Froth flotation and subsequent dewatering circuit optimization
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.005 | 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 it