机器学习结合激光诱导击穿光谱技术铁矿石分类方法

铁矿石是非常重要的矿产资源，它的开发利用对钢铁产业的发展有很大的影响，铁矿石的选检与分类是冶金行业必不可少的环节，不同种类的铁矿石及其品质会直接影响与其他物质的配比，因此对铁矿石的选检分类研究在冶金行业具有重要意义。激光诱导击穿光谱技术(LIBS)是近年来发展起来的一项成分检测技术，具有无损、快速、原位在线检测等优点，在化学成分检测及样品分类领域有一定的优势。为了提高铁矿石的分类精度，提出将激光诱导击穿光谱技术与机器学习相结合对赤铁矿、褐铁矿、菱铁矿、云母赤铁矿、磁铁矿、磁赤铁矿、鲕状赤铁矿、黄铁矿、钴磁铁矿、磁黄铁矿等10种天然铁矿石进行分类研究。在研究中，首先通过激光诱导击穿光谱技术烧蚀10种天然铁矿石样品获得其对应的光谱数据；然后通过设定阈值的方法选定最大光谱强度对应的10个光谱特征；最后通过KNN、RF、SVM机器学习模型对选定的特征光谱进行分类训练及测试。结果表明:KNN、RF、SVM三种机器学习模型的分类准确度分别为83.0%、80.7%、90.3%。从分类准确度可以看出，激光诱导击穿光谱技术与机器学习相结合可以实现对铁矿石的快速、精确分类，这将为冶金行业的铁矿石选检分类提供一种全新的方法。

Classification of iron ore based on machine learning and laser induced breakdown spectroscopy

Iron ore is a very important mineral resource. Its development and utilization have a great impact on the development of the iron and steel industry. The selection and classification of iron ore is an indispensable link in the metallurgical industry. Different types of iron ores and its grade will directly affect the ratio of other substances, so the research on the selection and classification of iron ore is of great significance in the metallurgical industry. Laser-induced breakdown spectroscopy (LIBS) is a recently developed component detection technology. It has the advantages of non-destructive, fast, in-situ online detection, etc., and has certain advantages in the field of chemical composition detection and sample classification. In order to study the method of improving the classification accuracy of iron ores, 10 kinds of natural iron ores, including hematite, limonite, siderite, mica hematite, magnetite, maghmite, oolitic hematite, pyrite, cobalt-bearing magnetite, pyrrhotine, were classified with LIBS and machine study. In this study, 10 kinds of natural iron ores, were ablated by LIBS to obtain their corresponding spectral data; then the 10 features corresponding to the maximum spectral intensity were obtained by setting a threshold; the classification training and testing on selected feature spectra were performed with KNN, RF, and SVM models. The results show that the classification accuracy of the three machine learning models: KNN, RF and SVM are 83.0%, 80.7%, and 90.3%, respectively. It can be seen from the classification accuracy that combination of LIBS and machine learning can achieve rapid and accurate classification of iron ores, which will provide a new method for classification of iron ores in the metallurgical industry.