Outlier screening for iron-making data on blast furnaces

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.