Fine Coal Beneficiation using an Air Dense Medium Fluidized Bed
Bibliographic record
Abstract
The potential of using Air Dense Medium Fluidized Bed (ADMFB) separation for cleaning sub-bituminous coal was investigated. The effect of operating parameters such as the fluidizing air velocity and medium particle size on separation efficiency was determined with coal of various size fractions. Good separation efficiencies with raw coal in the 6 to 1 mm size fraction were achieved. Partition curves showed an E p value of 0.03 for coal in 5.6 to 3.35 mm size fraction. For the 1.00 to 0.42 mm size fraction, the separation efficiency deteriorated to an E p value of 0.10. To achieve an optimum separation efficiency, a separation medium with a narrow and distinct size fraction is needed to allow a superficial gas velocity sufficiently high to create a pseudo fluid medium bed while sufficiently low to avoid back mixing of fine coal and lifting of fine size mineral matter during fluidization.
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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 | 0.000 |
| 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) | 0.001 | 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".