Prediction of flotation efficiency of metal sulfides using an original hybrid machine learning model

Abstract

Froth flotation process is extensively used for selective separation of base metal sulfides from uneconomic mineral resources. Reliable prediction of process outcomes (metal recovery and grade) is vital to ensure peak performance. This work employs an innovative hybrid machine learning (ML) model—constructed by combining the random forest model and the firefly algorithm—to predict froth flotation efficiency of galena and chalcopyrite in relation to various experimental process parameters. The hybrid model's prediction performance was rigorously evaluated, and compared against four different standalone ML models. The outcomes of this study illustrate that the hybrid ML model has the prediction ability to process outcomes with high-fidelity, while consistently outperforming the standalone ML models.