Hypotheses on optimization of mining systems operating parameters using predictive analytics
V.N. Zakharov, M.V. Rylnikova, D.A. Klebanov, D.N. Radchenko
Institute of Comprehensive Exploitation of Mineral Resources of Russian Academy of Sciences, Moscow, Russian Federation
Russian Mining Industry №5 / 2023 р. 38-42
Abstract: The article assesses possible approaches to enhancing the efficiency of data collection to manage mining systems and it proposes an option to formulate hypotheses on optimizing their operating parameters in dynamically changing mining and geological, mining engineering and external conditions. The method of formulating and verifying hypotheses on implicit relationships between the parameters of mining and related systems appears to be an efficient tool for targeted data collection from digital sources and their storage for use in predictive analytics. The second approach is creation of artificial intelligence systems to work with data that aim to identify deviations and adjust the operating parameters of the mining system based on retrospective analysis of the collected arrays of historical information, without having hypotheses formulated in advance when managing the mining system. In addition to hypotheses on the regularities and relationships between the mining system parameters, the article emphasizes the importance of forecasting and accounting for the extent of changes in the parameters of the related systems. The related systems mean such systems as the environment and society, which interact with the mining systems in time and space. Moreover, the functioning of the latter, due to the global scale of man-caused transformation of the lithosphere, results in inevitable changes in the state of the related systems. Only the big data technologies can make it possible to reveal implicit regularities in changes in the parameters of each adjacent system, including identification of rational indicators of the mining systems operation. The paper emphasizes the data collection based on the principle of capturing all changes of the information from the digital sources. Based on this principle, we propose to standardize the approach to data collection for mining systems management.
Keywords: mining system, optimization of production, Big Data, predictive analytics, data collection, environment, optimization beneficiaries
Acknowledgments: The research was financially supported by the Russian Science Foundation Grant No.22-17-00142.
For citation: Zakharov V.N., Rylnikova M.V., Klebanov D.A., Radchenko D.N. Hypotheses on optimization of mining systems operating parameters using predictive analytics. Russian Mining Industry. 2023;(5):38–42. (In Russ.) https://doi.org/10.30686/1609-9192-2023-5-38-42
Article info
Received: 29.07.2023
Revised: 23.08.2023
Accepted: 28.08.2023
Information about the authors
Valerii N. Zakharov – Corresponding Member of RAS, Dr. Sci. (Eng.), Professor, Director, Institute of Comprehensive Exploitation of Mineral Resources Russian Academy of Sciences, Moscow, Russian Federation; ORCID: https://orcid.org/0000-0002-9309-2391
Marina V. Rylnikova – Dr. Sci. (Eng.), Professor, Institute of Comprehensive Exploitation of Mineral Resources of Russian Academy of Sciences, Moscow, Russian Federation; e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
Dmitry A. Klebanov – Cand. Sci. (Eng.), Head of Laboratory of Intelligent Systems and Digital Technologies, Institute of Comprehensive Exploitation of Mineral Resources of Russian Academy of Sciences, Moscow, Russian Federation; e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
Dmitry N. Radchenko – Cand. Sci. (Eng.), Associate Professor, Head of the Laboratory of Theoretical Fundamentals for Mining Systems Design, Institute of Comprehensive Exploitation of Mineral Resources Russian Academy of Sciences, Moscow, Russian Federation
References
1.Temkin I., Klebanov D., Deryabin S., Konov I. Predictive analytics in mining. Dispatch system is the core element of creating intelligent digital mine. In: Sukhomlin V., Zubareva E. (eds) Modern Information Technology and IT Education. SITITO 2018. Communications in Computer and Information Science. Vol. 1201. Springer, Cham; 2020, pp. 365–374. https://doi.org/10.1007/978-3-030-46895-8_28
2. Rylnikova M.V., Klebanov D.A., Makeev M.A., Kadochnikov M.V. Application of artificial intelligence and the future of big data analytics in the mining industry. Russian Mining Industry. 2022;(3):89–92. (In Russ.) https://doi.org/10.30686/1609-9192-2022-3-89-92
3. Trubetskoy K.N., Kuleshov A.A., Klebanov A.F., Vladimirov D.Ya. Contemporary management systems for mining and transport complexes. St. Petersburg: Nauka; 2007. 306 p. (In Russ.) Available at: https://www.geokniga.org/books/16307?
4. Temkin I.O., Klebanov D.A., Deryabin S.A., Konov I.S. Construction of intelligent geoinformation system for a mine using forecasting analytics techniques. Mining Informational and Analytical Bulletin. 2020;(3):114–125. (In Russ.) https://doi.org/10.25018/0236-1493-2020-3-0-114-125
5. Zakharov V.N., Kaplunov D.R., Klebanov D.A., Radchenko D.N. Methodical approaches to standardization of data acquisition, storage and analysis in management of geotechnical systems. Gornyi Zhurnal. 2022;(12):55–60. (In Russ.) https://doi.org/10.17580/gzh.2022.12.10
6. Klebanov F.S. Adeyology: a general theory of safety. Moscow: Korina-ofset; 2011. 136 p. (In Russ.)
7. Mullins C.S. Extract, Load, Transform (ELT). Available at: https://www.techtarget.com/searchdatamanagement/definition/Extract-Load-Transform-ELT
