TY - GEN
T1 - Design of inner and outer gray-box models to predict molten steel temperature in Tundish
AU - Ahmad, Iftikhar
AU - Kano, Manabu
AU - Hasebe, Shinji
AU - Kitada, Hiroshi
AU - Murata, Noboru
N1 - Funding Information:
This study was partially supported by the grant from ISIJ as an activity of research group, High Precision Process Control via Large Scale Database and Simulation Models, and also by Japan Society for the Promotion of Science (JSPS), Grant-in-Aid for Scientific Research (C) 24560940.
PY - 2013
Y1 - 2013
N2 - In order to realize stable production in the steel industry, it is important to control molten steel temperature in a continuous casting process. The present work aims to develop a gray-box model that predicts the molten steel temperature in the tundish (TD temp). In the proposed approach, two parameters in the first-principle model, i.e., overall heat transfer coefficients of ladle and tundish, are optimized for each past batch separately, then the relationship between the two parameters and measured process variables is modeled through random forests (RF). In this inner gray-box model, the statistical models update the physical parameters according to the operating condition. To enhance the accuracy of TD temp estimation, another RF model is developed which compensates errors of the inner gray-box. The proposed approach was validated through its application to real operation data at a steel work.
AB - In order to realize stable production in the steel industry, it is important to control molten steel temperature in a continuous casting process. The present work aims to develop a gray-box model that predicts the molten steel temperature in the tundish (TD temp). In the proposed approach, two parameters in the first-principle model, i.e., overall heat transfer coefficients of ladle and tundish, are optimized for each past batch separately, then the relationship between the two parameters and measured process variables is modeled through random forests (RF). In this inner gray-box model, the statistical models update the physical parameters according to the operating condition. To enhance the accuracy of TD temp estimation, another RF model is developed which compensates errors of the inner gray-box. The proposed approach was validated through its application to real operation data at a steel work.
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U2 - 10.3182/20131218-3-IN-2045.00089
DO - 10.3182/20131218-3-IN-2045.00089
M3 - Conference contribution
AN - SCOPUS:84896336743
SN - 9783902823595
T3 - IFAC Proceedings Volumes (IFAC-PapersOnline)
SP - 744
EP - 749
BT - 10th IFAC Symposium on Dynamics and Control of Process Systems, DYCOPS 2013 - Proceedings
PB - IFAC Secretariat
T2 - 10th IFAC Symposium on Dynamics and Control of Process Systems, DYCOPS 2013
Y2 - 18 December 2013 through 20 December 2013
ER -