Utilization and Implementation of Atmospheric Monitoring Systems in United States Underground Coal Mines and Application of Risk Assessment
Bibliographic record
Abstract
Explosions of gas and dust continue to be recognized as an extreme danger in underground coal mines and still occur despite significant technological advances. Mining researchers have been attempting to accurately measure and quantify ventilation and gas properties since early mining; however basic monitoring attempts were limited by the available technologies. Recent advancements in monitoring and communication technologies enable comprehensive atmospheric monitoring to become feasible on a mine-wide scale. Atmospheric monitoring systems (AMS) allow operators to monitor conditions underground in real-time. Real-time monitoring enables operators to detect and identify developing high risk areas of the mine, as well as quickly alert mining personnel underground. Real-time monitoring also can determine whether conditions are safe for mining, to operate ventilation systems more efficiently, and to provide an additional layer of monitoring atmospheric conditions underground. AMS utilizes numerous monitoring technologies that will allow underground coal mines to comprehensively monitor gas and ventilation parameters. AMS are utilized worldwide as well as in the United States, and can be modified to cater to specific hazards at different mines. In the United States, AMS are primarily used to monitor belt lines and electrical installations for smoke, CO, and CH₄, and to automatically alarm at set thresholds. The research in this study investigates and analyzed AMS across the world (specifically Australia, Canada, and United States). Two case studies presented in Chapter 5 focus on the utilization and implementation of AMS in two underground coal mines in the United States. These case studies identify challenges regarding installation, data management, and analysis of real-time atmospheric monitoring data. The second case study provides significant evidence that correlates mine ventilation fan outages and changes in barometric pressure to increases in methane from previous works. This research does not attempt to quantify data, but intends to provide engineers knowledge to utilize, design, and implement an AMS. Several incident scenarios are simulated using ventilation computer software, as well as the benefits of monitoring in past disasters are analyzed. This research does not intend to place blame, but intends to increase the understanding of utilizing and implementing AMS in underground coal mines.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".