Decarbonization of remote mine electricity supply and vehicle fleets
Bibliographic record
Abstract
The need to drastically reduce greenhouse gas emissions is becoming more and more apparent. To avoid a tipping point, scientists agree that emissions need to reach net zero by 2050, which will require unprecedented changes to the way electricity is generated, particularly for remote off-grid mines. In this paper, the potential to achieve 100% renewable penetration for a remote mine will be investigated, with an example case study for a mine in the Canadian Arctic. Five key pillars to achieve net zero operation will be discussed: energy efficiency, hybrid power, microgrid integration, alternative vehicles, and low carbon technologies. In addition, the state-of-the-art in renewable generation, energy storage, and hydrogen storage are presented. The key enablers for a mine to achieve 100% renewable penetration for electricity and vehicles are identified, along with benchmark costs and savings opportunities. Strategies and challenges for existing mines to achieve high renewable penetration are discussed. The remote mine of the future will be significantly different from today’s operations; changes to the operating strategy and process, energy generation, and vehicle fleet will be the key enablers.
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.
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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".