TY - GEN
T1 - Prediction of blast furnace operation using on-line Bayesian learning
AU - Kaneko, N.
AU - Sakamoto, S.
AU - Uchida, K.
AU - Ogai, H.
AU - Ito, M.
AU - Matsuzaki, S.
PY - 2008
Y1 - 2008
N2 - 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.
AB - 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.
KW - Bayes methods
KW - Jit modeling
KW - Prediction
KW - Process control
UR - http://www.scopus.com/inward/record.url?scp=58149086096&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=58149086096&partnerID=8YFLogxK
U2 - 10.1109/ICCAS.2008.4694181
DO - 10.1109/ICCAS.2008.4694181
M3 - Conference contribution
AN - SCOPUS:58149086096
SN - 9788995003893
T3 - 2008 International Conference on Control, Automation and Systems, ICCAS 2008
SP - 2240
EP - 2245
BT - 2008 International Conference on Control, Automation and Systems, ICCAS 2008
T2 - 2008 International Conference on Control, Automation and Systems, ICCAS 2008
Y2 - 14 October 2008 through 17 October 2008
ER -