A molecular-based model for prediction of liquid viscosity of pure organic compounds: A quantitative structure property relationship (QSPR) approach

Journal of the Taiwan Institute of Chemical Engineers

Volumn 44, Issue 3, 2013, Pages 359-364

  Gharagheizi, Farhad ^a   Mirkhani, Seyyed Alireza ^a   Keshavarz, Mohammad Hossein ^b   Farahani, Nasrin ^a   Tumba, Kaniki ^c  

^a ISLAMIC AZAD UNIVERSITY   (Iran)

^b MALEK ASHTAR UNIVERSITY OF TECHNOLOGY   (Iran)

^c MANGOSUTHU UNIVERSITY OF TECHNOLOGY   (South Africa)

Author keywords

Feed forward neural networks; Genetic algorithms; Quantitative structure viscosity relationship; Subset variable selection

Indexed keywords

ERROR OF THE MODELS; MOLECULAR DESCRIPTORS; MULTIVARIATE LINEAR REGRESSIONS; PREDICTIVE CAPABILITIES; PREDICTIVE MODELING; QUANTITATIVE STRUCTURE PROPERTY RELATIONSHIPS; QUANTITATIVE STRUCTURE-VISCOSITY RELATIONSHIP; SUBSET VARIABLE SELECTION;

FORECASTING; GENETIC ALGORITHMS; ORGANIC COMPOUNDS;

VISCOSITY OF LIQUIDS;

EID: 84892487812     PISSN: 18761070     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jtice.2012.12.015     Document Type: Article

References (35)

1. Viswanath D.S., Ghosh T.K., PDH L., Dutt N.K. Viscosity of Liquids: theory, estimation, experiment and data 2007, Springer, Dordrecht.
2. Cocchi M., De Benedetti P.G., Seeber R., Tassi L., Ulrici A. Development of quantitative structure-property relationships using calculated descriptors for the prediction of the physicochemical properties (nD, ρ, bp, e{open}, η) of a series of organic solvents. J Chem Inf Model 1999, 39:1190-1203.
3. Ivanciuc O., Ivanciuc T., Filip P.A., Cabrol-Bass D. Estimation of the liquid viscosity of organic compounds with a quantitative structure-property model. J Chem Inf Model 1999, 39:515-524.
4. Katritzky A.R., Chen K., Wang Y., Karelson M., Lucic B., Trinajstic N., et al. Prediction of liquid viscosity for organic compounds by a quantitative structure-property relationship. J Phys Org Chem 2000, 13:80-86.
5. Kauffman G.W., Jurs P.C. Prediction of surface tension, viscosity, and thermal conductivity for common organic solvents using quantitative structure-property relationships. J Chem Inf Model 2001, 41:408-418.
6. Sutter J.M., Dixon S.L., Jurs P.C. Automated descriptor selection for quantitative structure-activity relationships using generalized simulated annealing. J Chem Inf Model 1995, 35:77-84.
7. Rajappan R., Shingade P.D., Natarajan R., Jayaraman V.K. Quantitative structure-property relationship (QSPR) prediction of liquid viscosities of pure organic compounds employing random forest regression. Ind Eng Chem Res 2009, 48:9708-9712.
8. Liaw A., Wiener M. Resampling methods in R: the boot package. R News 2002, 2:1-39.
9. r<0.75) for organic liquids from group contributions. Chem Eng J 2002, 88:27-35.
10. Luckas M., Lucas K. Viscosity of liquids: an equation with parameters correlating with structural groups. AlChE J 1986, 32:139-141.
11. Marrero-Morejón J. Estimation of liquid viscosity at ambient temperature of pure organic compounds by using group-interaction contributions. Chem Eng J 2000, 79:69-72.
12. Murata A., Tochigi K., Yamamoto H. Prediction of the liquid viscosities of pure components and mixtures using neural network and asog group contribution methods. Mol Simul 2004, 30:451-457.
13. Nannoolal Y., Rarey J., Ramjugernath D. Estimation of pure component properties. Part 4: Estimation of the saturated liquid viscosity of non-electrolyte organic compounds via group contributions and group interactions. Fluid Phase Equilib 2009, 281:97-119.
14. Stefanis E., Constantinou L., Tsivintzelis I., Panayiotou C. New group-contribution method for predicting temperature-dependent properties of pure organic compounds. Int J Thermophys 2005, 26:1369-1388.
15. Yinghua L. Estimation of liquid viscosity of pure compounds at different temperatures by a corresponding-states group-contribution method. Fluid Phase Equilib 2002, 198:123-130.
16. Sastri S.R.S., Rao K.K. A new group contribution method for predicting viscosity of organic liquids. Chem Eng J 1992, 50:9-25.
17. Yaws C.L. Yaws' Handbook of Thermodynamic and Physical Properties of Chemical Compounds 2003.
18. Sanghvi R., Yalkowsky S.H. Estimation of the normal boiling point of organic compounds. Ind Eng Chem Res 2006, 45:2856-2861.
19. Talete srl. Dragon 5. 4 for windows (Software for molecular Descriptor Calculations) 2006.
20. Leardi R., Boggia R., Terrile M. Genetic algorithms as a strategy for feature selection. J Chemometr 1992, 6:267-281.
21. Todeschini R., Consonni, Mauri R., Pavan M. Detecting bad regression models: multicriteia fitness functions in regression analysis. Anal Chim Acta 2004, 515:199-208.
22. Gharagheizi F. QSPR analysis for intrinsic viscosity of polymer solutions by means of GA-MLR and RBFNN. Comput Mater Sci 2007, 40:159-167.
23. Gharagheizi F., Sattari M., Tirandazi B. Prediction of crystal lattice energy using enthalpy of sublimation: a group contribution-based model. Ind Eng Chem Res 2011, 50:2482-2486.
24. Gharagheizi F., Eslamimanesh A., Mohammadi A.H., Richon D. QSPR approach for determination of parachor of non-electrolyte organic compounds. Chem Eng Sci 2011, 66:2959-2967.
25. Gharagheizi F., Eslamimanesh A., Mohammadi A.H., Richon D. Determination of critical properties and acentric factors of pure compounds using the artificial neural network group contribution algorithm. J Chem Eng Data 2011, 56:2460-2476.
26. Gharagheizi F., Eslamimanesh A., Mohammadi A.H., Richon D. Use of artificial neural network - group contribution method to determine surface tension of pure compounds. J Chem Eng Data 2011, 56:2587-2601.
27. Gharagheizi F., Eslamimanesh A., Mohammadi A.H., Richon D. Representation/prediction of solubilities of pure compounds in water using artificial neural network - group contribution method. J Chem Eng Data 2011, 56:720-726.
28. Hagan M.T., Demuth H.B., Beale M. Neural Network Design 2002, International Thomson, Andover, MA.
29. Zendehboudi S., Ahmadi M.A., James L., Chatzis I. Prediction of condensate-to-gas ratio for retrograde gas condensate reservoirs using artificial neural network with particle swarm optimization. Energy Fuels 2012, 26:3432-3447.
30. Yousefi F., Karimi H. Application of equation of state and artificial neural network to prediction of volumetric properties of polymer melts. J Ind Eng Chem 2012.
31. Pazuki G.R., Nikookar M., Dehnavi M., Al-Anazi B. The prediction of permeability using an artificial neural network system. Pet Sci Technol 2012, 30:2108-2113.
32. Marinović S., Bolanča T., Ukić Š, Rukavina V, Jukić A. Prediction of diesel fuel cold properties using artificial neural networks. Chem Technol Fuels Oils 2012, 48:67-74.
33. Gharagheizi F., Eslamimanesh A., Mohammadi A.H., Richon D. Artificial neural network modeling of solubilities of 21 commonly used industrial solid compounds in supercritical carbon dioxide. Ind Eng Chem Res 2011, 50:221-226.
34. Eslamimanesh A., Gharagheizi F., Mohammadi A.H., Richon D. Artificial neural network modeling of solubility of supercritical carbon dioxide in 24 commonly used Ionic liquids. Chem Eng Sci 2011, 66:3039-3044.
35. Todeschini R., Consonni V. Molecular descriptors for chemoinformatics 2009, Wiley-VCH/John Wiley, Weinheim/Chichester. 2nd rev. and enl. ed.