Open AccessPrediction of flotation efficiency of metal sulfides using an original hybrid machine learning model
Author(s) -
Cook Rachel,
Monyake Keitumetse Cathrine,
Hayat Muhammad Badar,
Kumar Aditya,
Alagha Lana
Publication year - 2020
Publication title -
engineering reports
Language(s) - English
Resource type - Journals
ISSN - 2577-8196
DOI - 10.1002/eng2.12167
Subject(s) - galena , process (computing) , froth flotation , computer science , firefly algorithm , random forest , chalcopyrite , process engineering , base metal , machine learning , artificial intelligence , engineering , materials science , metallurgy , chemical engineering , sphalerite , particle swarm optimization , copper , operating system , welding , hydrothermal circulation
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.