Skip to main content
SpringerLink
Log in

Correlation of ternary liquid--liquid equilibrium data using neural network-based activity coefficient model

Neural Computing and Applications volume 24, pages 339–346 (2014)Cite this article

Abstract

Liquid--liquid equilibrium (LLE) data are important in chemical industry for the design of separation equipments, and it is troublesome to determine experimentally. In this paper, a new method for correlation of ternary LLE data is presented. The method is implemented by using a combined structure that uses genetic algorithm (GA)--trained neural network (NN). NN coefficients that satisfy the criterion of equilibrium were obtained by using GA. At the training phase, experimental concentration data and corresponding activity coefficients were used as input and output, respectively. At the test phase, trained NN was used to correlate the whole experimental data by giving only one initial value. Calculated results were compared with the experimental data, and very low root-mean-square deviation error values are obtained between experimental and calculated data. By using this model tie-line and solubility curve data of LLE can be obtained with only a few experimental data.

This is a preview of subscription content, access via your institution.

Access options

Buy single article

Instant access to the full article PDF.

USD 39.95

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

References

  1. Poling BE, Prausnitz JM, O’Connell JP (1998) Properties of gases and liquids, 5th edn, vol 8. McGraw-Hill, Boston, pp 1–204

    Google Scholar 

  2. Seader JD, Henley EJ (1998) Separation process principles, 1st edn. Wiley, New York, pp 31–89

    Google Scholar 

  3. 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 Equilibria 235:92–98

    Article  Google Scholar 

  4. Mohammadi AH, Martinez-Lopez JF, Richon D (2010) Determining phase diagrams of tetrahydrofuran+methane, carbondioxide or nitrogen clathrate hydrates using an artificial neural network algorithm. Chem Eng Sci 65:6059–6063

    Article  Google Scholar 

  5. Nguyen VD, Tan RR, Brondial Y, Fuchino T (2007) Prediction of vapor-liquid equilibrium data for ternary systems using artificial neural networks. Fluid Phase Equilibria 254:188–197

    Article  Google Scholar 

  6. Singh MK, Banerjee T, Khanna A (2005) Genetic algorithm to estimate interaction parameters of multicomponent systems for liquid-liquid equilibria. Comput Chem Eng 29:1712–1719

    Article  Google Scholar 

  7. Özmen D (2008) Determination and correlation of liquid-liquid equilibria for the (water + carboxylic acid + dimethyl maleate) ternary systems at T = 298.2K. Fluid Phase Equilibria 269:12–18

    Article  Google Scholar 

  8. Ghanadzadeh H, Ganji M, Fallahi S (2012) Mathematical model of liquid-liquid equilibrium for a ternary system using the GMDH-type neural network and genetic algorithm. Appl Math Model 36:4096–4105

    Article  Google Scholar 

  9. Daubert TE (1987) Chemical engineering thermodynamics, 2nd edn, vol 7. McGraw-Hill, Singapore, pp 259–290

    Google Scholar 

  10. Treybal RE (1963) Liquid extraction, 2nd edn. McGraw Hill Book Company, New York pp 56–121

    Google Scholar 

  11. Prausnitz JM, Lichtenthaler RN, de Azevedo EG (1999) Molecular thermodynamics of fluid-phase equilibria, 3rd edn. Prentice Hall, New Jersey, pp 213–304

    Google Scholar 

  12. Prausnitz JM, Anderson TF, Grens EA, Eckert CA, Hsieh R, O’Connell JP (1980) Computer calculations for multicomponent vapor-liquid and liquid-liquid equilibria. Prentice-Hall, Englewood Cliffs, pp 39–81

    Google Scholar 

  13. Sorensen JM (1980) Correlation of liquid-liquid equilibrium data, PhD. thesis, Technical University of Denmark, Lyngby, Denmark

  14. Haykin S (1999) Neural networks, 2nd edn. Prentice-Hall, New Jersey, pp 6–23

    MATH  Google Scholar 

  15. Şengül M, Özmen A, Yılmaz M (2003) A new synthesis algorithm for mixed lumped-distributed low-pass ladder networks via artificial neural networks. International conference on electronics and computer engineering, Bursa, Turkey.

  16. Tang KS, Man KF, Kwong S, He Q (1996) Genetic algorithms and their applications. IEEE Signal Process Mag 13:22–37

    Article  Google Scholar 

  17. Mitchell M (1999) An introduction to genetic algorithms, 5th edn. A Bradford Book The MIT Press, London, pp 7–12

    Google Scholar 

  18. Lagarias JC, Reeds JA, Wright MH, Wright PE (1998) Convergence properties of the nelder-mead simplex method in low dimensions. SIAM J Optim 9:112–147

    Article  MATH  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical-Electronics Engineering, Faculty of Engineering and Natural Sciences, Kadir Has University, Cibali, 34083, Istanbul, Turkey

    Atilla Özmen

Authors
  1. Atilla Özmen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Atilla Özmen.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Özmen, A. Correlation of ternary liquid--liquid equilibrium data using neural network-based activity coefficient model. Neural Comput & Applic 24, 339–346 (2014). https://doi.org/10.1007/s00521-012-1227-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-012-1227-4

Keywords

search

Navigation