Correlation of Vapor-Liquid Equilibria for Commonly Used Binary Systems in Supercritical Fluid Extraction Processes

Saeid Atashrouz, Hamed Mirshekar, Hamid Bagheri



In this paper, a comprehensive mathematical model is developed based on the Feed-ForwardBack Propagation Artificial Neural Network (FFBP-ANN). The model is employed for thecalculation of Vapor Liquid Equilibria (VLE) of four CO2-containing binary mixtures. Themixtures include CO2 - Tertpentanol was investigated at the temperature range from 313.14 to343.15 K. The following mixtures including CO2 - Isobutanol at 313.2 to 353.2 K, CO2 - methylacetate at 308.15 to 328.15 K and CO2 - diisopropyl ether at 265.15 to 333.15 K wereinvestigated as well. The related experimental data of open literature have been used to constructthe model. The results confirm that there is a reasonable conformity between the predicted valuesand the experimental data. Additionally, the ability of the ANN model is examined by comparison with the conventional thermodynamic models and ANN model predicted VLE datawith more accuracy.

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Lee, H. S., and Lee. H. (1998). High-pressure phase equilibria for the carbon dioxide–2-pentanol and carbon dioxide–water–2-pentanol systems. Fluid Phase Equilibria. 150-151:695-701.


Hongling, L., Rongjiao, Z., Wei X., Yanfen, L., Yongju, S., and Yiling, T. (2011).Vapor-Liquid Equilibrium Data of the Carbon Dioxide + Ethyl Butyrate and Carbon Dioxide + Propylene Carbonate Systems at Pressures from (1.00 to 13.00) MPa and Temperatures from (313.0 to 373.0) K. Journal of Chemical & Engineering Data. 56:1148-1157.


Lin, W., Jian-cheng, L., Hao, Y., and Kai-xun, C. (2011). Vapor-liquid Phase Equilibria for CO2 + Tertpentanol Binary System at Elevated Pressures. Chemical Research in Chinese Universities. 27(4):678-682.

Zhu, C., Wu, X., Zheng, D., He, W., and Jing, S. (2008). Measurement and correlation of vapor–liquid equilibria for the system carbon dioxide–diisopropyl ether. Fluid Phase Equilibrium. 264:259–263.


Ghanadzadeh, H., and Ahmadifar, H. (2008). Estimation of ( vapour + liquid ) equilibrium of binary systems (tert-butanol + 2-ethyl-1-hexanol) and (n-butanol + 2-ethyl-1-hexanol) using an artificial neural network. The Journal of Chemical Thermodynamics, 40:1152–1156. doi:

Safamirzaei, M., Modarress, H., and Mohsen-Nia, M. (2010). Modeling the hydrogen solubility in methanol, ethanol, 1-propanol and 1-butanol. Fluid Phase Equilibria, 289:32–39. doi:

Eslamimanesh, A., Gharagheizi, F., Mohammadi, A. H., and Richon, D. (2011). Artificial Neural Network modeling of solubility of supercritical carbon dioxide in 24 commonly used ionic liquids. Chemical Engineering Science. 66:3039–3044. doi:

Sencan, A., Köse, I. I., and Selbas, R. (2011). Prediction of thermophysical properties of mixed refrigerants using artificial neural network. Energy Conversion and Management, 52: 958–974.


Mohanty, S. (2005). Estimation of vapour liquid equilibria of binary systems, carbon dioxide–ethyl caproate, ethyl caprylate and ethyl caprate using artificial neural networks, Fluid Phase Equilibrium. 235: 92–98.


Safamirzaei, M., Modarress, H. (2012). Correlating and predicting low pressure solubility of gases in [bmim][BF4] by neural network molecular modeling. Thermochimica Acta. 545:125– 130.


Bakhbakhi, Y. (2011). Phase equilibria prediction of solid solute in supercritical carbon dioxide with and without a cosolvent: The use of artificial neural network. Expert Systems with Applications, 38:11355–11362.


Lin, W., Luo, J., Cheng, Y., and Kai-xun, C. (2011). Vapor-liquid Phase Equilibria for CO2+Tertpentanol Binary System at Elevated Pressures. Chemical Research in Chinese Universities. 27:678–682.

Lin, W., Xiaosong, H., and Kaixun, C. (2009). Phase Equilibrium of Isobutanol in Supercritical CO2. Chinese Journal of Chemical Engineering. 17:642-647. doi:

Schwinghammer, S., Siebenhofer, M. and Marr, R. (2006). Determination and modelling of the high-pressure vapour–liquid equilibrium carbon dioxide–methyl acetate. Journal of Supercritical Fluids. 38:1–6.


"Aspen Hysys 2006 Software- aspenONE".

Karimi, H., and Yousefi, F. (2007). Correlation of Vapour Liquid Equilibria of Binary Mixtures Using Artificial Neural Networks. Chinese Journal of Chemical Engineering. 15: 765-771


Zhang, G., Patuwo, B. E., Hu, M. Y. (1998). Forecasting with artificial neural networks: the state of the art. International Journal of Forecasting. 14:35-62.


Manohar, H. J., Saravanan, R., and Renganarayanan, S. (2006). Modelling of steam fired double effect vapour absorption chiller using neural network. Energy Conversion and Management. 47:2202–2210.


Malallah, A. and Nashawi, I. S. (2005). Estimating the fracture gradient coefficient using neural networks for a field in the Middle East. Journal of Petroleum Science and Engineering. 49:193– 211.doi:

Chakraborty, M., Bhattacharya, C., and Dutta, S. (2003). Studies on the applicability of artificial neural network (ANN) in emulsion liquid membranes. Journal of Membrane Science. 220:155–164. doi:

Beale, R., and Jackson, T., (1998). Neural Computing: An Introduction. London, UK, Institude of Physics Publishing, Bristol BSI 6BE.

Moghadassi, A. R., Nikkholgh, M. R., Hosseini, S. M., Parvizian, F. and Sanaeirad, A. (2011). Prediction of Vapor Liquid Equilibrium (VLE) Data for Binary Systems; Case Study: Methane/Tetrafluoromethane. ARPN Journal of Engineering and Applied Sciences. 6:100-107.

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