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

Abstract

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.