Opportunities for Adding Recycled Content to Primary Aluminum Products
Bibliographic record
Abstract
Rio Tinto is a leading producer of low-carbon primary aluminum due to its efficient processes and hydroelectricity. It has one of the lowest greenhouse gas (GHG) footprints in the world, which is below four tons of CO2 per ton of primary aluminum. Nevertheless, integrating end-of-life recycling into primary aluminum products, although challenging, plays an important role in further reducing GHG emissions during aluminum production. This is why much effort has been made in recent years throughout Rio Tinto plants to find innovative solutions to overcome this challenge. In 2022, the first circular economy initiative was deployed at Laterrière Works with the addition of a remelt furnace with an initial production capacity of 22,000 tons per year. This project has contributed to adding capacity to remelt both internal process scrap and external industrial scrap. A second initiative is the operation of a new recycling center at Arvida Works to commence in 2025 that will process 30,000 tons per year of end-of-life scrap. As a primary alloy producer, the main challenge for Rio Tinto is to integrate these materials into current and new products without affecting their quality and performance. This paper will present preliminary studies on the chemical compatibility of scrap with current alloys, and the approach used for managing their organic content.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.002 |
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".