Extracting cold energy from back-filled zones to pre-freeze new miningzones in Canadian uranium mines: numerical modeling and experimentalvalidation
Bibliographic record
Abstract
Athabasca basin in northern Saskatchewan is the home to high-grade uranium ores -known as the Saudi Arabia of the uranium world.McArthur River and Cigar Lake mines are two underground mines located in the Athabasca basin in northern Saskatchewan.Due to high uranium content and weak ground structure, the uranium ore is extracted using special mining techniques, namely raise boring in McArthur River and jet boring in Cigar Lake mine, in which the ground/orebodies need to be frozen before it is excavated.The artificial ground freezing (AGF) system employs a refrigeration plant to produce brine (calcium chloride) at -30 • C to freeze the ground by pumping it to the coaxial/bayonet tube heat exchangers installed in the drill holes.As the mine progresses, orebody in the old zones depletes, and new zones need to be developed.The depleted zone typically had been frozen for several years, for which ground temperature reaches close to brine temperature.This paper explores a novel idea of extracting "coolth" energy from the depleted zones to pre-freeze new zones.This could potentially shorten the overall freezing time in the new zone and reduce overall energy consumption and its associated carbon footprint.The concept is tested using a laboratory-scale AGF facility at McGill University.A mathematical model is also developed and validated against experimental data.The model is then used to simulate mine field conditions.The results suggest that the pre-freezing technique can shorten the total freezing time from 9 to 7 months with potential energy savings of up to 37%.Future work will focus on design improvement, techno-economic analysis, and possible implementation.
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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) | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".