Emission Impacts of Direct Reduced Iron‐Ore Processing Systems: A Systems Thinking Approach in Case Studies of Canada and Peru
Bibliographic record
Abstract
This study presents a novel methodology for quantifying CO2 emissions in direct reduced iron (DRI)‐grade iron‐ore processing plants using system dynamics modeling (SDM) with an emphasis on associated uncertainties. Two plants in Canada and Peru are analyzed, where similar ore grades (34.89% and 39.10%) but different ore types (hematite and magnetite) lead to distinct mineral processing systems. By incorporating the breakdown of power grid generation sources, the annual CO2 emissions are quantified, finding significantly higher emissions in Peru (22,241.63 tCO2e ton−1) compared to Canada (2,252.85 tCO2e ton−1). These results highlight the critical impact of local energy grids on emissions, underscoring the need to consider both ore characteristics and regional energy profiles in developing decarbonization strategies. This study offers a structured approach to assessing the CO2 impact of raw materials in low‐carbon steel production. It emphasizes the effect of the spatial distribution of raw materials in the steel‐making process. The analysis reveals that emissions from the plant in Peru are nearly 10 times higher than those in Canada, highlighting the significant influence of the local energy grid. These results underscore the influence of ore characteristics and regional energy profiles in developing effective decarbonization strategies.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".