A new empirical equation for minimum spouting/spout‐fluidization velocity in draft tube spout‐fluid beds at elevated temperature
Bibliographic record
Abstract
Draft tube spout‐fluid beds have been widely used in drying, coating, and granulation processes. In these processes, the spout‐fluid reactor is always operated at gas temperatures that exceed ambient conditions. Therefore, one objective of draft tube spout‐fluid bed research is to determine the critical factors that affect the minimum spouting velocity (Ums) and the minimum spout‐fluidization velocity (Umsf) at high temperatures. In this study, four types of particles were used to study the resulting pressure drops. Ums and Umsf were used in a conical draft tube spout‐fluid bed at temperatures of 293–500 K. In addition, a series of operating conditions and geometric configurations were investigated to systematically study the factors that affect Ums and Umsf. Overall, Ums and Umsf increased as the static bed height, entrainment zone height, draft tube diameter, and particle diameter increased. In contrast, Ums decreased as the superficial fluidizing gas velocity and spout nozzle diameter increased. In addition, Umsf increased as the superficial fluidizing gas velocity, spout nozzle diameter, and temperature increased. Furthermore, as the fluidizing gas flow increased, the minimum spouting velocity shifted from increasing to decreasing as the temperature gradually increased. Two general correlations between the Ums and Umsf values and the above factors were proposed. In addition, the influences of operating conditions and geometrical parameters on the minimum spouting velocity should be considered in the design and use of draft tube spout‐fluid beds.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".