Liquid load point determination in a reactive distillation packing by X‐ray tomography
Bibliographic record
Abstract
Abstract In this paper, we report on the use of X‐ray tomography to determine the liquid load point in 0.1 m diameter modular catalytic distillation packings Katapak‐SP11 and Katapak‐SP12. The liquid load point corresponds to the overall packed bed liquid load above which there is an increment in the liquid flowing outside the catalytic baskets and the catalytic baskets themselves are saturated with liquid. From tomographic images, we show that several factors affect the wetting and filling of catalytic baskets. The complex hybrid structure of catalytic packings influences the liquid distribution inside the elements. The liquid preferentially fills the external catalytic baskets because they receive the liquid not only from the packing element situated above but also from the wall wipers. Moreover, liquid hold‐up inside a catalytic basket section depends significantly on the vertical position in the packing element and on the position of the packing in the column packed bed. The counter–current gas flow speeds up the process of liquid filling of the baskets, also for low liquid loads. The non‐uniform distribution of liquid in catalytic basket which is observed experimentally makes the identification of a unique liquid load point not straightforward.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".