Table of Contents
ISRN Chemical Engineering
Volume 2013, Article ID 930484, 11 pages
http://dx.doi.org/10.1155/2013/930484
Research Article

Prediction the Vapor-Liquid Equilibria of CO2-Containing Binary Refrigerant Mixtures Using Artificial Neural Networks

1Chemical Engineering Department, Gas and Petrochemical Engineering Faculty, Persian Gulf University, Bushehr 7516913817, Iran
2Department of Petrochemical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Mahshahr Campus, P.O. Box 415, Mahshahr, Iran

Received 9 May 2013; Accepted 18 July 2013

Academic Editors: J. A. A. González, K. Okumura, and J. E. Ten Elshof

Copyright © 2013 Ahmad Azari et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. J. S. Lim, J. M. Jin, and K.-P. Yoo, “VLE measurement for binary systems of CO2 + 1,1,1,2-tetrafluoroethane (HFC-134a) at high pressures,” The Journal of Supercritical Fluids, vol. 44, no. 3, pp. 279–283, 2008. View at Publisher · View at Google Scholar · View at Scopus
  2. K. Jeong, J. Im, G. Lee, Y.-J. Lee, and H. Kim, “Vapor-liquid equilibria of the carbon dioxide (CO2) + 2,2-dichloro-1,1,1-trifluoroethane (R123) system and carbon dioxide (CO2) + 1-chloro-1,2,2,2-tetrafluoroethane (R124) system,” Fluid Phase Equilibria, vol. 251, no. 1, pp. 63–67, 2007. View at Publisher · View at Google Scholar · View at Scopus
  3. J. H. Kim and M. S. Kim, “Vapor-liquid equilibria for the carbon dioxide + propane system over a temperature range from 253.15 to 323.15 K,” Fluid Phase Equilibria, vol. 238, no. 1, pp. 13–19, 2005. View at Publisher · View at Google Scholar · View at Scopus
  4. F. Rivollet, A. Chapoy, C. Coquelet, and D. Richon, “Vapor-liquid equilibrium data for the carbon dioxide (CO2) + difluoromethane (R32) system at temperatures from 283.12 to 343.25 K and pressures up to 7.46 MPa,” Fluid Phase Equilibria, vol. 218, no. 1, pp. 95–101, 2004. View at Publisher · View at Google Scholar · View at Scopus
  5. G. Silva-Oliver and L. A. Galicia-Luna, “Vapor-liquid equilibria for carbon dioxide + 1,1,1,2-tetrafluoroethane (R-134a) systems at temperatures from 329 to 354 K and pressures upto 7.37 MPa,” Fluid Phase Equilibria, vol. 199, no. 1-2, pp. 213–222, 2002. View at Publisher · View at Google Scholar · View at Scopus
  6. A. Valtz, C. Coquelet, and D. Richon, “Vapor-liquid equilibrium data for the hexafluoroethane + carbon dioxide system at temperatures from 253 to 297 K and pressures up to 6.5 MPa,” Fluid Phase Equilibria, vol. 258, no. 2, pp. 179–185, 2007. View at Publisher · View at Google Scholar · View at Scopus
  7. A. Valtz, X. Courtial, E. Johansson, C. Coquelet, and D. Ramjugernath, “Isothermal vapor-liquid equilibrium data for the carbon dioxide (R744) + decafluorobutane (R610) system at temperatures from 263 to 353K,” Fluid Phase Equilibria, vol. 304, no. 1-2, pp. 44–51, 2011. View at Publisher · View at Google Scholar · View at Scopus
  8. H. Madani, A. Valtz, C. Coquelet, A. H. Meniai, and D. Richon, “(Vapor + liquid) equilibrium data for (carbon dioxide + 1,1-difluoroethane) system at temperatures from (258 to 343) K and pressures up to about 8 MPa,” The Journal of Chemical Thermodynamics, vol. 40, no. 10, pp. 1490–1494, 2008. View at Publisher · View at Google Scholar · View at Scopus
  9. C. Si-Moussa, S. Hanini, R. Derriche, M. Bouhedda, and A. Bouzidi, “Prediction of high-pressure vapor liquid equilibrium of six binary systems, carbon dioxide with six esters, using an artificial neural network model,” Brazilian Journal of Chemical Engineering, vol. 25, no. 1, pp. 183–199, 2008. View at Google Scholar · View at Scopus
  10. M. N. Safamirzaei, H. Modarress, and M. Mohsen-Nia, “Modeling the hydrogen solubility in methanol, ethanol, 1-propanol and 1-butanol,” Fluid Phase Equilibria, vol. 289, no. 1, pp. 32–39, 2010. View at Publisher · View at Google Scholar · View at Scopus
  11. H. Ghanadzadeh, M. Ganji, and S. Fallahi, “Mathematical model of liquid-liquid equilibrium for a ternary system using the GMDH-type neural network and genetic algorithm,” Applied Mathematical Modelling, vol. 36, no. 9, pp. 409–4105, 2012. View at Publisher · View at Google Scholar · View at Scopus
  12. V. H. Alvarez and M. D. A. Saldaña, “Thermodynamic prediction of vapor-liquid equilibrium of supercritical CO2 or CHF3 + ionic liquids,” The Journal of Supercritical Fluids, vol. 66, pp. 29–35, 2012. View at Publisher · View at Google Scholar · View at Scopus
  13. F. Gharagheizi, A. Eslamimanesh, P. Ilani-Kashkouli, A. H. Mohammadi, and D. Richon, “Determination of vapor pressure of chemical compounds: a group contribution model for an extremely large database,” Industrial & Engineering Chemistry Research, vol. 51, pp. 7119–7125, 2012. View at Google Scholar
  14. A. Z. Hezave, M. Lashkarbolooki, and S. Raeissi, “Correlating bubble points of ternary systems involving nine solvents and two ionic liquids using artificial neural network,” Fluid Phase Equilibria, vol. 352, pp. 34–41, 2013. View at Publisher · View at Google Scholar
  15. M. Lashkarblooki, A. Z. Hezave, A. M. Al-Ajmi, and S. Ayatollahi, “Viscosity prediction of ternary mixtures containing ILs using multi-layer perceptron artificial neural network,” Fluid Phase Equilibria, vol. 326, pp. 15–20, 2012. View at Publisher · View at Google Scholar
  16. M. Lashkarbolooki, A. Z. Hezave, and S. Ayatollahi, “Artificial neural network as an applicable tool to predict the binary heat capacity of mixtures containing ionic liquids,” Fluid Phase Equilibria, vol. 324, pp. 102–107, 2012. View at Publisher · View at Google Scholar · View at Scopus
  17. M. Safamirzaei and H. Modarress, “Correlating and predicting low pressure solubility of gases in [bmim][BF4] by neural network molecular modeling,” Thermochimica Acta, vol. 545, pp. 125–130, 2012. View at Publisher · View at Google Scholar
  18. A. Şencan Şahin and H. Yazıcı, “Thermodynamic evaluation of the Afyon geothermal district heating system by using neural network and neuro-fuzzy,” Journal of Volcanology and Geothermal Research, vol. 233-234, pp. 65–71, 2012. View at Google Scholar
  19. Y. Sun, G. Bian, W. Tao, C. Zhai, M. Zhong, and Z. Qiao, “Thermodynamic optimization and calculation of the YCl3-ACl (A=Li, Na, K, Rb, Cs) phase diagrams,” Calphad, vol. 39, pp. 1–10, 2012. View at Publisher · View at Google Scholar
  20. X. Xiao, G. Q. Liu, B. F. Hu et al., “A comparative study on Arrhenius-type constitutive equations and artificial neural network model to predict high-temperature deformation behaviour in 12Cr3WV steel,” Computational Materials Science, vol. 62, pp. 227–234, 2012. View at Publisher · View at Google Scholar
  21. K. M. Yerramsetty, B. J. Neely, and K. M. Gasem, “A non-linear structure-property model for octanol-water partition coefficient,” Fluid Phase Equilibria, vol. 332, pp. 85–93, 2012. View at Publisher · View at Google Scholar
  22. F. Yousefi and H. Karimi, “P-V-T properties of polymer melts based on equation of state and neural network,” European Polymer Journal, vol. 48, pp. 1135–1143, 2012. View at Publisher · View at Google Scholar · View at Scopus
  23. F. Yousefi, H. Karimi, and M. M. Papari, “Modeling viscosity of nanofluids using diffusional neural networks,” Journal of Molecular Liquids, vol. 175, pp. 85–90, 2012. View at Publisher · View at Google Scholar
  24. M. Safamirzaei and H. Modarress, “Application of neural network molecular modeling for correlating and predicting Henry's law constants of gases in [bmim][PF6] at low pressures,” Fluid Phase Equilibria, vol. 332, pp. 165–172, 2012. View at Publisher · View at Google Scholar
  25. K. Shahbaz, S. Baroutian, F. S. Mjalli, M. A. Hashim, and I. M. Alnashef, “Densities of ammonium and phosphonium based deep eutectic solvents: prediction using artificial intelligence and group contribution techniques,” Thermochimica Acta, vol. 527, pp. 59–66, 2012. View at Publisher · View at Google Scholar · View at Scopus
  26. F. Gharagheizi, P. Ilani-Kashkouli, N. Farahani, and A. H. Mohammadi, “Gene expression programming strategy for estimation of flash point temperature of non-electrolyte organic compounds,” Fluid Phase Equilibria, vol. 329, pp. 71–77, 2012. View at Publisher · View at Google Scholar
  27. I. P. Koronaki, E. Rogdakis, and T. Kakatsiou, “Thermodynamic analysis of an open cycle solid desiccant cooling system using artificial neural network,” Energy Conversion and Management, vol. 60, pp. 152–160, 2012. View at Publisher · View at Google Scholar · View at Scopus
  28. T. Hatami, M. Rahimi, H. Daraei, E. Heidaryan, and A. A. Alsairafi, “PRSV equation of state parameter modeling through artificial neural network and adaptive network-based fuzzy inference system,” Korean Journal of Chemical Engineering, vol. 29, no. 5, pp. 657–667, 2012. View at Publisher · View at Google Scholar · View at Scopus
  29. M. Saber, “Comparative analysis of hydrate formation pressure applying cubic Equations of State (EoS), Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS),” International Journal of Thermodynamics, vol. 15, pp. 91–101, 2012. View at Google Scholar
  30. F. Gharagheizi, A. Eslamimanesh, P. Ilani-Kashkouli, A. H. Mohammadi, and D. Richon, “QSPR molecular approach for representation/prediction of very large vapor pressure dataset,” Chemical Engineering Science, vol. 76, pp. 99–107, 2012. View at Publisher · View at Google Scholar · View at Scopus
  31. F. Hajabdollahi, Z. Hajabdollahi, and H. Hajabdollahi, “Soft computing based multi-objective optimization of steam cycle power plant using NSGA-II and ANN,” Applied Soft Computing, vol. 12, no. 11, pp. 3648–3655, 2012. View at Publisher · View at Google Scholar
  32. H. Karimi and F. Yousefi, “Application of artificial neural network-genetic algorithm (ANN-GA) to correlation of density in nanofluids,” Fluid Phase Equilibria, vol. 336, pp. 79–83, 2012. View at Publisher · View at Google Scholar
  33. S. Zendehboudi, M. A. Ahmadi, L. James, and I. Chatzis, “Prediction of condensate-to-gas ratio for retrograde gas condensate reservoirs using artificial neural network with particle swarm optimization,” Energy & Fuels, vol. 26, pp. 3432–3447, 2012. View at Publisher · View at Google Scholar
  34. S. Urata, A. Takada, J. Murata, T. Hiaki, and A. Sekiya, “Prediction of vapor-liquid equilibrium for binary systems containing HFEs by using artificial neural network,” Fluid Phase Equilibria, vol. 199, no. 1-2, pp. 63–78, 2002. View at Publisher · View at Google Scholar · View at Scopus
  35. R. Sharma, D. Singhal, R. Ghosh, and A. Dwivedi, “Potential applications of artificial neural networks to thermodynamics: vapor—liquid equilibrium predictions,” Computers & Chemical Engineering, vol. 23, no. 3, pp. 385–390, 1999. View at Publisher · View at Google Scholar · View at Scopus
  36. S. Mohanty, “Estimation of vapour liquid equilibria of binary systems, carbon dioxide-ethyl caproate, ethyl caprylate and ethyl caprate using artificial neural networks,” Fluid Phase Equilibria, vol. 235, no. 1, pp. 92–98, 2005. View at Publisher · View at Google Scholar · View at Scopus
  37. C. A. Faúndez, F. A. Quiero, and J. O. Valderrama, “Phase equilibrium modeling in ethanol + congener mixtures using an artificial neural network,” Fluid Phase Equilibria, vol. 292, no. 1-2, pp. 29–35, 2010. View at Publisher · View at Google Scholar · View at Scopus
  38. M. C. Iliuta, I. Iliuta, and F. Larachi, “Vapour-liquid equilibrium data analysis for mixed solvent-electrolyte systems using neural network models,” Chemical Engineering Science, vol. 55, no. 15, pp. 2813–2825, 2000. View at Publisher · View at Google Scholar · View at Scopus
  39. S. Mohanty, “Estimation of vapour liquid equilibria for the system carbon dioxide-difluoromethane using artificial neural networks,” International Journal of Refrigeration, vol. 29, no. 2, pp. 243–249, 2006. View at Publisher · View at Google Scholar · View at Scopus
  40. A. Mun, A. K. Mutlag, and M. S. Hameed, “Vapor-liquid equilibrium prediction by PE and ANN for the extraction of unsaturated fatty acid esters by supercritical CO2,” Network, vol. 6, no. 9, pp. 122–134, 2011. View at Google Scholar · View at Scopus
  41. A. Ghaemi, S. Shahhoseini, M. Ghannadi, and M. Farrokhi, “Prediction of vapor-liquid equilibrium for aqueous solutions of electrolytes using artificial neural networks,” Journal of Applied Sciences, vol. 8, no. 4, pp. 615–621, 2008. View at Google Scholar · View at Scopus
  42. M. Bilgin, “Isobaric vapour—liquid equilibrium calculations of binary systems using a neural network,” Journal of the Serbian Chemical Society, vol. 69, no. 8-9, pp. 669–674, 2004. View at Publisher · View at Google Scholar · View at Scopus
  43. H. Yamamoto and K. Tochigi, “Prediction of vapor—liquid equilibria using reconstruction-learning neural network method,” Fluid Phase Equilibria, vol. 257, no. 2, pp. 169–172, 2007. View at Publisher · View at Google Scholar · View at Scopus
  44. B. Zarenezhad and A. Aminian, “Predicting the vapor-liquid equilibrium of carbon dioxide + alkanol systems by using an artificial neural network,” Korean Journal of Chemical Engineering, vol. 28, no. 5, pp. 1286–1292, 2011. View at Publisher · View at Google Scholar · View at Scopus
  45. R. Abedini, I. Zanganeh, and M. Mohagheghian, “Simulation and estimation of vapor-liquid equilibrium for asymmetric binary systems (CO2-Alcohols) using artificial neural network,” Journal of Phase Equilibria and Diffusion, vol. 32, no. 2, pp. 105–114, 2011. View at Publisher · View at Google Scholar · View at Scopus
  46. S. Ganguly, “Prediction of VLE data using radial basis function network,” Computers & Chemical Engineering, vol. 27, no. 10, pp. 1445–1454, 2003. View at Publisher · View at Google Scholar · View at Scopus
  47. C. A. Faúndez, F. A. Quiero, and J. O. Valderrama, “Correlation and prediction of VLE of water + congener mixtures found in alcoholic beverages using an artificial neural network,” Chemical Engineering Communications, vol. 198, no. 1, pp. 102–119, 2010. View at Publisher · View at Google Scholar · View at Scopus
  48. H. Karimi and F. Yousefi, “Correlation of vapour liquid equilibria of binary mixtures using artificial neural networks,” Chinese Journal of Chemical Engineering, vol. 15, no. 5, pp. 765–771, 2007. View at Publisher · View at Google Scholar · View at Scopus
  49. H. Ghanadzadeh and H. Ahmadifar, “Estimation of (vapour + liquid) equilibrium of binary systems (tert-butanol + 2-ethyl-1-hexanol) and (n-butanol + 2-ethyl-1-hexanol) using an artificial neural network,” The Journal of Chemical Thermodynamics, vol. 40, no. 7, pp. 1152–1156, 2008. View at Publisher · View at Google Scholar · View at Scopus
  50. T. Hiaki, M. Nanao, S. Urata, and J. Murata, “Vapor—liquid equilibria for 1,1,2,3,3,3-hexafluoropropyl, 2,2,2-trifluoroethyl ether with several organic solvents,” Fluid Phase Equilibria, vol. 194–197, pp. 969–979, 2002. View at Publisher · View at Google Scholar · View at Scopus
  51. S. Ketabchi, H. Ghanadzadeh, A. Ghanadzadeh, S. Fallahi, and M. Ganji, “Estimation of VLE of binary systems (tert-butanol + 2-ethyl-1-hexanol) and (n-butanol + 2-ethyl-1-hexanol) using GMDH-type neural network,” The Journal of Chemical Thermodynamics, vol. 42, no. 11, pp. 1352–1355, 2010. View at Publisher · View at Google Scholar · View at Scopus
  52. AspenONE, Aspen Hysys 2006 Software.
  53. M. O. McLinden, “Thermodynamic properties of CFC alternatives: a survey of the available data,” International Journal of Refrigeration, vol. 13, no. 3, pp. 149–162, 1990. View at Google Scholar · View at Scopus
  54. G. Zhang, B. E. Patuwo, and M. Y. Hu, “Forecasting with artificial neural networks: the state of the art,” International Journal of Forecasting, vol. 14, no. 1, pp. 35–62, 1998. View at Google Scholar · View at Scopus
  55. A. Malallah and I. S. Nashawi, “Estimating the fracture gradient coefficient using neural networks for a field in the Middle East,” Journal of Petroleum Science and Engineering, vol. 49, no. 3-4, pp. 193–211, 2005. View at Publisher · View at Google Scholar · View at Scopus
  56. M. Chakraborty, C. Bhattacharya, and S. Dutta, “Studies on the applicability of Artificial Neural Network (ANN) in emulsion liquid membranes,” Journal of Membrane Science, vol. 220, no. 1-2, pp. 155–164, 2003. View at Publisher · View at Google Scholar · View at Scopus
  57. R. Beale and T. Jackson, Neural Computing: An Introduction, Institude of Physics, London, UK, 1998.