Physical and Chemical Geotechnologies

Abstract

This paper focuses on the aspects of determining technological parameters and stresses involved in the high-speed extraction of thin coal seams. Given the increasing demands for efficiency and environmental sustainability in coal mining, special attention is given to developing methods that optimize extraction processes while reducing costs and environmental impact. The study is grounded in theoretical calculations and modeling that aid in understanding the mechanisms of stress distribution within the coal seam during high-speed extraction. Key parameters such as the seam geometry, physical and mechanical properties of coal, along with the speed and method of extraction are considered. The primary aim of the research is to develop analytical methods for accurately predicting the optimal working conditions of equipment and minimizing risks associated with high stress levels in the seam. This includes determining optimal cutting speeds, cutting depths, and other critical parameters that ensure maximum productivity with minimal deformation and destruction of the coal seam. As a result, the paper proposes a comprehensive approach to analyzing and optimizing the extraction processes of thin coal seams, considering not only economic efficiency but also safety and environmental aspects. The conducted studies and developed methodologies can be applied in practice to enhance the efficiency of the coal industry, contributing to technological advancement in the field.

Keywords: high-speed extraction, mine, coal seam, technological parameters, correlation analysis

References

1. Chimunhu, P., Topal, E., Ajak, A. D., & Asad, W. (2022). A review of machine learning applications for underground mine planning and scheduling. Resources Policy, 77, 102693. Link
2. Babets, D.V., Sdvyzhkova, O.O., Larionov, M.H., Tereshchuk, R.M. (2017): Estimation of rock mass stability based on probability approach and rating systems. Naukovyi Visnyk Natsionalnoho Hirnychoho Universytety, (2), 58–64.
3. Downes, R. (2020): Remote Control: Organisational Control and Monitoring in Distributed Teams. Victoria University of Wellington, 370. Link
4. Dychkovskyi, R., Falshtynskyi, V., Ruskykh, V., Cabana, E., & Kosobokov, O. (2018): A modern vision of simulation modelling in mining and near mining activity. E3S Web of Conferences, 60, 00014. Link
5. Dyczko, A. (2023): The geological modelling of deposits, production designing and scheduling in the JSW SA Mining Group. Gospodarka Surowcami Mineralnymi - Mineral Resources Management. Link
6. Babets, D., Sdvyzhkova, O., Hapieiev, S., Shashenko, O., & Vasyl, V. (2023). Multifactorial analysis of a gateroad stability at goaf interface during longwall coal mining – A case study. Mining of Mineral Deposits, 17(2), 9–19. Link
7. Ebtehaj, I., Bonakdari, H., Zaji, A. H., Azimi, H., & Khoshbin, F. (2015): GMDH-type neural network approach for modeling the discharge coefficient of rectangular sharp-crested side weirs. Engineering Science and Technology, an International Journal, 18(4), 746–757. Link
8. Hatimah, I. (2017): Corporate Social Responsibility (CSR) Training Model. 1st International Conference on Educational Sciences, 135 -155 Link
9. Ivakhnenko, A. (1968): The Group Method of Data Handling-A Rival of the Method of Stochastic Approximation. Soviet Automatic Control, 1(3), 43-55.
10. Dychkovskyi, R.O., Tymoshenko, Y.V., & Astafiev, D.O. (2014). Method of analytical investigation of wall advance speed and forms of line face influence on stress-strain state of a rock massif. Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu, 1, 11–16.
11. Dodangeh, E., Panahi, M., Rezaie, F., Lee, S., Tien Bui, D., Lee, C.-W., & Pradhan, B. (2020): Novel hybrid intelligence models for flood-susceptibility prediction: Meta optimization of the GMDH and SVR models with the genetic algorithm and harmony search. Journal of Hydrology, 590, 125423. Link
12. Dychkovskyi, R., Tabachenko, M., Zhadiaieva, K., & Cabana, E. (2019): Some aspects of modern vision for geoenergy usage. E3S Web of Conferences, 123, 01010. Link
13. Sdvyzhkova, O., Dmytro, B., Moldabayev, S., Rysbekov, K., & Sarybayev, M. (2020). Mathematical modeling a stochastic variation of rock properties at an excavation design. 20th International Multidisciplinary Scientific GeoConference Proceedings SGEM 2020, Science and Technologies in Geology, Exploration And Mining. Link
14. Tymoshenko Ye. V. (2014). Substantiation of technological parameters for extraction of thin flat coal seams by plow systems: dissertation for the degree of candidate of technical sciences. National Mining University. - Dnipropetrovsk, 153.
15. Abdolrahimi, S., Nasernejad, B., & Pazuki, G. (2014): Prediction of partition coefficients of alkaloids in ionic liquids based aqueous biphasic systems using hybrid group method of data handling (GMDH) neural network. Journal of Molecular Liquids, 191, 79–84. Link
16. Astafiev, D., Niedbalski, Z., Leschhorn, F., &Tymoshenko, Y. (2016). Technological, economic and ecological aspects of selective coal mining from ultra-thin seams in conditions of Ukraine. Mining of Mineral Deposits, 10(1), 83–88. Link
17. Matyjaszek, M., Fidalgo Valverde, G., Krzemień, A., Wodarski, K., & Riesgo Fernández, P. (2020): Optimizing Predictor Variables in Artificial Neural Networks When Forecasting Raw Material Prices for Energy Production. Energies, 13(8), 2017. Link
18. Kiani, M., Hosseini, S.H., Taji, M., & Gholinejad, M. (2021). Risk criteria classification and the evaluation of blasting operations in open pit mines by using the FDANP method. Mining of Mineral Deposits, 15(2), 70-81. Link