Prediction of biphasic separation in CO2 absorption using a molecular surface information-based machine learning model

• Published in: Environmental science--processes & impacts 2022-12, Vol.24 (12), p.2409-2418
• Main Authors: Kataoka, Taishi, Yingquan Hao, Hung, Ying Chieh, Orita, Yasuhiko, Shimoyama, Yusuke
• Format: Article
• Language: English
• Subjects: Absorbents; Absorption; Alkanolamines; Carbon dioxide; Carbon sequestration; Climate change; Energy conservation; Global warming; Glycol ethers; Learning algorithms; Machine learning; Organic solvents; Predictions; Regression analysis; Solvents; Support vector machines; Surface charge; Surface chemistry
• ISSN: 2050-7887; 2050-7895
• DOI: 10.1039/d2em00253a

Carbon dioxide capture technologies have become a focus to overcome global warming. Biphasic absorbents are one of the promising approaches for energy-saving CO2 capture processes. These biphasic absorbents are mainly composed of a mixed solvent composed of alkanolamine and organic solvents like glycol ether or alcohol. However, screening experiments of the mixed-solvent absorbents are required to search for biphasic absorbents due to their complicated phase behavior. In this work, we developed a prediction method for the phase states of the mixed-solvent absorbents using a quantum calculation and machine learning models, including random forest, logistic regression, and support vector machine models. There are 61 mixed-solvent absorbents containing alkanolamine/glycol ether or alcohol in the dataset. The machine learning models successfully predicted the phase states of the mixed-solvent absorbents before and after CO2 absorption with accuracies of more than 90%. Furthermore, we analyzed the contributions of explanatory variables for prediction using the learned model. As a result, we found that molecular surface charge of the amine species is more important than those of the other organic solvents to determine the phase behaviors during CO2 absorption.