Machine learning models coupled with ionic fragment σ-profiles to predict ammonia solubility in ionic liquids

Abstract

Emitting NH3 into the atmosphere leads to significant air pollution, while NH3 itself serves as an essential component for fertilizers and refrigerants in industry. Thus, recovering and reusing NH3 is highly valuable. Ionic liquids (ILs) have shown great potential for NH3 capture, where the accurate prediction of solubility is a critical point for selecting ILs and designing a separation process. This work combined the Ionic Fragment Contribution (IFC) strategy with machine learning (ML) to develop four models (IFC-ML) to predict NH3 solubility in ILs. A dataset containing 785 solubility data points, covering 10 cations and 10 anions, was collected. From this dataset, the S1–S6 descriptors based on the IFC method were used as inputs for the ML models, together with temperature (T) and pressure (P). Among the models, the IFC-GBR model was recommended for predicting NH3 solubility in ILs due to its higher coefficient of determination (R2) of 0.9945 and lower mean squared error (MSE) of 0.0003 than the others. Additionally, in comparison with previous conductor-like screening model for real solvents (COSMO-RS) and extreme learning machine (ELM) methods, the IFC-GBR method showed a more accurate prediction of the NH3 solubility in ILs over a wider range of temperatures and pressures, providing additional chemical insights into IL-NH3 system that cations played a more important role for NH3 solubility. These results highlighted the developed IFC-GBR (gradient boosting regressor) model offered valuable insights for helping guide the process design of absorbing NH3 through IL-based technology.