Drop behaviour characteristics in different operating regimes in an L‐shaped pulsed sieve‐plate column
Bibliographic record
Abstract
Abstract In this study, the influence of different operating regimes on drop behaviour in an L‐shaped pulsed sieve‐plate column has been investigated and the effects of pulsation intensity and phase superficial velocities on drop diameters are evaluated. The toluene‐water system with 0.03 L/L (3 vol%) acetone as a mass transfer agent is used. Experimental observations reveal that with increasing pulsation intensity, drop sizes slightly decrease and by reaching to the end of mixer‐settler regime, they significantly decrease followed by a mild decrease in the dispersion regime. Moreover, it is observed that mean drop size firstly varies inversely as both phase superficial velocities change, while it slightly increases with further increase in superficial velocities. Finally, some of the most recommended correlations are compared to the experimental data. It is revealed that these correlations show the highest deviation in the lower end of the mixer‐settler regime (> 40 %), while in the dispersion regime it becomes < 15 %, indicating that they are only applicable in the region of practical regimes. Accordingly, two of these correlations are modified in order to cover different operating regimes by means of two correlations proposed to predict the transition regime in each section of the column. The AARE values of the modified correlations are found to be < 14.7 %.
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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) | 0.000 | 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".