Applying Nonnegative Matrix Factorization for Underground Mining Method Selection Based on Mining Projects' Historical Data

DOI Web Site 参考文献29件
  • MANJATE Elsa Pansilvania Andre
    Division of Sustainable Resources Engineering, Graduate School of Engineering, Hokkaido University, Instituto Superior Politécnico de Tete,
  • OHTOMO Yoko
    Division of Sustainable Resources Engineering, Faculty of Engineering, Hokkaido University,
  • ARIMA Takahiko
    Division of Sustainable Resources Engineering, Faculty of Engineering, Hokkaido University,
  • ADACHI Tsuyoshi
    Graduate School of International Resource Sciences, Akita University
  • Miguel BENE Bernardo
    Instituto Superior Politécnico de Tete,
  • KAWAMURA Youhei
    Division of Sustainable Resources Engineering, Faculty of Engineering, Hokkaido University, North China Institute of Science and Technology,

抄録

<p>Mining methods selection (MMS) is one of the most critical and complex decision-making tasks in mine planning. The selection of underground mining methods is considered to be the most problematic due to the complexity associated with the orebody geometry, geology, and geotechnical properties. This study integrated artificial intelligence and machine learning in the MMS process by introducing the recommendation systems  (RS) approach in MMS through the nonnegative matrix factorization (NMF) algorithm. As such, the weighted nonnegative matrix factorization (WNMF) algorithm is applied to build a model for underground MMS. The study's input dataset is based on thirty mining projects' historical data. In the experiments, we evaluate the capability of the WNMF to predict underground mining methods using five input variables: ore strength, host-rock strength, orebody thickness, shape, and dip. The results show that the WNMF model achieved an average prediction accuracy of 67.5%, considered reasonable and realistic. Further findings reveal that the WNMF model is sensitive to the imbalanced class dataset used in the experiments, thus, suggesting the need to improve the dataset's quality. These results reveal the model's effectiveness in predicting underground mining methods; therefore, with continuous improvement, the WNMF model can be effectively applied in underground MMS.</p>

収録刊行物

参考文献 (29)*注記

もっと見る

キーワード

詳細情報 詳細情報について

問題の指摘

ページトップへ