A Systematic Review of Coal Mine Dust Suppression Methods Based on Numerical Simulations and Experimental Investigations
Bibliographic record
Abstract
Large quantities of dust are generated during coal mining and transportation, posing a threat to workers’ health. Therefore, this article conducts a systematic review of the literature on coal mine dedusting. This study examines coal mine dust suppression methods by integrating numerical simulations and experiments, focusing on four aspects: the structural improvement of the dust remover, chemical modification, the optimization of the operating environment, and the ventilation system. The structural improvement of a dust remover primarily involves optimizing the nozzle’s structure and size, particularly the Laval structure. The findings indicate that alterations in the surface structure of the Laval nozzle’s contraction section have minimal effect on the airflow velocity. Chemical modification of the dust remover can enhance the wetting properties of coal dust and includes non-phytochemical and phytochemical modification. Molecular Dynamics (MD) simulations are frequently employed in chemical modification. The optimization of the operating environment for dust removers focuses predominantly on spray pressure optimization.
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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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".