Hybrid Renewable Hydrogen Energy Solution for Remote Cold-Climate Open-Pit Mines
Bibliographic record
Abstract
Contemporary off-grid mining operations rely on diesel fuel for the provision of their total energy including electricity, heat, and haulage. Given the high cost of diesel and its imposed greenhouse gas emissions, mining companies are looking for more affordable and cleaner sources of energy for their operations. Although renewable energy systems, such as solar photovoltaic and wind provide efficient solutions to address this challenge, full decarbonization has shown to be very challenging, mainly due to the high cost of battery storage along with the inability to meet total site energy demand. Integrating hydrogen and thermal storage with battery banks can facilitate a full transitioning off diesel. In this sense, the present study intends to offer an innovative decarbonized solution by integrating wind turbines with a multi-storage system (battery, hydrogen, and thermal storage) to supply the total energy (electricity, heat, and haulage) for remote open-pit mines. Among the different proposed fully decarbonized configurations in this study, it is shown that a renewable system with a hydrogen-powered fleet and hybridized battery/hydrogen storage configuration can present the most economically viable case for open-pit mines with a considerably less life-of-mine cost.
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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".