Predicting the supercritical carbon dioxide extraction of oregano bract essential oil

Songklanakarin Journal of Science and Technology

Volumn 33, Issue 5, 2011, Pages 531-538

  Moghadassi, Abdolreza ^a   Hosseini, Sayed Mohsen ^a   Parvizian, Fahime ^a   Al Hajri, Ibrahim ^b   Talebbeigi, Mehdi ^c  

^a ARAK UNIVERSITY   (Iran)

^b Public Authority for Applied Education and Training   (Kuwait)

^c Research and Development Center   (Iran)

Author keywords

Artificial neural network; Efficiency; Oregano bract essential oil; Prediction; Supercritical extraction

Indexed keywords

EID: 82355164491     PISSN: 01253395     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (21)

1. Bozorgmehry, R.B., Abdolahi, F. and Moosavian, M.A. 2005. Characterization of basic properties for pure proper- ties for pure substances and petroleum fractions by neural network. Fluid phase equilibria. 231, 188-193.
2. Bulsari, A.B. 1995. Neural Networks for Chemical Engineers, Elsevier, Science Press, Amsterdam, Netherlands.
3. Chouai, A., Laugier, S. and Richon, D. 2002. Modeling of thermodynamic properties using neural networks: Application to refrigerants. Fluid Phase Equilibria. 199, 53-62.
4. Demuth, H. and Beale, M. 2002. Neural Network Toolbox User's Guide, the Mathworks Inc, MA.
5. Gaspar, F., Lu, T., Santos, R. and Al-Duri, B. 2003. Modelling the extraction of essential oils with compressed carbon dioxide. Journal of Supercritical Fluids. 25, 247-260.
6. Ganguly, S. 2003. Prediction of VLE data using radial basis function network. Computers & Chemical Engineering. 27, 1445-1454.
7. Guimaraes, P.R.B. and McGreavy, C. 1995. Flow of information through an artificial neural network. Computers & Chemical Engineering. 19, 741-746.
8. Gharbi, R. 1997. Estimating the isothermal compressibility coefficient of under saturated Middle East crude's using neural networks. Energy and Fuels. 11, 372-376.
9. Habiballah, W.A., Startzman, R.A. and Barrufet, M.A. 1994. Use of Neural Networks for prediction of vapor liquid equilibrium K-values for light hydrocarbon mixtures. Proceedings of Society of Petroleum Engineers (SPE) annual technical conference and exhibition. Reservoir Engineering 1, 347 (paper no. #28597).
10. Hagan, M.T., Demuth, H.B. and Beal, M. 1996. Neural Net- work Design, PWS Publishing Company, Boston, U.S.A.
11. Izadifar, M. and Abdolahi, F. 2006. Comparison between neural network and mathematical modeling of super- critical CO2 extraction of black pepper essential oil. Journal of Supercritical Fluids. 38, 37-43.
12. Lang, R.I.W. 2000. A Future for Dynamic Neural Networks, Department of Cybernetics, University of Reading, U.K.
13. Moghadassi, A.R., Parvizian, F., Hosseini, S.M. and Fazlali, A.R. 2009a. A New Approach for Estimation of PVT Properties of Pure Gases Based on Artificial Neural Network Model. Brazilian Journal of Chemical Engineering. 26, 199-206.
14. Moghadassi, A., Parvizian, F. and Hosseini, S.M. 2009b. A New Approach Based on Artificial Neural Networks for Prediction of High Pressure Vapor-liquid Equili- brium. Australian Journal of Basic and Applied Sci- ence. 3(3), 1851-1862.
15. Mohanty, S. 2006. Estimation of vapour liquid Equilibria for the system, carbon dioxide- difluormethane using artificial neural networks. International Journal of Refrigeration. 29, 243-249.
16. th International Petroleum exhibition and conference, Abu Dhabi, U.A.E.
17. Petersen, P., Fredenslund, A. and Rasmussen, P. 1994. Artifi- cial neural networks as a predictive tool for vapour-liquid equilibrium. Computers & Chemical Engineering. 18, Suppl. S63-S67.
18. Sharma, R., Singhal, D., Ghosh, R. and Dwivedi, A. 1995. Potential applications of artificial neural networks to thermodynamics: vapour-liquid equilibrium predic- tions. Computers & Chemical Engineering. 23, 385-390.
19. Sozen, A., Arcakilioglu, E. and Ozalp, M. 2004. Investigation of thermodynamic properties of refrigerant /absorbent couples using artificial neural networks. Chemical En- gineering and Processing: Process Intensification. 43, 1253-1259.
20. Urata, S., Takada, A., Murata, J., Hiaki, T. and Sekiya, A. 2002. Prediction of vapour-liquid equilibrium for binary systems containing HFEs by using artificial neural network. Fluid Phase Equilibria. 199, 63-78.
21. Zahedi, G., Fazlali, A.R., Hosseini, S.M., Pazuki, G.R. and Sheikhattar, L. 2009. Prediction of asphaltene precipitation in crude oil. Journal of Petroleum Science and Engineering. 68, 218-222.