Post-Combustion CO2 capture by vacuum swing adsorption using a hydrophobic metal-organic framework (MOF), CALF-20: Multi-objective optimization and experimental validation
Bibliographic record
Abstract
In this paper, the ability of CALF-20, a hydrophobic metal-organic framework (MOF), to capture CO2 from dry flue gas (15/85 mol% of CO2/N2) using two different adsorption configurations, basic four-step vacuum swing adsorption (VSA), and four-step with light-product pressurization (LPP) was evaluated. Pareto curves, for the simultaneous maximization of CO2 purity and recovery, were generated. Five points from each Pareto curve representing five different process conditions were chosen to experimentally validate the model prediction. The experiments resulted in a CO2 purity and recovery of 95%, and 70%, respectively for the basic four-step cycle, while the four-step LPP resulted in 95% purity and 90% recovery, meeting US Department of Energy targets. The temperature history, the pressure transient, and the flow rate of different steps were in good agreement with the model predictions. The results of this study confirmed that CALF-20 is capable of separating CO2 from dry flue gas, demonstrating the potential of the MOF for practical separations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".