Outlier screening for ironmaking data on blast furnaces
| Autor: | Shao-fei Chen, Qing Lyu, Xiao-jie Liu, Xin Li, Hong-yang Li, Jun Zhao |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Data processing
Blast furnace business.industry Computer science Mechanical Engineering Big data 0211 other engineering and technologies Metals and Alloys Word error rate 02 engineering and technology Linear interpolation 021001 nanoscience & nanotechnology computer.software_genre Data type ComputingMethodologies_PATTERNRECOGNITION Geochemistry and Petrology Mechanics of Materials Data quality Outlier Materials Chemistry Data mining 0210 nano-technology business computer 021102 mining & metallurgy |
| Zdroj: | International Journal of Minerals, Metallurgy and Materials. 28:1001-1010 |
| ISSN: | 1869-103X 1674-4799 |
| DOI: | 10.1007/s12613-021-2301-7 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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