Strategies used to control the costs of underground ventilation in some Brazilian mines
Bibliographic record
Abstract
In an underground mine, the ventilation is responsible for 25% to 50% of its electrical energy consumption. In countries such as South Africa, United States and Canada researchers have started to achieve a significant reduction in energy consumption without neglecting aspects of the quantity and quality of air required for the best performance of the system, in compliance with safety standards and worker comfort. In Brazil, on demand this ventilation application began in 2013 at the Ipueira mine (Bahia, controlled by Ferbasa company), and was soon after applied by the Cuiabá, Córrego do Sitio I and Lamego mines; all three mines administered by Anglo Gold Ashanti. Each mine adopted frequency inverters for the main ventilation, whereby the fan rotation is adjusted according to demand and speed drivers. This measure resulted in the saving of thousands of reais, since the flow is proportional to the velocity, the pressure is proportional to the square of the velocity, and the power is proportional to the cubed velocity. Therefore, a reduction of 20% in the flow will save about 50% of the energy required. The Cuiabá mine presents the most modern and automated system in the country. The fans are controlled and monitored through a control room. In addition, sensors scattered in the mine, control the required flow rate. The Lamego mine has a similar but simpler system. This article proposes to discuss the application and improvement of the process of ventilation on demand in Brazilian mines where this system is applied.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".