Techno-economic feasibility assessment of a diesel exhaust heat recovery system to preheat mine intake air in remote cold climate regions
Bibliographic record
Abstract
Underground mines in Arctic and Subarctic regions require the preheating of mine intake air during winter. The cold fresh air of those remote areas can be as severe as −40 °C and commonly needs to be heated to around +3 °C. This extensive amount of heating is usually provided by employing large-size air heaters, fueled by diesel, propane, natural gas, or heavy oil, leading to high energy costs and large carbon footprints. At the same time, the thermal energy content of a diesel generator sets (gen-sets) exhaust is known to be one-third of the total heating value of its combusted fuel. Exhaust heat recovery from diesel gen-sets is a growing technology that seeks to mitigate the energy costs by capturing and redirecting this commonly rejected exhaust heat to other applications such as space heating or pre-heating of the mine intake air. The present study investigated the possibility of employing a simple system based on off-the-shelf heat exchanger technology, which can recover the waste heat from the exhaust of the power generation units (diesel gen-sets) in an off-grid, cold, remote mine in Canada for heating of the mine intake air. Data from a real mine was used for the analysis along with environmental data of three different location-scenarios with distinct climates. After developing a thermodynamic model, the heat savings were calculated, and an economic feasibility evaluation was performed. The proposed system was found highly viable with annual savings of up to C$6.7 million and capable enough to provide an average of around 75% of the heating demand for mine intake air, leading to a payback period of about eleven months or less for all scenarios. Deployment of seasonal thermal energy storage has also been recommended to mitigate the mismatch between supply and demand, mainly in summertime, possibly allowing the system to eliminate fuel costs for intake air heating.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".