Mass Transfer Performance and Hydrodynamics of a Bubble Column Reactor at Offshore Floating Conditions
Bibliographic record
Abstract
Hydrodynamics and mass transfer characteristics are analyzed in a bubble column subjected to simulated ship motions using a hexapod robot with six-degree-of-freedom motions. Wire-mesh sensors have been used for collecting local gas holdup and flow patterns under nonreactive conditions. Additionally, the electrical conductivity of the liquid phase during CO 2 uptake was extracted to determine hydroxide ion consumption rates as an indicator of mass transfer. The two-phase flow patterns in the bubble column operating under offshore conditions deviate significantly from the stationary ones due to the buoyancy-driven lateral migration of bubbles. The consumption rates of hydroxide ions during the chemical absorption of CO 2 revealed that the amplitude of oscillations imposed on the bubble column is the dominant factor for the mass transfer in moving columns. Contrarily, the effect of the oscillation frequency is negligible, which is attributed to bubble coalescence and bubble flow maldistribution in the bubble column subjected to rotational oscillations. Bubble-free zones are formed in the liquid phase because of the buoyancy effects in the column tilted from the vertical axis, while the frequency of the oscillations does not add any additional effects to the bubble kinematics. The latter is attributed to the low shear rates maintained over the range of frequencies simulating marine swells.
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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 | 0.000 | 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".