
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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Laboratory


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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Laboratory


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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Laboratory


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
The control method concept of the bulk material behavior in the pelletizing drum for improving the results of DEM-modeling
Keywords:A probe | Cast iron production | DEM-modeling | Digital twin | Drum pelletizers | Pelletizing automation | Track recovery | Wear pattern | Wear rate
Date of publication: 2019-01-01
Journal: CIS Iron and Steel Review
Authors: Boikov, A.V, Savelev, R.V, Payor, V.A, Erokhina, O.O.
ISSN:24141089
Q1
(Scimago)
One of the problems of the use of drum pelletizers in metallurgy is the lining wear, as well as the economic costs associated with it, including increased energy costs during operation and the need to periodically stop the units and then replace the lining. Most significantly the trajectory of particle motion affects the lining wear profile and wear intensity. It is assumed that during the implementation of the technological process, a monodisperse occurs, which has the greatest effect on the wear profile. In addition, the lining wear is influenced by the impact of particles at an acute angle, with a maximum impact caused by a collision at an angle of 39°18′. At present there are no universal solutions for determining the degree of lining wear in real time with a corresponding adjustment of the pelletizing process parameters. Creating a system for monitoring the lining wear is necessary for timely repair and maintenance of equipment to prevent an emergency situation, as well as increase the service life of the aggregates. This article proposes a concept of a method that allows to evaluate the trajectory of the charge during the technological process according to the coordinates of the movement and acceleration of the probe in the unit during the implementation of the technological process. The digitization and analysis of data obtained from the probe will allow to assess the integrity of the lining surface, the degree of lining wear and places with increased wear rate in real time with the possibility of adjusting technological processes to increase the lining service life. The obtained data will allow to clarify the computer model of the process by assessing the behavior of the charge in the unit and create a reserve for the further implementation of digital twin equipment.

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.

DEM Calibration Approach: Random Forest
Date of publication: 2018-12-10
Journal: Journal of Physics: Conference Series
Authors: Boikov, A.V, Savelev, R.V, Payor, V.A.
ISSN:17426596
A lot of researchers are developing new DEM parameters calibration approaches based on an experiment plan or the use of learning algorithms. This research is aimed at improving iterative algorithms frequently used for calibration. The big time consumption as a main problem of iterative algorithms is questioned. It is proposed to use Random forest algorithm to determine DEM parameters impact on the measured bulk responses. Measured responses are the parameters obtained by image processing using a technical vision system. As a result of 200 experiments processing, DEM parameters impact values on each bulk response were generated and presented as histograms. Obtained results were interpreted on the basis of the bulk material behavior and its physical properties. There is a discussion on the possibility of developing a universal DEM parameters calibration method based on the iterative algorithm.
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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