(656a) A New Computational-Experimental Screening Methodology Identifies More Effective Solvents for CO2 Capture

Carbon capture and storage technologies are projected to increasingly contribute to cleaner energy transitions by significantly reducing CO₂ emissions from fossil fuel-driven power and industrial plants. The state-of-the-art technology for CO₂ capture is the chemical absorption with aqueous alkanolamines. The important equipment cost and parasitic energy due to the thermal regeneration of the solvent prevents its widespread application. Unfortunately, solvents showing a lower regeneration energy tend to absorb CO₂ more slowly, resulting in much larger equipment. Therefore, new industrial solvents are often a mixture of amines with relatively low regeneration energy (e.g. tertiary amines) and an activator, piperazine, which increases the overall CO₂ absorption rate. The search for new tertiary amines with better kinetics is thus of paramount importance. Improving the efficiency of experimental screening using computational tools is challenging due to the complex nature of chemical absorption. We have developed a novel computational approach that combines kinetic experiments, molecular simulations and machine learning for the in silico screening of hundreds of prospective candidates and identify a class of tertiary amines that absorbs CO₂ faster than a typical commercial solvent when mixed with piperazine, which was confirmed experimentally.

Figure 1 illustrates the workflow of the methodology. First, we have developed a computational approach based on molecular modeling to predict the CO₂ absorption rates and energies in aqueous tertiary amines. The predictions are validated using experimental data. The key to the new model's excellent accuracy is to include the solvation effects in the computation of the free energy barrier for the reaction of CO₂ absorption [1].

In a second step we have generated a diverse library of 100 tertiary amines which represent a large portion of chemotypes relevant for industrial amine-based chemical CO₂ absorption. Using the molecular simulations-based model, we have calculated the CO₂ absorption rates and energies for those 100 aqueous amine solvents. As a check, some points have been verified experimentally [2].

In a third step we have used the dataset of the CO₂ absorption rates and energies for the 100 aqueous amines to build quantitative structure-property relationship models. These models allowed us to perform a large-scale virtual screening of commercially available amines and to identify the amines with optimal CO₂ absorption rate and energy. For several amines the predictions have been verified experimentally. Subsequently, for the best performing amine, piperazine has been added. The approach and the results will be presented.

[1] Rozanska, X., Wimmer, E. & de Meyer, F. Quantitative Kinetic Model of CO₂ Absorption in Aqueous Tertiary Amine Solvents. J. Chem. Inf. Model. 61, 1814–1824 (2021)

[2] Orlov, A., Valtz, A., Coquelet, C., Rozanska, X., Wimmer, E., de Meyer, F. Computational screening methodology identifies effective solvents for CO₂ capture. Communications Chemistry 5, 37 (2022)