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ECONOMY, ORGANIZATION AND MANAGEMENT
ArticleName Analytical modeling for the modern mining industry
DOI 10.17580/em.2019.02.07
ArticleAuthor Vostrikov A. V., Prokofeva E. N., Goncharenko S. N., Gribanov I. V.
ArticleAuthorData

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

Abstract

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.

keywords Expert analytical modeling, mining, digital technologies, intelligent systems, geoinformation data
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