Application prospects and investigation of image recognition technology in tuyeres monitoring of blast furnace
By extracting the frame image of BF tuyeres'video data,and combining with advanced image recognition algorithm to monitor the working state of tuyeres'area,and analyzing the corresponding blast furnace parameter adjustment strategy in real time,which is conducive to reducing the air rest rate and duration,and at the same time making up for the process defects of the response lag and inaccurate results in judging tuyere's state by relying on manual experience at this stage,so as to ensure the long-term stability and smooth movement of blast furnace.Based on the tuyeres'video data of a domestic steel plant between June 1 to June 31,2023,four common tuyere abnormalities in ironmaking process were sorted out,and main causes and countermeasures based on the principle of ironmaking were analyzed,including hanging slag,slag inflow,coal cutoff and water leakage.Then,the application route of image recognition technology in blast furnace tuyere recognition and monitoring is summarized,including image preprocessing,tuyere identification and early warning,and implantation of expert experience,and the widely used image recognition algorithms are introduced,including convolutional neural network.Transformer mechanism and graph neural network,and the latter two algorithms are affirmed and respected.Finally,based on the graph con-volutional neural network,the monitoring and analysis system 1.0 of the blast furnace tuyere is developed,and its functions are briefly introduced.Adhering to the development principle of low latency and high precision,aiming to explore image recognition's application route of blast furnace tuyere in the future by combing the tuyere anomaly and image recognition algorithm,so as to provide a theoretical reference for China's steel enterprises to select reasonable tuyere monitoring technology and improve the intelligent level of tuyere identification and monitoring.
NETL
NSTL
万方数据
