A comparative classification efficiency of hydro cyclone and high frequency screen – an experimental study
Bibliographic record
Abstract
In coal preparation plants, classifying cyclones are commonly used to generate the pre-classified feed to the Reflux Classifier (RC) from coal fines. However, the efficiency of classification cyclones is low and generates a high amount of fines in the RC feed resulting in poor product quality. To address this issue, high frequency (HF) screen was explored as an alternative to classifying cyclone to generate desired RC feed. In the HF wet screening experimentation, the effect of four critical operational parameters such as feed rate, feed density, spray water and aperture size on the classification efficiency was investigated comprehensively. The overall screening efficiency of HF screen is in the range of 89-98%. HF wet screen generates lesser average percentage of fines (<250μm) at approximately 10% compared to 30% for the classifying cyclone. The classifying cyclone performance curve was modelled using modified Plitt's equation and imperfection in the range of 0.3 - 0.5. The corrected cut size (d_50c) of the classifying cyclone was in the range of 130 -180 μm in comparison to ~250 μm for HF wet screen. The best operating conditions observed were at the aperture size of 230 μm, feed rate of 236 TPH, feed solids concentration of 12.1% with the spray water of 45 m 3 /hr.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".