Sustainable open pit mining through GHG-conscious short-term production scheduling
Bibliographic record
Abstract
The mining industry is a notable contributor to global greenhouse gas (GHG) emissions, posing challenges to achieving the Paris Agreement’s goal of capping emissions at 30 Gt CO2-equivalent annually by 2030. This paper introduces a novel Mixed Integer Linear Programming (MILP) model tailored for short-term open-pit mine planning that integrates environmental considerations, particularly GHG emissions, alongside economic objectives. The model handles complex operational challenges including block sequencing, multiple transport destinations, and stockpile management. Additionally, it provides the opportunity to examine the adoption of In-Pit Crushing and Conveying (IPCC) systems as the main transport method, an innovative approach aimed at reducing emissions from haulage – which accounts for over 35% of GHG emissions in open-pit mining. Applied to a case study in an iron ore mine, the model not only considers the environmental benefits of IPCC systems compared to traditional truck and shovel (TS) operations but also highlights significant reductions in haulage costs and carbon tax liabilities. The findings demonstrate that fixed IPCC (FIPCC) systems, in particular, offer substantial decreases in GHG emissions, presenting a compelling case for their broader adoption in the industry.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".