Prediction of CO2 solubility in Ionic liquids for CO2 capture using deep learning models.

Abstract

Ionic liquids (ILs) are highly effective for capturing carbon dioxide (CO₂). The prediction of CO₂ solubility in ILs is crucial for optimizing CO₂ capture processes. This study investigates the use of deep learning models for CO₂ solubility prediction in ILs with a comprehensive dataset of 10,116 CO₂ solubility data in 164 kinds of ILs under different temperature and pressure conditions. Deep neural network models, including Artificial Neural Network (ANN) and Long Short-Term Memory (LSTM), were developed to predict CO₂ solubility in ILs. The ANN and LSTM models demonstrated robust test accuracy in predicting CO₂ solubility, with coefficient of determination (R²) values of 0.986 and 0.985, respectively. Both model's computational efficiency and cost were investigated, and the ANN model achieved reliable accuracy with a significantly lower computational time (approximately 30 times faster) than the LSTM model. A global sensitivity analysis (GSA) was performed to assess the influence of process parameters and associated functional groups on CO₂ solubility. The sensitivity analysis results provided insights into the relative importance of input attributes on output variables (CO₂ solubility) in ILs. The findings highlight the significant potential of deep learning models for streamlining the screening process of ILs for CO₂ capture applications.