Development of a renewable technology for air heating and thermal cooling of sub-arctic mines using spray freezing
Bibliographic record
Abstract
Mining industry is associated with high energy consumption and greenhouse gas (GHG) emissions due to intensive extraction processes and reliance on fossil fuel , specifically propane and diesel . In remote mines located in sub-arctic climates, heating and cooling operations can take up to half of this energy consumption, highlighting the importance of exploring innovative clean alternatives. The present study investigates one emerging solution to address this energy demand, known as spray freezing, in which the solidification of water droplets is used to provide the heating and cooling needs of mines. A multiscale thermo-hydraulic framework for spray freezing is developed, coupling the multi-stage droplet solidification process with a reduced-order spray-droplet dynamics model. Parametric studies are conducted using the Monte-Carlo method to quantify the effects of operating parameters on the system performance . It is found that the heat rate and cooling capacity of the spray freezing system are predominantly influenced by water flow rate and air temperature. Increasing the water flow rate from 7.5 kg/s to 30 kg/s can increase the heat rate to up to 400%. The ice generation of the system depends most on the air temperature, increasing significantly when the temperature drops below the water nucleation point, approximately -14 °C. Eventually, a multi-variate regression method is used to derive three user-friendly correlations that predict the heat rate, outlet air temperature, and ice generation of the spray freezing system, allowing a quick evaluation of the system performance in on-site applications.
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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) | 0.001 | 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".