Operational state detection in hydrocyclones with convolutional neural networks and transfer learning
作者: K.C. GigliaC. Aldrich
作者单位: 1Western Australian School of Mines, Curtin University, GPO Box U1987, 6845 WA, Australia
2Department of Process Engineering, Stellenbosch University, Private Bag X1, Matieland, 7602 Stellenbosch, South Africa
刊名: Minerals Engineering, 2020, Vol.149
来源数据库: Elsevier Journal
DOI: 10.1016/j.mineng.2020.106211
关键词: HydrocyclonesImage analysisConvolutional neural networksProcess monitoringDeep learningTransfer learning
英文摘要: Abstract(#br)In the mineral processing industries, the performance of hydrocyclones used in solids classification tasks influences the efficiency of downstream processing. Typical operation involves a fan shaped underflow profile, while undesirable roping and underflow blockage conditions result in excessive coarse particles in the overflow and have visually different underflow characteristics. Different approaches were investigated previously as potential sensing options for undesirable state detection, and while some have been commercialised, they currently have not found widespread implementation in the industry. Analysis of hydrocyclone underflow by use of image analysis has been one method investigated, in which it has been shown that meaningful features can be extracted for...
全文获取路径: Elsevier  (合作)
影响因子:1.207 (2012)

  • learning 学识
  • neural 神经系统的
  • monitoring 监视
  • convolutional 卷积
  • Transfer 转运牌汽车
  • transfer 转换
  • state 状态
  • analysis 分析
  • detection 探测