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基于支持向量回归与极限学习机的高炉铁水温度预测

王振阳 江德文 王新东 张建良 刘征建 赵宝军

王振阳, 江德文, 王新东, 张建良, 刘征建, 赵宝军. 基于支持向量回归与极限学习机的高炉铁水温度预测[J]. 工程科学学报, 2021, 43(4): 569-576. doi: 10.13374/j.issn2095-9389.2020.05.28.001
引用本文: 王振阳, 江德文, 王新东, 张建良, 刘征建, 赵宝军. 基于支持向量回归与极限学习机的高炉铁水温度预测[J]. 工程科学学报, 2021, 43(4): 569-576. doi: 10.13374/j.issn2095-9389.2020.05.28.001
WANG Zhen-yang, JIANG De-wen, WANG Xin-dong, ZHANG Jian-liang, LIU Zheng-jian, ZHAO Bao-jun. Prediction of blast furnace hot metal temperature based on support vector regression and extreme learning machine[J]. Chinese Journal of Engineering, 2021, 43(4): 569-576. doi: 10.13374/j.issn2095-9389.2020.05.28.001
Citation: WANG Zhen-yang, JIANG De-wen, WANG Xin-dong, ZHANG Jian-liang, LIU Zheng-jian, ZHAO Bao-jun. Prediction of blast furnace hot metal temperature based on support vector regression and extreme learning machine[J]. Chinese Journal of Engineering, 2021, 43(4): 569-576. doi: 10.13374/j.issn2095-9389.2020.05.28.001

基于支持向量回归与极限学习机的高炉铁水温度预测

doi: 10.13374/j.issn2095-9389.2020.05.28.001
基金项目: 中国博士后科学基金面上资助项目(2019M650490)
详细信息
    通讯作者:

    E-mail: wangzhenyang@ustb.edu.cn

  • 中图分类号: TF543.1

Prediction of blast furnace hot metal temperature based on support vector regression and extreme learning machine

More Information
  • 摘要: 选取某4000 m3级别高炉2014年至2019年时间范围内的日平均数据,以铁水温度为目标函数,首先对铁水温度的特征参量进行线性与非线性相关性分析、特征选择与规范化处理,获取了显著影响铁水温度的正负相关性特征参量。在此基础上,基于支持向量回归与极限学习机两种算法对铁水温度构建预测模型,模型均可对铁水温度实现有效预测,基于支持向量回归算法构建的预测模型较优,预测平均绝对误差为4.33 ℃,±10 ℃误差范围内的命中率为94.0%。
  • 图  1  铁水温度初选特征参量样本散点图

    Figure  1.  Scatter plot of the primary characteristic parameters of hot metal temperature

    图  2  铁水温度与初选特征参量之间相关系数。(a)Pearson相关系数;(b)Spearman相关系数

    Figure  2.  Correlation coefficient between hot metal temperature and primary characteristic parameters: (a) Pearson; (b) Spearman

    图  3  铁水温度测量值与预测值比对。(a)基于SVR算法;(b)基于ELM算法

    Figure  3.  Comparison of measured and predictive values of hot metal temperature:(a) prediction value based on support vector regression (SVR);(b) prediction value based on extreme learning machine (ELM).

    图  4  铁水温度预测值与测量值偏差。(a)基于SVR的铁温预测值与测量值偏差;(b)基于ELM的铁温预测值与测量值偏差;(c)基于SVR与ELM预测铁温误差概率密度分布函数

    Figure  4.  Deviation of predictive value of hot metal temperature from the measured value: (a) based on SVR; (b) based on ELM; (c) the probability density distribution function of hot metal temperature error based on SVR and ELM

    图  5  铁水温度预测的百分比误差散点分布统计图。(a)SVR;(b)ELM

    Figure  5.  Scatter distribution statistics of percentage error in hot metal temperature prediction: (a) SVR; (b) ELM

    表  1  铁水温度预测的初选特征参量

    Table  1.   Primary data items for hot metal temperature prediction

    Operating parametersState parameters
    Blast volumeVolume utilization coefficient
    Blast pressureSynthetic load
    Blast temperatureGas utilization efficiency
    Blast velocity energyDaily hot metal production
    Coke ratePressure difference
    Coal injection ratePermeability index
    Nut coke rateBosh gas volume
    Fuel rateBosh gas index
    Oxygen enrichmentCooling water temperature difference
    Pulverized coal injection per hourCurrent hot metal temperature
    Theoretical combustion temperatureHot metal Si content
    下载: 导出CSV

    表  2  铁水温度终选特征参量

    Table  2.   Final characteristic parameters of hot metal temperature

    No.Characteristic parameters
    1Fuel rate
    2Nut coke rate
    3Coke rate
    4Blast temperature
    5Bosh gas index
    6Permeability index
    7Hot metal Si content
    8Daily hot metal production
    9Current hot metal temperature
    10Synthetic load
    11Pressure difference
    12Gas utilization efficiency
    13Volume utilization coefficient
    14Cooling water temperature difference
    下载: 导出CSV

    表  3  SVR与ELM算法铁水温度预测结果综合定量表征

    Table  3.   Quantitative characterization of SVR and ELM model prediction results of hot metal temperature

    ModelMAPE/%MAE /℃RMSE/℃HP(±10 ℃)/%
    SVR0.294.335.6094.0
    ELM0.314.696.0988.5
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-05-28
  • 网络出版日期:  2020-12-30
  • 刊出日期:  2021-03-31

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