Floating photovoltaic arrays to power the mining industry: A case study for the McFaulds lake (Ring of Fire)
Bibliographic record
Abstract
The article looks at the integration of crystalline and thin film (a‐Si) floating photovoltaic (PV) arrays for electricity generation in remote mine sites. Floating PV arrays rather than regular ground mounted PV arrays are considered more suitable for the site because it decreases the environmental impacts—in terms of not requiring landscaping or deforestation. The research provides a techno‐economic analysis for the integration of varying levels of PV with 40 MW of diesel generation. The main challenge was the consideration of the gen sets part load together with the variability of the solar resource at the site. Applications of alternative technologies at remote mine sites are fairly limited. Results show that at a diesel fuel cost greater than $129c/L a‐Si floating PV would offer some financial benefits. At this price, this is not applicable to floating crystalline PV arrays because the infrastructure required to keep them floating would offset the cost savings from the PV array. Further savings could be achieved if energy storage or load shedding could be implemented at the mine, or extra revenue could be generated through carbon credits. Solar energy for remote mine sites is not a solution to 100% of its electricity demands, unless an energy storage is available, so diesel generation is still a requirement. © 2015 American Institute of Chemical Engineers Environ Prog, 35: 898–905, 2016
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".