Document Type : Original Research Paper

Authors

• Ali Nemati vardin ¹
• Masoud Monjezi ²
• Hasel Amini Khoshalan ³
• Jafar Hamidi Khademi ¹
• Mojtaba Rezakhah ¹

¹ Department of Mining Engineering, Faculty of Engineering, Tarbiat Modares University, Tehran, Iran

² Tarbiat Modares University

³ Department of Mining Engineering, Faculty of Engineering, University of Kurdistan, Sanandaj, Iran

Abstract

Drilling is one of the most important operations in open-pit mining, and the penetration rate of drill bits is a key performance measure. This paper presents research on the penetration rate of drill bits based on mining rock mass rating, thrust pressure (weight on bit), rotational pressure, and Schmidt hammer rebound hardness. To achieve this, a dataset comprising the drilling operations of 85 blastholes from the Sungun copper mine in Iran was prepared and analyzed using statistical and intelligent methods. Multivariate regression analysis and artificial neural networks developed in Python, utilizing optimization algorithms such as gradient descent, stochastic gradient descent, and adaptive moment estimation, were applied to predict the penetration rate of drill bits in this study. The coefficient of determination (R²), mean absolute error (MAE), and root mean square error (RMSE) served as performance indicators to evaluate the methods employed. Among these, the adaptive moment estimation (Adam)-based model exhibited superior performance compared to alternative models, achieving values of R² = 0.96, MAE = 4.55, and RMSE = 4.30. Furthermore, the sensitivity analysis revealed that mining rock mass rating is the most influential factor on the rate of penetration, while thrust pressure has the least impact.

Keywords

• Drilling
• Rate of Penetration
• Intelligent Methods, Adaptive Moment Estimation
• stochastic gradient descent

Main Subjects

• Drilling & Blasting