Development of Strategies to Reduce Ventilation and Heating Costs in a Swedish Sublevel Caving Mine—a Unique Case of LKAB’s Konsuln Mine
Bibliographic record
Abstract
Abstract This paper outlines a unique case of the development of strategies to reduce ventilation and heating costs in Konsuln iron ore mine in northern Sweden. The mine, located just south of Luossavaara-Kiirunavaara Aktiebolag’s Kiruna iron ore mine, was developed as a test mine 2018–2020 for the Sustainable Underground Mining (SUM) project. Besides functioning as a test mine, Konsuln also contributes ore production. The existing mine ventilation system was designed for the current production rate of 0.8 million tons per annum (Mtpa). There is a plan to increase this rate to between 1.8 and 3 Mtpa in the future, and this requires the primary fans to be upgraded. Therefore, a study was carried out to determine whether using ventilation on demand (VOD) could avoid this fan upgrade and reduce Konsuln’s ventilation and heating power costs in the future. The study also investigated whether using battery electric vehicles (BEVs) along with VOD or as a standalone strategy could further reduce these power costs. In addition, the study analyzed the suitability of heating power reduction strategies presently or previously used in the Nordic countries and Canada to investigate potential additional strategies to reduce the heating power cost, the largest portion of Konsuln’s ventilation and heating power costs. The study found using VOD can avoid the expensive upgrading of the existing primary fans and reduce Konsuln’s ventilation and heating power costs in the future. Using BEVs can further reduce these costs. Finally, none of the Nordic and Canadian heating power reduction strategies is suitable for Konsuln because they require unique conditions that do not exist in Konsuln.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".