Impact of a Compound Collector on the Recovery of a Low-Rank Coal by Flotation
Bibliographic record
Abstract
Flotation remains the cornerstone for recovering low-rank coal leveraging on differences in mineral surface properties.However, the efficiency of standard non-ionic surfactants like kerosene and diesel oil has been suboptimal.This study investigated the impact of combining pine oil (PO) with oleic acid (OA) on low-grade coal flotation response, employing Response Surface Methodology based on a Box-Behnken matrix to design the flotation experiments, with combustible matter recovery (%) as the primary response.Chemical analysis identified the notable presence of silica and alumina, while mineralogical investigations revealed the dominance of kaolinite and anhydrite as major gangue constituents.The coal surface was characterised by roughness, irregular strips and plenty of cracks.Zeta potential studies elucidated collector adsorption states, highlighting the influence of pH on surface charge variations.Fouriertransform infrared spectroscopy analysis indicated the presence of polar functional groups on the coal surface with observed modifications after compound collector addition.Flotation results revealed that the oleic acid-pine oil mixture, particularly at a 1:1 ratio, yields an exceptional recovery of 85.46% at a slurry pH of 8.5.The optimum condition to obtain maximum combustible matter recovery of 86.48% was found to be at an oleic acid dosage of 100 g/t, a pine oil dosage of 100 g/t and a pH of 10.The study highlighted the pivotal role of slurry pH in influencing recovery with higher pH levels correlating with increased recoveries.In conclusion, fatty acids, specifically oleic acid, emerged as potential polar collectors to combine with non-ionic collectors for the recovery of low-grade coal.The findings advocate for further exploration of surface chemistry, mineralogical interactions and alternative collectors.Additionally, scaling up studies and environmental impact assessments are recommended to propel the practical applicability of the compound collector system in industrial settings.
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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) | 0.000 | 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".