Utilizing Renewable Hydrogen For Fuel-Cell Mine Haul Vehicles In Canada: A Techno Economic Assessment
Bibliographic record
Abstract
To reduce emissions from diesel-fuelled mine-haul fleets in Canada, hydrogen has been considered a viable alternative. However, emissions from electrolysis can increase depending on the carbon dioxide (CO2) intensity of the electrical source. This study found that total emissions can be reduced by 50% with grid-connected electrolysis and up to 90% when connected to a renewable energy source such as a wind turbine. The study results indicate that the current cost of ownership for fuel-cell electric vehicles (FCEVs) and hydrogen production from wind energy is approximately 18%-30% higher than diesel fuel. As technology learnings increase, utilizing hydrogen in mine trucks will be economically viable to diesel-fueled mine-haul fleets as future costs are projected to drop by 2030. This techno-economic prefeasibility study investigates the amount of emissions reduction and cost-savings from diesel-fuelled mine-haul fleets by utilizing electrolysis from either grid-electricity or wind-energy in FCEVs within the Canadian mining industry.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".