Machine learning predictive framework for CO2 thermodynamic properties in solution

CO2 is the major greenhouse gas (GHG) emission throughout the world. For scientific and industrial purposes, chemical absorption is regarded as an efficient method to capture CO2. However, the observation of thermodynamic properties of CO2 in solution environment requires too much time and resources. To address this issue and provide an ultra-fast solution, here, we use machine learning as a powerful data-mining strategy to predict the CO2 solubility, density and viscosity of potassium lysinate (PL) and its blended solutions with monoethanolamine (MEA), with totally 433 data groups extracted from previous experimental literatures. Specifically, we compared the predictive performances of back-propagation neural network (BPNN) and general regression neural network (GRNN). Results show that for BPNN with only one hidden layer and a small number of hidden neurons could provide good predictive performance for CO2 solubility and aqueous solution viscosity, while a GRNN could perform better for the prediction of aqueous solution density. Finally, it is concluded that such a machine learning based predictive framework could help to provide an ultra-fast prediction for CO2-related thermodynamic properties in solution environment.