Investigation of wet spraying system to control dust pollution in mines (A case study )
Bibliographic record
Abstract
Industrial dust has a significant effect on the environment of mines, which leads to an increase in illness among workers. To decrease the impact of dust on the climate, a wet spraying system is useful for controlling dust in mining companies. In this paper, by using the wet (water) spraying system, dust control in the area of the crusher and pellet plant (discharge tower) has been investigated by Goharzamin Iron Ore company. Goharzamin Iron Ore company has an essential role producing of steel in Iran. There are a 15 million tons gyratory crusher, three iron concentrate plants with an annual capacity of 6 million tons, and a pelletizing plant with annual capacity of 5 million tons. The dust was controlled in the gyratory crusher area of Goharzamin Iron Ore company by using a wetting spraying system. Results showed that the rates of PM10 for the east, west, north and south sides of the gyratory crusher and also the center of this system are equal 851.2, 647.5, 643.9 and 781.2, and 1116.3 μg/m3, respectively. Foremore, after turning on the wet spraying system in this area, these values are reduced to 128.3, 112.8, 115.9, 123.7, and 189.9 μg/m3, respectively. The results showed that the water spray system in the gyratory crusher area reduced the PM2.5 (Particulate matter) and PM10 particles by 67% and 80%, respectively.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".