Pressure Swing Adsorption

Pressure swing adsorption (PSA) is a cyclical process used to separate gas mixtures by exploiting differences in adsorption affinities under varying pressure conditions, primarily aiming for efficient and cost-effective gas purification or separation. Current research emphasizes developing accurate predictive models, often employing machine learning techniques like deep neural networks, graph neural networks, and random forests, to accelerate the design and optimization of PSA units, particularly for CO2 capture. These models are increasingly used in conjunction with optimization algorithms like genetic algorithms and particle swarm optimization to identify optimal operating parameters and improve overall process efficiency, leading to significant computational speedups compared to traditional methods. This work has direct implications for industrial applications requiring gas purification, such as carbon capture and hydrogen production.