The effects of automation on the environmental impact of deep underground metal ore mining operations
Bibliographic record
Abstract
The growing demand for increased production has resulted in the need to develop deeper underground mines to extract more resources. However, the mining process becomes less economically attractive as the ventilation and ore transportation costs drastically increase when operating at large depths. This has led to the industry investigating automated battery-electric and biodiesel fueled machinery instead of diesel machines to reduce emissions, and hence ventilation costs, as well improve productivity and thereby, the economic viability of deep mine projects. A life cycle assessment (LCA) approach has been developed to evaluate the environmental impact from introducing automated equipment in underground copper mines. This is a novel application for an LCA, and as a gauge of model accuracy, it was found that calculated greenhouse gas (GHG) emissions for an underground mine site in Canada were within 5.6% of their reported emissions. The model was then expanded using data collected from automation trials at a Canadian mine to predict changes due to the introduction of various levels of automation with regards to the impact potentials of global warming, acidification, eutrophication and human toxicity. All impact levels were quantified and found to decrease due to automation. Data from this site study was then used to further develop the LCA model to predict changes in environmental impacts for underground copper mine sites in Australia, Canada, Poland, USA and Zambia. Site specific parameters and processes that contribute to their overall environmental impacts were identified, and the calculated CO2 emissions were within 4.2-5.6% of the reported values. The mining industry is moving toward introducing significantly more technology to enhance both productivity and safety. This thesis investigates using an LCA approach to add a third dimension; improved environmental impacts that contribute to more sustainable mining
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".