首页|基于周期配准和趋势分解的多关联高炉炉温预测

基于周期配准和趋势分解的多关联高炉炉温预测

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原文链接
  • NETL
  • NSTL
  • 万方数据
高炉煤气流可表征高炉炉况运行状态,而十字测温温度反映了高炉煤气流的分布状态.本文提出了一种基于周期配准与季节性趋势分解(seasonal and trend decomposition using loess,STL)的多变量关联高炉十字测温温度动态建模方法,以提高煤气流的准确估计.首先,通过滑动窗口方法划分周期窗口,并进行多变量间的周期配准,匹配精准的多变量关联关系;其次,引入稳健型季节性趋势分解(RobustSTL)方法,保留关键参数信息,提取全局变化趋势,提高在线估计模型的准确度;再次,使用门控循环单元(gated recurrent unit,GRU)建立十字测温多变量关联的多步预测模型;最后,利用十字测温数据集进行实验验证,结果表明,本文提出的预测模型取得了较好的性能提升.
Multi-correlation blast furnace temperature prediction based on periodic registration and trend decomposition
The blast furnace gas flow can characterize the operating state of a blast furnace,while the cross-temperature measurement reflects the distribution state of the blast furnace gas flow.This paper proposed a dynamic modeling method for multivariate correlation of blast furnace cross-temperature measurement based on periodic registration and seasonal and trend decomposition using loess(STL),which can improve the accurate estimation of gas flow.Firstly,the periodic partitioning and periodic registration among multiple variables are performed by sliding window,which is helpful to achieve precise multivariate correlation.Next,the RobustSTL is introduced to retain key information,extract global changes,and enhance the accuracy of the online estimation model.Then,the gated re-current unit(GRU)is employed to establish a multi-step prediction model for the multivariate corre-lation of cross-temperature measurement.Finally,experimental verification is conducted using the cross-temperature measurement dataset,and the results show that the proposed predictive model achieves significant performance improvement.

cross-temperature measurement of blast furnaceperiodic partitioningperiodic registra-tionRobustSTL decompositiontime series prediction of multi-correlation

张亚娴、张森、杨永亮、肖文栋

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北京科技大学自动化学院,北京 100083

北京科技大学工业过程知识自动化教育部重点实验室,北京 100083

北京科技大学顺德创新学院,广东佛山 528399

高炉十字测温 周期划分 周期配准 稳健型季节性趋势分解 多关联时序预测

国家自然科学基金国家自然科学基金北京市自然科学基金广东省基础与应用基础研究基金

6217303261903028J2100052022A1515140109

2024

10.3969/j.issn.1000-7059.2024.02.007

冶金自动化
冶金自动化研究设计院

冶金自动化

影响因子：0.685
ISSN：1000-7059
年,卷(期)：2024.48(2)
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