| 高炉炼铁数据的异常值处理 |
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| 作者姓名： | Jun Zhao Shao-fei Chen Xiao-jie Liu Xin Li Hong-yang Li Qing Lyu |
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| 作者单位： | College of Metallurgy, Northeastern University, Shenyang 110819, China;Tangshan Branch of HBIS Group Co., Ltd., Tangshan 063020, China;College of Metallurgy and Energy, North China University of Technology, Tangshan 063210, China;College of Metallurgy, Northeastern University, Shenyang 110819, China;College of Metallurgy and Energy, North China University of Technology, Tangshan 063210, China |
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| 摘 要： | Blast furnace data processing is prone to problems such as outliers. To overcome these problems and identify an improved method for processing blast furnace data, we conducted an in-depth study of blast furnace data. Based on data samples from selected iron and steel companies, data types were classified according to different characteristics; then, appropriate methods were selected to process them in order to solve the deficiencies and outliers of the original blast furnace data. Linear interpolation was used to fill in the divided continuation data, the K-nearest neighbor (KNN) algorithm was used to fill in correlation data with the internal law, and periodic statistical data were filled by the average. The error rate in the filling was low, and the fitting degree was over 85%. For the screening of outliers, corresponding indicator parameters were added according to the continuity, relevance, and periodicity of different data. Also, a variety of algorithms were used for processing. Through the analysis of screening results, a large amount of efficient information in the data was retained, and ineffective outliers were eliminated. Standardized processing of blast furnace big data as the basis of applied research on blast furnace big data can serve as an important means to improve data quality and retain data value.
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| 关 键 词： | ,,,,,,, |
Outlier screening for ironmaking data on blast furnaces |
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| Jun Zhao,Shao-fei Chen,Xiao-jie Liu,Xin Li,Hong-yang Li,Qing Lyu.Outlier screening for ironmaking data on blast furnaces[J].International Journal of Minerals,Metallurgy and Materials,2021,28(6):1001-1010. |
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| Authors: | Jun Zhao Shao-fei Chen Xiao-jie Liu Xin Li Hong-yang Li Qing Lyu |
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| Abstract: | Blast furnace data processing is prone to problems such as outliers. To overcome these problems and identify an improved method for processing blast furnace data, we conducted an in-depth study of blast furnace data. Based on data samples from selected iron and steel companies, data types were classified according to different characteristics; then, appropriate methods were selected to process them in order to solve the deficiencies and outliers of the original blast furnace data. Linear interpolation was used to fill in the divided continuation data, the K-nearest neighbor (KNN) algorithm was used to fill in correlation data with the internal law, and periodic statistical data were filled by the aver-age. The error rate in the filling was low, and the fitting degree was over 85％. For the screening of outliers, corresponding indicator paramet-ers were added according to the continuity, relevance, and periodicity of different data. Also, a variety of algorithms were used for processing. Through the analysis of screening results, a large amount of efficient information in the data was retained, and ineffective outliers were elimin-ated. Standardized processing of blast furnace big data as the basis of applied research on blast furnace big data can serve as an important means to improve data quality and retain data value. |
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| Keywords: | blast furnace data missing outliers data processing data mining |
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