Open pit limit optimization considering the pumped storage benefit after mine closure: a case study
Bibliographic record
Abstract
Abstract Repurposing a closed mine as lower reservoir is a cost-effective way for the construction of pumped storage hydropower (PSH) plant. This method can eliminate the expenses of mine reclamation, reservoir construction, and land acquisition, resulting in significant cost savings and benefits for the PSH project, known as the PSH benefit. The construction of PSH plants within a closed mine is divided into surface mode and semi-underground mode in this paper. Through a general comparison of two in-situ cases, the finding highlight that the surface mode can achieve a larger potential installed capacity and lower construction cost. Furthermore, the PSH benefit is quantified and internalized as an economic parameter in the ultimate pit limit (UPL) optimization by allocating it into unit ore. Taken an undisclosed open-pit iron mine as example, the UPL is optimized by considering the PSH benefit. The internalized PSH benefit is calculated to be 6.59 CN¥/t when the installed capacity is 2000 MW, and ore amount within the optimized UPL is increased by 1.4%. The results indicated that the PSH benefit does influence the shape and size of UPL, but not significantly. Besides, converting several bottoms in a single open-pit into lower and upper reservoirs presents more challenges for UPL optimization, which further explorations is needed.
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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 | 0.001 | 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) | 0.003 | 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".