Abstract
Ionic liquids (ILs) are highly effective for capturing carbon dioxide (CO(2)). The prediction of CO(2) solubility in ILs is crucial for optimizing CO(2) capture processes. This study investigates the use of deep learning models for CO(2) solubility prediction in ILs with a comprehensive dataset of 10,116 CO(2) 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 CO(2) solubility in ILs. The ANN and LSTM models demonstrated robust test accuracy in predicting CO(2) solubility, with coefficient of determination (R(2)) 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 CO(2) solubility. The sensitivity analysis results provided insights into the relative importance of input attributes on output variables (CO(2) solubility) in ILs. The findings highlight the significant potential of deep learning models for streamlining the screening process of ILs for CO(2) capture applications.