Decarbonizing Arctic Mining Operations with Wind-Hydrogen Systems: Case Study of Raglan Mine
Bibliographic record
Abstract
This study evaluates the techno-economic feasibility of integrating wind power with hydrogen-based storage to decarbonize the Raglan Mine in northern Canada. Using HOMER simulations with real 2021 operational data, six progressive scenarios were modeled, ranging from partial substitution of diesel generators to complete site-wide electrification, including heating, transport, and mining equipment. Results show that complete decarbonization (Scenario 6) is technically achievable and could avoid up to 143,000 tCO2eq annually (~2.15 Mt over 15 years), but remains economically prohibitive under current technology costs. In contrast, Scenario 2 Case 2, which combines solid oxide fuel cells with thermal charge controllers, emerges as the most viable near-term pathway, avoiding ~61,000 tCO2eq annually (~0.91 Mt over 15 years) while achieving improved return on investment. A qualitative multi-criteria framework highlights this configuration as the best trade-off between technical feasibility, environmental performance, and economic viability. At the same time, complete decarbonization remains a longer-term target contingent on cost reductions and policy support. Overall, the findings provide clear evidence that hydrogen storage, when coupled with wind power, can deliver substantial and measurable decarbonization benefits for Arctic mining operations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".