Froth flotation and subsequent dewatering circuit optimization
Bibliographic record
Abstract
The proper selection of dewatering equipment downstream of flotation is vital in maximizing preparation plant yield. As such, mismatched flotation and dewatering equipment can lead to catastrophic reductions in plant profitability. This paper covers optimum flotation and subsequent dewatering circuit configurations for thermal and coking coal worldwide. The benefits of using deslime column flotation together with screen bowl centrifuges for thermal coals will be discussed, with numerous successful applications mentioned. Likewise for coking coals, conventional versus column flotation applications will be reviewed. The benefits of the Australian practices of dewatering "by-zero" froth concentrates using disc or horizontal belt vacuum filters will also be quantified. The advantages of using pressure filtration in the USA and Canada compared to using centrifuges on by-zero froth concentrates will be discussed in detail, with industrial examples. In particular, the frothing problems seen in many plants using centrifuges to dewater conventional by-zero froth, and more particularly column froth concentrates, will be highlighted. The paper will conclude with a section describing successful applications of slimes flotation followed by pressure filtration.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".