
The speed of scientific and technological progress and the disappearance of certain activities associated with the penetration of automation into all areas of production and management processes are factors of possible growth for enterprises of the future. Digital integration, which integrates scientific directions, people, processes, users and data, will create the conditions for scientific and technological advances and breakthroughs, enabling scientific and economic shifts in related industries and, above all, in the global mineral market. In this regard, in 2018, for the purpose of training, research and development in the field of digital technologies for the enterprises of mineral and fuel and energy complexes, the "Educational Center of Digital Technologies" was established at the Mining University.
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Directions of scientific research

Study of efficient development and functioning of energy systems on new technological basis, energy saving principles, modern electrical engineering, RES

Theory and methodology of information support of subsoil use objects

Creation of a system of continuous training and professional development aimed at forming professional digital competences of specialists required to ensure the innovative development of the fuel and energy complex

Energy saving and energy efficiency improvement

Transition to advanced digital, intelligent production technologies, robotic systems at the enterprises
This direction implies the consideration of intellectual technologies of electric power systems management, including electric power transmission, electric power demand management, digital twins of electric power facilities, digital information models of electrical engineering systems.
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Within the framework of this direction, new methods of monitoring and management based on digital and information technologies are being developed, and information systems are being created to solve mining industry problems.
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This direction is aimed at the development and popularisation of engineering education, improvement of digital competencies of employees and students, as well as implementation of additional professional education programmes for representatives of fuel and energy complex companies.
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This direction implies research and substantiation of complex indicators of efficiency of energy generation, transport and consumption when supplied from traditional and renewable energy sources, taking into account the impact of global challenges and variation of external factors.
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Within the framework of this direction, research is carried out aimed at improving the efficiency of equipment and technological processes of mining, processing and transporting minerals.
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Scientific publications

Monitoring the technical condition of autonomous electrical systems with electric drive
Date of publication: 2019-12-18
Journal: E3S Web of Conferences
Authors: Korolev, N, Solovev, S.
ISSN:22671242
The article discusses the structure of autonomous electrical systems with an electric drive and an electric energy generation system. An approach is described for assessing the overall efficiency of technological cycle equipment with specification of parameters that must be considered when calculating it. The necessity of taking into account energy efficiency and operability indicators of autonomous electric complexes is substantiated. The effectiveness of the use of autonomous systems decreases throughout the entire life cycle due to wear and tear. As the operability of electromechanical equipment, an indicator of the residual resource is selected. The calculation of the residual resource, with the definition of its boundary values and recommendations for further use. Nominal energy efficiency parameters of autonomous electrotechnical complexes with electric drive deteriorate during operation, which also needs to be taken into account. To simplify the assessment of the operational state of electromechanical equipment, a state diagram is presented that allows you to track the dynamics of degradation of individual nodes. Taking into account the described indicators and means of control, it is possible to increase the efficiency of using autonomous complexes.
Evaluation of bulk material behavior control method in technological units using dem. Part 1
Keywords:Bulk materials | Classification of motion modes | DEM-modeling | Neural networks | Pelletizing drums
Date of publication: 2020-01-01
Journal: CIS Iron and Steel Review
Authors: Boikov, A.V, Savelyev, R.V, Payor, V.A, Erokhina, O.O.
ISSN:24141089
Q1
(Scimago)
Nowadays pelletizing drums are widely used in the steel industry. These units are characterized by high endurance and low cost of maintenance. However, use and control of these units in the process of coarsening have a number of issues. For most of the cases pelletizing drums are “black box” and control accuracy can not be estimated exactly. It is explained by low existing theoretical basis of this production process. Particularly it is tied up with the variability of the bulk materials (charges) parameters supplied to the unit. Overcome of this issues can be reached with development of intelligent control systems for drum pelletizing machines. Main requirement for such systems is possibility to level or consider the effect of charges properties variability in control. However, it is necessary to study the behavior of bulk materials inside the units. Visual assessment of pelletization does not allow to evaluate the ongoing physical processes. Development of mathematical and numerical models can help studying the process and take a lot of parameters into account including charges properties and even interaction with water. But the adequacy of the resulting models also has to be clarified using physical devices to record or capture bulk materials behavior inside the units. This research proposes a DEM simulation test of the concept for bulk material behavior control through the recognition of the mixture movement fragments using special capsules. This part is dedicated to the simulation model set up and extracting the particles trajectories for further processing.

Synthetic data generation for steel defect detection and classification using deep learning
Keywords:Computer vision | Machine learning | Steel defect detection | Synthetic data
Date of publication: 2021-07-01
Journal: Symmetry
Authors: Boikov, A, Payor, V, Savelev, R, Kolesnikov, A.
ISSN:20738994
Q2
(Scimago)
The paper presents a methodology for training neural networks for vision tasks on synthe-sized data on the example of steel defect recognition in automated production control systems. The article describes the process of dataset procedural generation of steel slab defects with a symmetrical distribution. The results of training two neural networks Unet and Xception on a generated data grid and testing them on real data are presented. The performance of these neural networks was assessed using real data from the Severstal: Steel Defect Detection set. In both cases, the neural networks showed good results in the classification and segmentation of surface defects of steel workpieces in the image. Dice score on synthetic data reaches 0.62, and accuracy—0.81.
Partner reviews
"Together with the Educational Center of Digital Technologies at St. Petersburg Mining University, we have been collaborating for several years to shape fundamental and applied challenges and ideas for the digitalisation of the mining industry."
"We are very glad to be part of the process that the Educational Center of Digital Technologies at St. Petersburg Mining University is engaged in. We are confident that this centre can become an assembly point for all those new solutions that will bring the mining industry to a new level."
The Committee for the Fuel and Energy Complex of the Leningrad Region expresses its gratitude to you for your support in holding the Festival and organising an informative exposition of the enterprise aimed at attracting the young generation to the fuel and energy complex profession.
Thanks to your efforts, we will be able to further educate young people full of strength and aspirations for knowledge and creativity in the field of energy saving.
We hope for further fruitful co-operation in the field of energy saving.
Thanks to your efforts, we will be able to further educate young people full of strength and aspirations for knowledge and creativity in the field of energy saving.
We hope for further fruitful co-operation in the field of energy saving.
On behalf of the Ministry of Energy of Russia, we would like to express our gratitude to the WeWatt team of young researchers for the great and necessary work for the industry, done under your leadership on a proactive and pro bono basis.
The results of this study will serve as a basis for further work in this area and will be useful to coal companies in carrying out digital transformation of production facilities, contributing to the effective and successful achievement of the goal.
The results of this study will serve as a basis for further work in this area and will be useful to coal companies in carrying out digital transformation of production facilities, contributing to the effective and successful achievement of the goal.
Institute for Problems of Integrated Subsoil Development, Dmitry Klebanov
Leonid Zhukov, Director of SITECH Division of Zeppelin Rusland Ltd.
Committee for Fuel and Energy Complex, Chairman of the Committee Y.V. Andreev
Ministry of Energy of the Russian Federation


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