Developing New Models for Flyrock Distance Assessment in Open-Pit Mines | ||
| Journal of Mining and Environment | ||
| مقاله 4، دوره 13، شماره 2، تیر 2022، صفحه 375-389 اصل مقاله (2.1 M) | ||
| نوع مقاله: Original Research Paper | ||
| شناسه دیجیتال (DOI): 10.22044/jme.2022.11805.2170 | ||
| نویسندگان | ||
| J. Shakeri1؛ H. Amini Khoshalan* 1؛ H. Dehghani2؛ M. Bascompta3؛ K. Onyelowe4 | ||
| 1Department of Mining Engineering, Faculty of Engineering, University of Kurdistan, Sanandaj, Iran | ||
| 2Department of Mining Engineering, Hamedan University of Technology, Hamedan, Iran | ||
| 3Department of Mining Engineering, Polytechnic University of Catalonia, Barcelona, Spain | ||
| 4Department of Civil Engineering, Michael Okpara University of Agriculture, Umudike, Nigeria | ||
| چکیده | ||
| In this research work, a comprehensive study is conducted to predict flyrock as a typical and undesirable phenomenon occurring during the blasting operation in open-pit mining. Despite the availability of several empirical methods for predicting the flyrock distance, the complexity of flyrock analysis has resulted in the low performance of these models. Therefore, the statistical and robust artificial intelligence techniques are applied for flyrock prediction in the Sungun copper mine in Iran. For this purpose, the linear multivariate regression (LMR), imperialist competitive algorithm (ICA), adaptive neuro-fuzzy inference system (ANFIS), and artificial neural network (ANN) methods are applied to predict flyrock with effective parameters including the blasthole diameter, stemming, burden, powder factor, and maximum charge per delay. According to the attained results, the ANN model with the structure of 5-8-1, Levenberg-Marquardt as the learning algorithm, and log-sigmoid (logsig) as the transfer functions are selected as the optimal network with the RMSE and R2 values of 5.04 m and 95.6% to predict flyrock, respectively. Also it can be concluded that the ICA technique has a relatively high capability in predicting flyrock, with the LMR and ANFIS models placed in the next. Finally, the sensitivity analysis reveal that the powder factor and blasthole diameters have the most importance on the flyrock distance in the present work. | ||
| کلیدواژهها | ||
| Flyrock distance؛ linear multivariate regression؛ imperialist competitive algorithm؛ adaptive neuro-fuzzy inference system؛ artificial neural network | ||
| مراجع | ||
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