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Optimizing mining economics: Predicting blasting costs in limestone mines using the RES-based method | ||
| International Journal of Mining and Geo-Engineering | ||
| مقاله 7، دوره 58، شماره 2، شهریور 2024، صفحه 181-190 اصل مقاله (1.44 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22059/ijmge.2024.363654.595091 | ||
| نویسندگان | ||
| Hadi Fattahi* ؛ Hossein Ghaedi | ||
| Faculty of Earth Sciences Engineering, Arak University of Technology, Arak, Iran. | ||
| چکیده | ||
| The mining process involves several sequential stages, including drilling, blasting, loading, transportation, and mineral processing. Among these stages, blasting costs (BC) exhibit greater sensitivity compared to others. Inadequate blasting practices can lead to additional drilling, increased explosive consumption, and environmental consequences such as ground vibrations. The variability in blasting patterns and ore rock hardness results in variations in BC. Consequently, there's a need for a method that can establish a relationship between design, geotechnical parameters, and blasting costs while accounting for uncertainties in input parameters. In this study, the rock engineering system method (RES) was employed to construct a complex and non-linear model for predicting blasting costs, considering uncertainties in geotechnical parameters. Data from six limestone mines in Iran were utilized, incorporating 146 data points. The input parameters used for creating this relationship included hole diameter, burden, Emulsion, hole number, hole length, spacing, stemming, sub-drilling, rock hardness, ANFO, number of electric detonators, uniaxial compressive strength, and specific gravity. The model was built using 80% of the data (117 data points) to establish the RES-based method, with the remaining 20% (29 data points) dedicated to evaluating and validating the model. To assess its performance, the RES-based method was compared to other statistical regression techniques, utilizing statistical indicators such as root mean square error (RMSE), mean square error (MSE), and coefficient of determination (R2). The results demonstrated that the RES-based method significantly outperformed other statistical approaches, with impressive accuracy, as indicated by MSE=0.00608, RMSE=0.078, and R2=0.9518 in predicting explosion costs. Therefore, the model developed through this method can be effectively applied in mining and rock mechanics projects, providing a high level of accuracy. | ||
| کلیدواژهها | ||
| Blasting costs؛ RES-based method؛ Mining economy؛ Limestone mines؛ Predicting blasting costs | ||
| مراجع | ||
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