How process engineering can enable the screening and discovery of adsorbents for CO2 capture

In the last 20 years, significant efforts have been invested in developing adsorption processes for CO2 capture. The explosion in adsorbent synthesis and molecular simulations has resulted in the generation of hundreds of thousands of (hypothetical & real) adsorbents, e.g., Zeolites, Metal-Organic Frameworks (MOFs). This excitement seems to have led to an implicit assumption that the key bottleneck in developing large-scale adsorption processes is discovering the right adsorbent. Recent studies have demonstrated that the performance of an adsorbent is intimately linked to the process in which it is deployed, and any meaningful screening should consider the complexity of the process. Hence, screening these large databases to identify suitable candidates for scale-up is a challenging problem. The talk will have two parts.


Part 1. Process Engineering for screening & optimization: Simulation of cyclic adsorption processes is computationally intensive: they constitute the simultaneous propagation of heat and mass transfer fronts; there are no straightforward design tools. The cyclic nature of adsorption processes requires the transient calculation to evaluate the cyclic-steady state performance. This talk will focus on recent modelling and machine learning developments that have allowed us to screen large adsorbents databases and develop achievable separation/cost targets for adsorption with other technologies. These techniques have allowed us to understand the interplay between processes and materials. The talk will focus on Pressure-Vacuum Swing Adsorption for post-combustion CO2 capture. It will be based on our recent work related to the screening of adsorbents [1], machine-learning models for process optimization [2,3,4] scale-up and costing [4,5].


Part 2. CO2 capture on the first commercial MOF: The second part of the talk will deal with wet-flue-gas CO2 capture on CALF-20, the first MOF that has been successfully deployed for large-scale CO2 capture [6]. The details of characterizing CO2, H2O competition and scaling up the process for wet-gas capture will be discussed.


  1. Burns, T. D., et al. "Prediction of MOF Performance in Vacuum Swing Adsorption Systems for Post-combustion CO2 Capture Based on Integrated Molecular Simulations, Process Optimizations, and Machine Learning Models." Env. Sci. Technol. 54.7 (2020): 4536-4544.
  2. Pai, K.N., et al. "Generalized, adsorbent-agnostic, artificial neural network framework for rapid simulation, optimization and adsorbent-screening of adsorption processes." Ind. Engg. Chem. Res 59.38 (2020), 16730–16740.
  3. Pai, K. N., et al. "Experimentally validated machine learning frameworks for accelerated prediction of cyclic steady-state and optimization of pressure swing adsorption processes." Sep Purif. Technol. 241 (2020): 116651.
  4. Pai, K. N., et al. "Practically achievable performance limits for pressure-vacuum swing adsorption based post-combustion CO2 capture." ACS Sus. Chem. Engg. 10 (2021): 3838-3849.
  5. Subraveti, S. G., et al. "Techno-economic Assessment of Optimised Vacuum Swing Adsorption for Post-Combustion CO2 Capture from Steam-Methane Reformer Flue Gas." Sep. Purif. Technol (2020), 256 (2021): 117832.
  6. Lin, J-B, et al. "A scalable metal-organic framework as a durable physisorbent for carbon dioxide capture." Science 374, no. 6574 (2021): 1464-1469.


Arvind Rajendran

Professor at University of Alberta