Flotation response of coal washery reject fines: Characteristics, process optimization, and oxidation
Bibliographic record
Abstract
Abstract The present paper outlines the characterization, electrokinetic behaviour, and flotation response of rejected coking coal fines with 32.5% ash generated in a coal washery in Eastern India. The response methodology and central composite rotatable design (RSM‐CCRD) were used for the process modelling and optimization of the flotation process using diesel, methyl isobutyl carbinol (MIBC), and sodium hexametaphosphate as a collector, frother, and depressant to maximize ash reduction, yield, and combustible recovery. At optimum condition, a 9.7% clean ash coal was achieved with a 63% yield at collector, frother, and depressant dosages of 0.78, 0.31, and 0.80 kg/ton, respectively. The model prediction and experimental data corroborated sufficiently. Subsequently, within 1 year, the fines oxidized and did not float with the collector. Fourier‐transform infrared spectroscopy (FTIR) confirmed surface oxidation on oxidized coal. The oxidized coal responded favourably to acid oil, a vegetable oil refinery waste. With acid oil as a collector, the oxidized coal can upgrade to 12% clean ash coal with a 60% yield; the combustible recovered is 80%. The work indicates successful upgradation of fresh and oxidized coal using the froth flotation process.
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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 | 0.000 | 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) | 0.001 | 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".