Journal
ENGINEERING REPORTS
Volume 2, Issue 6, Pages -Publisher
WILEY
DOI: 10.1002/eng2.12167
Keywords
complex sulfide ore; firefly algorithm; froth flotation; machine learning; random forests
Categories
Funding
- Division of Civil, Mechanical and Manufacturing Innovation [1661609, 1932690]
- Div Of Civil, Mechanical, & Manufact Inn
- Directorate For Engineering [1932690, 1661609] Funding Source: National Science Foundation
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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.
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