Bibliographic record
Abstract
This chapter is a systematic summary of the characteristics and applications of spouted beds, called powder–particle spouted beds (PPSBs), consisting of fine powders contacted with coarse particles. The main aspect of conventional spouting that is relevant to PPSBs is the final elutriation of the dried or partially dried fines, often produced by attrition, from a coarse particle bed. Figure 10.1 shows the operating conditions of several kinds of fluidization for particles of density 2500 kg/m, according to Geldart's classification, indicating also the fine particle size and gas velocity ranges in four reported studies of PPSBs. The two solid lines show the superficial gas velocity at minimum fluidization ( U mf ) and the terminal velocity of a single particle ( U t ). In a PPSB, as one of the operating conditions, the gas velocity is determined by the diameter and density of the coarse particles and is usually greater than U t of the fine powders, so the fines are elutriated from the coarse particle bed. Description of powder–particle spouted beds Conceptual illustrations of a PPSB are shown in Figure 10.2. Group D particles in Geldart's classification usually act as the coarse particles and Group A, B, or C particles as the fine powders. As shown in Figure 10.2a, for a Group C–D particle system, the coarse particles in the bed are first spouted and raw powders (fine particles) in a dry or partially dried state are then continuously fed to the bottom of the spouted bed with spouting gas.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.010 |
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".