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| Optimization of blast furnace parameters based on #br#
big data technology and process experience |
| LIU Song1,LIU Fulong2，LIU Erhao3,L Qing1,SHI Quan1,LIU Xiaojie1 |
(1. College of Metallurgy and Energy， North China University of Science and Technology,
Tangshan 063009， Hebei， China;2. General Institute of Steel Research, Hebei Steel Group Co., Ltd.,
Shijiazhuang 050023， Hebei， China;3. Chengde Steel Group Co., Ltd., Chengde 067000, Hebei, China) |
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Abstract In order to evaluate the operation of blast furnace more accurately, the guidelines of blast furnace are quantified. According to the characteristics of blast furnace production process, the big data technology to the datadriven analysis of blast furnace production parameters was applied, hot metal production and blast furnace energy consumption, and proposes a new method to optimize blast furnace production parameters. Firstly, the data collection, cleaning, filtering and integration of the historical data of blast furnace in a steel plant are carried out, and the data warehouse of the blast furnace is established. Then, a variety of clustering algorithms are combined to complete the detailed division of the blast furnace condition changes. The combination of process experience and recursive feature elimination algorithm is used to comprehensively select strong correlation variables that can reflect fluctuations in furnace conditions. The statistical method is applied to analyze the optimal range of core parameters corresponding to class a furnace conditions, which is of great significance for guiding onsite production and maintaining the longterm stable and smooth operation of the blast furnace.
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