Explosive utilization efficiency enhancement: An application of machine learning for powder factor prediction using critical rock characteristics
Blessing Olamide Taiwo,
Angesom Gebretsadik,
Hawraa H. Abbas,
Mohammad Khishe,
Yewuhalashet Fissha,
Esma Kahraman,
Ahsan Rabbani,
Adams Abiodun Akinlabi
Affiliations
Blessing Olamide Taiwo
Department of Mining Engineering, Federal University of Technology, Akure, Nigeria
Angesom Gebretsadik
Department of Resources Engineering, Graduate School of Engineering, Division of Sustainable Resources Engineering, Hokkaido University, Sapporo, 001-0015, Japan; Department of Mining Engineering, Aksum University, 7080, Aksum, Tigray, Ethiopia
Hawraa H. Abbas
College of Information Technology Engineering, Al-Zahraa University for Women, 56001, Karbala, Iraq; Department of Electrical and Electronics Engineering, University of Kerbala, Karbala, 56001, Iraq
Mohammad Khishe
Department of Electrical Engineering, Imam Khomeini Naval Science University of Nowshahr, Nowshahr, Iran; Innovation Center for Artificial Intelligence Applications, Yuan Ze University, Taiwan; Applied Science Research Center, Applied Science Private University, 11937, Amman, Jordan; Corresponding author. Department of Electrical Engineering, Imam Khomeini Naval Science University of Nowshahr, Nowshahr, Iran.
Yewuhalashet Fissha
Department of Mining Engineering, Aksum University, 7080, Aksum, Tigray, Ethiopia; Department of Geosciences, Geotechnology and Materials Engineering for Resources, Graduate School of International Resource Sciences, Akita University, Japan
Esma Kahraman
Department of Mining Engineering, Cukurova University, Adana, 01250, Turkey
Ahsan Rabbani
Department of Civil Engineering, Sai Nath University, Ranchi, India
Adams Abiodun Akinlabi
Department of Mining Engineering, Federal University of Technology, Akure, Nigeria
Maximizing the use of explosives is crucial for optimizing blasting operations, significantly influencing productivity and cost-effectiveness in mining activities. This work explores the incorporation of machine learning methods to predict powder factor, a crucial measure for assessing the effectiveness of explosive deployment, using important rock characteristics. The goal is to enhance the accuracy of powder factor prediction by employing machine learning methods, namely decision tree models and artificial neural networks. The analysis finds key rock factors that have a substantial impact on the powder factor, hence enabling more accurate planning and execution of blasting operations. The analysis uses data from 180 blast rounds carried out at a dolomite mine in south-south Nigeria. It incorporates measures such as root mean square error (RSME), mean absolute error (MAE), R-squared (R2), and variance accounted for (VAF) to determine the best models for predicting powder factor. The results indicate that the decision tree model (MD4) outperforms alternative approaches, such as artificial neural networks and Gaussian Process Regression (GPR). In addition, the research presents an efficient artificial neural network equation (MD2) for estimating the values of optimum powder factor, demonstrating outstanding blasting fragmentation. In conclusion, this research provides significant information for improving the accuracy of powder factor prediction, which is especially advantageous for small-scale blasting operations.
