1. College of Mechanical Engineering, North China University of Science and Technology, Tangshan 063210, Hebei, China; 2. Steel Research Institute, HBIS Group Co. , Ltd. , Shijiazhuang 050023, Hebei, China; 3. Chengde Branch, Hebei Iron and Steel Co. , Ltd. , Chengde 067000, Hebei, China; 4. Institute for Metallurgical Engineering and Technology, North China University of Science and Technology, Tangshan 063210, Hebei, China; 5. College of Metallurgy and Energy, Ministry of Education Key Laboratory of Modern Metallurgy Technology, North China University of Science and Technology, Tangshan 063210, Hebei, China
Abstract:Aiming at the problem of blast furnace hearth erosion, the research progress of intelligent technology of blast furnace hearth is introduced, and the technology of realizing the visualization of hearth lining is analyzed. Based on the comparison of the hearth erosion model and the development of the large data forecasting model, the model of hearth erosion big data fusion technology is puts forward. Based on the decision tree and genetic algorithm to optimize the BP neural network, the model takes the hot metal composition and temperature, cooling parameters, operation parameters as input parameters, and adopts the method of fusion of big data technology to build the prediction model of hearth erosion. Big data technology provides new ideas for the development of steel industry and further promotes the intelligent iron-making of blast furnace.
张伟阳, 郝良元, 钟文达, 邓勇, 程相文, 吕庆. 基于大数据技术的炉缸侵蚀模型[J]. 钢铁, 2020, 55(8): 160-168.
ZHANG Wei-yang, HAO Liang-yuan, ZHONG Wen-da, DENG Yong, CHENG Xiang-wen, LÜ Qing. Erosion model of hearth based on big data technology[J]. Iron and Steel, 2020, 55(8): 160-168.
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