Skip to main content
Log in

Mathematical Model of the Phase Diagrams of Ionic Liquids-Based Aqueous Two-Phase Systems Using the Group Method of Data Handling and Artificial Neural Networks

  • Published:
Theoretical Foundations of Chemical Engineering Aims and scope Submit manuscript

Abstract

Development of ionic liquid-based aqueous two-phase systems as a new viewpoint in the expansion of research in the field of biological materials separation depends on accurate determination of phase diagram. In this work, the efficiency of artificial neural network was studied aiming to forecast the formation possibility of phase diagrams of aqueous two-phases systems for the ability of range of ionic liquids composed of different anions with a selected salt. In order to investigate effects of the anion of ionic liquids on phase diagram, this study was performed on 472 of experimental data. On the basis of the accurate set of statistical measurements obtained, a good agreement between the experimental data points and the predicted values was gained. Furthermore, the group method of data handling was applied to model the molality of ionic liquids and a reasonable agreement was obtained between experimental data and the predicted values of this model.

This is a preview of subscription content, log in via an institution to check access.

Access this article

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

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Hatti-Kaul, R., Aqueous Two-Phase Systems: Methods and Protocols (Methods in Biotechnology), New York: Humana, 2000.

    Book  Google Scholar 

  2. Shahriari, Sh., Taghikhani, V., Vossoughi, M., Safekordi, A.A., Alemzadeh, I., and Pazuki, G.R., Measurement of partition coefficients of β-amylase and amyloglucosidase enzymes in aqueous two-phase systems containing poly(ethylene glycol) and Na2SO4/KH2PO4 at different temperatures, Fluid Phase Equilibr., 2010, vol. 292, pp. 80–86.

    Article  CAS  Google Scholar 

  3. Shahriari, Sh., GhayourDoozandeh, S., and Pazuki, G.R., Partitioning of cephalexin in aqueous two-phase systems containing poly(ethylene)glycol and sodium citrate salt at different temperatures, J. Chem. Eng. Data, 2012, vol. 57, pp. 256–262.

    Article  CAS  Google Scholar 

  4. Gutowski, K.E., Broker, G.A., Willauer, H.D., Huddleston, G.J., Swatloski, R.P., Holbrey, J.D., and Rogers, R.D., Controlling the aqueous miscibility of ionic liquids: Aqueous biphasic systems of water-miscible ionic liquids and water-structuring salts for recycle, metathesis, and separations, J. Am. Chem. Soc., 2003, vol. 125, pp. 6632–6633.

    Article  CAS  Google Scholar 

  5. Claudio, A.F.M., Ferreira, A.M., Shahriari, Sh., Freire, M.G., and Coutinho, J.A.P., Critical assessment of the formation of ionic-liquid-based aqueous two-phase systems in acidic media, J. Phys. Chem. B., 2011, vol. 115, pp. 11145–11153.

    Article  CAS  Google Scholar 

  6. Ventura, S.P.M., de Barros, R.L.F., de Pinho Barbosa, J.M., Soares, C.M.F., Lima, Á.S., and Coutinho, J.A.P., Production and purification of an extracellular lipolyticenzyme using ionic liquid-based aqueous two-phase systems, Green Chem., 2012, vol. 14, pp. 734–740.

    Article  CAS  Google Scholar 

  7. Freire, M.G., Claúdio, A.F.M., Araújo, J.M.M., Coutinho, J.A.P., Marrucho, I.M., Lopes, J.N.C., and Rebelo, L.P.N., Aqueous biphasic systems: A boost brought about by using ionic liquids, Chem. Soc. Rev., 2012, vol. 41, pp. 4966–4995.

    Article  CAS  Google Scholar 

  8. Shahriari, Sh., Neves, C.M.S.S., Freire, M.G., and Coutinho, J.A.P., Role of the hofmeister series in the formation of ionic-liquid-based aqueous biphasic systems, J. Phys. Chem. B., 2012, vol. 116, pp. 7252–7258.

    Article  CAS  Google Scholar 

  9. Freire, M.G., Neves, C.M.S.S., Marrucho, I.M., Lopes, J.N.C., Rebelo, L.P.N., and Coutinho, J.A.P., High-performance extraction of alkaloids using aqueous two-phase systems with ionic liquids, Green Chem., 2010, vol. 12, pp. 1715–1718.

    Article  CAS  Google Scholar 

  10. Zafarani-Moattar, M.T. and Hamzehzadeh, Sh., Partitioning of amino acids in the aqueous biphasic system containing the water-miscible ionic liquid 1-butyl-3-methylimidazolium bromide and the waterstructuring salt potassium citrate, Biotechnol. Prog., 2011, vol. 27, pp. 986–997.

    Article  CAS  Google Scholar 

  11. Shahriari, Sh., Tomé, L.C., Araújo, J.M.M., Rebelo, L.P.N., Coutinho, J.A.P., Marrucho, I.M., and Freire, M.G., Aqueous biphasic systems: A benign route using cholinium-based ionic liquids, RSC Adv., 2013, vol. 3, pp. 1835–1843.

    Article  CAS  Google Scholar 

  12. Najdanovic, V., Canongia, L., Trindade, J., and Rebelo, L.P.N., Salting-out in aqueous solutions of ionic liquids and K3PO4: Aqueous biphasic systems and salt precipitation, Int. J. Mol. Sci., 2007, vol. 8, pp. 736–748.

    Article  Google Scholar 

  13. Wu, B., Zhang, Y., and Wang, H., Phase behavior for ternary systems composed of ionic liquid + saccharides + water, J. Phys. Chem. B., 2008, vol. 112, pp. 6426–6429.

    Article  CAS  Google Scholar 

  14. Pereira, J.F.B., Lima, A.S., Freire, M.G., and Coutinho, J.A.P., Ionic liquids as adjuvants for the tailored extraction of biomolecules in aqueous biphasic systems, Green Chem., 2010, vol. 12, pp. 1661–1669.

    Article  CAS  Google Scholar 

  15. Ventura, S.P.M., Neves, C.M.S.S., Freire, M.G., Marrucho, I.M., Oliveira, J., and Coutinho, J.A.P., Evaluation of anion influence on the formation and extraction capacity of ionic-liquid-based aqueous biphasic systems, J. Phys. Chem. B., 2009, vol. 113, pp. 9304–9310.

    Article  CAS  Google Scholar 

  16. Neves, C.M.S.S., Ventura, S.P.M., Freire, M.G., Marrucho, I.M., and Coutinho, J.A.P., Evaluation of cation influence on the formation and extraction capability of ionic-liquid-based aqueous biphasic systems, J.Phys. Chem. B., 2009, vol. 113, pp. 5194–5199.

    Article  CAS  Google Scholar 

  17. Pereira, J.F.B., Ventura, S.P.M., Silva, F.A., Shahriari, Sh., and Coutinho, J.A.P., Aqueous biphasic systems composed of ionic liquids and polymers: a platform for the purification of biomolecules, Sep. Purif. Technol., 2013, vol. 113, pp. 83–89.

    Article  CAS  Google Scholar 

  18. Gurney, K., An Introduction to Neural Networks, London: Routledge, 1997.

    Book  Google Scholar 

  19. Mjalli, F.S., Neural network model-based predictive control of liquid–liquid extraction contactors, Chem. Eng. Sci., 2005, vol. 60, pp. 239–253.

    Article  CAS  Google Scholar 

  20. Amiri, M., Davande, H., Sadeghian, A., and Chartier, S., Feedback associative memory based on a new hybrid model of generalized regression and self-feedback neural networks s, Neural Networks, 2010, vol. 23, pp. 892–904.

    Article  Google Scholar 

  21. Faundez, C.A., Quiero, F.A., and Valderrama, J.O., Correlation of solubility data of ammonia in ionic liquids for gas separation processes using artificial neural networks, Fluid Phase Equilib., 2010, vol. 292, pp. 29–35.

    Article  CAS  Google Scholar 

  22. Hosseini, S.M., Amiri, M., Najarian, S., and Dargahi, J., Application of artificial neural networks for estimation of tumor characteristics in biological tissues, Int. J. Med. Robot. Comp., 2007, vol. 3, pp. 235–244.

    Article  Google Scholar 

  23. Atashrouz, S., Pazuki, G., and Alimoradi, Y., Estimation of the viscosity of nine nanofluids using a hybrid GMDH-type neural network system, Fluid Phase Equilib., 2014, vol. 372, pp. 43–48.

    Article  CAS  Google Scholar 

  24. Abdolrahimi, S., Nasernejad, B., and Pazuki, G., Prediction of partition coefficients of alkaloids in ionic liquids based aqueous biphasic systems using hybrid group method of data handling (GMDH) neural network, J. Mol. Liq., 2014, vol. 191, pp. 79–84.

    Article  CAS  Google Scholar 

  25. Shahriari, Sh. and Shahriari, Shi., Predicting ionic liquid based aqueous biphasic systems with artificial neural networks, J. Mol. Liq., 2014, vol.197, pp. 65–72.

    Article  CAS  Google Scholar 

  26. Zeinolabedini Hezave, A., Lashkarbolooki, M., and Raeissi, S., Using artificial neural network to predict the ternary electrical conductivity of ionic liquid, Fluid Phase Equilib., 2012, vol. 314, pp. 128–133.

    Article  Google Scholar 

  27. Arpornwichanop Vitae, A. and Shomchoam, N., Control of fed-batch bioreactors by a hybrid on-line optimal control strategy and neural network estimator, Neurocomputing, 2009, vol. 72, pp. 2297–2302.

    Article  Google Scholar 

  28. Valderrama, J.O., Reategui, A., and Rojas, R.E., Density of ionic liquids using group contribution and artificial neural networks, Ind. Eng. Chem. Res., 2009, vol. 48, pp. 3254–3259.

    Article  CAS  Google Scholar 

  29. Valderrama, J.O., Muñoz, J.M., and Rojas, R.E., Viscosity of ionic liquids using the concept of mass connectivity and artificial neural networks, Korean J. Chem. Eng., 2011, vol. 28, pp. 1451–1457.

    Article  CAS  Google Scholar 

  30. Gautam, Sh. and Simon, L., Prediction of equilibrium phase compositions and β-glucosidase partition coefficient in aqueous two-phase systems, Chem. Eng. Commun., 2007, vol. 194, pp. 117–128.

    Article  CAS  Google Scholar 

  31. Shahriari, Sh., Taghikhani, V., Vossoughi, M., Pazuki, G.R., Alemzadeh, I., and Safekordi, A.A., in 20th European Symposium on Computer Aided Process Engineering, Pierucci, S. and Buzzi Ferraris, G., Eds., Amsterdam: Elsevier, 2010.

  32. Mcculloch, W.S. and Pitts, W.H., A logical calculus of the ideas immanent in nerous activity, Bull. Math. Biophys., 1943, vol. 5, pp. 115–138.

    Article  Google Scholar 

  33. Basheer, I.A. and Hajmeer, M., Artificial neural networks: Fundamentals, computing, design, and application, J. Microb. Meth., 2000, vol. 43, pp. 3–31.

    CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Shahla Shahriari.

Additional information

The article is published in the original.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shahriari, S., Atashrouz, S. & Pazuki, G. Mathematical Model of the Phase Diagrams of Ionic Liquids-Based Aqueous Two-Phase Systems Using the Group Method of Data Handling and Artificial Neural Networks. Theor Found Chem Eng 52, 146–155 (2018). https://doi.org/10.1134/S0040579518010165

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1134/S0040579518010165

Keywords

Navigation