College of Information Science and Engineering, Northeastern University, Shenyang, 110819, China; Key Laboratory of Intelligent Diagnosis and Safety for Metallurgical Industry, Liaoning Province, Northeastern University, Shenyang 110819, China.
Institute of Research and Development, Duy Tan University, Da Nang 550000, Viet Nam.
Spectrochim Acta A Mol Biomol Spectrosc. 2021 Mar 5;248:119168. doi: 10.1016/j.saa.2020.119168. Epub 2020 Nov 12.
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%.
在铁矿石的第一选别阶段，矿石分类准确率对后续工作起着决定性的作用。因此，如何快速准确地识别铁矿石是一项重要任务。传统的化学、物理和人工识别方法存在成本高和耗时耗力的缺点。本研究提出了一种新的铁矿石识别方法，将深度学习与可见-近红外反射光谱相结合，建立了铁矿石分类模型。我们从鞍山铁矿地区采集了铁矿石样本，并使用分光光度计测量了光谱数据。然后，提出了一个基于卷积神经网络和改进的极限学习机算法的深度神经网络框架，并基于该框架建立了一个铁矿石分类模型。结果表明，所提出的模型可以有效地识别铁矿石的类型，整体准确率达到 98.11%。
