Phase Distribution in a High Pressure Slurry Bubble Column via a Single Source Computed Tomography
Bibliographic record
Abstract
Due to their numerous advantages, Slurry Bubble Column Reactors (SBCR) are gaining wide importance in the petroleum, petrochemical, chemical, and biochemical industries. Most previous literature studies on the fluid dynamics and the phase holdup distribution in SBCR are limited to low superficial gas velocity, low solids loading, and the atmospheric pressure. Moreover, the presence of a solids phase poses difficulties in probe measurement techniques such as pitot tube and optical probe and see through measurement techniques such as particle image velocimetry (PIV), Laser Doppler Velocimetry (LDV). Single source γ-ray Computed Tomography (CT) has been used to measure cross-sectional phase holdup distribution in two-phase systems. In the present study, a new methodology has been developed that combines a single source CT with overall gas holdup measurement, along with valid assumptions, to measure the cross-sectional holdup distribution of the three phases in a slurry bubble column. The implementation of developed methodology has been demonstrated using air-water-glass beads (150 µm) at selected conditions of superficial gas velocity and operating pressure.
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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 | 0.000 |
| 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 | 0.000 | 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".