Modeling of dynamic data-driven approach for the distributed steel rolling heating furnace temperature field

Bao, Qingfeng; Zhang, Sen; Guo, Jun; Xu, Zhengguang; Zhang, Zhenquan

doi:10.1007/s00521-022-06917-y

Cited by 5 publications

(1 citation statement)

References 39 publications

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“…With the development of intelligent algorithms, some intelligent algorithms and big data methods are playing increasingly important roles in modeling the temperature field and thermal expansion of mills. Bao et al established a datadriven dynamic neural network model for temperature forecasting in heated strips [19]. Li et al established a comprehensive forecasting model for hot-rolled strips based on a multi-granularity cascaded forest framework through real-time data collection and analysis, and they achieved accurate predictions under the conditions of limited samples [20].…”

Section: Introductionmentioning

confidence: 99%

Online Partition-Cooling System of Hot-Rolled Electrical Steel for Thermal Roll Profile and Its Industrial Application

Wang,

Sun,

Yang

et al. 2024

Processes

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The shape and convexity are crucial quality assessment indicators for hot-rolled electrical steel strips. Besides bending rolls, shifting rolls, and the original roll profile, the thermal roll profile also plays a significant role in controlling the shape and convexity during the hot-rolling process. However, it is always overlooked due to its dynamic uncertainty. To solve this problem, it is necessary to achieve online cooling-status control for the local thermal expansion of rolls. Based on the existing structure of a mill, a pair of special partition-cooling beams with an intelligent cooling system was designed. For high efficiency and practicality, a new online predictive model was established for the dynamic temperature field of the hot-rolling process. An equivalent treatment was applied to the boundary condition corresponding to the practical cooling water flow. In addition, by establishing the corresponding target distribution curve for the partitioned water flow cooling, online water-flow-partitioning control of the thermal roll profile was achieved. In the practical application process, a large number of onsite results exhibited that the predicted error was within 5% compared to the experimental results. The temperature difference between the upper and lower rolls was within 5 °C, and the temperature difference on both sides of the rolls was controlled within 0.7 °C. The hit rate of convexity (C40) increased by 33%. It was demonstrated that the partition-cooling processes of hot rolling are effective for the local shape and special convexity. They are able to serve as a better control method in the hot-rolling process.

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Section: Introductionmentioning

confidence: 99%