Improving the top coal recovery ratio in longwall top coal caving mining using drawing balance analysis
Bibliographic record
Abstract
The recovery ratio of longwall top coal caving (LTCC) technology is an important measure of its effectiveness. However, the recovery ratio of single-opening sequential caving technology in thick and extra-thick coal seams needs improvement. To address this, an independent cluster-group caving technology is proposed in this study. Four numerical simulation experiments were conducted to compare the recovery ratio and drawing balance of four-opening independent cluster-group caving technology and single-opening sequential caving technology. Results show that the recovery ratio in four-opening independent cluster-group caving technology is approximately 6% higher than in single-opening sequential caving technology when the thickness of the broken gangue layer and the coal seam are the same. Additionally, a judgment formula for the broken immediate roof thickness is provided when the top coal recovery ratio is seriously affected. The independent cluster-group caving technology demonstrates stronger stability and better adaptability under different conditions, as its caving sequence can prevent larger thickness changes and gangue disturbances during the drawing process. Overall, this study highlights the potential of independent cluster-group caving technology to improve the recovery ratio of LTCC technology in thick and extra-thick coal seams.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".