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Synthetic-data modelOpen · synthetic

Zinc Leach Recovery Predictor

Predicts zinc recovery through leaching and purification from feed and acidity conditions.

ZincZinc Processing IntelligenceRecovery predictionv0.2.2Apache-2.0
4k 112 17

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

FieldTypeDescription
feed_gradefloat · %Concentrate zinc grade
leach_acidityfloatLeach acidity profile
impuritiesfloat[] · ppmFe, Co, Ni, Cd

Output schema

FieldTypeDescription
recoveryfloat · %Predicted zinc recovery

Training data & evaluation

Training data type

Synthetic leaching dataset

Evaluation metrics

MAE 1.5 % 0.90

Limitations

  • Does not model jarosite/goethite iron-removal route selection.

Discussion

  • s.

    s.iyer

    ML researcher · 3d

    Validation note

    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.

    l.zhang

    Process engineer · 1w

    Improvement

    Adding an uncertainty head would make this far more useful for prioritization. Happy to open a PR against the reference repo.

    11
Reference, not production-proven

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.