Preliminary study on missing data processing of blast furnace ironmaking
CHEN Shao-fei1,2,3, LIU Xiao-jie1,2,3, LI Hong-yang1,2,3, BU Xiang-ping4, LÜ Qing1,2,3, LIU Fu-long5
1. School of Metallurgy and Energy, North China University of Science and Technology,Tangshan 063009, Hebei, China; 2. Ministry of Education Key Laboratory of Modern Metallurgical Technology,Tangshan 063009, Hebei, China; 3. Key Laboratory of Modern Metallurgical Technology, North China University of Science and Technology,Tangshan 063009, Hebei, China; 4. Hangzhou Pailie Technology Co., Ltd., Hangzhou 310000, China; 5. General Research Institute of Hegang Group, Shijiazhuang 050000, Hebei, China
Abstract：Aiming at the problem of missing data in the process of blast furnace ironmaking, a systematic data filling model with a single dimension and multi-dimension was proposed. The development status and comparison of the advantages and disadvantages of the method of filling the missing data were summarized and expounded. On this basis, through the classification and comparison of the actual production data of a steel plant in Hebei province, combined with the advantages and disadvantages of the filling method, aiming at blast furnace ironmaking data, a set of solutions combined with simple statistical methods, linear interpolation methods, machine learning methods and other methods were proposed to realize the in-depth integration and processing of blast furnace data and meet the demand for data mining. At the same time, the furnace top temperature and oxygen pipeline temperature were selected as data samples, and the sample data are filled with the near point intermediate value method, the near point mean value method, linear interpolation method and Adaboost algorithm. And the filling effect was ideal, which can fully verify the feasibility of the scheme.
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2021, Vol. 31 
