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.
Article PDF
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Avoid common mistakes on your manuscript.
References
G. Brunner, Gas extraction: An introduction to fundamentals of supercritical fluids and the application to separation processes, Springer, New York (1994).
M. A. McHugh and V. Krukonis, Supercritical fluid extraction: Principles and practice, Butterworth-Heinemann (1986).
J. B. Hannay and J. Hogart, Proceedings of Royal Society, 29, 324 (1879).
L. T. Taylor, Supercritical fluid extraction, Wiley, New York (1996).
R. Dohrn and G. Brunner, Fluid Phase Equilib. J., 106, 213 (1995).
F. Gharagheizi, A. Eslamimanesh, A. H. Mohammadi and D. Richon, Ind. Eng. Chem. Res., 50, 221 (2011).
S. Guha and G. Madras, Fluid Phase Equilib. J., 4736, 1 (2001).
A. Chafer, A. T. Fornari, A. Berna and R. P. Stateva, J. Supercrit. Fluids, 32, 89 (2004).
Z. Huang, W.D. Lu, S. Kawi and Y. C. Chiew, J. Chem. Eng. Data, 49, 1323 (2004).
A. Berna, A. Chafer, J. B. Monton and S. Subirats, J. Supercrit. Fluids, 20, 157 (2001).
A. Chafer, A. Berna, J. B. Monton and R. Munoz, J. Supercrit. Fluids, 24, 103 (2002).
H. Yang and C. Zhong, J. Supercritical Fluids, 33, 99 (2005).
H. Bae, J. Jeon and H. Lee, Fluid Phase Equilib. J., 222, 119 (2004).
Q. Li, C. Zhong, Z. Zhang and Q. Zhou, Korean J. Chem. Eng., 21, 1173 (2004).
K. Cheng, M. Tang and Y. Chen, Fluid Phase Equilib. J., 214, 169 (2003).
J. Jin, C. Zhong, Z. Zhang and Y. Li, Fluid Phase Equilib. J., 226, 9 (2004).
J. Chrastil, J. Phys. Chem., 86, 3016 (1982).
J. Jin, Z. Zhang, Q. Li, Y. Li and E. Yu, J. Chem. Eng. Data, 50, 801 (2005).
E. M. El-M. Shokir, Neural Network Determines Shaly-Sand Hydrocarbon Saturation, Oil Gas J., April 23 (2001).
E. M. El-M. Shokir, A. Ateeq and A. Al-Sughayer, J. Can. Pet. Technol., 45, 41 (2006).
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).
L. Fausett, Fundamentals of neural networks, architectures, algorithms, and applications, Prentice Hall, Englewood Cliffs, NJ (1994).
S. Haykin, Neural networks: A comprehensive foundation, Prentice Hall, 2nd Ed. (1998).
C. R. Yonker and R.D. Smith, J. Phys. Chem., 92, 2374 (1938).
M. P. Ekart, K. L. Bennett, S. M. Ekart, G. S. Gurdial, C. L. Liotta and C. A. Eckert, AIChE J., 39, 235 (1993).
N. R. Foster, H. Singh, S. L. J. Yun, D. L. Tomasko and S. J. Macnaughton, Ind. Eng. Chem. Res. J., 32, 2849 (1993).
S. T. Ting, S. Macnaughton, D. Tomasko and N. Foster, Ind. Eng. Chem. Res. J., 32, 1471 (1993).
M. Sauceau, J. Letourneau, B. Freiss, D. Richon and J. Fages, J. Supercrit. Fluids, 31, 133 (2004).
L. Qunsheng, Z. Zeting, Z. Chongli, L. Yancheng and Z. Qingrong, Fluid Phase Equilib. J., 207, 183 (2003).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
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
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11814-014-0065-8