화학공학소재연구정보센터
홈 로그인 로그아웃 연락처 사이트맵
Korean Journal of Chemical Engineering, Vol.31, No.8, 1496-1504, August, 2014
DOI10.1007/s11814-014-0065-8  Export Citation
Development of artificial neural network models for supercritical fluid solvency in presence of co-solvents
Eissa Mohamed El-Moghawry Shokir1, 2, †, Emad Souliman Al-Homadhi2, Osama Al-Mahdy2, and Ayman Abdel-Hamid El-Midany1
  • 1Faculty of Engineering, Mining, Petroleum and Metallurgical Department, Cairo University, Giza, Egypt
  • 2College of Engineering, Petroleum & Natural Gas Engineering Department, King Saud University, Riyadh, Kingdom of Saudi Arabia
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This paper presents the application of artificial neural networks (ANN) to develop new models of liquid solvent dissolution of supercritical fluids with solutes in the presence of cosolvents. The neural network model of the liquid solvent dissolution of CO2 was built as a function of pressure, temperature, and concentrations of the solutes and cosolvents. Different experimental measurements of liquid solvent dissolution of supercritical fluids (CO2) with solutes in the presence of cosolvents were collected. The collected data are divided into two parts. The first part was used in building the models, and the second part was used to test and validate the developed models against the Peng-Robinson equation of state. The developed ANN models showed high accuracy, within the studied variables range, in predicting the solubility of the 2-naphthol, anthracene, and aspirin in the supercritical fluid in the presence and absence of co-solvents compared to (EoS). Therefore, the developed ANN models could be considered as a good tool in predicting the solubility of tested solutes in supercritical fluid.
Keywords:Supercritical;Fluid;Solvents;Cosolvents;Artificial Neural Networks
  1. Brunner G, Gas extraction: An introduction to fundamentals of supercritical fluids and the application to separation processes, Springer, New York (1994).
  2. McHugh MA, Krukonis V, Supercritical fluid extraction: Principles and practice, Butterworth-Heinemann (1986).
  3. Hannay JB, Hogart J, Proce. Royal Soc., 29, 324 (1879)
  4. Taylor LT, Supercritical fluid extraction, Wiley, New York (1996).
  5. Dohrn R, Brunner G, Fluid Phase Equilib., 106(1-2), 213 (1995)
  6. Gharagheizi F, Eslamimanesh A, Mohammadi AH, Richon D, Ind. Eng. Chem. Res., 50(1), 221 (2011)
  7. Guha S, Madras G, Fluid Phase Equilib., 4736, 1 (2001)
  8. Chafer A, Fornari T, Berna A, Stateva RP, J. Supercrit. Fluids, 32(1-3), 89 (2004)
  9. Huang Z, Lu WD, Kawi S, Chiew YC, J. Chem. Eng. Data, 49(5), 1323 (2004)
  10. Berna A, Chafer A, Monton JB, Subirats S, J. Supercrit. Fluids, 20(2), 157 (2001)
  11. Chafer A, Berna A, Monton JB, Munoz R, J. Supercrit. Fluids, 24(2), 103 (2002)
  12. Yang HY, Zhong CL, J. Supercrit. Fluids, 33(2), 99 (2005)
  13. Bae HK, Jeon JH, Lee H, Fluid Phase Equilib., 222, 119 (2004)
  14. Li Q, Zhong C, Zhang Z, Zhou Q, Korean J. Chem. Eng., 21(6), 1173 (2004)
  15. Cheng KW, Tang M, Chen YP, Fluid Phase Equilib., 214(2), 169 (2003)
  16. Jin JS, Zhong CL, Zhang ZT, Li Y, Fluid Phase Equilib., 226, 9 (2004)
  17. Chrastil J, J. Phys. Chem., 86, 3016 (1982)
  18. Jin JS, Zhang ZT, Li QS, Li Y, Yu EP, J. Chem. Eng. Data, 50(3), 801 (2005)
  19. Shokir EMEM, Neural Network Determines Shaly-Sand Hydrocarbon Saturation, Oil Gas J., April 23 (2001).
  20. Shokir EMEM, Alsughayer AA, Al-Ateeq A, J. Can. Pet. Technol., 45(11), 41 (2006)
  21. Shokir EMEM, Goda HM, Sayyouh MH, Al-Fattah K, Selection and evaluation EOR method using artificial intelligent, SPE Paper 79163 Presented at the 26th Annual SPE International Technical Conference and Exhibition in Abuja, Nigeria, August 5-7 (2002).
  22. Fausett L, Fundamentals of neural networks, architectures, algorithms, and applications, Prentice Hall, Englewood Cliffs, NJ (1994).