Lithium Impurity Risk Model
Estimates the risk of impurity breakthrough to battery-grade product from feed profile and purification 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 |
|---|---|---|
| feed_impurities | float[] · ppm | Mg, Ca, B, Na in feed |
| reagent_dosing | float[] | Precipitation / IX dosing |
| ph | float | Purification pH |
Output schema
| Field | Type | Description |
|---|---|---|
| impurity_risk | float | Breakthrough risk 0 to 1 |
| driver_ranking | string[] | Top contributing impurities |
Training data & evaluation
Training data type
Synthetic impurity-control dataset
Evaluation metrics
Limitations
- Battery-grade thresholds are configurable; defaults are illustrative.
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.