Prediction of blast furnace operation using on-line Bayesian learning

N. Kaneko*, S. Sakamoto, K. Uchida, H. Ogai, M. Ito, S. Matsuzaki

*この研究の対応する著者

研究成果: Conference contribution

2 被引用数 (Scopus)

抄録

The large scale database-based online modeling, called LOM, is a type of Just-In-Time modeling for blast furnace. In this paper, we propose a new type of LOM using a nonlinear local model to improve the performance of the long-term prediction. To estimate the parameter of the nonlinear local model, we use on-line Bayesian learning scheme with Sequential Monte Carlo. The prediction performance of the new LOM is demonstrated by using the real process data of blast furnace.

本文言語English
ホスト出版物のタイトル2008 International Conference on Control, Automation and Systems, ICCAS 2008
ページ2240-2245
ページ数6
DOI
出版ステータスPublished - 2008 12 1
イベント2008 International Conference on Control, Automation and Systems, ICCAS 2008 - Seoul, Korea, Republic of
継続期間: 2008 10 142008 10 17

出版物シリーズ

名前2008 International Conference on Control, Automation and Systems, ICCAS 2008

Conference

Conference2008 International Conference on Control, Automation and Systems, ICCAS 2008
国/地域Korea, Republic of
CitySeoul
Period08/10/1408/10/17

ASJC Scopus subject areas

  • 制御およびシステム工学

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