Prediction of CO 2 absorption by physical solvents using a chemoinformatics-based machine learning model

Hao Li (1) , Dan Yan (2) , Zhien Zhang (3) , Eric Lichtfouse (4)

1 University of Texas at Austin [Austin]
2 THU - Tsinghua University [Beijing]
3 OSU - Ohio State University [Columbus]
4 CEREGE - Centre européen de recherche et d'enseignement des géosciences de l'environnement

The rising atmospheric CO2 level is partly responsible for global warming. Despite numerous warnings from scientists during the past years, nations are reacting too slowly, and thus, we will probably reach a situation needing rapid and effective techniques to reduce atmospheric CO2. Therefore, advanced engineering methods are particularly important to decrease the greenhouse effect, for instance, by capturing CO2 using solvents. Experimental testing of many solvents under different conditions is necessary but time-consuming. Alternatively, modeling CO2 capture by solvents using a nonlinear fitting machine learning is a rapid way to select potential solvents, prior to experimentation. Previous predictive machine learning models were mainly designed for blended solutions in water using the solution concentration as the main input of the model, which was not able to predict CO2 solubility in different types of physical solvents. To address this issue, here, we developed a new descriptor-based chemoinformatics model for predicting CO2 solubility in physical solvents in the form of mole fraction. The input factors include organic structural and bond information, thermodynamic properties, and experimental conditions. We studied the solvents from 823 data, including methanol (165 data), ethanol (138), n-propanol (98), n-butanol (64), n-pentanol (59), ethylene glycol (52), propylene glycol (54), acetone (51), 2-butanone (49), ethylene glycol monomethyl ether (46 data), and ethylene glycol monoethyl ether (47), using artificial neural networks as the machine learning model. Results show that our descriptor-based model predicts the CO2 absorption in physical solvents with generally higher accuracy and low root-mean-squared errors. Our findings show that using a set of simple but effective chemoinformatics-based descriptors, intrinsic relationships between the general properties of physical solvents and their CO2 solubility can be precisely fitted with machine learning.

Mots clés: machine learning; prediction; physical solvent; solubility; co2; greenhouse gas

Mots clés: chemical descriptors; absorption; chemoinformatics

Domaines: Chimie de coordination; Chimie organique; Chimie théorique et/ou physique; Milieux et Changements globaux; Ingénierie de l'environnement; Milieux fluides et réactifs

Hao Li, Dan Yan, Zhien Zhang, Eric Lichtfouse. Prediction of CO 2 absorption by physical solvents using a chemoinformatics-based machine learning model. Environmental Chemistry Letters, 2019, 17 (3), pp.1397 - 1404. ⟨10.1007/s10311-019-00874-0⟩. ⟨hal-02267200⟩