Research on Intelligent Perception and Application Analysis of Blast Furnace Hot Metal Flow

The taphole is the only visible window for observing the blast furnace hearth state, and hot metal flow carries key hearth information. To address the problems of current hot metal flow monitoring, such as reliance on manual work, difficulty in quantification, poor real-time performance, as well as insufficient perception stability and low data utilization in existing research, this study proposes a full-chain intelligent solution for blast furnace taphole hot metal flow monitoring: by building an image acquisition system adapted to extreme working conditions, selecting ResNet50 as the state perception model, and combining Canny edge detection with the local morphological extremum analysis algorithm to extract core contour parameters; supplemented by the anti-vibration self-adjustment algorithm and the multi-taphole automatic switching strategy, the robustness and operation efficiency of the system are significantly improved. On this basis, a coke sticking early-warning model is constructed, splashing in different periods is quantitatively classified, and the spatiotemporal difference in hot metal flow is revealed. Finally, a full-chain technical system of “data acquisition–intelligent perception–working condition diagnosis–decision support” is formed, which promotes the digital and intelligent upgrading of hot metal flow monitoring and provides solid support for the safe operation.