Schungite raw material quality evaluation using image processing method

Aleksandr N. Chertov; Elena V. Gorbunova; Roman V. Sadovnichii; Natalia N. Rozhkova

doi:10.1117/12.2270389

26 June 2017 Schungite raw material quality evaluation using image processing method

Aleksandr N. Chertov, Elena V. Gorbunova, Roman V. Sadovnichii, Natalia N. Rozhkova

Proceedings Volume 10334, Automated Visual Inspection and Machine Vision II; 103340T (2017) https://doi.org/10.1117/12.2270389
Event: SPIE Optical Metrology, 2017, Munich, Germany

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.

Citation Download Citation

Aleksandr N. Chertov, Elena V. Gorbunova, Roman V. Sadovnichii, and Natalia N. Rozhkova "Schungite raw material quality evaluation using image processing method", Proc. SPIE 10334, Automated Visual Inspection and Machine Vision II, 103340T (26 June 2017); https://doi.org/10.1117/12.2270389

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