WU Yaming, HUANG Yun, TAO Linhe, WU Zhikang, CAI Xuebin, ZUO Haibin
Reasonable operation of the furnace type is the key to its long life, stable operation, and sound economic and technical indicators for the blast furnace. Based on the production data of a certain steel plant's blast furnace, the blast furnace type optimization method was studied, providing scientific guidance for blast furnace operation. First, the Isolation Forest and Boxplot methods were adopted to identify and process noise in the data, and then Principal Component Analysis （PCA） was used for dimensionality reduction to eliminate noise and data redundancy, providing a high-quality data foundation for subsequent cluster analysis. Next, the application effects of two clustering algorithms, K-means and DBSCAN, were compared. The K-means algorithm achieved the best silhouette coefficient when the number of clusters was 14, indicating that the blast furnace type could be divided into 14 categories; the DBSCAN algorithm exhibited a lower Davies-Bouldin Index（DBI） when Neighborhood Radius （Eps） and Minimum Neighborhood Sample Count（min_samples） were 6.25 and 2, showing the best clustering effect and the ability to effectively identify clusters of any shape, especially suitable for handling the complexity and nonlinearity of blast furnace production data. To evaluate the advantages and disadvantages of different furnace types, an evaluation method for operating furnace types based on comprehensive production indicators was established, selecting coke ratio, fuel ratio, output, and iron loss as key performance indicators and assigning different weights. The results show that the fourth type of furnace type performs the best in terms of blast furnace operation indicators and can be used as the operating target for a reasonable furnace type. To achieve blast furnace type optimization, the implicit relationship between blast furnace operating parameters and furnace types was explored using the Random Forest method, determining the key feature parameters that affect furnace types, including burden matrix parameters, permeability index, gas utilization rate, and standard wind speed. By analyzing the evolution of furnace types and the trend of blast furnace parameters, it is found that the deterioration of furnace types is mainly related to the decrease in permeability, which leads to uneven airflow distribution, reduces gas utilization rate, and increases pressure drop. A new method for optimizing blast furnace type management is established, providing valuable data analysis and operational guidance for on-site personnel, helping to improve blast furnace operation level, reduce energy consumption and costs, and achieve long life, stable operation, and efficient production of blast furnaces.
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