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

• Published in: IEEE transactions on industrial informatics Vol. 17; no. 6; pp. 4107 - 4116
• Main Authors: Li, Jie, Liu, Wen-Qiang, Han, Yang, Liu, Wei-Xing, Yang, Ai-Min, Li, Fei, Li, Da-Liang
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.06.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Blast furnace slag; Blast furnaces; Centroids; Cluster analysis; Clustering; Compensation; Cotton; Crucibles; Deep learning; Feature extraction; hierarchical clustering; High temperature; High temperature environments; Iron; Machine learning; Mathematical analysis; Mathematical models; Melting; Quenching and tempering; Silicon dioxide; Slag; smart positioning; Tailings; Target recognition; Target tracking; Thermal compensation; thermal dynamic compensation; Thermal quenching; Tracking; Visualization
• ISSN: 1551-3203; 1941-0050
• DOI: 10.1109/TII.2020.3024922

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 SiO 2 , In the process of quenching and tempering slag from iron tailings, melted SiO 2 particles will randomly walk in the crucible, therefore, how to intelligently identify and track targets (SiO 2 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 SiO 2 particles. Through centroid tracking and positioning technology, intelligent edge feature extraction technology and fitting method of experimental data through high temperature melting process of SiO 2 particles quantitative characterization functions of visual characteristics of SiO 2 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.