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Schungite raw material quality evaluation using image processing method
Author(s): Aleksandr N. Chertov; Elena V. Gorbunova; Roman V. Sadovnichii; Natalia N. Rozhkova
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Paper Abstract

In modern times when technologies are developing rapidly, the high-carbon schungite rocks of Karelia are promising mineral raw material for production of active fillers for composite materials, radio shielding materials, silicon carbide, stable aqueous dispersions, sorbents, catalysts, carbon nanomaterials, and other products. An intensive evolution of radiometric separation and sorting methods based on different physical phenomena occurring in the interaction of minerals and their constituent chemical elements with different types of radiation open new enrichment opportunities for schungite materials. This is especially pertinent to optical method of enrichment, which is a part of radiometric methods. The present work is devoted to the research and development of preliminary quality assessment principles for raw schungite on the basis of image processing principles and perspectives of the optical separation for its [schungite] enrichment. Obtained results of preliminary studies allow us to describe the selective criteria for separation of mentioned raw material by optical method, as well as to propose the method of quality indicator assessing for schungite raw materials. All conceptual and theoretical fundamentals are corroborated by the results of experimental studies of schungite rock samples with breccia and vein textures with different sizes from Maksovo deposit.

Paper Details

Date Published: 26 June 2017
PDF: 10 pages
Proc. SPIE 10334, Automated Visual Inspection and Machine Vision II, 103340T (26 June 2017); doi: 10.1117/12.2270389
Show Author Affiliations
Aleksandr N. Chertov, ITMO Univ. (Russian Federation)
Elena V. Gorbunova, ITMO Univ. (Russian Federation)
Roman V. Sadovnichii, Karelian Research Ctr. (Russian Federation)
Natalia N. Rozhkova, Karelian Research Ctr. (Russian Federation)


Published in SPIE Proceedings Vol. 10334:
Automated Visual Inspection and Machine Vision II
Jürgen Beyerer; Fernando Puente León, Editor(s)

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