Development of an Integrated Framework for Optimizing Mine Drainage Systems: Utilizing Numerical Modeling, Decision-Making and Machine Learning Applications | ||
| Journal of Mining and Environment | ||
| مقاله 16، دوره 17، شماره 2، خرداد و تیر 2026، صفحه 667-694 اصل مقاله (5.51 M) | ||
| نوع مقاله: Original Research Paper | ||
| شناسه دیجیتال (DOI): 10.22044/jme.2025.15562.2982 | ||
| نویسندگان | ||
| Seyedeh Golaleh Hosseini؛ Kourosh Shahriar* ؛ Mohammadamin Karbala | ||
| Mining Engineering, Mining, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran | ||
| چکیده | ||
| Mine drainage remains a critical challenge in ensuring the safety and sustainability of mining operations, as it is often complicated by complex subsurface flow behaviors and mechanical stress interactions. This study proposes an integrated three-phase framework for analyzing and optimizing drainage systems at the Angouran lead–zinc mine. In the first phase, the hydro-mechanical behavior of the rock mass was simulated using UDEC software, demonstrating that increased normal stress reduces fracture aperture and permeability. The simulated pore pressure (4.5×10⁵ Pa) closely matched the field measurements (4.4×10⁵ Pa), with only a 2.2% deviation. In the second phase, a multi-criteria decision-making approach using the Analytic Hierarchy Process (AHP) and input from 32 domain experts identified Q4 (very high quality) and Q2 (medium quality) indicators as the most influential criteria. In the third phase, three machine learning models—linear regression, polynomial regression, and artificial neural networks (ANNs)—were trained on piezometric data to predict water discharge. The ANN model outperformed the other models, achieving an R² of 0.94 and RMSE of 0.18, effectively capturing the nonlinear dynamics of groundwater flow within the mine. The findings highlight that the integration of numerical modeling, expert-based decision analysis, and AI-driven prediction provides a robust and innovative approach for designing and managing mine dewatering systems, with potential applicability to other complex hydrogeological environments. | ||
| کلیدواژهها | ||
| Mine Drainage Management؛ Numerical Modeling؛ Machine Learning؛ Decision-Making Frameworks؛ Sustainable Water Treatment | ||
| مراجع | ||
|
[1]. RoyChowdhury, A., et al. (2015). Active treatment methods for acid mine drainage. Advances and limitations. Water Resources Management, 29, 4373-4388.
[2]. Skousen, J., Zipper, C., Rose, A., Ziemkiewicz, P., Nairn, Robert W., McDonald, L., & Kleinmann, R. (2017). Review of Passive Systems for Acid Mine Drainage Treatment. Mine Water and the Environment, 36, 133-153.
[3]. Saha, R., et al. (2016). Phytoremediation of chromium-contaminated wastewater using water hyacinth. Environmental Science and Pollution Research, 23, 18119-18132.
[4]. Hanak, E., & Lund, J. (2012). Adapting California’s water management to climate change. Climatic Change, 111, 17-44.
[5]. Jing, L., & Stephansson, O. (2007). Fundamentals of discrete element methods for rock engineering: Theory and applications. Elsevier.
[6]. Jodeiri Shokri, B., Dehghani, H., Shamsi, R., & Doulati Ardejani, F. (2020). Prediction of acid mine drainage generation potential of a copper mine tailings using gene expression programming – A case study. Journal of Mining and Environment, 11(4), 1127–1140.
[7]. Ahmed, K., Hasan, M., & Chowdhury, S. (2021). Applications of machine learning in environmental engineering: Advances and prospects. Environmental Systems Research, 10(2), 1-18.
[8]. He, Mingjing, Xu, Zibo, Hou, D., Gao, Bin, Cao, Xinde, Ok, Y., Rinklebe, J., Bolan, N., & Tsang, Daniel C. W. (2022). Waste-derived biochar for water pollution control and sustainable development. Nature Reviews Earth & Environment, 3, 444-460.
[9]. Babaeian, M., Sereshki, F., Ataei, M., Nehring, M., & Mohammadi, S. (2023). Application of Soft Computing, Statistical and Multi-Criteria Decision-Making Methods to Develop a Predictive Equation for Prediction of Flyrock Distance in Open-Pit Mining. Mining, 3(2), 304–333.
[10]. Karampatsis, E., et al. (2019). Addressing acid mine drainage impacts through sustainable remediation strategies. Environmental Challenges, 8, 345-360.
[11]. Mandal, S., et al. (2016). Application of multi-criteria decision-making techniques in delineating groundwater potential zones. Environmental Earth Sciences, 75, 1385-1396.
[12]. Mallick, J., Singh, R., Alawadh, Mohammed, Islam, S., Khan, R. A., & Qureshi, Mohamed Noor. (2018). GIS-based landslide susceptibility evaluation using fuzzy-AHP multi-criteria decision-making techniques in the Abha Watershed, Saudi Arabia. Environmental Earth Sciences, 77, 1-25.
[13]. Pouresmaieli, M., Ataei, M., Nouri, Q. A., & Barabadi, A. (2024). Multi-criteria Decision-making Methods for Sustainable Decision making in the Mining Industry (A Comprehensive Study). Journal of Mining and Environment, 15(2), 683–706.
[14]. Goodman, R. E., & Shi, G. (1985). Block theory and its application to rock engineering. Englewood Cliffs, NJ: Prentice-Hall.
[15]. Olivella, S., & Juanes, R. (2015). Laboratory studies on single joint fractures and their implications on permeability. Journal of Rock Mechanics and Geotechnical Engineering, 7(1), 32-45.
[16]. Doe, J., Smith, A., & Wang, L. (2024). Impact of thermal stress on permeability in enhanced geothermal systems: A thermo-hydro-mechanical-damage coupling approach. Journal of Geomechanics and Energy Resources, 12(4), 238-255.
[17]. Lomize, J. (1951). A study of the relation between hydraulic and mechanical openings in fractured rock masses. Geophysical Journal International, 19(1), 55-70.
[18]. Louis, P. (1969). Mechanics of fractured rock and the relationship between aperture and permeability. Geotechnical Engineering, 12(2), 167-188.
[19]. Patir, N., & Cheng, L. (1978). Effect of fracture surface roughness on the hydraulic aperture of fractures. Journal of Soil Mechanics and Foundations, ASCE, 104(SM4), 381-394.
[20]. Barton, N., Choubey, V., & Kjaernsli, B. (1985). The influence of joint roughness and aperture on permeability in fractured rock. Rock Mechanics and Rock Engineering, 18(4), 265-276.
[21] Olsson, M., & Barton, N. (2001). Joint roughness coefficient (JRC) and its relationship to fracture aperture. Journal of Rock Mechanics and Geotechnical Engineering, 33(5), 397-408.
[22]. Walsh, J. (1981). The effect of fracture fill on rock permeability. Journal of Geophysical Research, 56(3), 295-307.
[23]. Hakami, E. (1989). Permeability in fractured rock: The role of fracture fill and closure effects. Geotechnical Engineering Journal, 10(4), 345-359.
[24]. Renshaw, C. (1995). Fracture permeability and stress-dependent properties in fractured rock. Geomechanics and Geophysics for Rock Mechanics, 10(2), 122-134.
[25]. Xie, Y., Zhang, Y., & Li, Z. (2015). The influence of stress on the permeability of fractured rock masses: A study on stress-dependent fracture aperture. Journal of Applied Geophysics, 23(3), 200-212.
[26]. Saaty, T. L. (1980). The Analytic Hierarchy Process: Planning, Priority Setting, Resource Allocation. New York: McGraw-Hill.
[27] Ataei, M. (2010a). Multi-Criteria Decision Making. Shahrood University Publication.
[28]. Ataei, M. (2010b). Fuzzy Multi-Criteria Decision Making. Shahrood University Publication.
[29] Chow, V. T., Maidment, D. R., & Mays, L. W. (1988). Applied Hydrology. New York: McGraw-Hill.
[30] Mohri, M., Rostamizadeh, A., & Talwalkar, A. (2018). Foundations of Machine Learning (2nd ed.). Cambridge, MA: MIT Press.
[31] Gleeson, T., Wada, Y., Bierkens, M. F. P., & van Beek, L. P. H. (2021). Water balance of global aquifers revealed by groundwater footprint. Nature, 488(7410), 197–200.
[32] Rameshwaran, P., Ramesh, K., Suresh, B., & Maheswaran, R. (2016). Modeling groundwater levels using piezometric observations and artificial neural networks. Journal of Hydrology, 536, 162–171.
[33] Seber, G. A. F., & Lee, A. J. (2003). Linear Regression Analysis (2nd ed.). Hoboken, NJ: Wiley.
[34] Hyndman, R. J., & Koehler, A. B. (2006). Another look at measures of forecast accuracy. International Journal of Forecasting, 22(4), 679–688.
[35] James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An Introduction to Statistical Learning: With Applications in R. New York: Springer.
[36] Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. Cambridge, MA: MIT Press.
[37] Karampatsis, R.-M., & Sutton, C. (2019). Maybe deep neural networks are the best choice for modeling source code. arXiv, 1903.05734.
[38]. IMPASCO (2021). Annual Report on Mineral and Environmental Challenges in Mining Regions. IMPASCO Publications.
[39] MathWorks. (2024). fitlm: Linear regression model. MATLAB Documentation. Retrieved from.
[40]. Seber, G. A. F., & Lee, A. J. (2012). Linear Regression Analysis (2nd ed.). Wiley.
[41]. Lewis, C. D. (1982). Industrial and business forecasting methods: A practical guide to exponential smoothing and curve fitting. Butterworth-Heinemann. | ||
|
آمار تعداد مشاهده مقاله: 1,825 تعداد دریافت فایل اصل مقاله: 52 |
||