Mixing characteristics of irregular binaries in a promoted gas–solid fluidized bed: A mathematical model
Bibliographic record
Abstract
Abstract The vertical as well as horizontal dispersion for the gas–solid fluidization has been analyzed in the present work. The fluidization and solids mixing characteristics of large irregular particles (Geldart‐BD type) for both homogeneous and heterogeneous materials (size‐variant and density‐variant respectively) have been investigated in a 15 × 100 cm2 cylindrical column. A theoretical model for concentration of jetsam particles has been developed as a function of height of any layer of particles (measured from the grid) by considering the counterflow of solids and circulation model together with the dispersion model for the vertical and the horizontal displacement of the particles in the fluidized bed. The mixing index at any position in the bed has thereby been expressed as a function of the concentration of the jetsam. The values of mixing index calculated from the above model as well as the experimentally measured ones for both the homogeneous and the heterogeneous systems have been compared with each other. Attempt has also been made to develop correlations for the mixing index using various system parameters based on the dimensional analysis approach for the unpromoted and the promoted beds. The mixing index values thus obtained through the dimensional analysis approach have been compared with those obtained from the developed theoretical model. The values of the mixing index calculated for promoted beds have also been compared with the corresponding values for the unpromoted beds. Segregation effect is found to be stronger for the heterogeneous systems than the homogeneous ones. The density difference is the major factor affecting the segregation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".