Interpretable Machine Learning for Identifying Key Variables Influencing Gold Recovery and Grade

Abstract

Gold flotation performance is influenced by multiple interacting variables, yet most predictive studies in this area emphasize accuracy while neglecting interpretability, limiting their practical value for process engineers. This study applies explainable machine learning techniques to identify and interpret key variables, controlling cumulative gold recovery and grade using a small, experimentally derived dataset (n = 11) from Ballarat gold ore flotation. A Gradient Boosting Regressor, combined with SHAP (Shapley Additive Explanations), permutation importance, and feature importance analyses, was employed to uncover both linear and non-linear relationships. Power, head grade, and processing time consistently emerged as dominant predictors, while interaction effects (e.g., head grade × collector, size × head grade) provided additional explanatory insights. The findings reveal actionable process implications, including trade-offs between energy input and flotation efficiency, and highlight operational conditions for improved recovery and grade. This study demonstrates that interpretable machine learning can bridge the gap between statistical modeling and process optimization, delivering transparent, domain-specific insights even in data-constrained environments.