The application of machine learning models based on particles characteristics during coal slime flotation |
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| Affiliation: | 1. Shenzhen Engineering Research Center for Coal Comprehensive Utilization (SCCCU), School of Innovation and Entrepreneurship, Southern University of Science and Technology, Shenzhen 518055, China;2. Department of Mechanical Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong 999077, China;3. Department of Chemistry, Southern University of Science and Technology, Shenzhen 518055, China;4. Clean Energy Institute, Academy for Advanced Interdisciplinary Studies, Southern University of Science and Technology, Shenzhen 518055, China;5. Department of Chemical Engineering, University of Birmingham, Birmingham B15 2TT, UK |
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| Abstract: | In this study, four different machine learning (ML) models were used to simulate the migration behavior of minerals during coal slime flotation based on particle characteristics (shape, size, compositions, and types): random forest (RF), logistic regression (LR), AdaBoosting (Ada), and k-nearest neighbors (KNN). For ML model development, 70% of the total data was used for the training phase, and 30% was used for the testing phase. F-score and area under the curve (AUC) were used as the most vital indicators for evaluating the different ML models. Compared to the other ML models, the RF model had the best accuracy for simulating particle migration behavior during flotation. Furthermore, the RF model avoided the drawback of having to be retrained when the feed conditions changed. The results revealed that particle size and particle composition play the most significant role in coal slime flotation. |
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| Keywords: | Flotation Machine learning Particle behavior Random forest (RF) Particle characteristics |
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