DOI QR코드

DOI QR Code

Mean fragmentation size prediction in an open-pit mine using machine learning techniques and the Kuz-Ram model

  • Seung-Joong Lee (Infra Solution Business Team, Hanwha Corporation / Global) ;
  • Sung-Oong Choi (Department of Energy and Resources Engineering, Kangwon National University)
  • 투고 : 2023.02.05
  • 심사 : 2023.07.31
  • 발행 : 2023.09.10

초록

We evaluated the applicability of machine learning techniques and the Kuz-Ram model for predicting the mean fragmentation size in open-pit mines. The characteristics of the in-situ rock considered here were uniaxial compressive strength, tensile strength, rock factor, and mean in-situ block size. Seventy field datasets that included these characteristics were collected to predict the mean fragmentation size. Deep neural network, support vector machine, and extreme gradient boosting (XGBoost) models were trained using the data. The performance was evaluated using the root mean squared error (RMSE) and the coefficient of determination (r2). The XGBoost model had the smallest RMSE and the highest r2 value compared with the other models. Additionally, when analyzing the error rate between the measured and predicted values, XGBoost had the lowest error rate. When the Kuz-Ram model was applied, low accuracy was observed owing to the differences in the characteristics of data used for model development. Consequently, the proposed XGBoost model predicted the mean fragmentation size more accurately than other models. If its performance is improved by securing sufficient data in the future, it will be useful for improving the blasting efficiency at the target site.

키워드

과제정보

This research was partly supported by Energy & Mineral Resources Development Association of Korea (EMRD) grant funded by the Korea government (MOTIE) (Educational-Industrial Cooperation Consortium of Energy and Mineral Resources Development-Training Program for Specialists in Smart Mining) and National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT, ME, MOTIE) (NRF-2017M3D8A2085342, the National Strategic Project, Carbon Upcycling).

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