Influence of particle size distribution on fluidized bed hydrodynamics
Bibliographic record
Abstract
Past literature has shown that the production efficiency of a fluidized bed can be affected by changing the particle size distribution. The hydrodynamics of fine particle fluidization were studied with FCC and glass bead powders which have different surface-volume mean particle diameter (40-110 μM) and particle size distributions (narrow cut, wide cut and bimodal) under ambient conditions. Increasing the mean particle size increases the minimum fluidization velocity, minimum bubbling velocity and dense phase velocity (U[sub d]) while decreasing the voidages at minimum fluidization and minimum bubbling and the dense phase voidage (∈[sub d]) as well as the fractional bubble free bed expansion. Increasing the particle size spread increases U[sub d] and decreases ∈[sub d] for FCC, but no clear conclusion can be made for glass bead powders. Increasing the static bed height decreases U[sub d] and ∈[sub d] of FCC powders though it has no effect on minimum fluidization and bubbling properties. The magnitude of pressure fluctuations increases with increasing superficial gas velocity and as the size spread of the FCC powder becomes more narrow. However, the frequency of fluctuations is independent of each of these factors. Therefore, the quality and production efficiency of the fluidization process should improve with the use of a wide and continuous size distribution powder.
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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 | 0.002 |
| 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) | 0.001 | 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".