
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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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
Development of course feedback questionnaires of continuing professional education in the mining industry
Date of publication: 2019-01-01
Journal: Innovation-Based Development of the Mineral Resources Sector: Challenges and Prospects - 11th conference of the Russian-German Raw Materials, 2018
Authors: Zhukovskiy, Y.L, Koteleva, N.I, Kovalchuk, M.S.
Annotation
The presented study shows the impact of changes in the content of continuing education programs on the assessment of the quality of courses by students in the center of additional vocational education. The developed system of evaluation and input and output questionnaires made it possible to identify point changes that need to be implemented to improve the quality of both the course attendees and the customers. The presented system of impact on the quality of professional development through continuous balancing of the thematic content of the program of additional professional education allows managing the development of the competencies of employees. Point changes in the proportions of the content blocks of the programs made it possible to achieve the optimal set of topics that satisfies the needs of the customer in terms of developing certain competences in the employees, as well as the course participants themselves in terms of interest in learning and personal growth. The developed approach makes it possible for the company of the customer to introduce a system of continuous professional training, advanced training and retraining of personnel by controlling the level of competence of trained employees to improve performance.

Evaluation of bulk material behavior control method in technological units using dem. Part 2
Keywords:Bulk materials | Classification of motion modes | DEM-modeling | LSTM | Neural networks | Pelletizing drums | RNN
Date of publication: 2020-01-01
Journal: CIS Iron and Steel Review
Authors: Boikov, A.V, Savelev, R.V, Payor, V.A, Potapov, A.V.
ISSN:24141089
Q1
(Scimago)
The research is dedicated to the development of special devices (capsules) that can be used to control the mining ore behavior in the technological unit in order to increase processes efficiency. In the first part of the article, the choice of the discrete element method for gen-erating various particle trajectories in the unit (drum pelletizer) was substantiated. This part describes the specific technologies that were used to recognize the pelletizing mode. In par-ticular, conversation of paths to sensor readings is implemented using the Matlab Sensor Fusion and Tracking Toolbox. The obtained readings were processed using two neural network classifiers (DNN and LSTM). As a result, stable models for recognizing the pelletizing modes of the unit were obtained. LSTM recognition accuracy is greater than DNN. The developed approach can be used to recognize the operating modes of other technological units. In addition, data on particles trajectories can be used to improve DEM models of technological processes. Future work consists of the capsule physical implementation and testing the recognition algorithm on a real unit.

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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