Dynamic Compensation Model of BF Slag Homogenization Thermal Based on Advanced Deep Learning Algorithm

In recent years, deep learning has been widely used in the field of image visualization, and has attracted the attention of researchers. The main component of iron tailings is SiO2, In the process of quenching and tempering slag from iron tailings, melted SiO2 particles will randomly walk in the crucible, therefore, how to intelligently identify and track targets (SiO2 particles) in sequence image to promote the low consumption and efficient production of high value-added slag cotton has become an urgent problem. Aims at the temperature requirements of slag cotton preparation process, using intelligent algorithm with deep learning and hierarchical clustering, in-depth analysis of the visual information of the high temperature melting process of SiO2 particles. Through centroid tracking and positioning technology, intelligent edge feature extraction technology and fitting method of experimental data through high temperature melting process of SiO2 particles quantitative characterization functions of visual characteristics of SiO2 particles melting process under high temperature environment were obtained in this article. Approximation of the melting process of iron tailings in high temperature environments and embed this function into the static mathematical model of process thermal compensation an accurate thermal dynamic intelligent compensation model for slag cotton preparation process is realized.