Prediction of blast furnace operation using on-line Bayesian learning

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publication2008 International Conference on Control, Automation and Systems, ICCAS 2008
Pages2240-2245
Number of pages6
DOIs
Publication statusPublished - 2008 Dec 1
Event2008 International Conference on Control, Automation and Systems, ICCAS 2008 - Seoul, Korea, Republic of
Duration: 2008 Oct 142008 Oct 17

Publication series

Name2008 International Conference on Control, Automation and Systems, ICCAS 2008

Conference

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

Keywords

  • Bayes methods
  • Jit modeling
  • Prediction
  • Process control

ASJC Scopus subject areas

  • Control and Systems Engineering

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