Procedure for modeling and synthesizing of technological systems for coal mines based on integration with the smart paradigms
O.Yu. Kozlova1, V.V. Agafonov2, V.V. Kozlov3
1 MIREA – Russian Technological University, Moscow, Russian Federation
2 National University of Science and Technology “MISIS”, Moscow, Russian Federation
3 Moscow Polytechnic University (Tuchkovsky Branch), Tuchkovо, Russian Federation
Russian Mining Industry №2/ 2026 p. 74-77
Abstract: The procedure for synthesizing design solutions for technological systems in coal mines is updated and formalized within the framework of combining components of fuzzy logic, artificial neural networks, probabilistic reasoning and evolutionary algorithms. At the same time, hybridization and integration of the smart project information processing methods is linked to special procedures that allow for "soft computing" as part of Data Mining, ensuring their new functionality. Methodological features of the proposed scientific and methodological approach provide an opportunity for joint application of the two models (probabilistic-statistical and deterministic). This is consistent with the Bayesian approach to justifying design solutions and clearly allows for the most comprehensive consideration of the uncertainty factor, expanding the boundaries of the possibility of purposeful intellectual generation of the best alternatives for design solutions in the synthesis of technological systems for coal mines.
Keywords: coal mine, technological system, modeling of technological systems, fuzzy logic, neural networks, result uncertainty, investment risks
For citation: Kozlova O.Yu., Agafonov V.V., Kozlov V.V. Procedure for modeling and synthesizing of technological systems for coal mines based on integration with the smart paradigms. Russian Mining Industry. 2026;(2):74–77. https://doi.org/10.30686/1609-9192-2026-2-74-77
Information about the article
Received: 29.12.2025
Revised: 24.02.2026
Accepted: 25.02.2026
Information about the authors
Olga Yu. Kozlova – Cand. Sci. (Eng.), Associate Professor at the Department of Higher Mathematics-3, MIREA – Russian Technological University, Moscow, Russian Federation; e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
Valery V. Agafonov – Dr. Sci. (Eng.), Professor at the Department of Geotechnologies for Subsurface Development, Mining Institute, National University of Science and Technology “MISIS”, Moscow, Russian Federation; e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
Valery V. Kozlov – Dr. Sci. (Eng.), Associate Professor, Moscow Polytechnic University (Tuchkovsky Branch), Tuchkovо, Russian Federation; e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
References
1. Kretov V.A., Kozlova O.Yu. Synthesis of organizational-technological, organizational-technical and organizational-management solutions that have the greatest synergistic effect within the mining enterprise. Ugol’. 2022;(8):108–111. (In Russ.) https://doi.org/10.18796/0041-5790-2022-8-108-111
2. Shupletsov A.F., Latysheva M.A., Skorobogatova Yu.A. The model of removing uncertainty using the theory of fuzzy sets as a mechanism for identifying industry bottlenecks. Issues of Social-Economic Development of Siberia. 2019;(2):89–94. (In Russ.) Available at: https://brstu.ru/static/unit/journal_2/docs/number-36/89-94.pdf (accessed: 15.12.2025).
3. Pavlysh V.N., Peretolchina G.B. The mathematical modeling of non-stationary processes in environment with left certain parameters. Problems of Artificial Intelligence. 2018;(2):33–45. (In Russ.) Available at: http://paijournal.guiaidn.ru/download_pai/2018_2/2_Павлыш_Перетолчина.pdf (accessed: 15.12.2025).
4. Trofimov Yu.V., Muravyov I.P., Averkin A.N., Lebedev A.D., Kuznetsov E.M., Trusov I.A. et al. A plithogenic-fuzzy multilayer perceptron (PN- MLP) integrating fuzzy, intuitionistic-fuzzy, neutrosophic and plithogenic logics with XAI 2.0 for a breast cancer CAD platform. Fuzzy Systems and Soft Computing. 2025;20(1):5–35. (In Russ.) https://doi.org/10.26456/fssc132
5. Latyshev A.V., Romakin V.A., Khachumov V.M., Khachumov M.V. Methods and models of automatic knowledge-based synthesis of technological processes. Program Systems: Theory and Applications. 2016;7(3):25–43. (In Russ.) Available at: https://www.mathnet.ru/rus/ps223 (accessed: 15.12.2025).
6. Semina L.A., Kovaleva I.V., Kudinova M.G., Gornostal R.G. A system of balanced indicators as a tool for increasing investment attractiveness in the formation of innovative projects. Journal of Applied Research. 2025;(10):84–89. (In Russ.)
7. Tebekin A.V. Decision making criteria based on Arthur D. Little’s matrix model (ADL/LC). Journal of Management Studies. 2025;11(3):3–24. (In Russ.) Available at: https://naukaru.ru/ru/nauka/article/103329/view (accessed: 15.12.2025).
8. Yelezhanova Sh., Turzhanov N., Idrissov S., Dyussembina Zh. Development of an innovative course reengineering of information processes. Academic Scientific Journal of Computer Science. 2023;(4):290–299. (In Russ.) Available at: https://journals.nauka-nanrk.kz/physics-mathematics/article/view/5885 (accessed: 15.12.2025).
9. Belousov К.I., Bashirov R.К., Zelyanskaya N.L., Labutin I.А., Ryabinin К.V., Chumakov R.V. Profiling of conceptual systems based on a complex of methods of psychosemantics and machine learning. Automatic Documentation and Mathematical Linguistics. 2023;(7):1–14. (In Russ.) https://doi.org/10.36535/0548-0027-2023-07-1
10. Mirasova K.N. Ayn rand in the global world. The New Past. 2022;(4):160–174. (In Russ.) https://doi.org/10.18522/2500-3224-2022-4-160-174
