mahdi Bayat. Correlating thermal conductivity of pure hydrocarbons and aromatics via perceptron artificial neural network (PANN) method[J]. , 2017, 25(5): 547-554.

参考文献

[1] F.A. Wang, J.Q. Zhu, H.S. Chen, W.C. Wang, Y.L. Jiang, A new model of thermal conductivity for liquids, Chem. Eng. J. 78(2000) 187-191.
[2] M.R. Riazi, G.A. Enezi, S. Soleimani, Estimation of transport properties of liquids, Chem. Eng. Commun. 176(1999) 175-193.
[3] R.C. Reid, J.M. Prausnitz, B.E. Poling, The Properties of Gases and Liquids, 4th edition McGraw-Hill, New York, 1987.
[4] M. Moetamast, Viscosity and Thermal Conductivity, Masukaza yakata, Tokyo, 1975.
[5] V.S. Touloukian, P.E. Liley, S.C. Saxena, Thermal Conductivity, Nonmetallic Liquids and Gases, Plenum Press, New York, 1970.
[6] F.A. Wang, Introduction to Chemical Engineering Data, Chem. Ind. Press, Beijing, 1979.
[7] D.T. Jamieson, Thermal conductivity of liquids, J. Chem. Eng. Data 24(1979) 244-246.
[8] D.T. Jamieson, G. Cartwright, Thermal conductivity of associated liquids, J. Chem. Eng. Data 25(1980) 199-201.
[9] A.S. Teja, P. Rice, A generalized corresponding states method for the prediction of the thermal conductivity of liquids and liquid mixtures, Chem. Eng. Sci. 36(1981) 417-422.
[10] A.S. Teja, G. Tardieu, Prediction of the thermal conductivity of liquids and liquid mixtures including crude oil fractions, Can. J. Chem. Eng. 66(1988) 980-986.
[11] J.G. Bleazard, A.S. Teja, Extension of the rough hard-sphere theory for transport properties to polar liquids, Ind. Eng. Chem. Res. 35(1996) 2453-2459.
[12] D.M. Klaas, D.S. Viswanath, A correlation for the prediction of thermal conductivity of liquids, Ind. Eng. Chem. Res. 37(1998) 2064-2068.
[13] Q. Lei, Y.C. Hou, R. Lin, A new correlation for thermal conductivity of liquids, Chem. Eng. Sci. 52(1997) 1243-1251.
[14] Q. Lei, R. Lin, D. Ni, Thermal conductivities of some organic solvents and their binary mixtures, J. Chem. Eng. Data 42(1997) 971-974.
[15] S.A. Shojaee, S. Farzam, A.Z. Hezave, M. Lashkarbolooki, S. Ayatollahi, A new correlation for estimating thermal conductivity of pure ionic liquids, Fluid Phase Equilib. 354(2013) 199-206.
[16] J. Millet, J.H. Dymod, C.A. Nieto de Castro, Transport Properties of Fluids-Their Correlation, Prediction and Estimation, Cambridge University Press, New York, 2005.
[17] V.G. Nemzer, L.V. Nemzer, V.V. Pugach, Thermal conductivity of poly (ethylsiloxane) liquids at high pressures, J. Chem. Eng. Data 41(1996) 848-851.
[18] A.L. Horvath, Molecular Design-Chemical Structure Generation from the Properties of PureOrganic Compounds, Elsevier, New York, 1992.
[19] R.C. Reid, J.M. Prausnitz, B.E. Poling, The Properties of Gases and Liquids, 4th ed. McGraw-Hill, New York, 1987.
[20] D.S. Lakshmi, D.H.L. Prasad, A rapid estimation method for thermal conductivity of pure liquids, Chem. Eng. J. 48(1992) 211-214.
[21] P.M. Mathias, V.S. Parekh, E.J. Miller, Prediction and correlation of the thermal conductivity of pure fluids and mixtures, including the critical region, Ind. Eng. Chem. Res. 41(2002) 989-999.
[22] G. Latini, P. Pierpaoli, P. F., Dynamic viscosity and thermal onductivity prediction of refrigerants and refrigerant mixtures, International Refrigeration and Air Conditioning Conference 1992, pp. 489-498.
[23] M. Nagvekar, T.E. Daubert, A group contribution method for liquid thermal conductivity, Ind. Eng. Chem. Res. 26(1987) 1362-1365.
[24] S.W. Benson, J.H. Buss, Additivity rules for the estimation of molecular properties. Thermodynamic properties, J. Chem. Phys. 29(1958) 546-572.
[25] S.R.S. Sastri, K.K. Rao, Quick estimating for thermal conductivity, Chem. Eng. 100(1993) 106-107.
[26] J.W. Miller Jr., R.S. Gordon, C.L. Yaws, Correlation constants for chemical compounds-thermal conductivity of gas, Chem. Eng. 83(25) (1976) 153-155.
[27] G. Carrera, J. Aires-de-Sousa, Estimation of melting points of pyridinium bromide ionic liquids with decision trees and neural networks, Green Chem. 7(2005) 20-27.
[28] L. Jianfei, L. Weitie, C. Xiaolong, L. Jingyuan, Optimization of fermentation media for enhancing nitrite-oxidizing activity by artificial neural network coupling genetic algorithm, Chin. J. Chem. Eng. 20(2012) 950-957.
[29] N. Bar, T.K. Bandyopadhyay, M.N. Biswas, S. Kumar Das, Prediction of pressure drop using artificial neural network for non-Newtonian liquid flow through piping components, J. Pet. Sci. Eng. 71(2010) 187-194.
[30] M. Lashkarbolooki, Z.S. Shafipour, A.Z. Hezave, Trainable cascade-forward backpropagation network modeling of spearmint oil extraction in a packed bed using SC-CO₂, J. Supercrit. Fluids 73(2013) 108-115.
[31] S. Nasseh, A. Mohebbi, Z. Jeirani, A. Sarrafi, Predicting pressure drop in venturi scrubbers with artificial neural networks, J. Hazard. Mater. 143(2007) 144-149.
[32] M. Lashkarbolooki, A.Z. Hezave, S. Ayatollahi, Viscosity prediction of ternary mixtures containing ILs using multi-layer perceptron artificial neural network, Fluid Phase Equilib. 326(2012) 15-20.
[33] I. Jahanandish, B. Salimifard, H. Jalalifar, Predicting bottomhole pressure in vertical multiphase flowing wells using artificial neural networks, J. Pet. Sci. Eng. 75(2011) 336-342.
[34] S.K. Lahiri, K.C. Ghanta, Development of an artificial neural network correlation for prediction of hold-up of slurry transport in pipelines, Chem. Eng. Sci. 63(2008) 1497-1509.
[35] M. Lashkarbolooki, A.Z. Hezave, A. Babapoor, Correlation of density for binary mixtures of methanol + ionic liquids using back propagation artificial neural network, Korean J. Chem. Eng. 30(2013) 213-220.
[36] F. Yousefi, Z. Amoozandeh, Statistical mechanics and artificial intelligence to model the thermodynamic properties of pure and mixture of ionic liquids, Chin. J. Chem. Eng. 24(12) (2016) 1761-1771.
[37] I.J. Turias, J.M. Gutiérrez, P.L. Galindo, Modelling the effective thermal conductivity of an unidirectional composite by the use of artificial neural networks, Compos. Sci. Technol. 65(2005) 609-619.
[38] S.S. Sablani, R.M. Shafiur, Using neural networks to predict thermal conductivity of food as a function of moisture content, temperature and apparent porosity, Food Res. Int. 36(2003) 617-623.
[39] R. Eslamloueyan, M.H. Khademi, Estimation of thermal conductivity of pure gases by using artificial neural networks, Int. J. Therm. Sci. 48(2009) 1094-1101.
[40] R. Boozarjomehri, F. Abdolahi, M.A. Moosavian, Characterization of basic properties for pure substances and petroleum fractions by neural network, Fluid Phase Equilib. 231(2005) 188-196.
[41] A.Z. Hezave, M. Lashkarbolooki, S. Raeissi, Using artificial neural network to predict the ternary electrical conductivity of ionic liquid systems, Fluid Phase Equilib. 314(2012) 128-133.
[42] H. Watanabe, H. Kato, Thermal conductivity and thermal diffusivity of twenty-nine liquids:alkenes, cyclic (alkanes, alkenes, alkadienes, aromatics), and deuterated hydrocarbons, J. Chem. Eng. Data 49(2004) 809-825.
[43] H. Watanabe, Accurate measurement of the thermal conductivity and thermal diffusivity of toluene and n-heptane, Int. J. Thermophys. 18(1997) 313-325.
[44] H. Watanabe, D.J. Seong, The thermal conductivity and thermal diffusivity of liquid n-alkanes:C_nH_2n+2 (n=5 to 10) and toluene, Int. J. Thermophys. 23(2002) 337-356.
[45] H. Watanabe, Thermal conductivity and thermal diffusivity of sixteen isomers of alkanes:C_n H_2n +₂ (n=6 to 8), J. Chem. Eng. Data 48(2003) 124-136.
[46] M. Lashkarbolooki, B. Vaferi, A. Shariati, A.Z. Hezave, Investigating vapor-liquid equilibria of binary mixtures containing supercritical or near-critical carbon dioxide and a cyclic compound using cascade neural network, Fluid Phase Equilib. 343(2013) 24-29.
[47] http://www.cs.stir.ac.uk/~lss/NNIntro/InvSlides.html.
[48] M. Lashkarbolooki, B. Vaferi, D. Mowla, Using artificial neural network to predict the pressure drop in a rotating packed bed, Sep. Sci. Technol. 47(2012) 2450-2459.
[49] C.M. Rodenbush, D.S. Viswanath, F.H. Fu-hung Hsieh, A group contribution method for the prediction of thermal conductivity of liquids and its application to the Prandtl number for vegetable oils, Ind. Eng. Chem. Res. 38(1999) 4513-4519.
[50] M.R. Riazi, G.A. Enezi, S. Soleimani, Application of the one-third rule in hydrocarbon and crude oil systems, Chem. Eng. Commun. 176(2007) 175-193.
[51] A. Vasquez, J.G. Briano, Thermal conductivity of hydrocarbon mixtures:a perturbation approach, Ind. Eng. Chem. Res. 32(1993) 194-199.
[52] V. Pachaiyappan, S.H. Ibrahim, N.R. Kuloor, Chem. Eng. 74(4) (1967) 150.
[53] R. Mohammad, M.R. Riazi, A. Faghri, Thermal conductivity of liquid and vapor hydrocarbon systems:pentanes and heavier at low pressures, Ind. Eng. Chem. Process. Des. Dev. 24(1985) 398-401.