Ore characterisation and sorting

被引:11
|
作者
Cutmore, NG
Liu, Y
Middleton, AG
机构
[1] Commonwealth Scientific and, Industrial Research Organization, Menai
关键词
iron ores; classification; neural networks; on-line analysis; process instrumentation;
D O I
10.1016/S0892-6875(97)00018-6
中图分类号
TQ [化学工业];
学科分类号
0817 ;
摘要
The on-line characterisation of minerals, and an ability to use this information to perform on-line sorting, opens up new opportunities for the mining industry to both improve their existing operations and exploit presently uneconomic mineral reserves. In the present study, the dielectric properties of iron ore samples have been determined over the 0.7-20 GHz frequency range, using a single microwave probe, and the key features of the measured spectra extracted using principal components analysis. The subsequent sorting of the samples, on the basis of the identified spectral features, is then automatically performed using an ANN based classification scheme. The technique has been demonstrated to successfully classify iron ore samples with minor differences in composition into ore groups that relate petrological features to metallurgical performance. (C) 1997 Elsevier Science Ltd.
引用
收藏
页码:421 / 426
页数:6
相关论文
共 50 条
  • [21] A review of intelligent ore sorting technology and equipment development
    Xianping Luo
    Kunzhong He
    Yan Zhang
    Pengyu He
    Yongbing Zhang
    International Journal of Minerals,Metallurgy and Materials, 2022, (09) : 1647 - 1655
  • [22] Laser Photometric Technique for Ore Sorting Processes.
    Barton, P.J.
    Aufbereitungs-Technik/Mineral Processing, 1978, 19 (06): : 252 - 254
  • [23] Adding value by dry gravimetrical sorting of hematite ore
    BHM Berg- und Huttenmannische Monatshefte, 2021, 166 (08): : 416 - 418
  • [24] A review of intelligent ore sorting technology and equipment development
    Xianping Luo
    Kunzhong He
    Yan Zhang
    Pengyu He
    Yongbing Zhang
    International Journal of Minerals, Metallurgy and Materials, 2022, 29 : 1647 - 1655
  • [25] Decision support for ore sorting and preconcentration in gold applications
    Bearman, R. A.
    Bowman, D. J.
    Dunne, R.
    MINERAL PROCESSING AND EXTRACTIVE METALLURGY-TRANSACTIONS OF THE INSTITUTIONS OF MINING AND METALLURGY, 2020, 129 (01): : 12 - 23
  • [26] Characterisation of a complex lateritic ore by Mossbauer spectroscopy and its relevance in beneficiation of the ore
    Sen, R
    Sarkar, A
    Banerjee, S
    Spottiswood, DJ
    NEUES JAHRBUCH FUR MINERALOGIE-MONATSHEFTE, 2002, (07): : 319 - 334
  • [27] Mineralogical characterisation of the Tizapa ore deposit, Mexico
    Alfonso, Pura
    Torro, Lisard
    Mesa, Claudia
    Parcerisa, David
    Maria Mata-Perello, Josep
    Gonzalez-Partida, Eduardo
    Canet, Carles
    Garcia-Valles, Maite
    LET'S TALK ORE DEPOSITS, VOLS I AND II, 2011, : 779 - 781
  • [28] Characterisation and reduction of ironstone ore by CO gas
    Maroufi, S.
    Ciezki, G.
    Jahanshahi, S.
    Ostrovski, O.
    TRANSACTIONS OF THE INSTITUTIONS OF MINING AND METALLURGY SECTION C-MINERAL PROCESSING AND EXTRACTIVE METALLURGY, 2016, 125 (02): : 95 - 102
  • [29] Sensor-Based Ore Sorting-A Review of Current Use of Electromagnetic Spectrum in Sorting
    Modise, Ernest Gomolemo
    Zungeru, Adamu Murtala
    Mtengi, Bokani
    Ude, Albert Uchenna
    IEEE ACCESS, 2022, 10 : 112307 - 112326
  • [30] Ore characterisation for -: and simulation of -: primary autogenous grinding
    Hahne, R
    Pålsson, BI
    Samskog, PO
    MINERALS ENGINEERING, 2003, 16 (01) : 13 - 19