A techno-economic analysis of diesel exhaust injection into mine tailings for carbon sequestration
Bibliographic record
Abstract
The environmental impact of off-grid mines in remote, cold climates is significantly intensified by their dependence on fossil fuels for power and heating. A promising solution lies in the potential to capture and permanently store carbon within mine tailings, thus allowing the mining industry to take a leading role in carbon removal initiatives and provide sustainable solutions. This study explores energy-optimal design scenarios for flue gas injection into mine waste to capture carbon. The approach involves installing perforated pipes within dry stack tailings. The established reduced-order model in this research serves as a novel tool for decision-making, aiding in the selection of an appropriate perforation scheme for the injection pipes embedded in the tailings. A cost analysis is also performed to assess the financial viability of the proposed concept under different operating parameters. Operational expenses, particularly energy costs, are found to be influenced by the permeability of the tailings. In instances of lower permeabilities, larger injection pipes are required. The findings indicate that achieving viable operating costs for sequestering one tonne of carbon dioxide hinges on amenable pipe sizing and engineering. Additionally, the study estimates that maintaining a reasonable level (around 1%) between the power being decarbonized and the power required for the carbon sequestration operation is crucial.
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 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) | 0.000 | 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) | 0.001 | 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 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".