Iron ore identification method using reflectance spectrometer and a deep neural network framework.

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Abstract

In the first selection stage of iron ore, the ore classification accuracy plays a decisive role in subsequent work. Therefore, how to identify iron ore quickly and accurately is an important task. Traditional chemical, physical and manual identification methods have the disadvantages of high costs and high time consumption. This research proposes a new iron ore identification method, that combines deep learning with visible-infrared reflectance spectroscopy to establish an iron ore classification model. We collected iron ore samples from the Anshan iron ore area and measured the spectral data with a spectrometer. Then, a deep neural network framework is proposed based on the convolution neural network and the improved extreme learning machine algorithm, and an iron ore classification model is established based on the framework. The results show that the proposed model can effectively identify the types of iron ore, and the overall accuracy reaches 98.11%.
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