Hybrid Renewable Hydrogen Energy Solution for Application in Remote Mines
Bibliographic record
Abstract
Mining operations in remote locations rely heavily on diesel fuel for the electricity, haulage and heating demands. Such significant diesel dependency imposes large carbon footprints to these mines. Consequently, mining companies are looking for better energy strategies to lower their carbon footprints. Renewable energies can relieve this over-reliance on fossil fuels. Yet, in spite of their many advantages, renewable systems deployment on a large scale has been very limited, mainly due to the high battery storage system. Using hydrogen for energy storage purposes due to its relatively cheaper technology can facilitate the application of renewable energies in the mining industry. Such cost-prohibitive issues prevent achieving 100% penetration rate of renewables in mining applications. This paper offers a novel integrated renewable–multi-storage (wind turbine/battery/fuel cell/thermal storage) solution with six different configurations to secure 100% off-grid mining power supply as a stand-alone system. A detailed comparison between the proposed configurations is presented with recommendations for implementation. A parametric study is also performed, identifying the effect of different parameters (i.e., wind speed, battery market price, and fuel cell market price) on economics of the system. The result of the present study reveals that standalone renewable energy deployment in mine settings is technically and economically feasible with the current market prices, depending on the average wind speed at the mine location.
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".