Numerical and experimental analysis of a diesel exhaust heat recovery system for intake air heating of remote Arctic underground mines
Bibliographic record
Abstract
Remote mines operating in cold areas of Canada and other Arctic countries are often subjected to subfreezing temperatures that can get as low as -40°C. When those mines are underground, they need to heat their intake airflow up to a comfortable temperature for the adequate operation of machinery and personnel. Remote mines are also frequently not connected to the electrical power grid and need to depend on diesel generators to produce their electric power. As it has been demonstrated by several authors in literature, these commercial diesel generators consistently discard almost 70% of the total energy that is input as fuel. Such energy being neglected mostly in the form of heat through exhaust and other means. Knowing so much energy exists in the exhaust, usually in high grade, a system is proposed to recover thermal energy from the exhaust of the diesel generators, transport it and deliver it to the cold intake airflow of a remote underground mine. The overall alternative heating system is modeled analytically (with MATLAB) using real climate history data from a Canadian remote mine to evaluate its performance. Also, a pilot-test scale experimental setup is designed, constructed and tested and the heat exchanger utilized for intake air heating is further numerically modeled with computational fluid dynamics (using Ansys Fluent) to investigate its behavior in detail. Results from all the models created point to the system effectively recovering a significant part of the waste heat and delivering it to the cold airflow. It is also shown that due to the high temperature gradients created by the subfreezing temperatures the intake air heating unit holds the potential to deliver most of the recovered heat, with the exhaust heat recovery unit mostly driving the performance of the system.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".