Overcoming the gamma‐ray computed tomography data processing pitfalls for bubble column equipped with vertical internal tubes
Bibliographic record
Abstract
Abstract This study identifies and addresses some major pitfalls that are involved in the visualization and quantification of the gas‐liquid distributions and their profiles in a bubble column with internals using the gamma‐ray computed tomography (CT) technique. Some of these pitfalls encountered in the scanning of bubble columns with internals are using an improper reference scan, and applying the same experimental scanning procedure and mathematical relationships for estimating the gas holdup in the column without internals to the column with internals. The experimental results revealed that the selection of the inappropriate reference scan for CT experiments would significantly affect the reconstructed linear attenuation coefficient values and consequently the gas holdup results. Additionally, the reconstructed linear attenuation values showed good agreement with theoretical values when considering air as reference scans. However, disagreement is observed when using the empty column with internals as a reference scan. Moreover, it was found that using the proper reference scan eliminated the errors not only for the reconstructed linear attenuation coefficients but also for the gas holdup values near the wall region. Furthermore, the CT technique was capable of capturing the small thickness (5 mm) of the wall for phantom and bubble columns as well as the internals when the air was used as the reference scan. Finally, a new methodology has been implemented to exclude the internals from the cross‐sectional images, and the azimuthally averaged gas holdup profiles to provide accurate and reliable results for comparison and validation purposes for the bubble column with internals.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".