Simulation of Binary CO<sub>2</sub>/CH<sub>4</sub> Mixture Breakthrough Profiles in MIL‐53 (Al)
Bibliographic record
Abstract
MIL‐53 (Al) aluminum terephthalate, a commercial metal‐organic framework, has been studied as a potential candidate for pressure swing adsorption separation of CO2/CH4 binary mixtures. Pure gas isotherms of CH4 and CO2 measured over 0–6 MPa and at room temperature are fitted with the Dubinin‐Astakhov (D‐A) model. The D‐A model parameters are used in the Doong‐Yang Multicomponent adsorption model to predict the binary mixture isotherms. A one‐dimensional multicomponent adsorption breakthrough model is then used to perform a parametric study of the effect of adsorbent particle diameter, inlet pressures, feed flow rates, and feed compositions on the breakthrough performance. Commercial MIL‐53 with a particle diameter of 20 μm renders high tortuous flow; therefore it is less effective for separation. More effective separation can be achieved if MIL‐53 monoliths of diameters above 200 μm are used. Faster separation is possible by increasing the feed pressure or if the starting compositions are richer in CO2. More CH4 is produced per cycle at higher feed pressures, but the shortened time at higher pressures can result in the reduction of the CH4 purity.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".