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
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