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MEI Online: Analytical Techniques & Applied Mineralogy: Refereed Papers


Click for more info on Process Mineralogy '18


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Full papers of hyperlinked titles can be downloaded via ScienceDirect.



V.G. Greb, A. Guhl, H. Weigand, B. Schulz, M. Bertau, Understanding phosphorus phases in sewage sludge ashes: A wet-process investigation coupled with automated mineralogy analysis, Minerals Engineering, Vol. 99, 2016

L. Little, A.N. Mainza, M. Becker, J.G. Wiese, Using mineralogical and particle shape analysis to investigate enhanced mineral liberation through phase boundary fracture, Powder Technology, Vol. 301, 2016

N. Khajehzadeh, O. Haavisto, L. Koresaar, On-stream and quantitative mineral identification of tailing slurries using LIBS technique, Minerals Engineering, Vol. 98, 2016

N.Kh. Salakjani, P. Singh, A.N. Nikoloski, Mineralogical transformations of spodumene concentrate from Greenbushes, Western Australia. Part 1: Conventional heating, Minerals Engineering, Vol. 98, 2016

E. Whiteman, N.O. Lotter, S.R. Amos, Process mineralogy as a predictive tool for flowsheet design to advance the Kamoa project, Minerals Engineering, Vol. 96-97, 2016

S. Mohammadi-Jam, K.E. Waters, Inverse gas chromatography analysis of minerals: Pyrite wettability, Minerals Engineering, Vol. 96-97, 2016

Q. Lin, S.J. Neethling, L. Courtois, K.J. Dobson, P.D. Lee, Multi-scale quantification of leaching performance using X-ray tomography, Hydrometallurgy, Vol. 164, 2016

R. Lastra, D. Paktunc, An estimation of the variability in automated quantitative mineralogy measurements through inter-laboratory testing, Minerals Engineering, Vol. 95, 2016

K.P. van der Wielen, G. Rollinson, Texture-based analysis of liberation behaviour using Voronoi tessellations, Minerals Engineering, Vol. 89, 2016

J. Li, M. Hitch, Characterization of the microstructure of mechanically-activated olivine using X-ray diffraction pattern analysis, Minerals Engineering, Vol. 86, 2016

E. Donskoi, A. Poliakov, R. Holmes, S. Suthers, N. Ware, J. Manuel, J. Clout, Iron ore textural information is the key for prediction of downstream process performance, Minerals Engineering, Vol. 86, 2016



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