Study on the analysis of flow field and enhancing the fine coal flotation by the impingement flow
Bibliographic record
Abstract
Abstract The hydrophobicity and floatability of fine coal slime are severely diminished by surface coatings of gangue minerals, complicating coal–gangue separation in slurry systems. Traditional pulping methods struggle to efficiently remove fine mud from coal particles, reducing recovery efficiency. To address this, a self‐designed impact flow slurry conditioning device was developed to enhance reagent adsorption on coal surfaces. Combining computational fluid dynamics (CFD) simulations, reagent adsorption rate analysis, and contact angle measurements, this study optimized slurry impact velocity to evaluate flow field dynamics and conditioning mechanisms. Flotation experiments revealed that strain rate increased with impact velocity, peaking at 774 s −1 (5 m/s), while the minimum vortex scale reached 1.04 μm at 4 m/s. At 4 m/s, the collector adsorption rate and coal contact angle were maximized, achieving a combustible recovery rate of 98.18%, indicating optimal flotation performance. The impact flow method effectively strips surface gangue coatings, enhances coal‐gangue separation, and improves coal hydrophobicity and floatability. The device integrates a disturbing cone and plate to generate localized turbulence and shear fields, significantly boosting reagent adsorption efficiency and overcoming structural limitations of traditional stirring equipment. These innovations provide critical insights into shear‐driven adsorption mechanisms and advance coal slurry flotation technology, offering a scalable solution for industrial applications. This research establishes a foundation for developing efficient, high‐performance coal processing systems.
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How this classification was reachedexpand
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 | 0.001 | 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) | 0.000 | 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 teacher head, 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".