2.845

2023影响因子

(CJCR)

  • 中文核心
  • EI
  • 中国科技核心
  • Scopus
  • CSCD
  • 英国科学文摘

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

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
  • [1] de Castro J A, Nogami H, Yagi J I. Transient mathematical model of blast furnace based on multi-fluid concept with application to high PCI operation. ISIJ International, 2000, 40(7): 637-646
    [2] Nogami H, Chu M S, Yagi J I. Multi-dimensional transient mathematical simulator of blast furnace process based on multi-fluid and kinetic theories. Computers and Chemical Engineering, 2005, 29(11-12): 2438-2448
    [3] Gao C H, Ge Q H, Jian L. Rule extraction from fuzzy-based blast furnace SVM multiclassifier for decision-making. IEEE Transactions on Fuzzy Systems, 2014, 22(3): 586-596
    [4] 黄龙诚. 基于机理与数据混合驱动的高炉分布式炉温建模方法研究[硕士学位论文], 浙江大学, 中国, 2013

    Huang Long-Cheng. Blast Furnace Distributed Temperature Modeling Method Research Based on Mechanism and Data Hybrid Driven [Master dissertation], Zhejiang University, China, 2013
    [5] 李志玲. 基于主成分分析和偏最小二乘的高炉炉温预测模型的研究[硕士学位论文], 内蒙古科技大学, 中国, 2011

    Li Zhi-Ling. Study for Prediction Model of Blast Furnace Temperture Based on Principal Component Analysis and Partial Least Squares [Master dissertation], Inner Mongolia University of Science & Technology, China, 2011
    [6] 王文慧. 基于小波分析理论的高炉炉温预测模型研究[硕士学位论文], 浙江大学, 中国, 2005

    Wang Wen-Hui. Study for Prediction Model of Silicon Content in Molten Iron Based on Wavelet Analysis [Master dissertation], Zhejiang University, China, 2005
    [7] Chen W, Wang B X, Han H L. Prediction and control for silicon content in pig iron of blast furnace by integrating artificial neural network with genetic algorithm. Ironmaking & Steelmaking, 2010, 37(6): 458-463
    [8] Zeng J S, Gao C H, Liu X G, Yang K P, Luo S H. Using non-linear GARCH model to predict silicon content in blast furnace hot metal. Asian Journal of Control, 2008, 10(6): 632-637
    [9] Saxén H, Pettersson F, Gunturu K. Evolving nonlinear time-series models of the hot metal silicon content in the blast furnace. Materials and Manufacturing Processes, 2007, 22(5): 577-584
    [10] 刘学艺, 刘祥官, 王文慧. 贝叶斯网络在高炉铁水硅含量预测中的应用. 钢铁, 2005, 40(3): 17-20

    Liu Xue-Yi, Liu Xiang-Guan, Wang Wen-Hui. Application of Bayesian network to predicting silicon content in hot metal. Iron and Steel, 2005, 40(3): 17-20
    [11] Gao C H, Jian L, Luo S H. Modeling of the thermal state change of blast furnace hearth with support vector machines. IEEE Transactions on Industrial Electronics, 2012, 59(2): 1134-1145
    [12] Jian L, Gao C H, Li L, Zeng J S. Application of least squares support vector machines to predict the silicon content in blast furnace hot metal. ISIJ international, 2008, 48(11): 1659-1661
    [13] Gao C H, Zhou Z M, Qian J X. Chaotic identification and prediction of silicon content in hot metal. Journal of Iron and Steel Research International, 2005, 12(5): 3-5, 46
    [14] 郜传厚, 渐令, 陈积明, 孙优贤. 复杂高炉炼铁过程的数据驱动建模及预测算法. 自动化学报, 2009, 35(6): 725-730

    Gao Chuan-Hou, Jian Ling, Chen Ji-Ming, Sun You-Xian. Data-driven modeling and predictive algorithm for complex blast furnace ironmaking process. Acta Automatica Sinica, 2009, 35(6): 725-730
    [15] 南晓强, 李群湛, 赵元哲, 邱大强. 计及风电预测可信度的经济调度及辅助决策方法. 电力系统自动化, 2013, 37(19): 61-67

    Nan Xiao-Qiang, Li Quan-Zhan, Zhao Yuan-Zhe, Qiu Da-Qiang. An economic dispatch and decision making method based on credibility of wind power forecasting. Automation of Electric Power Systems, 2013, 37(19): 61-67
    [16] Zhao T T, Wang Q J, Bennett J C, Robertson D E, Shao Q X, Zhao J S. Quantifying predictive uncertainty of streamflow forecasts based on a Bayesian joint probability model. Journal of Hydrology, 2015, 528: 329-340
    [17] Khosravi A, Nahavandi S, Creighton D, Atiya A F. Comprehensive review of neural network-based prediction intervals and new advances. IEEE Transactions on Neural Networks, 2011, 22(9): 1341-1356
    [18] Chernick M R. Bootstrap Methods: A Guide for Practitioners and Researchers (Second Edition). Hoboken, N.J.: Wiley-Interscience, 2011.
    [19] 杨甲沛, 李锵, 刘郑, 袁晓琳. 基于自适应学习速率的改进型BP算法研究. 计算机工程与应用, 2009, 45(11): 56-58

    Yang Jia-Pei, Li Qiang, Liu Zheng, Yuan Xiao-Lin. Research of improved BP algorithm based on self-adaptive learning rate. Computer Engineering and Applications, 2009, 45(11): 56-58
    [20] Nix D A, Weigend A S. Estimating the mean and variance of the target probability distribution. In: Proceedings of the 1994 IEEE World Congress on Computational Intelligence, 1994 IEEE International Conference on Neural Networks. Orlando, FL: IEEE, 1994. 55-60
  • 加载中
图(4) / 表(5)
计量
  • 文章访问数:  1712
  • HTML全文浏览量:  247
  • PDF下载量:  951
  • 被引次数: 0
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

目录

    /

    返回文章
    返回