84
Views
3
CrossRef citations to date
0
Altmetric
Original Articles

Artificial neural network for the second virial coefficient of organic and inorganic compounds: An ANN for B of organic and inorganic compounds

, ORCID Icon, ORCID Icon, ORCID Icon &

References

  • Arlot, S., and Celisse, A. (2010). A survey of cross-validation procedures for model selection, Stat. Surv., 4, 40–79.
  • Atashrouz, S., Mirshekar, H., and Mohaddespour, A. (2017). A robust modeling approach to predict the surface tension of ionic liquids, J. Mol. Liq., 236, 344–357.
  • Bartolini, C. M., Caresana, F., Comodi, G., Pelagalli, L., Renzi, M., and Vagni, S. (2011). Application of artificial neural networks to micro gas turbines, Energy Conv. Manage., 52, 781–788.
  • Bian, X. Q., Han, B., Du, Z. M., Jaubert, J. N., and Li, M. J. (2016). Integrating support vector regression with genetic algorithm for CO2-oil minimum miscibility pressure (MMP) in pure and impure CO2 streams, Fuel, 182, 550–557.
  • Bishop, C. M. (1994). Neural networks and their applications, Rev. Sci. Instrum., 65, 1803–1832.
  • de Souza, L. E., and Canuto, S. (2001). Efficient estimation of second virial coefficients of fused hard-sphere molecules by an artificial neural network, Phys. Chem. Chem. Phys., 3, 4762–4768.
  • Di Nicola, G., Falone, M., Pierantozzi, M., and Stryjek, R. (2014). An improved group contribution method for the prediction of second virial coefficients, Ind. Eng. Chem. Res., 53, 13804–13809.
  • Di Nicola, G., and Pierantozzi, M. (2015). Surface tension of alcohols: A scaled equation and an artificial neural network, Fluid Phase Equilib., 389, 16–27.
  • Di Nicola, G., Coccia, G., Pierantozzi, M., and Falone, M. (2016a). A semi-empirical correlation for the estimation of the second virial coefficients of refrigerants, Int. J. Refrig., 68, 242–251.
  • Di Nicola, G., Pierantozzi, M., Petrucci, G., and Stryjek, R. (2016b). A semi-empirical correlation for the estimation of the second virial coefficients of refrigerants, Int. J. Refrig., 68, 242–251.
  • Di Nicola, G., Pierantozzi, M., Petrucci, G., and Stryjek, R. (2016c). Equation for the thermal conductivity of liquids and an artificial neural network, J. Thermophys. Heat Transfer, 30, 651–660.
  • Dymond, J., Marsh, K., Wilhoit, R., and Wong, K. (2002). Virial coefficients of pure gases, Landolt-Bornstein Numer. Data Funct. Relat. Sci. Technol.
  • Esfe, M. H., Saedodin, S., Bahiraei, M., Toghraie, D., Mahian, O., and Wongwises, S. (2014). Thermal conductivity modeling of MgO/EG nanofluids using experimental data and artificial neural network, J. Therm. Anal. Calorim., 118, 287–294.
  • Gharagheizi, F., Eslamimanesh, A., Sattari, M., Mohammadi, A. H., and Richon, D. (2015). Computation of the second virial coefficient of chemical compounds using a corresponding states based method, Adv. Chem. Res., 24, 91–112.
  • Greenman, R. M., Stepniewski, S. W., Jorgensen, C. C., and Roth, K. R. (2002). Designing compact feedforward neural models with small training data sets, J. Aircraft, 39, 452–459.
  • Hamzehie, M. E., Fattahi, M., Najibi, H., Van der Bruggen, B., and Mazinani, S. (2015). Application of artificial neural networks for estimation of solubility of acid gases (H2S and CO2) in 32 commonly ionic liquid and amine solutions, J. Nat. Gas Sci. Eng., 24, 106–114.
  • Iglesias-Silva, G. A., and Hall, K. R. (2001). An equation for prediction and/or correlation of second virial coefficients, Ind. Eng. Chem. Res., 40, 1968–1974.
  • Jafar, R., Shahrour, I., and Juran, I. (2010). Application of artificial neural networks (ANN) to model the failure of urban water mains, Math. Comput. Model., 51, 1170–1180.
  • Leroy, A. M., and Rousseeuw, P. J. (1987). Robust Regression and Outlier Detection, Wiley Series in Probability and Mathematical Statistics, Wiley, New York.
  • Mason, E. A., and Spurling, T. H. (1969). The Virial Equation of State, 2nd ed., Pergamon Press: Oxford, New York.
  • Mohagheghian, E., Zafarian-Rigaki, H., Motamedi-Ghahfarrokhi, Y., and Hemmati-Sarapardeh, A. (2015). Using an artificial neural network to predict carbon dioxide compressibility factor at high pressure and temperature, Korean J. Chem. Eng., 32, 2087–2096.
  • Mulero, Á., Pierantozzi, M., Cachadiña, I., and Di Nicola, G., (2017). An Artificial Neural Network for the surface tension of alcohols, Fluid Phase Equilib., 449, 28–40.
  • Natrella, M. 2010. NIST/SEMATECH e-Handbook of statistical methods.
  • Oreški, S. (2012). Comparison of neural network and empirical models for prediction of second virial coefficients for gases, Proc. Eng., 42, 303–312.
  • Petrucci, G., Ghidini, C., and Rospocher, M., (2016). Using recurrent neural network for learning expressive ontologies, ArXiv preprint arXiv:1607.04110.
  • Pierantozzi, M., Di Nicola, G., Latini, G., and Coccia, G. (2017). Artificial neural network modelling of liquid thermal conductivity for alcohols, Phys. Chem. Liq., 0, 1–18.
  • Pitzer, K. S., and Curl Jr., R. (1957). The volumetric and thermodynamic properties of fluids. III. Empirical equation for the second virial coefficient, J. Am. Chem. Soc., 79, 2369–2370.
  • Poling, B. E., Prausnitz, J. M., and O’Connell, J. P. (2001). The Properties of Gases and Liquids, 5th ed., McGraw-Hill, New York.
  • Priddy, K. L., and Keller, P. E. (2005). Artificial Neural Networks: An Introduction, SPIE Press: Bellingham, Washington USA.
  • Ramos-Estrada, M., Tellez-Morales, R., Iglesias-Silva, G. A., and Hall, K. (2004). A generalized correlation for the second virial coefficient based upon the Stockmayer potential, Latin Am. Appl. Res., 34, 41–47.
  • Tsonopoulos, C. (1974). An empirical correlation of second virial coefficients, AIChE J., 20, 263–272.
  • Turco, A. (2011). Factor analysis usage and interpretation, Technical Report, ESTECO.
  • Vaferi, B., Karimi, M., Azizi, M., and Esmaeili, H. (2013). Comparison between the artificial neural network, SAFT and PRSV approach in obtaining the solubility of solid aromatic compounds in supercritical carbon dioxide, J. Supercrit. Fluids, 77, 44–51.
  • Virendra, U., Rajiah, A., and Prasad, D. (1995). Dependence of the second virial coefficient on temperature, Chem. Eng. J. Biochem. Eng. J., 56, 73–76.
  • Weber, L. (1994). Estimating the virial coefficients of small polar molecules, Int. J. Thermophys., 15, 461–482.
  • Wilding, W. V., Rowley, R. L., and Oscarson, J. L. (1998). DIPPR® project 801 evaluated process design data, Fluid Phase Equilib., 150, 413–420.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.