Modelling study on phase equilibria behavior of ionic liquid-based aqueous biphasic systems

Yuqiu Chen, Xiaodong Liang, John M. Woodley, Georgios M. Kontogeorgis*

*Corresponding author for this work

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The ability to predict the phase equilibria behavior is of crucial relevance in the early design stage of biphasic liquid-liquid systems. Ionic liquid-based aqueous biphasic systems (IL-ABS) have demonstrated superior performance in many applications such as the recovery of bio-products and the recycling of hydrophilic ILs from aqueous solutions. In order to better utilize these novel biphasic liquid-liquid systems, modelling studies on phase equilibria behavior are carried out in this work. First, the IL database developed in our previous work is extended to these unconventional biphasic systems. In total, 17,449 experimental binodal data points covering 171 IL-ABS at different temperatures (278.15K-343.15K) are collected. Then, all involved IL-ABS are correlated using a popular three-parameter mathematical description and the optimal parameters of each IL-ABS are obtained. Afterwards, we try to build a linear group contribution (GC) model to predict the phase equilibria behavior of IL-ABS, but it fails due to the high complexity of these biphasic systems. For this reason, we finally turn to applying a well-known machine learning algorithm, i.e., artificial neural network (ANN), to build a nonlinear GC model for such a purpose. This model gives a mean absolute error (MAE) of 0.0175 and squared correlation coefficient (R2) of 0.9316 for the 13,789 training data points, and for the 3,660 test data points they are 0.0177 and 0.9195, respectively. The results indicate that the proposed nonlinear ANN-GC model, to some extent, is capable to predict the phase equilibria behavior of IL-ABS. Besides the efforts of building GC models, we also discuss some main issues that govern the phase equilibria behavior of IL- ABS, which could be a guidance in the design of IL-ABS.
Original languageEnglish
Article number116904
JournalChemical Engineering Science
Number of pages13
Publication statusPublished - 2022


  • Ionic liquids
  • ABS
  • Group contribution
  • Matching learning
  • Artificial neural network


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