热风炉炉壳的温度传感器布置优化研究
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1.太原科技大学电子信息工程学院;2.太原科技大学车辆与交通工程学院;3.太原度衡科技有限公司

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TP301.6

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国家自然科学基金项目(面上项目,重点项目,重大项目)


Research on Optimization of Temperature Sensor Layout for Hot Air Furnace Shell
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School of Electronic Information Engineering, Taiyuan University of Science and Technology

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    摘要:

    热风炉作为炼铁的重要设备,监测炉壳表面的温度变化,有助于制定控制策略并确保现场设备的安全性。针对热成像系统误差较大和无法对热风炉表面全部区域监测的局限性,本文通过接触式测温的方法,设计出传感器布置的目标函数;采用肘部法则和模拟退火算法,得到温度传感器最佳数量和位置的最优解;采用径向基函数的方法重构传感器优化前后的热风炉表面温度场,得到不同区域的均方根误差值范围。结果表明,相较于传感器均匀分布的温度场,优化后的温度场均方根误差值范围从0.02~0.165变化到0.02~0.145,既减少了均方根误差值范围,又提高了温度场重构的准确性,可为热风炉工业测温提供参考价值。

    Abstract:

    As an essential equipment in ironmaking, formulating control strategies and ensuring the safety of the equipment on-site were facilitated by monitoring the temperature changes on the surface of the furnace shell for hot air furnace. In response to the limitations of thermal imaging systems, which have significant errors and are unable to monitor the entire area of the hot stove surface, contact temperature measurement was utilized for sensor arrangement optimization. The elbow rule and simulated annealing algorithm were employed to determine the optimal number and location of temperature sensors. By using the radial basis function method, the temperature field on the hot blast stove's surface was reconstructed before and after sensor optimization. The range of root-mean-square error values in different regions was then determined. The results indicate that compared to the temperature field with a uniform distribution of sensors, the range of the root mean square error value of the optimized temperature field changes from 0.02 to 0.165 to 0.02 to 0.145. This reduction not only narrows the range of the root mean square error value but also enhances the accuracy of the temperature field reconstruction, which can be used as a reference for the industrial temperature measurement of the hot air furnace.

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张俊,李占龙,李虹,等. 热风炉炉壳的温度传感器布置优化研究[J]. 科学技术与工程, , ():

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  • 收稿日期:2023-11-05
  • 最后修改日期:2024-05-21
  • 录用日期:2024-05-21
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