Experimental study of gas pressure drop in rotating packed bed with rotational‐stationary packing
Bibliographic record
Abstract
Abstract Rotating packed bed (RPB) with rotational‐stationary packing is a novel instrument that could greatly enhance mass transfer compared with the conventional packed columns. The structure of rotational‐stationary packing makes the gas repeatedly enter the packing to increase the end effect. Meanwhile, owing to the resistance of the packing to the gas, the gas pressure drop of rotating packed bed with rotational‐stationary packing becomes the necessary factor that can evaluate the equipment. The gas pressure drop of the dry bed and wet bed were determined at various operating variables, including the rotational speed, the gas flow rate, and the liquid flow rate. In this work, the gas pressure drop was classified on the basis of the gas flow path and the corresponding theoretical formulas were provided. The pressure drop of the dry bed increased with increasing rotational speed and gas flow rate was observed, and the liquid flow rate had little influence on the pressure drop of the wet bed. Finally, the experimental values were fitted to the theoretical formula and the relationship between total pressure drop and rotational speed, gas flow rate in a RPB with rotational‐stationary packing was presented. The theoretical calculated values agreed well with the experimental data with a deviation within 6 %. These will provide the gas pressure drop theoretical calculation formula for a kind of RPB with rotational‐stationary packing.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".