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

• Published in: Scientific reports 2024-06, Vol.14 (1), p.14730-19, Article 14730
• Main Authors: Ali, Mazhar, Sarwar, Tooba, Mubarak, Nabisab Mujawar, Karri, Rama Rao, Ghalib, Lubna, Bibi, Aisha, Mazari, Shaukat Ali
• Format: Article
• Language: English
• Subjects: 639/166; 639/301; 639/4077; 639/705; 704/172; ANN; Carbon dioxide; Carbon sequestration; CO2 capture; Computer applications; Deep learning; Global sensitivity analysis; Humanities and Social Sciences; Ionic liquids; Long short-term memory; LSTM; multidisciplinary; Neural networks; Predictions; Science; Science (multidisciplinary); Sensitivity analysis; Solubility
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-65499-y

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.