Wiener Model Identification of Blast Furnace Ironmaking Process

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概要

• 論文の詳細を見る

• To account for the nonlinearity of blast furnace ironmaking process, a nonlinear Wiener model identification algorithm is presented. The system consists of a linear time invariant (LTI) subsystem followed by a static nonlinearity. The inverse of the nonlinearity is assumed to be a linear combination of known nonlinear basis functions and the linear subspace algorithm is used to identify the model. The inputs to the model are parameters regarded to be most responsible for the fluctuation of thermal state in blast furnace while the output to the model is silicon content in hot metal. The identified Wiener model is then tested on datasets obtained from No. 6 Blast Furnace from Baotou Steel. It is found that the blast furnace of concern is a short memory system, so that for each prediction the Wiener method is retrained. It is shown that the retrained model well improves the predictive accuracy.

• 社団法人 日本鉄鋼協会の論文
• 2008-12-15

著者

• Liu Xiang-guan

  The Institute Of System Optimum Technique Zhejiang University

• ZENG Jiu-sun

  The Institute of System Optimum Technique, Zhejiang University

• GAO Chuan-hou

  The Institute of System Optimum Technique, Zhejiang University

• LUO Shi-hua

  School of Information Management, Jiangxi University of Finance and Economics

• JIAN Ling

  College of Mathematics and Computer Science, China University of Petroleum

• Gao Chuan-hou

  The Institute Of System Optimum Technique Zhejiang University

• Jian Ling

  College Of Mathematics And Computational Science China University Of Petroleum

• Jian Ling

  College Of Mathematics And Computer Science China University Of Petroleum

• Luo Shi-hua

  School Of Information Management Jiangxi University Of Finance And Economics

• Zeng Jiu-sun

  The Institute Of System Optimum Technique Zhejiang University

関連論文

• Wiener Model Identification of Blast Furnace Ironmaking Process
• Identification of Multi-fractal Characteristics of Silicon Content in Blast Furnace Hot Metal
• Application of Least Squares Support Vector Machines to Predict the Silicon Content in Blast Furnace Hot Metal

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