Skip to main content
SpringerLink
Log in

Development of artificial neural network models for supercritical fluid solvency in presence of co-solvents

  • Polymer, Industrial Chemistry, Fluidization, Particle Technology
  • Published:
Korean Journal of Chemical Engineering Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. G. Brunner, Gas extraction: An introduction to fundamentals of supercritical fluids and the application to separation processes, Springer, New York (1994).

    Book  Google Scholar 

  2. M. A. McHugh and V. Krukonis, Supercritical fluid extraction: Principles and practice, Butterworth-Heinemann (1986).

    Google Scholar 

  3. J. B. Hannay and J. Hogart, Proceedings of Royal Society, 29, 324 (1879).

    Article  Google Scholar 

  4. L. T. Taylor, Supercritical fluid extraction, Wiley, New York (1996).

    Google Scholar 

  5. R. Dohrn and G. Brunner, Fluid Phase Equilib. J., 106, 213 (1995).

    Article  CAS  Google Scholar 

  6. F. Gharagheizi, A. Eslamimanesh, A. H. Mohammadi and D. Richon, Ind. Eng. Chem. Res., 50, 221 (2011).

    Article  CAS  Google Scholar 

  7. S. Guha and G. Madras, Fluid Phase Equilib. J., 4736, 1 (2001).

    Google Scholar 

  8. A. Chafer, A. T. Fornari, A. Berna and R. P. Stateva, J. Supercrit. Fluids, 32, 89 (2004).

    Article  CAS  Google Scholar 

  9. Z. Huang, W.D. Lu, S. Kawi and Y. C. Chiew, J. Chem. Eng. Data, 49, 1323 (2004).

    Article  CAS  Google Scholar 

  10. A. Berna, A. Chafer, J. B. Monton and S. Subirats, J. Supercrit. Fluids, 20, 157 (2001).

    Article  CAS  Google Scholar 

  11. A. Chafer, A. Berna, J. B. Monton and R. Munoz, J. Supercrit. Fluids, 24, 103 (2002).

    Article  CAS  Google Scholar 

  12. H. Yang and C. Zhong, J. Supercritical Fluids, 33, 99 (2005).

    Article  CAS  Google Scholar 

  13. H. Bae, J. Jeon and H. Lee, Fluid Phase Equilib. J., 222, 119 (2004).

    Article  Google Scholar 

  14. Q. Li, C. Zhong, Z. Zhang and Q. Zhou, Korean J. Chem. Eng., 21, 1173 (2004).

    Article  CAS  Google Scholar 

  15. K. Cheng, M. Tang and Y. Chen, Fluid Phase Equilib. J., 214, 169 (2003).

    Article  CAS  Google Scholar 

  16. J. Jin, C. Zhong, Z. Zhang and Y. Li, Fluid Phase Equilib. J., 226, 9 (2004).

    Article  CAS  Google Scholar 

  17. J. Chrastil, J. Phys. Chem., 86, 3016 (1982).

    CAS  Google Scholar 

  18. J. Jin, Z. Zhang, Q. Li, Y. Li and E. Yu, J. Chem. Eng. Data, 50, 801 (2005).

    Article  CAS  Google Scholar 

  19. E. M. El-M. Shokir, Neural Network Determines Shaly-Sand Hydrocarbon Saturation, Oil Gas J., April 23 (2001).

    Google Scholar 

  20. E. M. El-M. Shokir, A. Ateeq and A. Al-Sughayer, J. Can. Pet. Technol., 45, 41 (2006).

    Article  Google Scholar 

  21. E. M. El-M. Shokir, H. M. Goda, M. H. Sayyouh and K. Al-Fattah, 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).

    Google Scholar 

  22. L. Fausett, Fundamentals of neural networks, architectures, algorithms, and applications, Prentice Hall, Englewood Cliffs, NJ (1994).

    Google Scholar 

  23. S. Haykin, Neural networks: A comprehensive foundation, Prentice Hall, 2nd Ed. (1998).

    Google Scholar 

  24. C. R. Yonker and R.D. Smith, J. Phys. Chem., 92, 2374 (1938).

    Google Scholar 

  25. M. P. Ekart, K. L. Bennett, S. M. Ekart, G. S. Gurdial, C. L. Liotta and C. A. Eckert, AIChE J., 39, 235 (1993).

    Article  Google Scholar 

  26. N. R. Foster, H. Singh, S. L. J. Yun, D. L. Tomasko and S. J. Macnaughton, Ind. Eng. Chem. Res. J., 32, 2849 (1993).

    Article  CAS  Google Scholar 

  27. S. T. Ting, S. Macnaughton, D. Tomasko and N. Foster, Ind. Eng. Chem. Res. J., 32, 1471 (1993).

    Article  CAS  Google Scholar 

  28. M. Sauceau, J. Letourneau, B. Freiss, D. Richon and J. Fages, J. Supercrit. Fluids, 31, 133 (2004).

    Article  CAS  Google Scholar 

  29. L. Qunsheng, Z. Zeting, Z. Chongli, L. Yancheng and Z. Qingrong, Fluid Phase Equilib. J., 207, 183 (2003).

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Engineering, Mining, Petroleum and Metallurgical Department, Cairo University, Giza, Egypt

    Eissa Mohamed El-Moghawry Shokir & Ayman Abdel-Hamid El-Midany

  2. College of Engineering, Petroleum & Natural Gas Engineering Department, King Saud University, Riyadh, Kingdom of Saudi Arabia

    Eissa Mohamed El-Moghawry Shokir, Emad Souliman Al-Homadhi & Osama Al-Mahdy

Authors
  1. Eissa Mohamed El-Moghawry Shokir
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Emad Souliman Al-Homadhi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Osama Al-Mahdy
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ayman Abdel-Hamid El-Midany
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Eissa Mohamed El-Moghawry Shokir.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shokir, E.M.EM., Al-Homadhi, E.S., Al-Mahdy, O. et al. Development of artificial neural network models for supercritical fluid solvency in presence of co-solvents. Korean J. Chem. Eng. 31, 1496–1504 (2014). https://doi.org/10.1007/s11814-014-0065-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11814-014-0065-8

Keywords

Search

Navigation