基于光谱联合分析的鞍山式铁矿原位测定方法

铁矿组分含量对衡量铁矿石品质优劣具有重要意义。传统的测定方法存在周期长、操作复杂、工作量大、效率低等不足，而目前的光谱原位测试一般仅使用单一光谱范围，难以确定铁矿石中所有组分的含 量。选取鞍山式铁矿石作为试验样品，对样品进行了反射光谱与发射光谱的联合测试分析，并应用高光谱分析技术对矿石中的组分含量进行了定量反演。结果表明：通过将试验样品的反射光谱与发射光谱进行联合分 析与建模，可以反演出样品中Fe3O4、Fe2O3、SiO2各组分的含量以及铁品位、磁性率等重要指标；然后将所建模型应用于矿区现场分析与验证，得到的铁矿分布情况与矿区现场铁矿富集情况基本一致，全铁品位平均 预测误差为3.5%，效果较好。所提出的光谱联合分析方法具有原位测试、操作方便、速度快、效率高、时效性强、无污染等优点，在将来进一步优化模型的基础上，减小测试误差，为以后鞍山式铁矿原位测试和岩矿 智能感知，以至于无人采矿、智能采矿和智能配矿提供了新的技术支撑。

A Measuring Method in Situ Based on Combined analysis of BIF Iron Ore

The determination of iron ore composition is very important to measure iron ore quality. The traditional method has the defects of heavy workload,low efficiency and long period. It is difficult to determine the content of all components in iron ore only by using single spectra. In the study,Anshan type iron ore was used as experimental samples. The reflectance spectra and emission spectra of the experimental samples were combined. The composition contents of samples were determined by application of spectral analysis technology. The results showed that the contents of Fe₃O₄、Fe₂O₃ and SiO₂ in the samples,as well as the grade and magnetic susceptibility can be inversed by combining reflectance spectra and emission spectra.Then the model was applied to the field. The distribution of iron ore was basically consistent with the iron ore enrichment in the mining area. The average predictive residual of the grade in iron ore was 3.5%. The prediction results of the model were ideal. The method has the advantages of low working strength,conveniently,quickly,efficiently and no pollution. It can reduce the test error on the basis of further model optimization in the future. It provides a new technical method for intelligent perception of rock and mineral resources,unmanned mining,intelligent mining and intelligent ore blending in the future.