An interpretable and generalised machine learning model for predicting flotation performance

Clement Lartey

doi:10.1016/j.mineng.2025.109492

An interpretable and generalised machine learning model for predicting flotation performance

2025-06-19

0

OA

AI

DOI：10.1016/j.mineng.2025.109492

Original

Original request for help

Save

Abstract

En 中文

• 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

An interpretable and generalised machine learning model for predicting flotation performance

Abstract

Journal

Organization