Improvement of the Fluidizability of Cohesive Powders through Mixing with Small Proportions of Group A Particles
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Bibliographic record
Abstract
Abstract The gas fluidization behaviour of fine cohesive powders, classified as Geldart group C, is known to be characterized by cracks and channels leading to severe non‐homogeneities in the bed. Geldart group A particles, on the other hand, are known to show more homogeneous and regular fluidization behaviour. This paper studies the effects of the addition of small proportions of group A on the fluidization behaviour of a group C powder. Differential pressure fluctuations data at a sampling frequency of 200 Hz were recorded for two cases. In the first case, the bed contained only group C powder while in the second case small amounts of group A particles were added to the existing group C powder. Visual/image observations coupled with time series analysis showed that the addition of small proportions of group A particles substantially improved the fluidization behaviour of the bed even at low superficial gas velocities, leading to a more uniform fluidization. Evaluation of mean and standard deviations has shown the advantage of mixing the two powders as it allowed larger pressure fluctuations and smaller standard deviations. Power spectra, on the other hand, showed that unlike group C, for which fluctuations were small in magnitude and broadband in structure, the mixture showed stronger periodic behaviour as result of the attenuation of the small and rapid fluctuations caused by the flow of gas in the cracks and channels. Advanced methods such as the principal component analysis of the embedded trajectories allowed a quantitative comparison between the fluidization behaviour of the two systems.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it