Studies on Bed Density in a Gas-Vibro Fluidized Bed for Coal Cleaning
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Dry coal beneficiation has played a vital role during the initial stage of coal cleaning in recent years. Successful utilization of a gas–solid fluidized bed for >6 mm coal cleaning motivates scholars to explore the possibility of fine coal cleaning using dry beneficiation methods. In this study, pulsed flow was introduced into a fluidized bed to optimize bubble behavior, thus improving the density stability. The equation of minimum fluidization velocity ( U mfp ) in a gas-vibro fluidized bed for coal preparation was investigated theoretically. An equation has been proposed for predicting U mfp while considering changes in the friction coefficient ( C f ) in the gas-vibro fluidized bed. Based on two-phase theory, the correlation of bed density was determined by analyzing the bubble behavior in the gas-vibro fluidized bed. The theoretical bed density was then compared with experimental data of the bed density and separation density. The predicted bed density in monodisperse and binary dense medium systems was found to be consistent with the experimental results. Overall, the equation of bed density is suitable for estimating the separation density in the gas-vibro fluidized bed.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| 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".