An interpretable and generalised machine learning model for predicting flotation performance

Clement Lartey

doi:10.1016/j.mineng.2025.109492

2025-06-19

DOI：10.1016/j.mineng.2025.109492

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摘要

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• 开发了一种稳健的浮选模型来预测铜回收率。 • 该模型经过严格训练以确保稳健的泛化能力。 • 模型解释为预测和工艺交互提供了见解。 • 预测浮选性能的性能层级为：XGBoost > GPR > SVR。

Keyword:

Froth flotation

Machine learning

Model generalisation

Model interpretation

Copper recovery

期刊

Minerals Engineering

IF:

论文数:

8.5K

被引数:

2.6W

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