An Ionic Liquid Mixture Design for CO2 Capture through Bayesian Optimization and Molecular Dynamics Simulation

CO2 emissions into the atmosphere have become a global concern in recent years. The amount of CO2 generated in post-combustion processes has deserved the attention of the international scientific community. Thus, a variety of processes have emerged that try to address this problem from different points of view, such as the traditional absorption process that uses some type of amine as a solvent. Several other alternatives have been tried to solve the problems presented by this process. One of these alternatives consists of using ionic liquids as solvents in the CO2 absorption process. An important characteristic of ionic liquids is that their vapor pressure is very low, which makes them practically non-volatile. The task of designing ionic liquids for this purpose has gained interest in recent years. In previous work, our group designed ionic liquids using a computer-aided molecular design methodology, posing the problem as a MINLP problem. (Valencia-Márquez, et. al. 2017), (Silva-Beard, et. al. 2022). In this work, we use Molecular Dynamics Simulation (MDS), to calculate the capacity of absorption of a mixture of ionic liquids and propose an approach of experiments guided by Bayesian optimization to find an optimal mixture of ionic liquids that maximizes the amount of CO2 captured. The results suggest that following the procedure proposed it is possible to reduce the number of numerical experiments and therefore, the CPU time. © 2024 Elsevier B.V.

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