Aluminium Scrap Blend Optimizer
Recommends scrap blend ratios that hit target chemistry while minimizing impurity risk and cost. Reference optimization baseline.
This model is a reference implementation. Industrial use requires validation on plant-specific data. Customer-specific fine-tuning remains private unless explicitly approved for publication.
Input schema
| Field | Type | Description |
|---|---|---|
| scrap_streams | object[] | Available scrap streams + assays |
| target_chemistry | float[] · wt% | Target window |
Output schema
| Field | Type | Description |
|---|---|---|
| blend_ratios | float[] | Recommended stream fractions |
| impurity_risk | float | Predicted impurity risk |
Training data & evaluation
Training data type
Synthetic scrap blends
Evaluation metrics
Limitations
- Optimizer assumes assays are accurate; add uncertainty for production.
Discussion
- s.Validation note
s.iyer
ML researcher · 3d
Reproduced the reported metric on the public split with a fixed seed. Numbers line up within noise, so it's a good baseline to build on.
18 - l.Improvement
l.zhang
Process engineer · 1w
Adding an uncertainty head would make this far more useful for prioritization. Happy to open a PR against the reference repo.
11
Validate on your data, keep the result private
Reference models are a starting point. We can fine-tune privately on plant-specific data, with customer-owned IP and no public disclosure.