Title :
Prediction of blast furnace operation using on-line bayesian learning
Author :
Kaneko, N. ; Sakamoto, S. ; Uchida, K. ; Ogai, H. ; Ito, M. ; Matsuzaki, S.
Author_Institution :
Dept. of Electr. Eng. & Biosci., Waseda Univ., Tokyo
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.
Keywords :
Monte Carlo methods; blast furnaces; just-in-time; production engineering computing; blast furnace operation prediction; large scale database; nonlinear local model; online Bayesian learning; online modeling; sequential Monte Carlo; Automatic control; Automation; Bayesian methods; Blast furnaces; Control system synthesis; Databases; Large-scale systems; Monte Carlo methods; Parameter estimation; Predictive models; Bayes methods; JIT modeling; Prediction; Process control;
Conference_Titel :
Control, Automation and Systems, 2008. ICCAS 2008. International Conference on
Conference_Location :
Seoul
Print_ISBN :
978-89-950038-9-3
Electronic_ISBN :
978-89-93215-01-4
DOI :
10.1109/ICCAS.2008.4694181
