National Research University Higher School of Economics, Moscow, Russia:

Vostrikov A. V., Associate Professor, Candidate of Engineering Sciences
Prokofeva E. N., Associate Professor, Candidate of Engineering Sciences, eprokofyeva@hse.ru
Gribanov I. V., Analyst

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

Goncharenko S. N., Professor, Doctor of Egineering Sciences

The modern mining industry has huge innovative potential for the introduction and development of digital revolution products. It has always been the most important industry of modelling development, as many operations and processes here are directly empirical and provide a large amount of data for quantitative analysis, which is now well suited to the use of digital intelligent technologies. With the development of digital technologies, effective integrated modeling techniques and the introduction of new process management, knowledge and data analysis tools are needed. Analytical models here are primarily designed to symbolize object properties in dynamics. Intelligent models and solutions based on the use of information technologies and methods of working with big data were becoming most popular, and the processes of integrated monitoring, personalization, risk management, search and generation of solutions, web orientation of programs and technologies and formation of network organizational structures of management were becoming particularly important. Mining enterprises have specific risks: mining and geological risks, risks of loss of market share and investment attractiveness due to biased valuation of useful fossil reserves, risks related to cybersecurity and innovation. Enterprises need to implement new technologies in a comprehensive manner, and information innovation is becoming very important in the face of a lack of financial resources. Expert systems, fuzzy logic, neural networks and genetic algorithms are the most relevant applications in international practice of geoinformation resource management, which largely determines the practical use of artificial intelligence methods and tools in interaction with pound-based management solutions. Modern analytical expertise includes the integration of process management systems, in particular those that are different, which is based on the development of a large number of integration technologies and techniques that apply different data models and are carried out through different procedures. The study examines the development of analytical models based on intelligent technologies, which are now increasingly used in various areas of the mining industry.

1. Forecast of scientific and technological development of the Russian Federation for the period until 2030 (approved. The government of the Russian Federation on 3 January 2014).
Available at: http://www.garant.ru/products/ipo/prime/doc/70484380/#ixzz4aP1zXHhy (accessed: 27.03.2019).
2. The Development Strategy of Information Technology Industry in the Russian Federation for 2014–2020, and the Prospect for 2025; Available at: http://government.ru/docs/8024/ (accessed: 27.03.2019).
3. Yeralin Z. M., Goncharenko S. N. Models for solving key problems of strategic development of uranium mines. GIAB. 2019. No. 4. pp. 199–208.
4. Prokofeva E. N., Vostrikov A. V. Assessment of the quality of information flow management in organizations. Vestnik RIAT. 2017. No. 2. pp. 45–48.
5. Prokofeva E. N., Vostrikov A. V., Fernandez E., Borisov N. Navigation satellite systems as the audit foundation for mining companies. Eurasian Mining. 2017. No. 1. pp. 30–32. DOI: 10.17580/em.2017.01.08
6. Prokofeva E. N., Vostrikov A. V., Shapovalenko G. N., Alvarez A. The development of effective geomonitoring for mining area with industrial review. Eurasian Mining. 2017. No. 2. pp. 61–63. DOI: 10.17580/em.2017.02.15
7. Everything about mining. Extractive industries. Available at: http://industry-portal24.ru (acccessed: 5.04.2019).
8. Mining – Info portal. Available at: http://mining-info.ru/sovremennye-sistemy-modelirovaniya-p/ (acccessed: 27.03.2019).
9. Zotov L., Frolova N., Shum C. Gravity Changes over Russian River Basins from GRACE. Planetary Exploration and Science: Recent Results and Advances. Berlin : Birkhauser, Springer, 2015.

10. Zotov L., Bizouard C., Shum C. A possible interrelation between Earth rotation and climatic variability at decadal time-scale. Geodesy and Geodynamics. 2016. Vol. 7, No. 3. pp. 216–222.
11. Aleskerov F. T., Karabekyan D., Ivanov A., Yakuba V. I. Individual manipulability of majoritarian rules for one-dimensional preferences. Procedia Computer Science. 2018. Vol. 139. pp. 212–220.
12. Vasin S., Gamidullaeva L., Shkarupeta E., Finogeev A., Palatkin I. Emerging trends and opportunities for industry 4.0 development in Russia. European Research Studies Journal. 2018. Vol. XXI, Iss. 3. pp. 63–76.
13. Tolstykh T., Shkarupeta E., Shishkin I., Dudareva O., Golub N. Evaluation of the digitalization potential of region’s economy. Advances in Intelligent Systems and Computing. 2018. Vol. 622. pp. 736–743. DOI: 10.1007/978–3-319–75383–6
14. Goncharenko S. N., Duong L. B., Petrov M. V., Stoyanova I. A. Modeling of parameters of innovation water-protection measures on the basis of industrial-technological indices of coal mining at Vietnam enterprises. Gornyi Zhurnal. 2014. No. 9. pp. 143–146.
15. Potekhin I., Mischenko V., Mottaeva A., Zheltenkov A. Evaluation of possibility to increasing sustainability of high-rise buildings through use university intellectual property. E3S Web of Conferences. 2018. Vol. 33. 03020. DOI: 10.1051/e3sconf/20183303020
16. Gaydin A. M. From Geotechnology to geoesthetic. Gornyi Zhurnal. 2009. No. 4. pp. 72–76.
17. Ilin S. A., Kovalenko V. S., Pastikhin D. V The overcoming of the open cut mining initial disadvantages: experience and results. Gornyi Zhurnal. 2012. No. 4. pp. 25–32.
18. Mineral Commodity Summaries 2016, United States Geological Survey. p 202. Available at: https://minerals.usgs.gov/minerals/pubs/mcs/2016/mcs2016.pdf (accessed: 10.03.2019).
19. A Report on the State of the Canadian Mining Industry. Factsand-Figures-2016. 119 р. Available at: http://mining.ca/sites/default/files/documents/Facts-and-Figures-2016.pdf (accessed: 09.03.2019).
20. Ludden J. BGS and the Comprehensive Spending Review 2015. Available at: http://britgeosurvey.blogspot.ru/2015/12/bgs-and-comprehensive-spending-review.html (accessed: 17.03.2019).
21. Fernández S., Fernández J. E., Álvarez A. Assessment of quality assurance models University institutional evaluation and academic achievement. European University Association. Spring, 2018. pp. 71–78; 100.
22. Ganitskiy V. I., Dayanits D. G., Vorobyev A. G., Eyrikh V. I. About development of innovation activity and it’s staffing in the mining industry. Gornyi Zhurnal. 2011. No. 12. pp. 27–30.
23. Aleskerov F., Ivanov A., Karabekyan D., Yakuba V. Manipulability of Aggregation Procedures in Impartial Anonymous Culture. Procedia Computer Science. 2015. Vol. 55. pp. 1250–1257.
24. Vartanov A. Z., Petrov I. V., Kobyakov A. A., Romanov S. M., Fedash A. V. Ecological and economic aspects of the transition of the mining enterprises on the principles of best available technologies. GIAB. 2015. No. 1. pp. 511–521.
25. Goncharenko S. N., Kobyakov A. A., Petrov I. V., Stoyanova I. A. Economic-mathematical modeling of the distribution of the value of the cost of the preservation and restoration of the environment in the areas of mass closure of coal mines. Ecological and economic problems of the mining industry and development of fuel and energy complex of Russia: Preprint. GIAB. Special articles (special issue). Moscow : Mining book, 2012. pp. 20–25.
26. Kaplunov D. R., Ryl'nikova M. V., Radchenko D. N. Utilization of renewable energy sources in hard mineral mining. Journal of Mining Science. 2015. No. 1. pp. 111–117.
27. Korinek J., Ramdoo I. Local content policies in mineral-exporting countries. OECD Trade Policy Papers. 2017 No. 209. DOI: 10.1787/4b9b2617-en.
28. Mining Digital Report 2017. PwC School of Mines. Available at: http://www.pwc.com (accessed: 09.03.2019).
29. Mining for efficiency reports. PwC School of Mines. Available at: http://www.pwc.com (accessed: 01.04.2019).
30. Temkin I., Deryabin S., Konov I. Soft computing models in an intelligent open-pit mines transport control system. Procedia Computer Science. 2017. Vol. 120. pp. 411–416.
31. Temkin I. O., Do Chi Thanh, Agabubaev A. T. Some algorithms of functioning analytical platform in the control system of ventilation methane abundant mine. GIAB. 2018. No. S16. pp. 3–15.
32. Temkin I. O., Klebanov D. A., Deryabin S. A., Konov I. S. Haul road condition determination under controlled interaction of robotic elements in open pit mining and transport system. Gornyi Zhurnal. 2018. No. 1. pp. 78–82. DOI: 10.17580/gzh.2018.01.14