On-stream mineral identification of tailing slurries of an iron ore concentrator using data fusion of LIBS, reflectance spectroscopy and XRF measurement techniques

Navid Khajehzadeh*, Olli Haavisto, Lauri Koresaar

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

13 Citations (Scopus)

Abstract

This article is extension of the earlier work (Khajehzadeh et al., 2016), where quantitative mineralogical information of slurry samples was achieved using an on-stream LIBS analyzer. Despite the great advances in the analytical methods and laser-based measurement techniques, the industrial developers are still demanding novel ideas enabling differentiation between minerals having similar elemental contents such as hematite and magnetite or silicon-bearing minerals such as quartz and other mixed silica minerals since they have different flotation properties. The available analytical techniques for LIBS spectral analysis (including the earlier work of this research) could not distinguish between such minerals with identical elemental contents. This work at first presents data fusion of LIBS and reflectance spectroscopy and then discusses the data fusion of reflectance spectroscopy and X-ray fluorescence (XRF) measurement techniques operating on the same slurry samples. The results will show that such data integrations enable on-stream and quantitative identification of slurry mineral contents specially for hematite, magnetite, quartz and ferrorichterite which are important minerals in iron ore beneficiation.

Original languageEnglish
Pages (from-to)83-94
Number of pages12
JournalMinerals Engineering
Volume113
DOIs
Publication statusPublished - 1 Nov 2017
MoE publication typeA1 Journal article-refereed

Keywords

  • Data fusion
  • Laser-induced breakdown spectroscopy
  • Mineral interpretation
  • Partial least squares
  • Reflectance spectroscopy
  • X-ray fluorescence

Fingerprint Dive into the research topics of 'On-stream mineral identification of tailing slurries of an iron ore concentrator using data fusion of LIBS, reflectance spectroscopy and XRF measurement techniques'. Together they form a unique fingerprint.

Cite this