Prediction carbon dioxide solubility in ionic liquids based on deep learning

Abstract

Ionic liquids have a great potential in capture and separation of carbon dioxide (CO2), and the solubility of CO2 in ionic liquids is one of key data for engineering applications. In this paper, the critical properties of ionic liquids are combined with deep learning models (CP-DNN, CP-CNN, CP-RNN) to establish theoretical prediction models of CO2 solubility in ionic liquids. The predictive performance of these framworks is able to meet or exceed the predicted effects of the method based on thermodynamic models (PR,SRK) and machine learning method (XGBoost). For CP-RNN, the coefficient of determination (R2) between experimental and predicted values is 0.988, CP-CNN is 0.999, and CP-DNN is 0.984. This research can avoid complex computational characterisation, it is to provide a theoretical method to further enrich and improve the data information analysis of the solubility of CO2 in ionic liquids.