CO<sub>2</sub> Removal in Packed-Bed Columns and Hollow-Fiber Membrane Reactors. Investigation of Reactor Performance
Bibliographic record
Abstract
The CO 2 absorption performance of hollow-fiber membranes and conventional packed-bed column reactors under similar operating conditions was evaluated. Two-scale, nonisothermal, steady-state models were used to simulate the reactors behavior. The membrane reactor model accounts for CO 2 diffusion in gas-filled membrane pores, CO 2 and amine diffusion accompanied by chemical reaction in liquid-filled membrane pores, and CO 2 and amine diffusion accompanied by chemical reaction in the liquid film zone surrounding the inside membrane wall. The packed-bed column reactor model interconnects a two-fluid 2D hydrodynamic platform with 2D mass and energy transport equations in the gas and liquid phases and nonlinear differential equations governing diffusion and reaction in the liquid film. In the absence of membrane wetting, the hollow-fiber membrane reactor outperforms the packed-bed column reactor with similar volume and specific surface area. This is not the case under membrane wetting conditions, when at low specific surface areas the packed-bed column reactor can outperform the membrane reactor. However, as the hollow-fiber membrane reactors can be stacked with very high specific surface areas in the same reactor volume, the performance of this type of reactor remains better even under partial wetted membrane conditions.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 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".