State of the art Underground Ventilation
Bibliographic record
Abstract
Ventilation is a key issue for underground facilities. The ventilation is needed to provide breathing air to personnel in the facilities, to provide good air quality by removing atmospheric contaminants such as gases and dust, to provide air conditioning (cooling/heating) for personnel in the facilities, and to provide oxygen and cooling for operating vehicles in the facilities. Electrical power to provide ventilation in an underground facility is one of the main components of the total power consumption in that facility. Depending on the surface climatic condition and the depth of the facility, air conditioning (heating/cooling) might be required. In a sub-arctic climate region such as in northern Sweden, the air has to be heated to prevent freezing airways and to provide a comfortable condition for the personnel, and when the facility is at a great depth (> 1000 m), due to high virgin rock temperature, the fresh air usually have to be cooled, like in deep mines in Australia, Canada, and South Africa. In Swedish deep mines, currently heat is not a serious issue, but it might become one in the future as the mines get deeper. The electrical power consumption can comprise up to 40% of the total underground mine power consumption, which is a significant proportion (e.g. Kocsis, 2009; Halim and Kerai, 2013). This feasibility study consists of a survey among the mining companies and tunnel owners (such as Trafikverket) and the universities that have done research and design projects within the field. The project has identified the current knowledge within this area, and the gap between the technology/practice used currently and the available technology, and the potential for further improvements in the future. A review on ventilation system in other areas such as buildings and farms was also carried out in order to identify any of its technologies that can be employed for improving ventilation system in underground facilities. This study found that there are several new technologies that can be used to address challenges faced by underground facilities in Sweden: improving air quality in the deep part and reducing ventilation costs. Several research proposals for investigating the application of these technologies are proposed in this report. The study also indicates that there is a lack of technical expertise in underground ventilation within the industries in Sweden. It was observed that in many cases, the person who is in charge of ventilation does not have a specialisation in underground ventilation. The same situation exists among universities in Sweden. An improvement has recently been made by Luleå University of Technology (LTU), in which it has recruited a Senior Lecturer with expertise in underground ventilation. The person has previously worked as a practitioner and as an academic in Australia, one of leading mining nations in the world. This is the first step to address this issue. However, there is still much work to be done. A specific training program to educate underground ventilation to practitioners in underground mines and traffic tunnels should be established in the future.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".