Neural network technologies in mining data on particle size distribution of muck pile rocks
V.S. Velikanov1, 2, 3, A.V. Dremin1, S.A. Chernukhin3, N.V. Lomovtseva3
1 DAVTECH LLC, Ekaterinburg, Russian Federation
2 Ural Federal University named after the first President of Russia B.N. Yeltsin, Ekaterinburg, Russian Federation
3 Ural State Mining University, Ekaterinburg, Russian Federation
Russian Mining Industry №4 / 2024 p.90-94
Abstract:
INTRODUCTION. Current development of the mining industry increases the role of tools that support decision making and impact its speed. These trends have already affected almost all spheres of human activities and are expanding into the applied areas as artificial intelligence systems. The use of neural networks in the data mining technology is a current trend that is continuously evolving. The study solves the problem of defining the particle size distribution of muck pile rocks using the neural network technologies. The U-Net artificial neural network was used to solve the scientific and practical problem. This network had been trained to allow it to gain experience and adapt to the ongoing changes in the input data on the particle size distribution for different mineral deposits as it accumulates data.
METHODS. A complex approach that included a system scientific analysis and generalization of previously published studies was used in addressing the tasks set. The U-Net architecture was used for preliminary assessment of the particle size distribution.
RESULTS. The lump size parameters of the muck pile rocks were determined using the Russian-made Davtech equipment as the hardware and software support for experimental studies.
CONCLUSIONS. The data obtained in the course of the study will make it possible to develop recommendations to optimize the control modes of an open-pit excavator, which will ultimately reduce the number of equipment failures and increase its service life.
Keywords: mining industry, particle size distribution, rocks, blasting, open-pit excavator, neural network
For citation: Velikanov V.S., Dremin A.V., Chernukhin S.A., Lomovtseva N.V. Neural network technologies in mining data on particle size distribution of muck pile rocks. Russian Mining Industry. 2024;(4):90–94. (In Russ.) https://doi.org/10.30686/1609-9192-2024-4-90-94
Article info
Received: 19.05.2024
Revised: 01.07.2024
Accepted: 05.07.2024
Information about the authors
Vladimir S. Velikanov – Dr. Sci. (Eng.), Academic Adviser, DAVTECH LLC, Professor, Department of Hoisting and Hauling Machines and Robots, Ural Federal University named after the first President of Russia B.N. Yeltsin, Ekaterinburg, Russian Federation, Professor, Department of Automatics and Computer Technologies, Ural State Mining University, Ekaterinburg, Russian Federation; https://orcid.org/0000-0001-5581-2733; e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
Aleksandr V. Dremin – Director General, DAVTECH LLC, Ekaterinburg, Russian Federation
Stanislav A. Chernukhin – Cand. Sci. (Eng.), Associate Professor, Department of Automatics and Computer Technologies, Ural State Mining University, Ekaterinburg, Russian Federation Nataliya V. Lomovtseva – Cand. Sci. (Educ.), Associate Professor, Vice-Rector for Educational Activities and Digitalization, Ural State Mining University, Ekaterinburg, Russian Federation; e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
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