Heat pumps as a tool for energy recovery from mining wastes
Bibliographic record
Abstract
Abstract Heat pumps extract heat energy from a low-temperature source and transfer it to a higher temperature sink, usually via a closed loop of volatile ‘refrigerant’ fluid in a compression/expansion cycle. They can be efficiently used for space heating (and cooling), extracting heat from seawater, rivers, lakes, groundwater, rocks, sewage, or mine water. Electrical energy powers the heat pump’s compressor. The ratio of total heat output to electrical energy input, called the coefficient of performance, typically ranges from 3.0 to 6.0. The use of mine water for space heating or cooling purposes has been demonstrated to be feasible and economic in applications in Scotland, Canada, Norway, and the USA. Mine water is an attractive energy resource due to: (1) the high water storage and water flux in mine workings, representing a huge renewable enthalpy reservoir; (2) the possibility of re-branding a potentially polluting environmental liability as a ‘green’ energy resource; and (3) the development of many mine sites as commercial/industrial parks with large space heating/cooling requirements. The exothermic nature of the pyrite oxidation reaction (> 1000 kJ/mol) implies added benefits if closed-loop systems can harness the chemical energy released in mine-waste tips. An appreciation of geochemistry also assists in identifying and solving possible problems with precipitation reactions occurring in heat pump systems.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.005 | 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".