Bibliographic record
Abstract
When liquid suspensions containing fine solids are treated in packed-bed bubble reactors, bed plugging develops and increases the resistance to two-phase flow. Accumulation of fines in the catalyst bed increases the reactor pressure gradient until eventually the unit must be shut down and the physical ly d eactivated catalyst replaced. Currently, physical models linking the two-phase flow to the space−time evolution of fines buildup are virtually nonexistent. An attempt has been made with this contribution to fill in this gap by developing a unidirectional dynamic multiphase flow model based on the volume-average equations of mass and momentum balance for the gas and suspension and the species balance for the fines. Coherent with experimental observations, the model hypothesizes that plugging develops through deep-bed filtration mechanisms. The model incorporates physical effects of porosity and effective specific surface area changes due to the fines capture by the collecting catalyst particles, inertial effects in the gas and suspension, and coupling effects between the filtration parameters and the interfacial momentum exchange force terms. For the rationalization of deep-bed filtration phenomena in packed-bed bubble reactors, parametric studies of the effects of liquid velocity and viscosity, gas density and velocity, fines concentration in the influent suspension, and fines diameter on the plugging dynamics are discussed.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".