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.

SUBMITTER: Ali M 

PROVIDER: S-EPMC11208552 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature