Accelerating computational fluid dynamics simulation of post-combustion carbon capture modeling with MeshGraphNets.

Packed columns are commonly used in post-combustion processes to capture CO emissions by providing enhanced contact area between a CO-laden gas and CO-absorbing solvent. To study and optimize solvent-based post-combustion carbon capture systems (CCSs), computational fluid dynamics (CFD) can be used to model the liquid-gas countercurrent flow hydrodynamics in these columns and derive key determinants of CO-capture efficiency. However, the large design space of these systems hinders the application of CFD for design optimization due to its high computational cost.

Comparative study of machine learning techniques for post-combustion carbon capture systems.

Computational analysis of countercurrent flows in packed absorption columns, often used in solvent-based post-combustion carbon capture systems (CCSs), is challenging. Typically, computational fluid dynamics (CFD) approaches are used to simulate the interactions between a solvent, gas, and column's packing geometry while accounting for the thermodynamics, kinetics, heat, and mass transfer effects of the absorption process. These simulations can then be used explain a column's hydrodynamic characteristics and evaluate its CO-capture efficiency.