EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 567 (2024) E3S Web Conf., 567 (2024) 01002 References
Open Access
Issue
E3S Web Conf.
Volume 567, 2024
8th International Conference “Physical & Chemical Geotechnologies” 2024
Article Number 01002
Number of page(s) 19
DOI https://doi.org/10.1051/e3sconf/202456701002
Published online 09 September 2024
  1. Wollover, D. (1999). Design-To-Cost Decision Support System (DTC-DSS). The Journal of Cost Analysis & Management, 1(1), 3–33. https://doi.org/10.1080/15411656.1999.10462397 [CrossRef] [Google Scholar]
  2. Polyanska, A., Pazynich, Y., Mykhailyshyn, K., Babets, D., & Toś, P. (2024). Aspects of energy efficiency management for rational energy resource utilization. Rudarsko-Geološko-Naftni Zbornik, 39(3), 13–26. https://doi.org/10.17794/rgn.2024.3.2 [CrossRef] [Google Scholar]
  3. Dudek, M. (2017). The analysis of the low-cost flexibility corridors. In 2017 IEEE International Conference on Innovations in Intelligent Systems and Applications (pp. 478–483). Gdynia, Poland: Gdynia Maritime University. https://doi.org/10.1109/inista.2017.8001207 [Google Scholar]
  4. Dyczko, A., Malec, M., & Prostański, D. (2020). The efficiency of longwall systems in the case of using different cutting technologies in the LW Bogdanka. Acta Montanistica Slovaca, (25), 504–516. https://doi.org/10.46544/ams.v25i4.06 [Google Scholar]
  5. Polyanska, A., Savchuk, S., Dudek, M., Sala, D., Pazynich, Y., & Cicho, D. (2022). Impact of digital maturity on sustainable development effects in energy sector in the condition of Industry 4.0. Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu, (6), 97–103. https://doi.org/10.33271/nvngu/2022-6/097 [CrossRef] [Google Scholar]
  6. Kassymkanova, K.K. (2023). Geophysical studies of rock distortion in mining operations in complex geological conditions. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, (48), 57–62. https://doi.org/10.5194/isprs-archivesXLVIII-5-W2-2023-57-2023 [CrossRef] [Google Scholar]
  7. Malanchuk, Y., Moshynskyi, V., Khrystyuk, A., Malanchuk, Z., Korniyenko, V., & Zhomyruk, R. (2024). Modelling mineral reserve assessment using discrete kriging methods. Mining of Mineral Deposits, 18(1), 89–98. https://doi.org/10.33271/mining18.01.089 [CrossRef] [Google Scholar]
  8. Krot, K., & Lewicka, D. (2011). Human side of innovation-individual and organisational environment-related aspects: the case of IBM. International Journal of Innovation and Learning, 9(4), 352. https://doi.org/10.1504/ijil.2011.040535 [Google Scholar]
  9. Richert, M., & Dudek, M. (2023). Selected Problems of the Automotive Industry – Material and Economic Risk. Journal of Risk and Financial Management, 16(8), 368. https://doi.org/10.3390/jrfm16080368 [CrossRef] [Google Scholar]
  10. Rysbekov, K.B., Bitimbayev, M.Z., Akhmetkanov, D.K., & Miletenko, N.A. (2022). Improvement and systematization of principles and process flows in mineral mining in the Republic of Kazakhstan. Eurasian Mining, (1), 41–45. https://doi.org/10.17580/em.2022.01.08 [CrossRef] [Google Scholar]
  11. Miah, S.J. (2013). Cloud-Based Intelligent DSS Design for Emergency Professionals. Data Mining, 991–1003. https://doi.org/10.4018/978-1-4666-2455-9.ch050 [CrossRef] [Google Scholar]
  12. Dyczko, A. (2023). Real-time forecasting of key coking coal quality parameters using neural networks and artificial intelligence. Rudarsko-Geološko-Naftni Zbornik, 38(3), 105–117. https://doi.org/10.17794/rgn.2023.3.9 [CrossRef] [Google Scholar]
  13. 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. [Google Scholar]
  14. Lewicka, D. (2010). The impact of HRM on creating proinnovative work environment. International Journal of Innovation and Learning, 7(4), 430. https://doi.org/10.1504/ijil.2010.032932 [CrossRef] [Google Scholar]
  15. Haidai, O., Ruskykh, V., Ulanova, N., Prykhodko, V., Cabana, E.C., Dychkovskyi, R., Howaniec, N., & Smolinski, A. (2022). Mine Field Preparation and Coal Mining in Western Donbas: Energy Security of Ukraine – A Case Study. Energies, 15(13), 4653. https://doi.org/10.3390/en15134653 [CrossRef] [Google Scholar]
  16. Russkikh, V., Yavors’kyy, A., Zubko, S., & Chistyakov, Ye. (2013). Study of rock geomechanical processes while mining two-level interchamber pillars. Annual Scientific-Technical Collection – Mining of Mineral Deposits, 149–152. https://doi.org/10.1201/b16354-25 [Google Scholar]
  17. Dychkovskyi, R.O., Avdiushchenko, A.S., Falshtynskyi, V.S., & Saik, P.B. (2013). On the issue of estimation of the coal mine extraction area economic efficiency. Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu, (4), 107–114. [Google Scholar]
  18. Saik, P., Cherniaiev, O., Anisimov, O., & Rysbekov, K. (2023). Substantiation of the Direction for Mining Operations That Develop under Conditions of Shear Processes Caused by Hydrostatic Pressure. Sustainability, 15(22), 15690. https://doi.org/10.3390/su152215690 [CrossRef] [Google Scholar]
  19. Piwniak, G.G., Bondarenko, V.I., Salli, V.I., Pavlenko, I.I., & Dychkovskiy, R.O. (2007). Limits to economic viability of extraction of thin coal seams in Ukraine. Technical, Technological and Economic Aspects of Thin-Seams Coal Mining International Mining Forum 2007, 129–132. https://doi.org/10.1201/noe041543670a0.ch16 [Google Scholar]
  20. Alpysbay, M., Orynbassarova, E., Sydyk, N., Adebiyet, B., & Kamza, A. (2024). Mining mapping and exploration using remote sensing data in Kazakhstan: a review. Engineering Journal of Satbayev University, 146(2), 37–46. https://doi.org/10.51301/ejsu.2024.i2.05 [Google Scholar]
  21. Jiang, Y., Chen, W., Zhang, X., Zhang, X., & Yang, G. (2024). Real-Time Monitoring of Underground Miners’ Status Based on Mine IoT System. Sensors, 24(3), 739. https://doi.org/10.3390/s24030739 [CrossRef] [PubMed] [Google Scholar]
  22. Tsoy, B., Myrzakhmetov, S., Yazikov, E., Bekbotayeva, A., & Bashilova, Y. (2021). Application of radio-wave geointoscopy method to study the nature of spreading the solutions in the process of uranium underground leaching. Mining of Mineral Deposits, 15(4), 1–7. https://doi.org/10.33271/mining15.04.001 [CrossRef] [Google Scholar]
  23. Khomenko, O., Rudakov, D., Lkhagva, T., Sala, D., Buketov, V., & Dychkovskyi, R. (2023). Managing the horizon-oriented in-situ leaching for the uranium deposits of Mongolia. Rudarsko-geološko-naftni zbornik, 38(5), 49–60. https://doi.org/10.17794/rgn.2023.5.5 [CrossRef] [Google Scholar]
  24. Rakishev, B., Kenzhetaev, Z., Mataev, M., & Togizov, K. (2022). Improving the efficiency of downhole uranium production using oxygen as an oxidizer. Minerals, 12(8), 1005. https://doi.org/10.3390/min12081005 [CrossRef] [Google Scholar]
  25. Kenzhetaev, Z., Togizov, K., Abdraimova, M., & Nurbekova, M. (2022). Selecting the rational parameters for restoring filtration characteristics of ores during borehole mining of uranium depositst. Mining of Mineral Deposits, 16(3), 1–10. https://doi.org/10.33271/mining16.03.001 [CrossRef] [Google Scholar]
  26. Pivnyak, G., Dychkovskyi, R., Cabana, E.C, Lozynskyi, V., & Saik, P. (2020). Actual challenges in materials science and processing technologies. Preface. Key Engineering Materials, (844), 4. [Google Scholar]
  27. Bazaluk, O., Lozynskyi, V., Falshtynskyi, V., Saik, P., Dychkovskyi, R., & Cabana, E. (2021). Experimental Studies of the Effect of Design and Technological Solutions on the Intensification of an Underground Coal Gasification Process. Energies, 14(14), 4369. https://doi.org/10.3390/en14144369 [CrossRef] [Google Scholar]
  28. Lozynskyi, V. (2023). Critical review of methods for intensifying the gas generation process in the reaction channel during underground coal gasification (UCG). Mining of Mineral Deposits, 17(3), 67–85. https://doi.org/10.33271/mining17.03.067 [CrossRef] [Google Scholar]
  29. Vladyko, O., Maltsev, D., Cabana, E.C., Shavarskyi, I., & Dychkovskyi, R. (2022). Formation of the models of mining enterprise management. Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu, (3), 30–36. https://doi.org/10.33271/nvngu/2022-3/030 [CrossRef] [Google Scholar]
  30. Dyczko, A., Kicki, J., & Paraszczak, J. (2005). Decision support system to improve equipment effectiveness and reduce production cost in KGHM “Polska Miedz”, Poland. Application of Computers and Operations Research in the Mineral Industry, 385–390. https://doi.org/10.1201/9781439833407.ch51 [CrossRef] [Google Scholar]
  31. Dyczko, A. (2023). The geological modelling of deposits, production designing and scheduling in the JSW SA Mining Group. Gospodarka Surowcami Mineralnymi – Mineral Resources Management, 39(1), 35–62. https://doi.org/10.24425/gsm.2023.144628 [Google Scholar]
  32. Malinowski, L. (2019). Influence of chosen technical constraints on stability of 3D geological model-based schedule in a complex longwall operation. E3S Web of Conferences, (123), 01024. https://doi.org/10.1051/e3sconf/201912301024 [CrossRef] [EDP Sciences] [Google Scholar]
  33. Dyczko, A. (2007). Thin coal seams, their role in the reserve base of Poland. Technical, Technological and Economic Aspects of Thin-Seams Coal Mining International Mining, 81–87. https://doi.org/10.1201/noe0415436700.ch10 [Google Scholar]
  34. Kopacz, M., Kulpa, J., Galica, D., Dyczko, A., & Jarosz, J. (2019). Economic valuation of coal deposits – The value of geological information in the resource recognition process. Resources Policy, (63), 101450. https://doi.org/10.1016/j.resourpol.2019.101450 [CrossRef] [Google Scholar]
  35. Kopacz, M., Malinowski, L., Kaczmarzewski, S., & Kamiński, P. (2020). Optimizing mining production plan as a trade-off between resources utilization and economic targets in underground coal mines. Gospodarka Surowcami Mineralnymi – Mineral Resources Management, 36(4), 49–74. https://doi.org/10.24425/gsm.2020.133948 [Google Scholar]
  36. Polak, R. (2014). Systemy przetwarzania danych – ich rola i znaczenie w realizacji idei Inteligentnej Kopalni. Wiadomości Górnicze, 65(10), 512–522. [Google Scholar]
  37. Dyczko, A. (2023). Production management system in a modern coal and coke company based on the demand and quality of the exploited raw material in the aspect of building a service-oriented architecture. Journal of Sustainable Mining, 22(1), 2–19. https://doi.org/10.46873/23003960.1371 [CrossRef] [Google Scholar]
  38. Turumbetov, T. (2022). Integrated monitoring for the rock mass state during large-scale subsoil development. Frontiers in Environmental Science, (10), 852591. https://doi.org/10.3389/fenvs.2022.852591 [Google Scholar]
  39. Vlasov, S., Moldavanov, Y., Dychkovskyi, R., Cabana, E., Howaniec, N., Widera, K., Bąk, A., & Smoliński, A. (2022). A Generalized View of Longwall Emergency Stop Prevention (Ukraine). Processes, 10(5), 878. https://doi.org/10.3390/pr10050878 [CrossRef] [Google Scholar]
  40. Kuchta, M., Newman, A., & Topal, E. (2004). Implementing a Production Schedule at LKAB’s Kiruna Mine. Interfaces, 34(2), 124–134. https://doi.org/10.1287/inte.34.2.124.30465 [CrossRef] [Google Scholar]
  41. Cowan, J., Beatson, R., & Ross, H. (2003). Practical Implicit Geological Modelling. 5th International Mining Geology Conference, 14. [Google Scholar]
  42. Kemajl, Z., Stojance, M., Gzim, I., & Ledi, M.L. (2024). Comprehensive analysis of the mining accident forecasting and risk assessment methodologies: Case study – Stanterg Mine. Mining of Mineral Deposits, 18(2), 11–17. https://doi.org/10.33271/mining18.02.011 [CrossRef] [Google Scholar]
  43. Sosnowski, P., Dyczko, A., & Kamiński, P. (2024). Quality management in a 3D geological model – reliability of predicted hard coal quality parameters. E3S Web of Conferences, (526), 01003. https://doi.org/10.1051/e3sconf/202452601003 [CrossRef] [EDP Sciences] [Google Scholar]
  44. Kononenko, M., Khomenko, O., Kosenko, A., Myronova, I., Bash, V., & Pazynich, Y. (2024). Raises advance using emulsion explosives. E3S Web of Conferences, (526), 01010. https://doi.org/10.1051/e3sconf/202452601010 [CrossRef] [EDP Sciences] [Google Scholar]
  45. Hussan, B., Lozynska, M.I., Takhanov, D.K., Oralbay, A.O., & Kuzmin, S.L. (2021). Assessing the quality of drilling-and-blasting operations at the open pit limiting contour. Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu, (6), 42–48. https://doi.org/10.33271/nvngu/2021-6/042 [CrossRef] [Google Scholar]
  46. Golda, K., Paszek, L., & Kulpa, J. (2024). Geological and technological viewpoint on 3D Deposit Model – examples of use in Pniówek Coal Mine. E3S Web of Conferences, (526), 01007. https://doi.org/10.1051/e3sconf/202452601007 [CrossRef] [EDP Sciences] [Google Scholar]
  47. Kopacz, M., Kulpa, J., Galica, D., & Olczak, P. (2020). The influence of variability models for selected geological parameters on the resource base and economic efficiency measures – Example of coking coal deposit. Resources Policy, (68), 101711. https://doi.org/10.1016/j.resourpol.2020.101711 [CrossRef] [Google Scholar]
  48. Krawczyk, A. (2018). A concept for the modernization of underground mining master maps based on the enrichment of data definitions and spatial database technology. E3S Web of Conferences, (26), 00010. https://doi.org/10.1051/e3sconf/20182600010 [CrossRef] [EDP Sciences] [Google Scholar]
  49. Kosydor, P., Warchala, E., Krawczyk, A., & Piórkowski, A. (2020). Determinants of large-scale spatial data processing in Polish mining. AIP Conference Proceedings, 040007. https://doi.org/10.1063/5.0000335 [CrossRef] [Google Scholar]
  50. Ahuja, I.P.S., & Khamba, J.S. (2008). Total productive maintenance: literature review and directions. International Journal of Quality & Reliability Management, 25(7), 709–756. https://doi.org/10.1108/02656710810890890 [CrossRef] [Google Scholar]
  51. Jain, A., Bhatti, R., & Singh, H. (2014). Total productive maintenance (TPM) implementation practice. International. Journal of Lean Six Sigma, 5(3), 293–323. https://doi.org/10.1108/ijlss-062013-0032 [CrossRef] [Google Scholar]
  52. Díaz-Reza, J.R., García-Alcaraz, J.L., & Martínez-Loya, V. (2018). TPM Literature Review. Impact Analysis of Total Productive Maintenance, 23–39. https://doi.org/10.1007/978-3-03001725-5_2 [Google Scholar]
  53. Spik, J., & Zelko, M. (2010). The Advanced Technologies Development Trends for the Raw Material Extraction and Treatment Area. In book Products and Services. https://doi.org/10.5772/10394 [Google Scholar]
  54. Padmavathi, R., Aravinda, K., Vetrivel, M., Santhana Lakshmi, C., Satheesh Kumar, R., & Sivakumar, S. (2024). An effective approach for electric motor fault diagnosis using deep learning. Przegląd Elektrotechnichny, 1(6), 255–258. https://doi.org/10.15199/48.2024.06.53 [Google Scholar]
  55. Hall, R.A., Knights, P.F., & Daneshmend, L.K. (2000). Pareto analysis and condition-based maintenance of underground mining equipment. Mining Technology, 109(1), 14–22. https://doi.org/10.1179/mnt.2000.109.1.14 [CrossRef] [Google Scholar]
  56. Hamoudeh, Y., & Ghodrati, B. (2014). Analysis of Mine Mobile Fleet Maintenance vs. Mine Roads Maintenance for Optimum Performance. Mine Planning and Equipment Selection, 1369–1380. https://doi.org/10.1007/978-3-319-02678-7_131 [CrossRef] [Google Scholar]
  57. Polak, R. (2012) Koncepcja monitoringu i transmisji danych technologicznych dotyczących pracy samojezdnych maszyn górniczych KGHM PM SA. Instytut Gospodarki Surowcami Mineralnymi i Energią PAN, 36–48. [Google Scholar]
  58. Uraikul, V., Chan, C.W., & Tontiwachwuthikul, P. (2007). Artificial intelligence for monitoring and supervisory control of process systemmis. Engineering Applications of Artificial Intelligence, 20(2), 115–131. https://doi.org/10.1016/j.engappai.2006.07.002 [CrossRef] [Google Scholar]
  59. Worden, K., Staszewski, W.J., & Hensman, J.J. (2011). Natural computing for mechanical systems research: A tutorial overview. Mechanical Systems and Signal Processing, 25(1), 4–111. https://doi.org/10.1016/j.ymssp.2010.07.013 [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.