Techno-Economic Assessment of Integrating Thermal Energy Storage with Diesel Exhaust Waste Heat Recovery System for Remote Mines in Cold Climates
Bibliographic record
Abstract
Detachment from the national gridline makes the remote mines in the cold climate regions of Canada solely dependent on diesel generators for power generation purposes.Notwithstanding, more than 30% of the consumed diesel by these generators is frittered away as heat through the exhaust.To endure the long harsh winters, these mines also require substantial amounts of heating which is usually provided by burning diesel or propane.In such a scenario, the installation of a diesel exhaust heat recovery system in these remote mines has been considered as a sustainable strategy to preheat the mine intake air.However, this combined heat and power generation strategy cannot provide all the necessary heating due to the daily misbalance between the heating demand and the available heat in the exhaust of the diesel generator.Coupling seasonal thermal energy storage with the waste heat recovery system is a possibility that seeks to resolve such issue.This study investigates the integration of a seasonal thermal energy storage with a diesel exhaust heat recovery system in a remote mine in northern Canada by analyzing several possible alternatives regarding capacity and rates of energy loss.The financial impact of these parameters has been added to show the viability of the proposed strategy.
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 | 0.001 |
| 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 | 0.000 |
| 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".