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Assessment of chromite liberation spectrum on microscopic images by means of a supervised image classification

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Date
2017
Author
Camalan, Mahmut
Çavur, Mahmut
Hosten, Cetin
Abstract
Assessment of mineral liberation spectrum with all its aspects is essential for plant control and optimization. This paper aims to estimate 2D mineral map and its associated liberation spectrum of a particular chromite sample from optical micrographs by using Random Forest Classification a powerful machine-learning algorithm implemented on a user-friendly and an open-source software. This supervised classification method can be used to accurately generate 2D mineral map of this chromite sample. The variation of the measured spectra with the sample size is studied showing that images of 200 particles randomly selected from the optical micrographs are sufficient to reproduce liberation spectrum of this sample. In addition the 2D spectrum obtained with this classification method is compared with the one obtained from the Mineral Liberation Analyzer (MLA). Although 2D mineralogical compositions obtained by the two methods are quite similar microscopic analysis estimates poorer liberation than MLA due to the residual noise (misclassified gangue) generated by the classification. Nevertheless we cannot compare the reliabilities of the two methods as there is not a standard produce yet to quantify the accuracy of MLA analysis. (C) 2017 Elsevier B.V. All rights reserved.

Source

Powder Technology

Volume

322

Pages

214-225

URI

https://hdl.handle.net/20.500.12469/314
https://doi.org/10.1016/j.powtec.2017.08.063

Collections

  • Araştırma Çıktıları / Scopus [1565]
  • Araştırma Çıktıları / WOS [1518]
  • Yönetim Bilişim Sistemleri / Management Information Systems [145]

Keywords

Mineral liberation
Optical microscope
Random forest tree
Image classification
Mineral liberation analyzer

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DSpace software copyright © 2002-2015  DuraSpace
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Theme by 
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