引入水温差时间序列的高炉炉缸热状态预测方法  

Prediction method of blast furnace hearth thermal state by introducing time series of water temperature difference

作　　者：张小乐 于凯 张胜男 崔桂梅[2] ZHANG Xiaole;YU Kai;ZHANG Shengnan;CUI Guimei(Inner Mongolia Anke Safety Production Testing and Inspection Co.,Ltd.,Baotou 014010,China;School of Information Engineering,Inner Mongolia University of Science and Technology,Baotou 014010,China)

机构地区：[1]内蒙古安科安全生产检测检验有限公司,内蒙古包头014010 [2]内蒙古科技大学信息工程学院,内蒙古包头014010

出　　处：《中国测试》2022年第6期74-79,共6页China Measurement & Test

基　　金：国家自然科学基金(61763039)。

摘　　要：为准确预测炉缸热状态,考虑到炉缸热状态受高炉冷却壁段水温差影响而产生的时滞性,该文提出一种基于高炉水温差时间序列的新型炉缸热状态趋势评估预测方法。首先,采用相关性分析法计算出泊松相关系数并对历史炉温、与炉温预测相关的过程参数及水温差时间序列进行有效性提取;其次,综合高炉各项指标参数及相关历史信息,建立引入水温差时间序列的炉缸热状态评判预测模型,其中以PSO-LSSVM、BP-NN(BP神经网络)预测模型为例;最后,以某大型高炉为基础,预测分析其工业数据。预测模型的高命中率结果表明,引入高炉水温差时间序列方法可更准确地评估预测炉缸热状态。In order to predict the thermal state of hearth accurately and consider the time lag effect of the water temperature difference in the cooling stave sections of the bf hearth thermal state,a new trend prediction method for the thermal state based on the time series of the bf water temperature difference is proposed.Firstly,the correlation analysis method is used to calculate the Poisson correlation coefficient and extract the effective historical furnace temperature,water temperature difference time series and process parameters related to furnace temperature prediction.Secondly,the index parameters of the three-dimensional BF and their related historical information are comprehensively used to to establish a hearth thermal state prediction model by introducing the time series of water temperature difference,which takes the PSO-LSSVM and BP neural network(BP-NN)prediction models as examples.Finally,based on a large-scale blast furnace,the industrial data are predicted and analyzed.The high hitting rate of the two prediction models shows that the model introduced into the BF water temperature difference time series can more accurately predict the heat state of the hearth.

关 键 词：炉缸热状态 水温差 炉温 时间序列 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]