Laminar cooling process model development using RBF networks

Authors:
Minghao Tan;Xuejun Zong;Heng Yue;Jinxiang Pian;Tianyou Chai
Affiliations:
School of Information Science and Engineering, Shenyang University of Technology, Shenyang, Liaoning, China;College of Information Engineering, Shenyang Institute of Chemical Technology, Shenyang, Liaoning, China;Research Center of Automation, Northeastern University, Shenyang, Liaoning, China;Research Center of Automation, Northeastern University, Shenyang, Liaoning, China;Research Center of Automation, Northeastern University, Shenyang, Liaoning, China
Venue:
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
Year:
2006

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ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III

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Abstract

Due to the complex nature (e.g., highly nonlinear, time varying, and spatially varying) of the laminar cooling process, accurate mathematical modeling of the process is difficult. This paper developed a hybrid model of the laminar cooling process by integrating Radial Basis Function (RBF) networks into the first principles dynamical model. The heat transfer coefficients of water cooling in the dynamical model were found by RBF networks. The developed model is capable of predicting the through-thickness temperature evolutions of the moving strip during the laminar cooling process. Experimental studies using real data from a hot strip mill show the superiority of the proposed model.