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.
Recommended Citation
R. Cook et al., "Prediction of Flotation Efficiency of Metal Sulfides using an Original Hybrid Machine Learning Model," Engineering Reports, vol. 2, no. 6, article no. e12167, Wiley Open Access, Jun 2020.
The definitive version is available at https://doi.org/10.1002/eng2.12167
Department(s)
Materials Science and Engineering
Second Department
Civil, Architectural and Environmental Engineering
Third Department
Mining Engineering
Publication Status
Open Access
Keywords and Phrases
complex sulfide ore; firefly algorithm; froth flotation; machine learning; random forests
International Standard Serial Number (ISSN)
2577-8196
Document Type
Article - Journal
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2024 The Authors, All rights reserved.
Creative Commons Licensing
This work is licensed under a Creative Commons Attribution 4.0 License.
Publication Date
01 Jun 2020
Included in
Ceramic Materials Commons, Civil and Environmental Engineering Commons, Mining Engineering Commons, Structural Materials Commons
Comments
Division of Civil, Mechanical and Manufacturing Innovation, Grant 1661609