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Method of Predicting Ore Dilution Based on a Neural Network and Its Application

Author

Listed:
  • Xingdong Zhao

    (Geomechanics Research Center, Northeastern University, Shenyang 110819, China)

  • Jia’an Niu

    (Geomechanics Research Center, Northeastern University, Shenyang 110819, China)

Abstract

A back-propagation neural network prediction model with three layers and six neurons in the hidden layer is established to overcome the limitation of the equivalent linear overbreak slough (ELOS) empirical graph method in estimating unplanned ore dilution. The modified stability number, hydraulic radius, average deviation of the borehole, and powder factor are taken as input variables and the ELOS of quantified unplanned ore dilution as the output variable. The training and testing of the model are performed using 120 sets of data. The average fitting degree r 2 of the prediction model is 0.9761, the average mean square error is 0.0001, and the relative error of the prediction is approximately 6.2%. A method of calculating the unplanned ore dilution is proposed and applied to a test stope of the Sandaoqiao lead–zinc mine. The calculated unplanned ore dilution is 0.717 m, and the relative error (i.e., the difference between calculation and measurement of 0.70 m) is 2.4%, which is better than the relative errors for the empirical graph method and numerical simulation (giving dilution values of 0.8 and 0.55 m, respectively). The back-propagation neural network prediction model is confirmed to predict the unplanned ore dilution in real applications.

Suggested Citation

  • Xingdong Zhao & Jia’an Niu, 2020. "Method of Predicting Ore Dilution Based on a Neural Network and Its Application," Sustainability, MDPI, vol. 12(4), pages 1-23, February.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:4:p:1550-:d:322352
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    Citations

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    Cited by:

    1. Ghorbani, Yousef & Nwaila, Glen T. & Zhang, Steven E. & Bourdeau, Julie E. & Cánovas, Manuel & Arzua, Javier & Nikadat, Nooraddin, 2023. "Moving towards deep underground mineral resources: Drivers, challenges and potential solutions," Resources Policy, Elsevier, vol. 80(C).
    2. Chimunhu, Prosper & Topal, Erkan & Ajak, Ajak Duany & Asad, Waqar, 2022. "A review of machine learning applications for underground mine planning and scheduling," Resources Policy, Elsevier, vol. 77(C).

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