In this study, trace elements in apatite were utilized as input parameters and presented to the neural work, while apatite classification (altered auriferous, altered barren, …
Two continuous tests on a pilot scale were run. Test I employed magnetic separation of the classifier overflow followed by floatation whereas Test II employed straight floatation of the classifier overall.
We show that the XGBoost classifier efficiently and accurately classifies high-dimensional apatite trace element data according to the ore deposit type (overall accuracy: …
This work applied a machine learning (ML) method to classify the chemical compositions of apatites from both fertile and barren rocks, aiming to help determine the …
They consist of an open circuit rod mill and a ball mill in closed circuit with classifiers. The products of both mills are fed to a cyclone battery. The overflow goes to …
To address this issue, we used supervised decision boundary maps (SDBM) to visually illustrate and interpret the machine learning process. We constructed a SDBM to classify the ore genetics from 1551 trace element data …
We show that the XGBoost classifier eficiently and accurately classifies high-dimensional apatite trace element data according to the ore deposit type (overall accuracy: 94% and F1 score: 89%).
Compositional statistics, classification, and a machine learning classifier are then applied to this dataset to generate biplots that can be used to determine the broad source …
It provides a rapid and highly accurate approach to classifying high-dimensional data, such as the variation in apatite trace elements in different types of ore systems. After …
Phosphate flotation using mixed collectors has been proved to give greater selectivity and high recovery of apatite comparing with fatty acids, which require selective depressant to be effective.