Modelling, simulation, and membrane wetting estimation in gas‐liquid contacting processes
Bibliographic record
Abstract
Abstract A set of experiments for CO2 separation from CO2‐N2 mixture by absorption into water by means of a gas‐liquid membrane contacting process is modelled using the mass continuity equation by combining process conditions, membrane and fluids properties, and module geometric characteristics. The general case of non‐constant concentration of the diffusing component in the shell side (Case B) is used, which entails an integro‐differential boundary condition at the lumen‐wall. The computational method is compared with existing literature data in terms of the logarithmic averaged overall and lumen Sherwood numbers revalidating the superiority of the counter‐current to the co‐current mode of operation, while offering a theoretical prediction of the limited behaviour of the latter as a function of the equilibrium coefficient. The elaborate model is then applied in order to assess the extent of membrane wetting due to liquid penetration into the pores in terms of the resistance‐in‐series model by comparing with the experimental results derived in a commercial cross‐flow membrane module under the counter‐current mode of operation. It is revealed that the assumption of shell‐side constant concentration (Case A) underestimates the wetting leading to a false estimation of the extent of liquid penetration into membrane pores. For Case B, a wetting‐pattern appears showing a correlation of an increasing shell‐side liquid flow rate with a decreasing wetting parameter and, thus, relatively less contribution of the liquid‐filled membrane resistance to the overall membrane resistance with increasing liquid loading.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".