Influence of mineral particle size and choice of suitable parameters for ore sorting using near infrared sensors

Influence of mineral particle size and choice of suitable parameters for ore sorting using near infrared sensors
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矿物粒度的影响以及使用近红外传感器进行矿石分选的合适参数的选择

DOI:
10.1016/j.mineng.2014.07.014
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发表时间:
2014
影响因子:
4.8
通讯作者:
H. Glass
H. Glass
中科院分区:
工程技术2区
文献类型:
--
作者:
S. Iyakwari;H. Glass

文献摘要

被引文献

相似文献

基于近红外传感器的分选是一种新兴的预选技术,其在许多矿物加工应用中具有前景,例如从矿石中去除方解石和粘土废物。预选通过去除不需要的脉石,提高了处理效率,降低了总处理成本。由于复杂矿石的小尺度不均匀性,即各种矿物在测量区域内并排出现,因此可能不容易从近红外光谱中辨别矿物的总成分。因此,矿物鉴定和随后的分类涉及到从特征深度、宽度、位置和光谱反射率水平方面分析吸收特征。本研究探讨了矿物的近红外光谱特性作为粒度分数的函数,特别是对个别矿物常见的孔雀石丰富的铜矿石。将纯矿物样品粉碎并筛分成不同的粒度级,然后用近红外线扫描仪进行扫描。结果发现,存在的特征吸收功能和他们的波长位置是一个更好的识别参数比反射率水平。探讨了近红外分选技术在某典型富孔雀石铜矿石预选中的应用。
Near infrared sensor-based sorting is an emerging preconcentration technology which holds promise for many mineral processing applications, such as elimination of calcite and clay waste from ore. Preconcentration serves to increase the processing efficiency as well as to reduce the total processing cost through the rejection of unwanted gangue. Given small-scale heterogeneity in complex ores, i.e. assorted minerals occurring side-by-side inside an area of measurement, the total mineral composition may not be easily discerned from a near infrared spectrum. Hence, mineral identification and subsequent classification involves the analysis of absorption features in terms of feature depth, width, position, and level of spectral reflectance. This research investigates the near infrared spectral characteristics of minerals as a function of particle size fraction, specifically for individual minerals commonly found in malachite-rich copper ores. Samples of pure minerals are crushed and sieved into different size fractions and scanned with a near infrared line scanner. It was found that the presence of characteristic absorption features and their wavelength position were a better identification parameter than the reflectance level. The implication for preconcentrating a typical malachite-rich copper ore through near-infrared based sorting is discussed.