Ways to Reduce Ore Losses and Dilution in Iron Ore Underground Mining in Kryvbass

Introduction. Ukraine’s economic potential greatly depends on efficient operation of the national mining and metallurgical complex that provides 30% of GDP. One of the essential structural branches of the mining and metallurgical complex is iron ore mining industry. The quality of iron ore materials is the primary indicator ensuring their competitiveness in domestic and foreign markets. The quality of iron ore products is formed in the course of ore mining and processing into marketable products.
Problem statement. Worsening ore quality and losses is a serious problem in iron ore underground mining. It is caused by incomplete extraction of iron ore reserves (72-75% of the producing reserves) while breaking and drawing as well as by ore dilution with waste rocks, which causes an iron content reduction by 1.5-12% as compared with the initial iron content in the massif.
Purpose. The research aims at analyzing reasons for high ore losses and dilution in underground ore mining at Kryvyi Rih iron ore basin and at searching ways to reduce them.
Materials and methods. Analysis of known methods for solving the given problem has revealed that it is quite efficient to create an organizational and technical system that enables to forecast, to detect, and to promptly solve the reasons causing excessive ore losses and dilution.
Results. To efficiently control qualitative and quantitative parameters of ore at all stages of mining, transportation, and processing, an automated working place, Quality AWP, which provides data collection and representation at all stages of mining production at the central server is suggested to apply for continuous monitoring and analysis of ore quality characteristics.
Conclusions. Introduction of the complex of technical means and Quality AWP automated system enables tracking the ore qualitative characteristics on a continuous basis and reducing ore losses by 3% and dilution by 2%.

1. Azarian, A. A., Vilkul, Yu. H., Kaplenko, Yu. P., Karamanyts, F. I., Kolosov, V. O., Morkun, V. S., Pilov, P. I., Sydorenko, V. D., Temchenko, A. H., Fedorenko, P. I. (2006). Complex of resource- and energy-saving geotechnologies of mineral mining and processing, technical means of their monitoring with a system controlling and optimizing mining production. Kryvyi Rih: Mineral [in Ukrainian].
2. Azarian, A. A., Kolosov, V. A., Morhun, A. V., Popov, S. O., Stupnik, M. I. (2012). Instructions on normalizing, forecasting and recording indices of ore extraction in underground mining at iron ore deposits. Kryvyi Rih: Mineral [in Russian].
3. Posik, L. N., Koshelev, I. V., Bovin, V. P. (1960). Radiometric express-analysis of extracted ores. Moscow: Atomizdat [in Russian].
4. Kurchin, G. S. (2015). On the issue of normalizing ore losses and dilution on contacts in underground mining. Mine Surveying Bulletin, 4, 19-24 [in Russian].
5. Hyongdoo, J., Topal, E., Kawamura, Yo. (2015). Decision support system of unplanned dilution and ore-loss in underground stoping operations using a neurofuzzy system. Journal Applied Soft Computing archive, 32, Issue C, 1-12.
https://doi.org/10.1016/j.asoc.2015.03.043
6. Liimatainen, J. (1996). Economic optimization models for capacity and cut off determination. Mine planning and equipment selection, hennies. Balkema, Rotterdam.
7. Pengenceran, M., Tanah, O. Quantifying dilution for underground mine operations. 
URL: http://joenaldoe.blogspot.ru/2011/10/mengukur-pengenceran-untuk-operasi....
(Last accessed: 02.11.2011).
8. Elbrond, J. (1994). Economic effect of ore loss and rock dilution. CIM Bulletin, 87(978), 131-134.
9. Ebrahimi, A. (2013). An attempt to standardize the estimation of dilution factor for open pit mining projects. (2013). World Mining Congress. Montreal. 
URL: http://www.cim.org/en/Publications-and-Technical-Resources/Publications/...
(Last accessed: 09.09.2013)
10. Azaryan, A. A, Azaryan, V. A, Trachuuk, A. A. (2013, October). Quick response quality control of mineral raw materials in the pipeline. European Science and Technology. Materials of the V International scientific and practice conference. Munich, Germany.
11. Morkun, V., Morkun, N., Pikilnyak, A. (2014). Ultrasonic facilities for the ground materials characteristics control. Metallurgical and Mining Industry, 2, 31-35. 
URL: http://www.metaljournal.com.ua/assets/Journal/a6.pdf (Last accessed: 24.04.2014).
12. Morkun, V., Morkun, N. (2018). Estimation of the Crushed Ore Particles Density in the Pulp Flow Based on the Dynamic Effects of High-Energy Ultrasound. Archives of Acoustics, 43(1), 61-67. 
URL: http://acoustics.ippt.gov.pl/index.php/aa/article/view/2066/pdf_320, doi: 10.24425/118080 
(Last accessed: 09.03.2018).
13. Kotov, I. A. (2016). Automation of intellectual systems of supporting operative control decisions by incorporating professional ontologies. Bulletin “NTU XIII”. Series: Informatics and simulation, 44(1216), 63-76 [in Ukrainian].
https://doi.org/10.20998/2411-0558.2016.44.04
14. Kotov, I. A. (2008). Representation of logical models of decision making in production expert systems on the basis of Petri net machine. Ore mining: scientific and technical collection of articles, 92, 189-193 [in Russian].
15. Azaryan, V. A. (2014). Model of dynamic stabilization of quality fluctuations in the ore flow. Bulletin of Kryvyi Rih National University, 37, 18-22 [in Russian].
16. Azaryan, V. A., Zhukov, S. A. (2017). System principles and assessment criterion of generalization of controlling the quality of ore flows. Collection of scientific articles of National Mining University, 52, 41-46 [in Russian].
17. Zubkevich, V. Yu. (2013). Vector representation of measuring the substance composition of the mixture variable. Bulletin of Kryvyi Rih National University, 33, 156-159 [in Russian].