Application of Improved Local Models of Large Scale Database-based Online Modeling to Prediction of Molten Iron Temperature of Blast Furnace
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概要
- 論文の詳細を見る
The large scale database-based online modeling called LOM is the one of local modeling method. This method has been developed to apply the just-in-time modeling for the blast furnace by us. In this paper, we propose two new types of local models in LOM to improve the prediction performance. One is used weighted multiple regression model as a linear local model of LOM. The other is used on-line Bayesian learning model as a nonlinear local model of LOM. In order to compare the prediction performance of the two types of local models in LOM, we evaluate the prediction performance by using the real process data of the blast furnace.
- 社団法人 日本鉄鋼協会の論文
- 2010-07-15
著者
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Kaneko Norio
Department Of Chemistry And Material Engineering Faculty Of Engineering Shinshu University
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Uchida Kenko
Department Of Electrical Engineering And Bioscience School Of Advanced Science And Engineering Wased
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Matsuzaki Shinroku
Environment & Process Technology Center Technical Development Bureau Nippon Steel Corp.
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Matsuzaki Shinroku
Environment & Process Technology Center Nippon Steel Corporation
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Kaneko Norio
Department Of Electrical Engineering And Bioscience School Of Advanced Science And Engineering Wased
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ITO Masahiro
Environment & Process Technology Center, Technical Development Bureau Nippon Steel Corp.
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OOGAI Haruhisa
The Graduate School of Information, Production and Systems, Waseda University
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Ogai Harutoshi
The Graduate School Of Information Production And Systems Waseda University
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Ito Masahiro
Environment & Process Technology Center Technical Development Bureau Nippon Steel Corp.
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