Analysis of a Batch Adsorber Analogue for Rapid Screening of Adsorbents for Postcombustion CO<sub>2</sub> Capture
Bibliographic record
Abstract
A simplified proxy model based on a well-mixed batch adsorber for vacuum swing adsorption (VSA) based CO2 capture from dry postcombustion flue gas is presented. A graphical representation of the model output allows for the rationalization of broad trends of process performance. The results of the simplified model are compared with a detailed VSA model that takes into account mass and heat transfer, column pressure drop, and column switching, in order to understand its potential and limitations. A new classification metric to identify whether an adsorbent can produce CO2 purity and recovery values that meet current U.S. Department of Energy (US-DOE) targets for postcombustion CO2 capture and to calculate the corresponding parasitic energy is developed. The model, which can be evaluated within a few seconds, showed a classification Matthew correlation coefficient of 0.76 compared to 0.39, the best offered by any traditional metric. The model was also able to predict the energy consumption within 15% accuracy of the detailed model for 83% of the adsorbents studied. The developed metric and the correlation are then used to screen the NIST/ARPA-E database to identify promising adsorbents for CO2 capture applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 teacher head, 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".