Fluidization characteristics of rice husk with and without coal using a bubbling fluidized cold bed model
Bibliographic record
Abstract
Abstract The fluidization behaviour was investigated using a cold flow bubbling fluidized bed setup with a column of 8 cm inner diameter. Minimum fluidization velocities ( U mf ) were experimentally determined for both mono‐component (rice husk or coal) and binary mixtures of rice husk and coal, using air as the fluidizing medium. For the binary mixtures, U mf,m was measured by varying the weight fraction and particle size of coal. It was observed that fluidization performance improved significantly with an increase in the coal weight fraction. Conversely, higher proportions of rice husk led to deteriorated fluidization behaviour due to its low bulk density and irregular particle shape. To predict the U mf,m for specific mixtures, two empirical correlations were developed for rice husk weight fractions of 20% and 40%. These correlations showed good agreement with experimental results, with relative errors within 7%.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 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".