Prediction of MOF Performance in Vacuum Swing Adsorption Systems for Postcombustion CO₂ Capture Based on Integrated...

Area of Science:

• Materials Science
• Chemical Engineering
• Environmental Science

Background:

• Postcombustion carbon dioxide capture and storage (CCS) is crucial for reducing greenhouse gas emissions.
• Current solvent-based CO2 capture methods are energy-intensive, hindering widespread adoption.
• Vacuum swing adsorption (VSA) presents a promising low-energy alternative for CCS.

Purpose of the Study:

• To screen a large number of metal-organic frameworks (MOFs) for their suitability in VSA-based low-energy CCS.
• To identify MOFs that meet specific CO2 purity and recovery targets.
• To assess the energy efficiency of MOFs in VSA processes compared to traditional methods.

Main Methods:

• Integrated atomistic simulations with a pilot-scale validated VSA simulator.
• Screened 1632 experimentally characterized MOFs.
• Developed machine learning models to predict MOF performance in VSA.

Main Results:

• 482 MOFs met the 95% CO2 purity and 90% CO2 recovery targets (95/90-PRTs).
• 365 MOFs demonstrated parasitic energies below solvent-based capture (target < 290 kWh_e/MT CO2), with a minimum of 217 kWh_e/MT CO2.
• Machine learning models achieved 91% accuracy in predicting a material's ability to meet the 95/90-PRT.

Conclusions:

• Full process simulations are essential for accurate parasitic energy and productivity estimates in VSA processes.
• MOFs show significant potential for developing efficient, low-energy CCS technologies.
• This integrated simulation approach effectively screens MOFs for practical VSA applications.