Prediction
of MOF Performance in Vacuum Swing Adsorption
Systems for Postcombustion CO2 Capture Based on Integrated Molecular Simulations, Process
Optimizations, and Machine Learning Models
posted on 2020-03-12, 16:35authored byThomas
D. Burns, Kasturi Nagesh Pai, Sai Gokul Subraveti, Sean P. Collins, Mykhaylo Krykunov, Arvind Rajendran, Tom K. Woo
Postcombustion
CO2 capture and storage (CCS) is a key
technological approach to reducing greenhouse gas emission while we
transition to carbon-free energy production. However, current solvent-based
CO2 capture processes are considered too energetically
expensive for widespread deployment. Vacuum swing adsorption (VSA)
is a low-energy CCS that has the potential for industrial implementation
if the right sorbents can be found. Metal–organic framework
(MOF) materials are often promoted as sorbents for low-energy CCS
by highlighting select adsorption properties without a clear understanding
of how they perform in real-world VSA processes. In this work, atomistic
simulations have been fully integrated with a detailed VSA simulator,
validated at the pilot scale, to screen 1632 experimentally characterized
MOFs. A total of 482 materials were found to meet the 95% CO2 purity and 90% CO2 recovery targets (95/90-PRTs)365
of which have parasitic energies below that of solvent-based capture
(∼290 kWhe/MT CO2) with a low value of
217 kWhe/MT CO2. Machine learning models were
developed
using common adsorption metrics to predict a material’s ability
to meet the 95/90-PRT with an overall prediction accuracy of 91%.
It was found that accurate parasitic energy and productivity estimates
of a VSA process require full process simulations.