Determination of parachor of various compounds using an artificial neural network-group contribution method

Industrial and Engineering Chemistry Research

Volumn 50, Issue 9, 2011, Pages 5815-5823

  Gharagheizi, Farhad ^a   Eslamimanesh, Ali ^b   Mohammadi, Amir H ^b,c   Richon, Dominique ^b  

^a Saman Energy Giti Co   (Iran)

^b PSL RESEARCH UNIVERSITY   (France)

^c UNIVERSITY OF KWAZULU NATAL   (South Africa)

Author keywords

[No Author keywords available]

Indexed keywords

ABSOLUTE AVERAGE DEVIATION; ARTIFICIAL NEURAL NETWORK; GROUP CONTRIBUTION METHOD; GROUP CONTRIBUTIONS; PREDICTIVE TOOLS; SQUARED CORRELATION COEFFICIENTS; STATISTICAL PARAMETERS;

CHEMICAL COMPOUNDS; CHEMICALS;

NEURAL NETWORKS;

EID: 79955526954     PISSN: 08885885     EISSN: 15205045     Source Type: Journal    
DOI: 10.1021/ie102464t     Document Type: Article

References (48)

1. Balasubrahmanyam, S. N. Einstein, "parachor" and molecular volume: Some history and a suggestion Curr. Sci. India 2008, 94, 1650-1658 (Pubitemid 351994102)
2. Danesh, A. PVT and Phase Behaviour of Petroleum Reservoir Fluids; Elsevier: Amsterdam, 1998.
3. Poling, B. E.; Prausnitz, J. M.; O'Connell, J. P. Properties of Gases and Liquids, 5th Ed.; McGraw-Hill: New York, 2001.
4. Macleod, D. B. Relation between surface tension and density Trans. Faraday Soc. 1923, 19, 38-42
5. Sugden, S. A Relation between surface tension, density, and chemical composition J. Am. Chem. Soc. 1924, 125, 1177-1189
6. Sugden, S. The Parachor and Valency; G. Routledge Sons, Ltd.: London, 1930.
7. Bayliss, S. Atomic radii from parachor data and from electron diffraction data J. Am. Chem. Soc. 1937, 59, 444-447
8. Schechter, D. S.; Guo, B. Parachors based on modern physics and their uses in IFT prediction of reservoir fluids. In SPE Proceedings of the Annual Conference, Dallas, TX, 1995; SPE Paper No. 30785.
9. Baker, O.; Swerdloff, W. Calculation of surface tensions, 3: Calculation of surface tension parachor values Oil Gas J. 1955, 53, 87
10. Fanchi, J. R. Calculation of parachors for compositional simulation, an update SPE Reservoir Eng. 1990, August) 433-436
11. Quayle, R. The parachor of organic compounds Chem. Rev. 1953, 53439-53589
12. Gharagheizi, F.; Eslamimanesh, A.; Mohammadi, A. H.; Richon, D. QSPR Approach for Determination of Parachor of Non-Electrolyte Organic Compounds, accepted manuscript, Chem. Eng. Sci. 2011.
13. Project 801, Evaluated Process Design Data, Public Release Documentation; Design Institute for Physical Properties (DIPPR), American Institute of Chemical Engineers (AIChE): New York, 2006.
14. Gharagheizi, F.; Abbasi, R.; Tirandazi, B. Prediction of Henry's law constant of organic compounds in water from a new group-contribution-based model Ind. Eng. Chem. Res. 2010, 49, 10149-10152
15. 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
16. 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, DOI: 10.1021/je101061t.
17. Chouai, A.; Laugier, S.; Richon, D. Modeling of thermodynamic properties using neural networks: Application to refrigerants Fluid Phase Equilib. 2002, 199, 53-62 (Pubitemid 34621375)
18. Piazza, L.; Scalabrin, G.; Marchi, P.; Richon, D. Enhancement of the extended corresponding states techniques for thermodynamic modelling. I. Pure fluids Int. J. Refrig. 2006, 29, 1182-1194 (Pubitemid 44313994)
19. Scalabrin, G.; Marchi, P.; Bettio, L.; Richon, D. Enhancement of the extended corresponding states techniques for thermodynamic modelling. II. Mixtures Int. J. Refrig. 2006, 29, 1195-1207 (Pubitemid 44313995)
20. Chapoy, A.; Mohammadi, A. H.; Richon, D. Predicting the hydrate stability zones of natural gases using Artificial Neural Networks Oil Gas Sci. Technol.-Rev. IFP 2007, 62, 701-706 (Pubitemid 350285564)
21. Mohammadi, A. H.; Richon, D. Hydrate phase equilibria for hydrogen + water and hydrogen + tetrahydrofuran + water systems: Predictions of dissociation conditions using an artificial neural network algorithm Chem. Eng. Sci. 2010, 65, 3352-3355
22. Mohammadi, A. H.; Richon, D. Estimating sulfur content of hydrogen sulfide at elevated temperatures and pressures using an artificial neural network algorithm Ind. Eng. Chem. Res. 2008, 47, 8499-8504
23. Mohammadi, A. H.; Richon, D. A mathematical model based on artificial neural network technique for estimating liquid water-hydrate equilibrium of water-hydrocarbon system Ind. Eng. Chem. Res. 2008, 47, 4966-4970
24. Mohammadi, A. H.; Afzal, W.; Richon, D. Determination of critical properties and acentric factors of petroleum fractions using artificial neural networks Ind. Eng. Chem. Res. 2008, 47, 3225-3232 (Pubitemid 351747440)
25. Mohammadi, A. H.; Richon, D. Use of artificial neural networks for estimating water content of natural gases Ind. Eng. Chem. Res. 2007, 46, 1431-1438 (Pubitemid 46353321)
26. Mohammadi, A. H.; Martínez-López, J. F.; Richon, D. Determining phase diagrams of tetrahydrofuran + methane, carbon dioxide or nitrogen clathrate hydrates using an artificial neural network algorithm Chem. Eng. Sci. 2010, 65, 6059-6063
27. Mehrpooya, M.; Mohammadi, A. H.; Richon, D. Extension of an Artificial Neural Network algorithm for estimating sulfur content of sour gases at elevated temperatures and pressures Ind. Eng. Chem. Res. 2010, 49, 439-442
28. Mohammadi, A. H.; Belandria, V.; Richon, D. Use of an artificial neural network algorithm to predict hydrate dissociation conditions for hydrogen + water and hydrogen + tetra- n -butyl ammonium bromide + water systems Chem. Eng. Sci. 2010, 65, 4302-4305
29. Gharagheizi, F. A new group contribution-based method for estimation of lower flammability limit of pure compounds J. Hazard Mater. 2009, 170, 595-604
30. Gharagheizi, F. New Neural Network Group Contribution model for estimation of lower flammability limit temperature of pure compounds Ind. Eng. Chem. Res. 2009, 48, 7406-7416
31. Gharagheizi, F.; Sattari, M. Estimation of molecular diffusivity of pure chemicals in water: A quantitative structure-property relationship study SAR QSAR Environ. Res. 2009, 20, 267-285
32. Gharagheizi, F. Prediction of standard enthalpy of formation of pure compounds using molecular structure Aust. J. Chem. 2009, 62, 376-381
33. Gharagheizi, F.; Tirandazi, B.; Barzin, R. Estimation of aniline point temperature of pure hydrocarbons: A Quantitative Structure-Property Relationship approach Ind. Eng. Chem. Res. 2009, 48, 1678-1682
34. Gharagheizi, F.; Mehrpooya, M. Prediction of some important physical properties of sulfur compounds using QSPR models Mol. Diversity 2008, 12, 143-155
35. Sattari, M.; Gharagheizi, F. Prediction of Molecular Diffusivity of Pure Components into Air: A QSPR Approach Chemosphere 2008, 72, 1298-1302
36. Gharagheizi, F.; Alamdari, R. F.; Angaji, M. T. A new neural network-group contribution method for estimation of flash point Energy Fuels 2008, 22, 1628-1635
37. Gharagheizi, F.; Fazeli, A. Prediction of Watson Characterization Factor of Hydrocarbon Compounds from Their Molecular Properties QSAR Comb. Sci. 2008, 27, 758-767
38. Gharagheizi, F.; Alamdari, R. F. A molecular-based model for prediction of solubility of C60 fullerene in various solvents Fullerenes, Nanotubes, Carbon, Nanostruct. 2008, 16, 40-57
39. Gharagheizi, F. A New Neural Network Quantitative Structure-Property Relationship for prediction of (Lower Critical Solution Temperature) of polymer solutions. e-Polym. 2007, Article No. 114.
40. Gharagheizi, F. QSPR studies for solubility parameter by means of genetic algorithm-based multivariate linear regression and generalized regression neural network QSAR Comb. Sci. 2008, 27, 165-170
41. Gharagheizi, F. A chemical structure-based model for estimation of upper flammability limit of pure compounds Energy Fuels 2010, 27, 3867-3871
42. Vatani, A.; Mehrpooya, M.; Gharagheizi, F. Prediction of standard enthalpy of formation by a QSPR Model Int. J. Mol. Sci. 2007, 8, 407-432 (Pubitemid 46871234)
43. Mehrpooya, M.; Gharagheizi, F. A Molecular approach for prediction of sulfur compounds solubility parameters, phosphorus sulfur and silicon and related elements Phosphorus Sulfur 2010, 185, 204-210
44. Kalogirou, S. A. Artificial Neural Networks in renewable energy systems applications: A review Renew. Sust. Energy Rev. 2001, 5, 373-401 (Pubitemid 32610523)
45. Hagan, M.; Demuth, H. B.; Beale, M. H. Neural Network Design; International Thomson Publishing, 2002.
46. Gharagheizi, F. QSPR analysis for intrinsic viscosity of polymer solutions by means of GA-MLR and RBFNN Comput. Mater. Sci. 2007, 40, 159
47. Gharagheizi, F.; An accurate model for prediction of autoignition temperature of pure compounds. J. Hazard. Mater. In press, DOI: 10.1016/j.jhazmat.2011.02.014.
48. Gharagheizi, F.; Sattari, M.; Tirandazi, B. Prediction of lattice crystal energy using enthalpy of sublimation: A group contribution-based model. Ind. Eng. Chem. Res. DOI: 10.1021/ie1001672j.