National University of Science and Technology — MISIS, Moscow, Russia:

V. V. Morozov, Professor, Doctor of Engineering Sciences, dchmggu@mail.ru

V. V. Rapshis, Post-Graduate Student

Erdenet Mining Corporation, Erdenet, Orhon, Mongolia:
Z. Ganbaatar, Counselor, Doctor of Engineering Sciences
L. Delgerbat, Chief Automated Process Control Specialist, Doctor of Engineering Sciences
A. M. Duda, Deputy Head of Automation and Computation Equipment Department

The article gives a description of system and algorithm for copper–molybdenum ore grade estimation based on material constitution and mineral composition of ore and on the ground of ore beneficiation control. The system includes X-ray-fluorescent and visiometric analyzers to be mounted above the crushed ore conveyor and allows real-time information on the material constitution, mineral composition and grade of ore. For higher-reliable X-ray fluorescence analysis, the analyzer was installed at an angle of 45° off the vertical and was furnished with mechanical protection from damage by ore fragments. For better identification, the multi-format image identification algorithm with modern chromatic image identification formats RGB and HSV was used. Grade of ore was calculated using a multi-criterial method, based on seven or more significant parameters of ore. The quantity of each of the five basic types of ore is determined in the preparation feed, and the ore enrichment process parameters are calculated adequately. The pre-set functions were used as the framework in the local automatic control systems. The descried algorithms of operational ore grade analysis and quality control are based on the continuous integrated radiometric analysis of ore samples taken by the analytical control system. The introduction of the proposed approaches ensures an increase in the grinding circuit output and the enhanced recovery of copper and molybdenum in concentrates.

1. Ulrig S. Process control by modern X-ray fluorescence (XRF) analysis. Proceedings of the XX International mineral processing congress. Germany, 1997. pp. 175–182.
2. Hyotyniemi H., Ylinen R. Modelling of visual flotation froth data. Automation in mining, mineral and metal processing. Preprints of a 9^th IFAC Symposium, Cologn, Germany, 1–3 September 1998. Pergamon, 1998. pp. 309–314.
3. Moylanen Ya., Timperi Yu., Kemppinen Kh. Printsipy kompyuternogo upravleniya flotatsionnym protsessom na baze novoy produktsii Outotec — videosistemy Frothmaster (Principles of computer control of flotation process on the basis of new Outotec products: Frothmaster videosystem). Izvestiya vuzov. Gornyy zhurnal = News of the Higher Institutions. Mining Journal. 2010. No. 2. pp. 89–92.
4. Sbárbaro D., del Villar R. Advanced Control and Supervision of Mineral Processing Plants. London : Springer-Verlag London Limited, 2010. 332 p.
5. Bashlykova T. V., Pakhomova G. A., Lagov B. S. et al. Tekhnologicheskie aspekty ratsionalnogo nedropolzovaniya: rol tekhnologicheskoy otsenki v razvitii i upravlenii mineralno-syrevoy bazoy strany (Technological aspects of rational soil use: technological assessment role in development and management of the country’s mineral resource base). Under the scientific editorship of Yu. S. Karabasov. Moscow : MISiS, 2015. 576 p.
6. Morozov V. V., Ganbaatar Z., Delgerbat L., Bokányi L., Stoliarov V. F. Advanced system for control milling and flotation processes based on an estimation of ore quality grade. Advanced materials research. 2012. Vol. 651. pp. 981–985.
7. Morozov V. V., Avdokhin V. M., Ganbaatar Z., Delgerbat L., Ulitenko K. Ya. Upravlenie protsessami rudopodgotovki i obogashcheniya na osnove nepreryvnogo analiza sortnosti rudy (Control of ore preparation and concentration processes on the basis of continuous analysis of ore quality grade). Gornyy informatsionnoanaliticheskiy byulleten = Mining Informational and Analytical Bulletin. 2012. Vol. 1, No. 12. pp. 569–583.
8. Morozov V., Davaasambuu D., Ganbaatar Z., Delgerbat L. et al. Modern systems of automatic control of processes of grinding and flotation of copper-molybdenum ore. 16^th IFAC Symposium on Control, Optimization and Automation in Mining, Minerals and Metal Processing. 2013. Vol. 15, Part 1. IFAC (ed.). pp. 166–171.
9. HSV (tsvetovaya model) (HSV (color model)). Available at: https://ru.wikipedia.org/w/index.php?title=HSV_%28%D1%86%D0%B2%D0%B5%D1%82%D0%BE%D0%B2%D0%B0%D1%8F_%D0%BC%D0%BE%D0%B4%D0%B5%D0%BB%D1%8C%29&stable=1 (in Russian)
10. Ganzhargal S. Metody razdeleniya rudopotokov po kachestvu pri planirovanii gornykh rabot pri otkrytoy razrabotke mestorozhdeniya «Erdetiyn-Ovoo» (Methods of quality separation of ore flows during the planning of mining operations during the open- cast mining of «Erdetiyn-Ovoo» deposit). Marksheyderiya i nedropolzovanie = Mine surveying and subsurface use. 2006. No. 4. pp. 29–31.
11. Rogozhin A. A., Ozhogina E. G., Kordyukov S. V., Lygina T. Z. Sovremennye trebovaniya k izucheniyu veshchestvennogo sostava pri tekhnologicheskoy otsenke prirodnogo i tekhnogennogo mineralnogo syrya (Modern requirements to investigation of substantial composition during the technological assessment of natural and anthropogenic mineral resources). Obogashchenie Rud = Mineral processing. 2006. No. 6. pp. 12–16.
12. Kozin V. Z. Oprobovanie mineralnogo syrya (Sampling of mineral raw materials). Ekaterinburg : Publishing House of Ural State Mining University, 2011. 316 p.
13. Perez C. A., Estévez P. A., Vera P. A., Castillo L. E., Aravena C. M. et al. Ore grade estimation by feature selection and voting using boundary detection in digital image analysis. International Journal of Mineral Processing. 2011. Vol. 101. pp. 28–36.
14. Chatterjee S., Bhattacherjee A., Samanta B., Pal S. K. Image-based quality monitoring system of limestone ore grades. Computers in Industry. 2010. Vol. 61. pp. 391–408.