Ferroalloy Composition Predictor
Predicts tapped alloy composition from furnace charge and operating conditions.
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 |
|---|---|---|
| charge_mix | float[] | Ore + reductant charge |
| furnace_power | float · MW | Furnace power |
| slag_chemistry | float[] | Slag composition indicators |
Output schema
| Field | Type | Description |
|---|---|---|
| alloy_composition | float[] · wt% | Predicted composition |
Training data & evaluation
Training data type
Synthetic furnace dataset
Evaluation metrics
Limitations
- Submerged-arc furnace assumption; other furnace types need retraining.
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.