Gipronikel Institute LLC, Saint Petersburg, Russia:

T. N. Khashkovskaya, Principal Specialist at the Laboratory for Geological Studies of Raw Materials, e-mail: KhashkovskayaTN@nornik.ru
D. V. Lyalinov, Senior Researcher at the Laboratory for Geological Studies of Raw Materials, e-mail: LyalinovDV@nornik.ru
E. N. Kolesnikova, 1^st Category Engineer at the Laboratory for Geological Studies of Raw Materials, e-mail: KolesnikovaEN@nornik.ru
V. I. Maksimov, Chief of Operations Support at the Laboratory for Geological Studies of Raw Materials, e-mail: MaksimovVI@nornik.ru

This paper describes a mineralogical study that looked at the copper-nickel ores of the Talnakh Ore Cluster. It also describes attempts to develop a method for predicting concentration performance on the basis of mapping data. The geological classification of the Talnakh ores developed in 1987–1992 is rather a classification of natural types and varieties as it fails to specify any particular concentration process indicators. The copper-nickel ores of the Talnakh Ore Cluster are multicomponent and it is quite difficult to develop a classification based on concentration performance that would account for the quality of concentrates and the recovery of non-ferrous and noble metals. In the period of 2015–2020, a series of experiments and a mineralogical study were conducted for 107 samples of impregnated ore, 105 samples of copper ore and 60 samples of high-grade ore under the contracts with the R&D Office of Nornickel’s Polar Division. Based on the results of the experiments, a classification was developed for impregnated, copper and high-grade ores based of one selected actual process indicator. Due to the use of a mining information system, each box of a block model representing an ore body (or, each mining unit with known characteristics) can be assigned appropriate concentration indicators. On the basis of geological mapping results, a method was applied for indirect calculation of expected concentration indicators based on a number of attributes. With the help of this method, the authors were able to determine the actual indicators, estimate the calculation error and find a way to improve the predictability of the model through analyzing additional samples and attributes.

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