Prediction and Optimization of Rock Fragmentation Induced by Blasting Using Hybrid Soft Computing Methods in the Narbaghi Copper Mine

Ahadi, Hamid; Monjezi, Masoud; Rezakhah, Mojtaba

doi:10.22034/IRSRM.2024.336581.1897

Prediction and Optimization of Rock Fragmentation Induced by Blasting Using Hybrid Soft Computing Methods in the Narbaghi Copper Mine

Document Type : Original Article

Authors

Hamid Ahadi

Masoud Monjezi

Mojtaba Rezakhah

Exploitation Division, Faculty of Mining and Materials, Tarbiat Modares University

10.22034/IRSRM.2024.336581.1897

Abstract

Blasting operations in open-pit mines constitute a crucial and costly stage of the production cycle. Poorly designed blast patterns can result in undesirable outcomes such as improper fragmentation, fly rock, and ground vibrations, leading to increased mining costs. This study presents a predictive and optimization model for rock fragmentation using data from 60 blasting operations in the Narbaghi copper mine. The input parameters include burden, spacing, bench height, number of holes, and number of rows. An artificial neural network (ANN) was employed for fragmentation prediction, yielding superior accuracy. A three-layer backpropagation ANN with 15 neurons in a single hidden layer and exponential and logarithmic sigmoid activation functions demonstrated the best performance, achieving an R² value of 0.94. Sensitivity analysis revealed that burden and spacing had the most significant impact, whereas bench height had the least effect on fragmentation. Finally, the optimal blast pattern was determined using the firefly algorithm, leading to a reduction in burden from 2.5 m to 1.8 m, spacing from 3.0 m to 2.25 m, and bench height from 6.0 m to 5.4 m. Consequently, the D50 value was optimized with a reduction of 59% to 88%

Keywords

Optimal Fragmentation

Blast Pattern

Fragmentation Prediction Fragmentation Optimization

Artificial Neural Network

Firefly Algorithm

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