Integrating machine learning model and computer-aided molecular design toward rational ionic liquid selection for separating fluorinated refrigerants

[Display omitted] •A machine learning model of ANN-GC is developed to predict FR-in-IL solubility.•CAILD is conducted to rationally design ILs for separating azeotropic FR blends.•MINLP problem is formulated and solved to identify the optimal ILs.•Quantum chemistry calculations are performed to rationalize the CAILD results. To reduce emissions and facilitate the reclamation of hydrofluorocarbons (HFCs), which are potent greenhouse gases, ionic liquid (ILs)-based absorption separation of HFCs from fluorinated refrigerant (FR) blends is considered as an effective approach. In this work, computer-aided ionic liquid design (CAILD) is carried out to rationally design ILs for separating FR blends, with the near-azeotropic R-32/R-125 system as an illustrative case study. Firstly, the machine learning algorithm, namely artificial neural network (ANN), is employed to develop a group contribution (GC) model for accurate prediction of FR-in-IL solubility. Then, a mixed-integer nonlinear programming (MINLP) problem is formulated and solved by integrating the ANN-GC model with two available GC models for predicting the melting point and viscosity of ILs. Consequently, the optimal three ILs including [C1C10Im][dca], [C1C11Im][dca], and [C1C12Im][dca] are identified. Finally, quantum chemistry calculations are conducted to disclose the separation mechanism from molecular perspective, further validating the reliability of the CAILD results.