QUANTITATIVE ASSESSMENT OF NATURAL VARIATION IN QUALITY OF COPPER ORE IN ROOF OF INTRUSION
The commercial reserves of the Norilsk copper–nickel ore show the trend of intensive reduction in metal content alongside with increasing variability of ore quality. This article describes justification and assessment of natural variation in ore quality in terms of MK-1 ore body of the Komsomolsky mine. Prediction of expected copper–nickel ore quality is discussed with a view to improving performance and economics of ore processing, as well as completeness of extraction from subsoil. From the analysis of huge initial data base, reliable correlations are established between copper–nickel processing performance, quality and material constitution stability. The implemented research finds that the study mine field is very complex in terms of natural variation in quality of copper–nickel ore occurring in the roof of intrusion (MK-1) and with relation to maintenance of material constitution stability in produced ore. High geological variability of quality in commercial reserves is the source of high quality variations in ore flows to processing plant. The Talnakh copper–nickel deposit can be considered as the future source of mineral reserves for the Norilsk mining and metallurgical industry and an expedient basis for development of future mining technologies for the Norilsk ore. The revealed indicators of natural variation in ore quality in subsoil predetermine measures to be undertaken to ensure uniform quality of produced ore. These organizational and production practices can be the framework for planning and modernization of mines in case of impoverishment of reserves in the Norilsk industrial region. Modification of current process flowsheet can improve produced ore composition stability and, thus, increase profit of the mine. This approach ensures economic efficiency of the entire mining and metallurgy industry in the Norilsk region.
For citation: Turtygina N. A., Okhrimenko A. V. Quantitative assessment of natural variation in quality of copper ore in roof of intrusion. MIAB. Mining Inf. Anal. Bull. 2019;(8):146-156. [In Russ]. DOI: 10.25018/0236-1493-2019-08-0-146-156.
: 622.273 DOI
: Turtygina N. A., Okhrimenko A. V.
Turtygina N.A., Cand. Sci. (Eng.), Assistant Professor,
Norilsk State Industrial Institute, 663310, Norilsk, Russia,
Okhrimenko A.V., Engineer mine planning,
Komsomolsky Mine, Norilsk, Russia.
Corresponding author: N.A. Turtygina, e-mail: firstname.lastname@example.org.Key words
Mine, quality, stability, orepass, ore, indicators, correlation dependences, content, technology, process flowsheet.References
1. Gosudarstvennyy doklad «O sostoyanii i ispol'zovanii mineral'no-syr'evykh resursov Rossiyskoy Federatsii v 2016 i 2017 godakh» [Governmental Report on Condition and Use of Mineral Reserves and Mineral Resources in the Russian Federation in 2016 and 2017]. http://www.mnr.gov.ru/ (accessed 15.05.2019).
2. Batugina N. S., Gavrilov V. L., Tkach S. M. Error of mine profit variation during development of complex-structure deposit. Gornyy zhurnal. 2018, no 12, pp. 26—30. [In Russ].
3. Lomonosov G. G. Proizvodstvennye protsessy podzemnoy razrabotki rudnykh mestorozhdeniy: monografiya [Underground ore mining processes: monograph], Moscow, Izd-vo «Gornaya kniga», 2013, 512 p.
4. Pavlishina D. N., Shumilov P. A., Tereshchenko S. V. Development of Ore Quality Control software module. Sbornik nauchnykh trudov KF Petr GU. Apatity, 2015, pp. 27—30. [In Russ].
5. Tereshchenko S. V., Marchevskaya V. V., Chernousenko E. V., Pavlishina D. N., Rukhlenko E. D. Analysis of priority trends in mineral processing. Gornyy informatsionno-analiticheskiy byulleten’. 2015, no 6, pp. 105—112. [In Russ].
6. Turtygina N. A. Miroshnikova L. K., Volkov N. A., Karpenko I. O. Promising technique of prediction and prospecting of mature Norilsk deposits with a view to produced ore quality control. Gornyy informatsionno-analiticheskiy byulleten’. 2016, no 2, pp. 313—318. [In Russ].
7. Turtygina N. A., Okhrimenko A. V. Numerical modeling results on in-situ ore pre-concentration technology. Gornyy informatsionno-analiticheskiy byulleten’. 2018, no S15, pp. 3—12. [In Russ].
8. Turtygina N. A., Okhrimenko A. V., Koval'chuk A. A. Planning of mining operations as an organizational method to control mineral quality in mine. Gornyy informatsionno-analiticheskiy byulleten’. 2018, no S17, pp. 3—11. DOI: 10.25018/0236-1493-2018-5-17-3-11. [In Russ].
9. Changik H., Ende W., Jianming X., Sunggi Y. Application of Multi-Gene Genetic Programming in Kriging Interpolation. Journal of Geoscience and Environment Protection, 2015, Vol. 3, no 5, pp. 27—34.
10. Pitombo C. S., Da Costa A. S. G., Salgueiro A. R. Proposal of a sequential method for spatial interpolation of mode choice. Boletim de Ciências Geodésicas, 2015, Vol. 21, no 2, pp. 274—289.
11. Poniewierski J. Negatively Geared Ore Reserves — A Major Peril of the Break-even Cutoff Grade. Good Practice and Communication: proceedings AusIMM Project Evaluation Conference, 8—9 March 2016. Carlton South: The Australasian Institute of Mining and Metallurgy, 2016. pp. 236—247.
12. Oggeri C., Oreste P. Underground Quarrying for Marble: Stability Assessment through Modelling and Monitoring. International Journal of Mining Science (IJMS), 2015, Vol. 1, no 1, pp. 35—42.