Document Type : Original Research Paper

Authors

• Eslam Ghojoghi ¹
• Hamid Mansouri ¹
• Mohamad Ali Ebrahimi Farsangi ²
• Esmat Rashedi ³

¹ Mining Engineering, Mining Engineering Department, Shahid Bahonar University of Kerman, Iran

² Mining Engineering Department, Shahid Bahonar University of Kerman, Iran

³ Communication Engineering, Department of Communication and Electrical Engineering, Faculty of Electrical and Computer Engineering, Graduate University of Advanced Technology, Kerman, Iran

Abstract

Back break is an undesirable consequence of rock blasting, which causes explosive energy loss and reduces operation efficiency. Consequently, it is essential to forecast it to exercise control and avert the occurrence of operational cost losses. The objective of this scientific research is to utilize Deep Neural Network, Extreme Gradient Boosting, and Lasso Regression in conjunction with Gravitational Search Algorithm to make predictions and minimization regarding the occurrence of blast-induced back break at Gol-e-Gohar 4 iron ore mine, Sirjan, Kerman, Iran. The constructed models comprise a set of nine input parameters, encompassing blasting design parameters, and rock geomechanical properties and produces back break as single output. The datasets used for training and evaluation consist of 266 blasting records extracted from Gol-e-Gohar 4 iron ore mine. The results obtained showed that the Deep Neural Network model with R2 of 0.81 and MSE of 0.70 has better performance over the Extreme Gradient Boosting and Lasso regression models to predict back break. Furthermore, the application of optimization algorithm resulted in optimized parameter values, which minimize back break.

Keywords

• Blasting
• Back break, Deep Neural Network, Gol-e-Gohar

Main Subjects

• Surface Mining