TY - JOUR
T1 - The application of machine learning models based on particles characteristics during coal slime flotation
AU - Zhao, Binglong
AU - Hu, Shunxuan
AU - Zhao, Xuemin
AU - Zhou, Baonan
AU - Li, Junguo
AU - Huang, Wei
AU - Chen, Guohua
AU - Wu, Changning
AU - Liu, Ke
PY - 2022/1
Y1 - 2022/1
N2 - 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.
AB - 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.
KW - Flotation
KW - Machine learning
KW - Particle behavior
KW - Particle characteristics
KW - Random forest (RF)
UR - http://www.scopus.com/inward/record.url?scp=85120491854&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85120491854&origin=recordpage
U2 - 10.1016/j.apt.2021.11.015
DO - 10.1016/j.apt.2021.11.015
M3 - RGC 21 - Publication in refereed journal
SN - 0921-8831
VL - 33
JO - Advanced Powder Technology
JF - Advanced Powder Technology
IS - 1
M1 - 103363
ER -