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基于Bootstrap的高炉铁水硅含量二维预报

蒋朝辉 董梦林 桂卫华 阳春华 谢永芳

蒋朝辉, 董梦林, 桂卫华, 阳春华, 谢永芳. 基于Bootstrap的高炉铁水硅含量二维预报. 自动化学报, 2016, 42(5): 715-723. doi: 10.16383/j.aas.2016.c150574
引用本文: 蒋朝辉, 董梦林, 桂卫华, 阳春华, 谢永芳. 基于Bootstrap的高炉铁水硅含量二维预报. 自动化学报, 2016, 42(5): 715-723. doi: 10.16383/j.aas.2016.c150574
JIANG Zhao-Hui, DONG Meng-Lin, GUI Wei-Hua, YANG Chun-Hua, XIE Yong-Fang. Two-dimensional Prediction for Silicon Content of Hot Metal of Blast Furnace Based on Bootstrap. ACTA AUTOMATICA SINICA, 2016, 42(5): 715-723. doi: 10.16383/j.aas.2016.c150574
Citation: JIANG Zhao-Hui, DONG Meng-Lin, GUI Wei-Hua, YANG Chun-Hua, XIE Yong-Fang. Two-dimensional Prediction for Silicon Content of Hot Metal of Blast Furnace Based on Bootstrap. ACTA AUTOMATICA SINICA, 2016, 42(5): 715-723. doi: 10.16383/j.aas.2016.c150574

基于Bootstrap的高炉铁水硅含量二维预报

doi: 10.16383/j.aas.2016.c150574
基金项目: 

国家自然科学基金创新研究群体科学基金 61321003

国家自然科学基金重大项目 61290325

中南大学中央高校基本科研业务费专项资金 2013zzts226

详细信息
    作者简介:

    董梦林中南大学信息科学与工程学院硕士研究生.主要研究方向为工业过程建模与优化控制研究,智能控制系统.E-mail: 244751367@qq.com

    桂卫华 中国工程院院士,中南大学信息科学与工程学院教授.主要研究方向为复杂工业过程建模与优化控制,工业大系统控制理论与应用.E-mail: gwh@mail.csu.edu.cn

    阳春华博士,中南大学信息科学与工程学院教授.主要研究方向为复杂工业过程建模与优化控制,智能自动化控制系统.E-mail: ychh@mail.csu.edu.cn

    谢永芳博士, 中南大学信息科学与工程学院教授. 主要研究方向为复杂工业过程建模与控制, 分散鲁棒控制.E-mail: yfxie@mail.csu.edu.cn

    通讯作者:

    蒋朝辉 博士, 中南大学信息科学与工程学院副教授. 主要研究方向为复杂工业过程建模与优化控制, 广义大系统控制理论与应用. 本文通信作者. E-mail:jzh0903@csu.edu.cn.

Two-dimensional Prediction for Silicon Content of Hot Metal of Blast Furnace Based on Bootstrap

Funds: 

Foundation for Innovative Re- search Groups of National Natural Science Foundation of China 61321003

Major Program of National Natural Science Foundation of China 61290325

Fundamental Research Funds for the Central Universities of Central South University 2013zzts226

More Information
    Author Bio:

    Master student at the School of Information Science and Engineering, Central South University. Her research interest covers mod-eling and optimal control of complex industrial process, and intelligent control system.

    Academician of Chinese Academy of Engineering, professor at the School of Information Science and Engineering, Central South University. His research interest covers modeling and optimal control of complex industrial process, industrial large system control theory and application.

    Ph. D., professor at the School of Information Science and Engineering, Central South University. Her research interest covers modeling and optimal control of complex industrial process, and intelligent automation control system.

    Ph. D., professor at the School of Information Science and Engineering, Central South University. His research interest covers modeling and optimal control of complex industrial process, and distributed robust control.

    Corresponding author: JIANG Zhao-Hui Ph. D., associate professor at the School of Information Science and Engineering, Central South University. His research interest covers modeling and optimal control of complex industrial process, descriptor large systems control theory and application. Corresponding author of this paper.E-mail:jzh0903@csu.edu.cn.
  • 摘要: 高炉铁水硅含量的实时准确预报对调控高炉炉温和稳定炉况具有重要作用, 但其预报结果一直存在准确度不高和缺乏可信度表征等问题, 特别是在炉况不稳、运行数据波动较大时, 预报结果的准确度和可信度急速下降, 不利于现场操作人员根据预报结果进行生产操作. 为此本文融合神经网络和Bootstrap预报区间方法, 构建高炉铁水硅含量的二维预报模型, 实现在预报硅含量值的同时给出了该预测值的可信度.应用实例表明, 本文提出的方法提高了硅含量点预测结果的准确度, 且预测区间宽度能正确地表征点预测结果的可信度, 对实际生产操作具有较好的指导意义.
  • 图  1  高炉铁水硅含量二维预报功能

    Fig.  1  The two-dimensional prediction function of the silicon content in hot metal of blast furnace

    图  2  高炉铁水硅含量二维预报模型预测结果图

    Fig.  2  Prediction results of the two-dimensional prediction model

    图  3  误差结果图

    Fig.  3  The predictive error result of the model

    图  4  硅含量预测值与实测值对比

    Fig.  4  The contrast of observed and predicted [Si]

    表  1  模型的候选输入变量

    Table  1  List of candidate input variables of the model

    变量名单位变量名单位
    Si(n-1)wt %理论燃烧温度
    Si(n - 2)wt %矿焦比kg/t
    料速t/h标准风速m/s
    顶压kpa热风温度oC
    全压差kpa鼓风动能kg . m/s
    富氧率wt %冷风流量m3/ min
    热风压力kpa冷风压力kpa
    实际风速m/s富氧压力kpa
    喷煤量t透气性指数m3/ min .kpa
    下载: 导出CSV

    表  2  模型的输入变量

    Table  2  List of the input variables of the model

    变量名相关性变量名相关性
    Si(n - 1)0.731富氧率0.251
    Si(n - 2)0.618热风温度-0.214
    冷风流量0.378料速-0.207
    实际风速-0.342透气性指数-0.113
    鼓风动能-0.304
    下载: 导出CSV

    表  3  四种预测模型的硅含量值的预测结果对比

    Table  3  Comparison of prediction results of the four models

    方法命中率(%)均方根误差
    单一神经网络750.1251
    偏最小二乘模型700.1384
    ARIMA模型730.1297
    二维预报模型840.0735
    下载: 导出CSV

    表  4  二维预报模型的预测结果统计

    Table  4  Statistics of prediction results of the two-dimensional

    绝对误差预测点个数预测区间平均宽度
    <0.051010.3118
    (0.05, 0.1)670.3207
    < 0.1320.4744
    下载: 导出CSV

    表  5  硅含量预测区间宽度和点预测结果的可信度关系

    Table  5  The relationship between width of prediction interval and reliability of point predictions

    预测点个数
    预测区间预测区间宽度范围< 0.1 < 0.1可信度(%)
    Ri< 0.37648095%
    R2(0.3, 0.45)7738096.25%
    Rs< 0.4515254037.5%
    下载: 导出CSV
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出版历程
  • 收稿日期:  2015-09-09
  • 录用日期:  2016-01-13
  • 刊出日期:  2016-05-01

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