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

ABSTRACT: 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 CO₂-capture efficiency. However, these approaches are computationally expensive, making it difficult to evaluate numerous designs and operating conditions to improve efficiency at industrial scales. In this work, we comprehensively explore the application of sta

SUBMITTER: Hu Y 

PROVIDER: S-EPMC11603113 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature