Please wait a minute...
浙江大学学报(工学版)
J4  2012, Vol. 46 Issue (5): 830-836    DOI: 10.3785/j.issn.1008-973X.2012.05.009
    
Quality prediction of hot metal in blast furnace using improved
support vector regression
LIU Yi1, LI Ping2, GAO Zeng-liang1
1. Institute of Process Equipment and Control Engineering, Zhejiang University of Technology, Hangzhou 310032,
China;2. Institute of Industrial Process Control, Zhejiang University, Hangzhou 310027,  China
Download:   PDF(0KB) HTML
Export: BibTeX | EndNote (RIS)      

Abstract  

A novel preprocessing method integrated two-level spaces of process variables was proposed to overcome the effect of noises and outliers in modeling data, which can degrade the performance of the related soft sensor model. The multivariate trimming was first utilized for preprocessing in the primary space. Then, a support vector clustering (SVC) strategy was proposed for outlier detection. The main idea of SVC is to map the data into the feature space and then to find a hypersphere with the minimal radius that contains most of the mapped data. Different from most of traditional preprocessing methods, SVC dose not assume that data are distributed (approximately) normally, and thus is more suitable for industrial ironmaking processes. After SVC-based preprocessing, a support vector regression soft sensor model was built. An experiment study for an industrial blast furnace is investigated and the results show its superiority, including efficient outlier detection, a more robust support vector regression model and better prediction performance, compared to traditional approaches．

Published: 01 May 2012
CLC:  TP 301.6  
  TQ 02  
Service
E-mail this article
Add to my bookshelf
Add to citation manager
E-mail Alert
RSS
Articles by authors
Cite this article:

LIU Yi, LI Ping, GAO Zeng-liang. Quality prediction of hot metal in blast furnace using improved
support vector regression. J4, 2012, 46(5): 830-836.

URL:

http://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2012.05.009     OR     http://www.zjujournals.com/eng/Y2012/V46/I5/830


用于高炉铁水质量预报的改进支持向量回归

针对建模数据中包含噪声和离群点会降低相应软测量模型准确性的问题,提出一种结合2层变量空间分析的预处理方法.用多变量修剪法在原始变量空间预处理；并提出支持向量聚类（SVC）的预处理方法,将建模数据映射到高维特征空间,构造一超球体来排除离群点.SVC无需像传统预处理方法假设数据服从正态或近似正态分布,更符合实际的高炉过程.预处理后的数据再用支持向量回归建立软测量模型.在一工业高炉铁水硅含量的建模和预报实验结果表明,所提出方法能够更有效排除离群点,且提高了支持向量回归模型的鲁棒性和预报性能.

［1］ 周传典. 高炉炼铁生产技术手册［M］. 北京: 冶金工业出版社, 2002: 188-214．
［2］ 刘祥官, 刘芳. 高炉炼铁过程优化与智能控制系统［M］. 北京: 冶金工业出版社, 2003: 1-270．
［3］ NOGAMI H, CHU MS, YAGI J. Multidimensional transient mathematical simulator of blast furnace process based on multifluid and kinetic theories ［J］. Computers and Chemical Engineering, 2005, 29(11): 2438-2448．
［4］ NISHIOKA K, MAEDA T, SHIMIZU M. A threedimensional mathematical modeling of drainage behavior in blast furnace hearth ［J］. ISIJ International, 2005, 45(5): 669-676．
［5］ 郜传厚, 渐令, 陈积明, 等. 复杂高炉炼铁过程的数据驱动建模及预测算法［J］. 自动化学报, 2009, 35(6): 725-730．
GAO Chuanhou, JIAN Ling, CHEN Jiming, et al. Datadriven modeling and predictive algorithm for complex blast furnace iron ［J］. Acta Automatica Sinica, 2009, 35(6): 725-730．
［6］ KANO M, NAKAGAWA Y. Databased process monitoring, process control, and quality improvement: Recent developments and applications in steel industry ［J］. Computers and Chemical Engineering, 2008, 32(1): 12-24．
［7］ GAO Chuanhou, CHEN Jiming, ZENG Jiusun, et al. A chaosbased iterated multistep predictor for blast furnace ironmaking process ［J］. American Institute of Chemical Engineers Journal, 2009, 55(4): 947-962．
［8］ CHEN J. A predictive system for blast furnaces by integrating a neural network with qualitative analysis ［J］. Engineering Applications of Artificial Intelligence, 2001, 14(1): 77-85．
［9］ HAO XJ, SHEN FM, DU G, et al. A blast furnace prediction model combining neural network with partial least square regression ［J］. Steel Research International, 2005, 76(10): 694-699．
［10］ GAO Chuanhou, ZHOU Zhimin, CHEN Jiming. Assessing the predictability for blast furnace system through nonlinear time series analysis ［J］. Industrial and Engineering Chemistry Research, 2008, 47(9): 3037-3045．
［11］ ZENG Jiusun, GAO Chuanhou. Improvement of identification of blast furnace ironmaking process by outlier detection and missing value imputation ［J］. Journal of Process Control, 2009, 19(9): 1519-1528．
［12］ 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［J］. 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 ［J］. Iron and Steel, 2005, 40(3): 17-20．
［13］ 王玉涛, 严其艳, 杨钢. 高炉铁水含硅量的动态神经网络多步预报［J］. 仪器仪表学报, 2006, 27(11): 1448-1451．
WANG Yutao, YAN Qiyan, YANG Gang. Multistep prediction of molten iron silicon content in blast furnace using dynamic neural network ［J］. Chinese Journal of Scientific Instrument, 2006, 27(11): 1448-1451．
［14］ 王华秋, 廖晓峰, 邹航, 等. 自反馈RBF网络在高炉热状态模型预测中的应用［J］. 系统工程与电子技术, 2008, 30(5): 929-934．
WANG Huaqiu, LIAO Xiaofeng, ZOU Hang, et al. Application of selffeedback RBF NN in prediction model for heat state of blast furnace ［J］. Systems Engineering and Electronics, 2008, 30(5): 929-934．
［15］ 赵敏. 高炉冶炼过程的复杂性机理及其预测研究［D］. 杭州: 浙江大学, 2008∶13-32．
ZHAO Min. Complexity mechanism and predictive research for BF ironmaking process ［D］. Hangzhou: Zhejiang University, 2008: 13-32．
［16］ TAYLOR J, CRISTIANINI N. Kernel methods for pattern analysis ［M］. Cambridge, UK: Cambridge University Pres, 2004: 38-75．
［17］ 渐令, 刘祥官. 支持向量机在铁水硅含量预报中的应用［J］. 冶金自动化, 2005, 29(3): 33-36.
JIAN Ling, LIU XiangGuan. Application of SVM to prediction of silicon content in hot metal ［J］. Metallurgical Industry Automation, 2005, 29(3): 33-36．
［18］ 郑俊华, 吴铁军. 高炉铁水硅含量预报的ICASVM建模方法［J］. 信息与控制, 2008, 37(2): 247-252．
ZHENG Junhua, WU Tiejun. ICASVM based modeling for predicting Silicon content in blast furnace hot metal ［J］. Information and Control, 2008, 37(2): 247-252．
［19］ 唐贤伦, 庄陵, 胡向东. 铁水硅含量的混沌粒子群支持向量机预报方法［J］. 控制理论与应用, 2009, 26(8): 838-842．
TANG Xianlun, ZHUANG Ling, HU Xiangdong. The support vector regression based on the chaos particle swarm optimization algorithm for the prediction of silicon content in hot metal ［J］. Control Theory & Applications, 2009, 26(8): 838-842．
［20］ CHIANG L H, PELL R J, SEASHOLTZ M B. Exploring process data with the use of robust outlier detection algorithms ［J］. Journal of Process Control, 2003, 13 (5): 437-449．
[1] LIU Jia-hai, YANG Mao-lin, LEI Hang, LIAO Yong. Multicore real-time task allocation algorithms with shared resource constraints[J]. J4, 2014, 48(1): 113-117.
[2] ZHAO Shi-kui, FANG Shui-liang, GU Xin-jian. Genetic algorithm with new initialization mechanism for flexible job shop scheduling[J]. J4, 2013, 47(6): 1022-1030.
[3] ZHANG Jun-chao, YUE Mao-xiong, LIU Hua-feng. Dynamic PET image reconstruction with Geometrical structure
prior constraints[J]. J4, 2012, 46(6): 961-966.
[4] FANG Shui-liang, YAO Yan-fei, ZHAO Shi-kui. Improved genetic algorithm for flexible job shop scheduling[J]. J4, 2012, 46(4): 629-635.
[5] YU Hai-qing, LIU Yi, CHEN Kun, JI Jun1, LI Ping. Robust recursive kernel learning modeling method with
application to blast furnace[J]. J4, 2012, 46(4): 705-711.
[6] LIU Jia-hai, YANG Mao-lin. Fair scheduling algorithm on multi-core platforms based platforms[J]. J4, 2011, 45(9): 1566-1570.
[7] NI He, Cheng-Gang, SUN Feng-Rui. Adaptive hybrid evolutionary modeling method and its application[J]. J4, 2010, 44(8): 1490-1495.