摘要
针对高炉炉缸侵蚀的问题,介绍了高炉炉缸智能技术研究进展,分析了实现炉缸内衬可视化的技术。基于炉缸侵蚀模型的比较及大数据预测模型的发展,提出了融合大数据技术的炉缸侵蚀模型技术思想。模型基于决策树和遗传算法优化的BP神经网络,将铁水成分及温度、冷却参数、操作参数作为输入参数,采用融合大数据技术的方法,构建了炉缸侵蚀预测模型。大数据技术为钢铁行业的发展提供了新思路,进一步推动了高炉智能化炼铁。
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.
作者
张伟阳
郝良元
钟文达
邓勇
程相文
吕庆
ZHANG Wei-yang;HAO Liang-yuan;ZHONG Wen-da;DENG Yong;CHENG Xiang-wen;LüQing(College of Mechanical Engineering,North China University of Science and Technology,Tangshan 063210,Hebei,China;Steel Research Institute,HBIS Group Co.,Ltd.,Shijiazhuang 050023,Hebei,China;Chengde Branch,Hebei Iron and Steel Co.,Ltd.,Chengde 067000,Hebei,China;Institute for Metallurgical Engineering and Technology,North China University of Science and Technology,Tangshan 063210,Hebei,China;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)
出处
《钢铁》
CAS
CSCD
北大核心
2020年第8期160-168,共9页
Iron and Steel
基金
河北省自然科学基金资助项目(E2020209069)
唐山市科学技术研究与发展计划资助项目(19150244E)
华北理工大学教育教学改革研究与实践资助项目(L1991)。
关键词
高炉
炉缸
侵蚀模型
大数据技术
预测模型
blast furnace
hearth
erosion model
big data technology
prediction model
