Comprehensive analysis of the mining accident forecasting and risk assessment methodologies: Case study – Stanterg Mine

Zeqiri Kemajl¹, Mijalkovski Stojance², Ibishi Gzim¹, Mojsiu Lavdie Ledi³

¹University Isa Boletini Mitrovice, Mitrovice, Kosovo

²Goce Delchev University Stip, Stip, North Macedonia

³Polytechnic University of Tirana, Tirana, Albania

Min. miner. depos. 2024, 18(2):11-17

https://doi.org/10.33271/mining18.02.011

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ABSTRACT

Purpose. This research aims to outline a methodology for accident forecasting and risk assessment in mining operations using the Stanterg Mine as a case study. It emphasises the crucial role of reliable mining accident reporting and accurate data processing in forecasting accidents and effectively managing risks during mining operations.

Methods. The research paper analyzes various methodologies for forecasting mining accidents using Excel and Simple Linear Regression Method (SLRM) to analyse data selected from the Stanterg Mine accidents.

Findings. The forecast indicates that on average there are about 3 accidents per month at the Stanterg Mine. The analysis, based on a one-month study of 42 reported accidents, and assuming a steady production rate, suggests an increased risk of accidents. This is supported by a thorough assessment using a 3×3 risk assessment matrix tailored to the Stanterg Mine. Stope mining is highlighted as the most hazardous area, associated with risks ranging from moderate to extreme levels.

Originality. Mining accident analysis at the Stanterg Mine involves an examination of the incidents, including factors leading to accidents, encountered hazards and their consequences. Accident forecasting entails studying historical data, identifying patterns, and using predictive modelling to anticipate future incidents. This proactive approach enables mining companies to proactively address risks and take preventative measures, reducing the probability of accidents.

Practical implications. The systematic processing and analysis of mining accidents has revealed valuable insights into the practical application of risk assessment in mining operations. The examination of accidents at the Stanterg Mine provides researchers with crucial knowledge for effective risk assessment and management in the mining sector.

Keywords: accident, risk, forecasting, assessment, mine

REFERENCES

Zeqiri, K. (2020). Investigation of the mining accidents at “Stan Terg” mine. Mining Science, 27, 39-46. https://doi.org/10.37190/msc202703
Zheng, X., & Liu, M. (2009). An overview of accident forecasting methodologies. Journal of Loss Prevention in the process Industries, 22(4), 484-491. https://doi.org/10.1016/j.jlp.2009.03.005
Zeqiri, K., Kortnik, J., & Mijalkovski, S. (2020). Determination of the risk at workplace, assessment and its rank calculation, in mining activities. GeoScience Engineering, 66(1), 69-75. https://doi.org/10.35180/gse-2020-0032
Pothina, R., & Ganguli, R. (2023). Contextual representation in NLP to improve success in accident classification of mine safety narratives. Minerals, 13(6), 770. https://doi.org/10.3390/min13060770
You, M., Li, Sh., Li, D., & Xu, Sh. (2021). Applications of artificial intelligence for coal mine gas risk assessment. Safety Science, 143, 105420. https://doi.org/10.1016/j.ssci.2021.105420
Mijalkovski, S., Peltechki, D., Zeqiri, K., Kortnik, J., & Mirakovski, D. (2020). Risk assessment at workplace in underground lead. Journal of the Institute of Electronics and Computer, 2, 121-141. https://doi.org/10.33969/JIEC.2020.21008
Gul, M. (2018). A review of occupational health and safety risk assessment approaches based on multi-criteria decision-making methods and their fuzzy versions. Human and Ecological Risk Assessment, 1723-1760. https://doi.org/10.1080/10807039.2018.1424531
Hegde, J., & Rokseth, B. (2020). Applications of machine learning methods for engineering risk assessment – A review. Safety Science, 122, 104492. https://doi.org/10.1016/j.ssci.2019.09.015
Qiu, Z., Liu, Q., Li, X., Zhang, J., & Zhang, Y. (2021). Construction and analysis of a coal mine accident causation network based on text mining. Process Safety and Environmental Protection, 153, 320-328. https://doi.org/10.1016/j.psep.2021.07.032
Tubis, A., Werbinska-Wojciechowska, S., & Wroblewski, A. (2020). Risk assessment methods in mining industry – A systematic review. Applied Science, 10(15), 5172. https://doi.org/10.3390/app10155172
Hadjigeorgiou, J. (2020). Understanding, managing and commu-nicating. Mining Technology, 129(3), 159-173. https://doi.org/10.1080/25726668.2020.1800909
Xu, N., Ma, L., Liu, Q., Wang, L., & Deng, Y. (2021). An improved text mining approach to extract safety risk factors from construction accident reports. Safety Science, 138, 105216. https://doi.org/10.1016/j.ssci.2021.105216
Noraishah Ismail, I., Ramli, A., & Abdul Aziz, H. (2021). Research trends in mining accidents study: A systematic literature review. Safety Science, 143, 105438. https://doi.org/10.1016/j.ssci.2021.105438
Stemn, E., Oppong Amoh, P., & Joe-Asare, Th. (2021). Analysis of artisanal and small-scale gold mining accidents and fatalities in Ghana. Resources Policy, 74, 102295. https://doi.org/10.1016/j.resourpol.2021.102295
Tsoukalas, V.D., & Fragiadakis, N.G. (2016). Prediction of occupational risk in the shipbuilding industry using multivariable linear regression and genetic algorithm analysis. Safety Science, 83, 12-22. https://doi.org/10.1016/j.ssci.2015.11.010
Huang, Y., & Zhou, Q. (2019). Mine accident prediction and analysis based on multimedia big data. Multimedia Tools and Applications, 82, 11145. https://doi.org/10.1007/s11042-019-7175-6
Majhi, D., Adhikari, N., Rao, M., & Sahoo, S. (2020). A fuzzy approach for accident prediction in IoT based mining system. Proceedings of Industry Interactive Innovations in Science, Engineering & Technology (I3SET2K19). http://doi.org/10.2139/ssrn.3517169
Health and Safety Unit. (2020). Trepca – Stan Terg mine. Injury note-book.
Zeqiri, K., Hetemi, M., Uka, U., Ibishi, G., & Mijalkovski, S. (2022). Analysis of near-miss incidents (NMI) reporting in mining operations. Mining of Mineral Deposits, 16(3), 25-30. https://doi.org/10.33271/mining16.03.025