화학공학소재연구정보센터
홈 로그인 로그아웃 연락처 사이트맵
Korean Journal of Chemical Engineering, Vol.35, No.10, 2097-2116, October, 2018
DOI10.1007/s11814-018-0125-6  Export Citation
A comprehensive comparison among four different approaches for predicting the solubility of pharmaceutical solid compounds in supercritical carbon dioxide
Gholamhossein Sodeifian1, 2, †, Seyed Ali Sajadian1, 2, Fariba Razmimanesh1, 2, and Nedasadat Saadati Ardestani1, 2
  • 1Department of Chemical Engineering, Faculty of Engineering, University of Kashan, Kashan, 87317-53153, Iran
  • 2Laboratory of Supercriritcal Fluids and Nanotechnology, University of Kashan, Kashan, 87317-53153, Iran
E-mail:
Supercritical technologies have been developed in the food, environmental, biochemical and pharmaceutical product processing during the recent decades. Obtaining accurate experimental solubilities of pharmaceutical compounds in supercritical carbon dioxide (SC-CO2) and their correlations are highly important and essential for the design of industrial operating units. In this study, the solubilities of six pharmaceutical compounds (Anti-HIV, Antiinflammatory and Anti-cancer) in SC-CO2 were correlated using four different models: cubic equation of state (EoS) model (SRK and modified-Pazuki EoSs), empirical and semi-empirical models (Chrastil, Mendez-Santiago-Teja, Spark et al. and Bian et al. models), regular solution model coupled with the Flory-Huggins equation, and an artificial neural network-based (ANN-based) model. In EoS calculations, twin-parametric van der Waals (vdW2) and Panagiotopoulos- Reid (mrPR) mixing rules were used for estimating the supercritical solution properties, with three different sets employed for obtaining critical and physicochemical properties of the solid compounds. To evaluate the capabilities of various approaches, a comprehensive comparison was carried out among the four models based on several statistical criteria, including AARD, Radj and F-value. Results of the analysis of variance (ANOVA) indicated that the ANN-based model provided the best results in terms of correlating the experimental solubility of the pharmaceutical compounds in SC-CO2.
Keywords:Supercritical Carbon Dioxide (SC-CO2);Pharmaceutical Compounds Solubility;Cubic Equations-of-state (EoS);Empirical and Semi-empirical Models;Artificial Neural Network (ANN)
  1. Tabaraki R, Toulabi A, Fluid Phase Equilib., 383, 108 (2014)
  2. Tabernero A, del Valle EMM, Galan MA, J. Supercrit. Fluids, 52(2), 161 (2010)
  3. Lyu J, Yang H, Ling W, Nie L, Yue G, Li R, Chen Y, Wang S, Frontiers in Energy (2017). https://doi.org/10.1007/s11708-017-0512-4.
  4. Sodeifian G, Sajadian SA, Ardestani NS, J. Supercrit. Fluids, 116, 46 (2016)
  5. Sodeifian G, Sajadian SA, Ardestani NS, J. Supercrit. Fluids, 119, 139 (2017)
  6. Sodeifian G, Sajadian SA, Daneshyan S, J. Supercrit. Fluids, 140, 72 (2018)
  7. Sodeifian G, Sajadian SA, J. Supercrit. Fluids, 133, 239 (2018)
  8. Brunner G, J. Supercrit. Fluids, 96, 11 (2015)
  9. Kiran E, J. Supercrit. Fluids, 110, 126 (2016)
  10. Tom JW, Debenedetti PG, J. Aerosol Sci., 22, 555 (1991)
  11. Yeo SD, Kiran E, J. Supercrit. Fluids, 34(3), 287 (2005)
  12. Yeoh HS, Chong GH, Azahan NM, Rahman RA, Choong TSY, Eng. J., 17, 67 (2013)
  13. Sodeifian G, Ardestani NS, Sajadian SA, Panah HS, Fluid Phase Equilib., 458, 102 (2018)
  14. Sodeifian G, Sajadian SA, Ardestani NS, J. Supercrit. Fluids, 107, 137 (2016)
  15. Sodeifian G, Sajadian SA, Ardestani NS, J. Supercrit. Fluids, 127, 146 (2017)
  16. Fang Z, Rapid production of micro-and nano-particles using supercritical water, Springer Science & Business Media (2010).
  17. Sodeifian G, Ardestani NS, Sajadian SA, Ghorbandoost S, J. Supercrit. Fluids, 114, 55 (2016)
  18. Williams JR, Clifford T, Supercritical fluid methods and protocols, Springer Science & Business Media (2000).
  19. Sodeifian G, Ghorbandoost S, Sajadian SA, Ardestani NS, J. Supercrit. Fluids, 110, 265 (2016)
  20. Sodeifian G, Sajadian SA, J. Supercrit. Fluids, 121, 52 (2017)
  21. Coimbra P, Duarte CMM, de Sousa HC, Fluid Phase Equilib., 239(2), 188 (2006)
  22. Cheng JS, Tang M, Chen YP, Fluid Phase Equilib., 194, 483 (2002)
  23. Huang CY, Lee LS, Su CS, J. Taiwan Inst. Chem. Engineers, 44, 349 (2013)
  24. Peng DY, Robinson DB, Ind. Eng. Chem. Fundam., 15, 59 (1976)
  25. Soave G, Fluid Phase Equilib., 84, 339 (1993)
  26. Valderrama JO, J. Chem. Eng. Jpn., 23, 87 (1990)
  27. Dashtizadeh A, Pazuki GR, Taghikhani V, Ghotbi C, Fluid Phase Equilib., 242(1), 19 (2006)
  28. Chrastil J, J. Phys. Chem., 86, 3016 (1982)
  29. Del Valle JM, Aguilera JM, Ind. Eng. Chem. Res., 27, 1551 (1988)
  30. Gordillo MD, Blanco MA, Molero A, de la Ossa EM, J. Supercrit. Fluids, 15(3), 183 (1999)
  31. Jouyban A, Chan HK, Foster NR, J. Supercrit. Fluids, 24(1), 19 (2002)
  32. Jouyban A, Rehman M, Shekunov BY, Chan HK, Clark BJ, York P, J. Pharm. Sci., 91, 1287 (2002)
  33. Bian XQ, Zhang Q, Du ZM, Chen J, Jaubert JN, Fluid Phase Equilib., 411, 74 (2016)
  34. Mendez-Santiago J, Teja AS, Fluid Phase Equilib., 158, 501 (1999)
  35. Sparks DL, Estevez LA, Hernandez R, Barlow K, French T, J. Chem. Eng. Data, 53(2), 407 (2008)
  36. Su CS, Chen YP, Fluid Phase Equilib., 254(1-2), 167 (2007)
  37. Su CS, Chen YP, J. Supercrit. Fluids, 43(3), 438 (2008)
  38. Eslamimanesh A, Gharagheizi F, Mohammadi AH, Richon D, Chem. Eng. Sci., 66(13), 3039 (2011)
  39. Gharagheizi F, Eslamimanesh A, Mohammadi AH, Richon D, Ind. Eng. Chem. Res., 50, 221 (2010)
  40. Mehdizadeh B, Movagharnejad K, Fluid Phase Equilib., 303(1), 40 (2011)
  41. Vaferi B, Karimi M, Azizi M, Esmaeili H, J. Supercrit. Fluids, 77, 44 (2013)
  42. Bakhbakhi Y, Mathematical Computer Modelling, 55, 1932 (2012)
  43. Sodeifian G, Sajadian SA, Razmimanesh F, Fluid Phase Equilib., 450, 149 (2017)
  44. Sodeifian G, Razmimanesh F, Sajadian SA, Panah HS, Fluid Phase Equilib., 472, 147 (2018)
  45. Ardjmand M, Mirzajanzadeh M, Zabihi F, Chinese J. Chem. Eng., 22, 549 (2014)
  46. Yalkowsky SH, Ind. Eng. Chem. Fundam., 18, 108 (1979)
  47. Pang T, McLaughlin E, Ind. Eng. Chem. Process Des. Dev., 24, 1027 (1985)
  48. Huang FH, Li MH, Lee LL, Starling KE, Chung FT, J. Chem. Eng. Jpn., 18, 490 (1985)
  49. Fedors RF, Polym. Eng. Sci., 14, 147 (1974)
  50. Ghoreishi SM, Heidari E, J. Supercrit. Fluids, 74, 128 (2013)
  51. Lashkarbolooki M, Vaferi B, Rahimpour MR, Fluid Phase Equilib., 308(1-2), 35 (2011)
  52. Sodeifian G, Sajadian SA, Ardestani NS, J. Taiwan Inst. Chem. Engineers, 60, 165 (2016)
  53. Garlapati C, Madras G, Thermochim. Acta, 500(1-2), 123 (2010)
  54. Sodeifian G, Haghtalab A, Appl. Rheol., 14, 180 (2004)
  55. Haghtalab A, Sodeifian G, Iran. Polym. J., 11, 107 (2002)
  56. Montgomery DC, Design and analysis of experiments, John Wiley & Sons (2008).
  57. Adachi Y, Lu BCY, Fluid Phase Equilib., 14, 147 (1983)
  58. Suleiman D, Estevez LA, Pulido JC, Garcia JE, Mojica C, J. Chem. Eng. Data, 50(4), 1234 (2005)
  59. Ch R, Madras G, Thermochim. Acta, 507, 99 (2010)
  60. Hezave AZ, Aftab S, Esmaeilzadeh F, J. Supercrit. Fluids, 68, 39 (2012)
  61. Yamini Y, Hojjati M, Kalantarian P, Moradi M, Esrafili A, Vatanara A, Thermochim. Acta, 549, 95 (2012)
  62. Poling BE, Prausnitz JM, Paul OCJ, Reid RC, The properties of gases and liquids, McGraw-Hill New York (2001).
  63. Curl R, Pitzer K, Ind. Eng. Chem., 50, 265 (1958)
  64. Lee BI, Kesler MG, AIChE J., 21, 510 (1975)