Strip mining of stratiform deposits with thick overburden: environmental challenges and constraints to sustainable extraction | ||
| International Journal of Mining and Geo-Engineering | ||
| مقاله 1، دوره 60، شماره 3، پاییز 2026، صفحه 249-258 اصل مقاله (1.2 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22059/ijmge.2026.404895.595329 | ||
| نویسندگان | ||
| Redouane Oubah* ؛ Youssef Zerradi ؛ Latifa Ouadif؛ Anas Driouch؛ Amine Soufi | ||
| Laboratory of Applied Geophysics, Geotechnics, Engineering Geology and the Environment, Mohammadia School of Engineers, Mohammed V University in Rabat, Morocco. | ||
| چکیده | ||
| The resource extraction industry relies heavily on strip mining to produce various minerals. Its popularity arises from its cost-effectiveness, making it a key surface mining method to meet the growing global demand for raw materials. The depletion of shallow ore deposits has led to an increased reliance on strip mining under extremely thick overburden layers, intensifying the environmental impact of mining operations. The first aim of this study was to evaluate the environmental effects of mining operations under these complex conditions, with a particular focus on their impact on land use, energy consumption, and climate change. Drawing upon multiple case studies, the research quantitatively explores how increasing overburden thickness affects the technical feasibility, energy demand, and footprint of mining activities. This analysis highlights the delicate balance between geological conditions and mining strategy which offers new insights into optimizing extraction efficiency while minimizing ecological disturbance. The feasibility of the utilization of an artificial neural network (ANN) based conceptual model was then explored to outline a predictive framework for environmental impacts under these conditions, intended to support future practical applications by combining the important operational factors studied with site-specific characteristics. The conception of the ANN model was structured with one hidden layer that is meant to capture complex connections between key mining factors and each one of the expected outcomes. | ||
| کلیدواژهها | ||
| Artificial neural network؛ Climate change؛ Environmental impact؛ Strip mining؛ Strip ratio | ||
| مراجع | ||
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آمار تعداد مشاهده مقاله: 354 تعداد دریافت فایل اصل مقاله: 140 |
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