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Zhen Zhang, Jue Tang, Man-sheng Chu, Quan Shi, Ming-yu Wang, Chuan-qiang Wang, Shi-bin Wang, Yun-tao Li. Online calculation and monitoring system of blast furnace operation profile based on data and mechanism dual drive[J]. Journal of Iron and Steel Research International, 2025, 32(12): 4188-4206. DOI: 10.1007/s42243-025-01605-2
Citation: Zhen Zhang, Jue Tang, Man-sheng Chu, Quan Shi, Ming-yu Wang, Chuan-qiang Wang, Shi-bin Wang, Yun-tao Li. Online calculation and monitoring system of blast furnace operation profile based on data and mechanism dual drive[J]. Journal of Iron and Steel Research International, 2025, 32(12): 4188-4206. DOI: 10.1007/s42243-025-01605-2

Online calculation and monitoring system of blast furnace operation profile based on data and mechanism dual drive

  • The operation furnace profile for the high heat load zone was one of the important factors affecting the stable and high-quality production of the blast furnace, but it was difficult to monitor directly. To address this issue, an online calculation model for the operation furnace profile was proposed based on a dual-driven approach combining data and mechanisms, by integrating mechanism experiment, numerical simulation, and machine learning. The experimentally determined slag layer hanging temperature was 1130 °C, and the thermal conductivity ranged from 1.32 to 1.96 m2 °C-1. Based on the 3D slag-hanging numerical simulation model, a database was constructed, containing 2294 sets of mechanism cases for the slag layer. The fusion of data modeling, heat transfer theory, and expert experience enabled the online calculation of key input variables for the operation furnace profile, particularly the quantification of the “black-box” variable of gas temperature. Simulated data were used as inputs, and light gradient boosting machine was applied to construct the online calculation model for the operation furnace profile. This model facilitated the online calculation of the slag layer thickness and other key indices. The coefficient of determination of the model exceeded 0.98, indicating high accuracy. A slag layer state judgment model was constructed, categorizing states as shedding, too thin, normal, and too thick. Real-time data were applied, and the average slag thickness in the high heat load area of the test data ranged from 40 to 80 mm, which was consistent with field experience. The absolute value of the Pearson correlation coefficient between slag layer thickness, thermocouple temperature, and heat load data was above 0.85, indicating that the calculated results closely aligned with the actual trends. A 3D visual online monitoring system for the operation furnace profile was created, and it has been successfully implemented at the blast furnace site.
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