A comparative study between LS-SVM method and semi empirical equations for modeling the solubility of different solutes in supercritical carbon dioxide

Chemical Engineering Research and Design

Volumn 89, Issue 11, 2011, Pages 2420-2427

  Mehdizadeh, Bahman ^a   Movagharnejad, Kamyar ^a  

^a BABOL NOSHIRVANI UNIVERSITY OF TECHNOLOGY   (Iran)

Author keywords

Genetic algorithm; Least square support vector machine; Semi empirical equation; Solubility; Supercritical carbon dioxide

Indexed keywords

COMPARATIVE STUDIES; LEAST SQUARE SUPPORT VECTOR MACHINES; MODEL PREDICTION; RELATIVE DEVIATIONS; SEMI-EMPIRICAL CORRELATION; SEMIEMPIRICAL EQUATION; SINGLE OUTPUT; SUPERCRITICAL CARBON DIOXIDES;

CARBON; CARBON DIOXIDE; GENETIC ALGORITHMS; LEAST SQUARES APPROXIMATIONS; SOLUBILITY; SUPPORT VECTOR MACHINES;

SUPERCRITICAL FLUID EXTRACTION;

EID: 80053571100     PISSN: 02638762     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.cherd.2011.03.017     Document Type: Article

References (37)

1. Asghari-Khiavi M., Yamini Y., Farajzadeh M. Solubilities of two steroid drugs and their mixtures in supercritical carbon dioxide. J. Supercrit. Fluids 2004, 30:111-117.
2. Cartwright H.M. Applications of Artificial Intelligence in Chemistry 1993, Oxford University Press, Oxford.
3. Chen Y., Chen Y., Tang M. Solubilities of cinnamic acid, phenoxyacetic acid and 4-methoxyphenylacetic acid in supercritical carbon dioxide. Fluid Phase Equilib. 2009, 275:33-38.
4. Chrastil J. Solubility of solids and liquids in supercritical gases. J. Phys. Chem. 1982, 86:3016-3022.
5. 2. Ind. Eng. Chem. Res. 1988, 27:1551-1559.
6. Dondeti P., Desai Y. Supercritical fluid technology in pharmaceutical research. Encyclopedia Pharmaceut. Technol. 1999, 18:219-248.
7. Ghaziaskar H., Kaboudvand M. Solubility of trioctylamine in supercritical carbon dioxide. J. Supercrit. Fluids 2008, 44:148-154.
8. González J., Molina M., Rodrói{dotless}guez F., Mirada F. Solubilities of hydroquinone and p-quinone in supercritical carbon dioxide. Fluid Phase Equilib. 2002, 200:31-39.
9. Gordillo M.D., Blanco M.A., Molero A., Martinez de la Ossa M.E. Solubility of the antibiotic penicillin G in supercritical carbon dioxide. J. Supercrit. Fluids 1999, 1:183-189.
10. Herrero M., Cifuentes A., Ibañez E. Sub- and supercritical fluid extraction of functional ingredients from different natural sources: plants, food-by-products, algae and microalgae: a review. Food Chem. 2006, 98:136-148.
11. Hojjati M., Yamini Y., Khajeh M., Vatanara A. Solubility of some statin drugs in supercritical carbon dioxide and representing the solute solubility data with several density-based correlations. J. Supercrit. Fluids 2007, 41:187-194.
12. Huang Sh., Tang M., Ho Sh., Chen Y. Solubilities of Nphenylacetamide, 2-methyl-Nphenylacetamide and 4-methyl-Nphenylacetamide in supercritical carbon dioxide. J. Supercrit. Fluids 2007, 42:165-171.
13. Huang Z., Yang X., Sun G., Songc Sh., Kawic S. The solubilities of xanthone and xanthene in supercritical carbon dioxide: structure effect. J. Supercrit. Fluids 2005, 36:91-97.
14. 2 extraction of black pepper essential oil. J. Supercrit. Fluids 2006, 38:37-44.
15. Jafari Nejad Sh., Abolghasemi S.H., Moosavian H., Maragheh M.A. Prediction of solute solubility in supercritical carbon dioxide: a novel semi-empirical model. Chem. Eng. Res. Des. 2010.
16. Jouyban A., Chan H.K., Foster N.R. Mathematical representation of solute solubility in supercritical carbon dioxide using empirical expressions. J. Supercrit. Fluids 2002, 24:19-35.
17. 2 using minimum number of experiments. J. Pharm. Sci. 2002, 91:1287-1295.
18. Kamali M.J., Mousavi M. Analytic, neural network, and hybrid modeling of supercritical extraction of ∝-pinene. J. Supercrit. Fluids 2008, 47:168-173.
19. Khimeche K., Alessi P., Kikic I., Dahmani A. Solubility of diamines in supercritical carbon dioxide Experimental determination and correlation. J. Supercrit. Fluids. 2007, 41:10-19.
20. Lahiri K., Ghanta K.C. Hybrid support vector regression and genetic algorithm technique-a novel approach in process modeling. Chem. Prod. Process Model. 2009, 4:1-24.
21. Li J.Z., Liu H.X., Yao X.J., Liu M.C., Hu Z.D., Fan B.T. Structure-activity relationship study of oxindole-based inhibitors of cyclin-dependent kinases based on least-squares support vector machines. Anal. Chim. Acta 2007, 581:333-342.
22. Li C., Zhu X., Cao G., Sui Sh., Hu M. Identification of the Hammerstein model of a PEMFC stack based on least squares support vector machines. J. Power Sources 2008, 175:303-316.
23. Mehdizadeh B., Movagharnejad K. A comparison between neural network method and semi empirical equations to predict the solubility of different compounds in supercritical carbon dioxide. Fluid Phase Equilib. 2011, 303:40-44.
24. Mèndez-Santiago J., Teja A.S. Solubility of solids in supercritical fluids: consistency of data and a new model for cosolvent systems. Ind. Eng. Chem. Res. 2000, 39:4767-4771.
25. Miller D., Hawthorne S., Clifford A. Solubility of chlorinated hydrocarbons in supercritical carbon dioxide from 3 13 to 413K and at pressures from 150 to 450bar. J. Supercrit. Fluids 1997, 10:57-63.
26. Niazi A., Ghasemi J., Yazdanpoor A. Simultaneous spectrophotometric determination of nitroaniline isomers after cloud point extraction by using least-squares support vector machines. Spectrochimi. Acta, Part A 2007, 68:523-530.
27. Niazi A., Goodarzi M., Yazdanpoor A. A comparative study between least-squares support vector machines and partial least squares in simultaneous spectrophotometric determination of cypermethrin, permethrin and tetramethrin. J. Braz. Chem. Soc. 2008, 10:536-542.
28. Shamsipur M., Fat'hi M., Yamini Y., Ghiasvand A. Solubility determination of nitrophenol derivatives in supercritical carbon dioxide. J. Supercrit. Fluids 2002, 23:225-231.
29. Suykens J.A.K., Vandewalle J. Least squares support vector machines classifiers. Neural Netw. Lett. 1999, 9:293-300.
30. Tabaraki R., Khayamian T., Ensafi A.A. Wavelet neural network modeling in QSPR for prediction of solubility of 25 anthraquinone dyes at different temperatures and pressures in supercritical carbon dioxide. J. Mol. Graph. Model. 2006, 25:46-54.
31. Vapnik V. Statistical Learning Theory 1998, John Wiley, New York.
32. Yang Z., Gu X.S., Liang X.Y., Ling L.C. Genetic algorithm-least squares support vector regression based predicting and optimizing model on carbon fiber composite integrated conductivity. Mater. Des. 2010, 31:1042-1049.
33. Yaxiong Zh. An improved QSPR study of standard formation enthalpies of acyclic alkanes based on artificial neural networks and genetic algorithm. Chemometr. Intell. Lab. Syst. 2009, 98:162-172.
34. Yamini Y., Arab J., Asghari-Khiavi M. Solubilities of phenazopyridine, propranolol, and methiamazole in supercritical carbon dioxide. J. Pharmaceut. Biomed. Anal. 2003, 32:181-187.
35. Yamini Y., Fat'hi M., Alizadeh N., Shamsipur M. Solubility of dihydroxybenzene isomers in supercritical carbon dioxide. Fluid Phase Equilib. 1998, 152:299-305.
36. Yeo S., Erdogan K. Formation of polymer particles with supercritical fluids: a review. J. Supercrit. Fluids 2004, 34:287-308.
37. Yu Z., Singh B., Rizvi S.S.H., Zollewg J.A. Solubilities of fatty acids, fatty acid esters, and fats and oils in supercritical carbon dioxide. J. Supercrit. Fluids 1994, 7:51-61.