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Explosive utilization efficiency enhancement: An application of machine learning for powder factor prediction using
Blessing Olamide Taiwo1, Angesom Gebretsadik2,3, Hawraa H Abbas4,5
1Department of Mining Engineering, Federal University of Technology, Akure, Nigeria.
Heliyon
|July 18, 2024
Summary
Machine learning models accurately predict powder factor in blasting operations. Decision tree models outperformed others, enhancing efficiency and cost-effectiveness in mining, particularly for small-scale operations.
Area of Science:
- Mining Engineering
- Geotechnical Engineering
- Computational Intelligence
Background:
- Optimizing explosive use is vital for mining productivity and cost-effectiveness.
- Accurate powder factor prediction is essential for effective explosive deployment.
- Rock characteristics significantly influence blasting outcomes.
Purpose of the Study:
- To enhance powder factor prediction accuracy using machine learning.
- To identify key rock factors impacting powder factor.
- To improve planning and execution of blasting operations.
Main Methods:
- Utilized machine learning models: Decision Trees and Artificial Neural Networks.
- Analyzed data from 180 blast rounds in a Nigerian dolomite mine.
- Employed performance metrics: RMSE, MAE, R-squared, and VAF.
Main Results:
- Decision Tree model (MD4) showed superior performance over ANNs and GPR.
- Identified critical rock factors influencing powder factor.
- Developed an Artificial Neural Network equation (MD2) for optimum powder factor estimation.
Conclusions:
- Machine learning, particularly Decision Trees, offers enhanced powder factor prediction accuracy.
- The findings are valuable for optimizing blasting operations, especially in small-scale mining.
- Accurate prediction leads to improved blasting fragmentation and operational efficiency.
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