Gas hold‐up in bubble columns: Operation with concentrated slurries versus high viscosity liquid
Bibliographic record
Abstract
Abstract The hydrodynamics of bubble columns with concentrated slurries of paraffin oil (density, ρL = 790 kg/m3; viscosity, μL = 0.0029 Pa·s; surface tension, σ = 0.028 N·m1) containing silica particles (mean particle diameter dp = 38 μm) has been studied in columns of three different diameters, 0.1, 0.19 and 0.38 m. With increasing particle concentration, the total gas hold‐up decreases significantly. This decrease is primarily caused by the destruction of the small bubble population. The hold‐up of large bubbles is practically independent of the slurry concentration. The measured gas hold‐up with the 36% v paraffin oil slurry shows remarkable agreement with the corresponding data obtained with Tellus oil (ρL = 862 kg/m3; μL = 0.075 Pa·s; σ = 0.028 N·m−1) as the liquid phase. Dynamic gas disengagement experiments confirm that the gas dispersion in Tellus oil also consists predominantly of large bubbles. The large bubble hold‐up is found to decrease significantly with increasing column diameter. A model is developed for estimation of the large bubble gas hold‐up by introduction of an wake‐acceleration factor into the Davies‐Taylor‐Collins relation (Collins, 1967), describing the influence of the column diameter on the rise velocity of an isolated spherical cap bubble.
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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) | 0.001 | 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 | 0.000 | 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".