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<p>The Mine-to-Mill (MTM) approach is crucial in mining due to the high energy consumption and costs of comminution processes like crushing and grinding, which account for over 50% of total energy use. Optimizing these processes, starting from blasting, enhances efficiency and profitability. Accurate rock mass characterization is key to blasting optimization, and Monitoring While Drilling (MWD) provides real-time geotechnical data foron-the-spot adjustments. Acoustic emission monitoring, a leading MWD technique combined with intelligent models, offers promising results in rock characterization. This study employed a Support Vector Machine (SVM) model to predict rock mass properties from drilling acoustic signals. The model demonstrated high accuracy, achieving R² values of 0.976 (training) and 0.808 (testing). The Mean Absolute Percentage Error (MAPE) was 4.36% and 29.52%, while the Root Mean Squared Error (RMSE) reached 0.0486 and 0.141, the Mean Absolute Error (MAE) was 0.021 and 0.103, and the Mean Squared Error (MSE) was 0.0024 and 0.0199 for training and testing, respectively. These results confirm the model’s reliability in estimating rock characteristics. Integrating acoustic emission monitoring with advanced modeling can enhance MTM strategies, reducing energy consumption, operational costs, and environmental impact in mining.</p>

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The use of acoustic emission technique in MWD for mine to mill approach as a smart tool for sustainable mining

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月14日

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  • Mohammad Hossein Jalalian,
  • Raheb Bagherpour,
  • Mehrbod Khoshouei

摘要

The Mine-to-Mill (MTM) approach is crucial in mining due to the high energy consumption and costs of comminution processes like crushing and grinding, which account for over 50% of total energy use. Optimizing these processes, starting from blasting, enhances efficiency and profitability. Accurate rock mass characterization is key to blasting optimization, and Monitoring While Drilling (MWD) provides real-time geotechnical data foron-the-spot adjustments. Acoustic emission monitoring, a leading MWD technique combined with intelligent models, offers promising results in rock characterization. This study employed a Support Vector Machine (SVM) model to predict rock mass properties from drilling acoustic signals. The model demonstrated high accuracy, achieving R² values of 0.976 (training) and 0.808 (testing). The Mean Absolute Percentage Error (MAPE) was 4.36% and 29.52%, while the Root Mean Squared Error (RMSE) reached 0.0486 and 0.141, the Mean Absolute Error (MAE) was 0.021 and 0.103, and the Mean Squared Error (MSE) was 0.0024 and 0.0199 for training and testing, respectively. These results confirm the model’s reliability in estimating rock characteristics. Integrating acoustic emission monitoring with advanced modeling can enhance MTM strategies, reducing energy consumption, operational costs, and environmental impact in mining.