A correlation for average droplet diameter in rotating packed beds
Bibliographic record
Abstract
Abstract A rotating packed bed (RPB) is a novel process intensification technology that increases mass transfer rate using a strong centrifugal acceleration. Inside an RPB, the inlet jet of the liquid absorbent is broken into tiny droplets. It is reported that RPBs provide 11 times larger mass transfer area compared to equal‐sized packed beds and two to three orders of magnitude higher mass transfer compared to equal‐sized stirred tanks. The novelty of the technology and lack of research, however, undermines the adoption of RPBs where a low mass transfer rate is the main bottleneck. In this work, we study the effect of bed size on the average droplet diameter in RPBs and investigate scale‐up criteria to preserve the average droplet diameter at a large scale. Furthermore, we develop a correlation for the average droplet diameter using the experimental data and simulation results obtained using a volume of fluid (VOF) method. This correlation is obtained from dimensional analysis. The effects of rotating speed, absorbent flow rate, wire mesh packing diameter, bed diameter, absorbent viscosity, density, and surface tension are included in the dataset. Among these parameters, rotating speed, centrifugal force, and surface tension have the highest correlation coefficients ( R 2 = 0.88, 0.83, and 0.42, respectively) with the average droplet diameter.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".