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An interpretable and generalised machine learning model for predicting flotation performance
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DOI：10.1016/j.mineng.2025.109492.png)
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Abstract
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• A robust flotation model was developed to predict copper recovery. • The model was rigorously trained to ensure robust generalisation. • Model interpretations offered insights into prediction and process interactions. • Performance hierarchy for predicting flotation performance was XGBoost>GPR>SVR
Keywords:
Froth flotation
Machine learning
Model generalisation
Model interpretation
Copper recovery
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