A neural network approach for spatial variation assessment – A nepheline syenite case study
作者: Camilo A. Mena SilvaSteinar L. EllefmoRoar SandøyBjørn E. SørensenKurt Aasly
作者单位: 1Department of Geoscience and Petroleum, Norwegian University of Science and Technology, NO-7491 Trondheim, Norway
2Sibelco Nordic AS, N-1309 Sandvika, Norway
刊名: Minerals Engineering, 2020, Vol.149
来源数据库: Elsevier Journal
DOI: 10.1016/j.mineng.2019.106178
关键词: GeometallurgyNeural networkConcentrate yieldMineralogyModellingSpatial modellingIndustrial mineralsNepheline syenite
英文摘要: Abstract(#br)The present geometallurgical study shows the application of a machine-learning methodology to the prediction of material properties from the Nabbaren nepheline syenite deposit in Norway. The approach used in this study created and tested a shallow neural network along with cluster analysis for the prediction of laboratory concentrate yield and modal mineralogy. The input is bulk chemistry data from the mining company open pit database. The methodology proposed unveils general trends in the deposit to a suitable operational scale for the open pit mine. The accuracy of the prediction models is good, with one of the prediction models achieving a strong correlation coefficient of 0.9. The application of a neural network approach showed a successful attempt in the prediction of...
全文获取路径: Elsevier  (合作)
影响因子:1.207 (2012)

  • nepheline 霞石
  • syenite 正长岩
  • spatial 空间的
  • network 网络
  • minerals 矿物
  • variation 变异
  • assessment 评价
  • modelling 模型制造
  • approach 
  • neural 神经系统的