Abstract

Practically achievable limits for pressure-vacuum swing adsorption (PVSA)-based postcombustion carbon capture are evaluated. The adsorption isotherms of CO₂ and N₂ are described by competitive Langmuir isotherms. Two low-energy process cycles are considered and a machine learning surrogate model is trained with inputs from an experimentally validated, detailed PVSA model. Several case studies are considered to evaluate two critical performance indicators, namely, minimum energy and maximum productivity. For each case study, the genetic algorithm optimizer that is coupled to the machine learning surrogate model searches tens of thousands of combinations of isotherms and process operating conditions. The framework pairs the optimum materials properties with the optimum operating conditions, hence providing the limits of achievable performance. The results indicate that pressures < 0.2 bar may be required to achieve process constraints for feeds with low CO₂ compositions (<0.15 mole fraction), indicating that PVSA may not be favorable. At higher CO₂ feed compositions, PVSA can be attractive and can be operated at practically achievable vacuum levels. Further, the gap between the energy consumption of available adsorbents and the achievable limits with the best hypothetical best adsorbent varies between 20 and 2.5% as the CO₂ feed composition changes between 0.05 and 0.4. This indicates a limited potential for the development of new adsorbents of PVSA-based CO₂ capture. Future work for PVSA should focus on gas streams with high CO₂ compositions.

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