Ventilation control system implementation and energy consumption reduction at Totten Mine with Level 4 Tagging and future plans
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Totten Mine has been operating a ventilation control system (VCS) with remote activation capabilities under a manual mode since 2014. The system consists of on/off and variable frequency drive fans, variable opening louvers and doors to regulate the airflow across levels, ramps and headings. Parameters that are monitored within the VCS include air volume, relative humidity, dry bulb temperature and carbon monoxide. All of the VCS components installed and commissioned in 2014 were operational during the mine production ramp-up from 2015 to 2016. The managed ventilation system demonstrated its ability to accommodate the airflow requirements of the mine in an effective and efficient manner during this time. However, changes to the mining plan generated an increase in required air volumes. It was determined these could be accomplished by further enhancements of the ventilation control system. As a result, during 2016 the automation and ventilation departments reviewed the alternatives available in the market and selected the NRG1-ECO software provided by Bestech to achieve a tag and tracking-based level of automated ventilation control. The implementation of the software started at the end of 2016 with a single level and was then expanded to additional levels. This paper presents the state of the current VCS at Totten Mine in terms of the levels that have been commissioned, the control strategies in use and the energy reduction achieved in the range of 50 to 60%. These are compared to the baseline established before the implementation. Challenges encountered during commissioning, maintenance and the plan for future implementation and software versions are also discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it