A knowledge-based system for temperature prediction in hot strip mills

Authors

Haibo Xie, University of WollongongFollow
Zhengyi Jiang, University of WollongongFollow
Dongbin Wei, University of WollongongFollow
Xianghua Liu, Northeastern UniversityFollow
Guodong Wang, Northeastern University

RIS ID

26870

Publication Details

Xie, H., Jiang, Z. Y., Wei, D., Liu, X. Wang, G. (2008). A knowledge-based system for temperature prediction in hot strip mills. Advanced Materials Research, 32 153-156.

Abstract

Rolling temperature is an important factor affecting mechanical properties of hot rolled strip significantly. Traditional techniques cannot meet higher precision control imperatives. In the present work, a novel knowledge-based system has been developed for the temperature prediction in hot strip mills. Neural network has been used for this purpose, which is an intelligent technique that can solve nonlinear problem of temperature control by learning from the samples. Furthermore, an annealing robust learning algorithm was presented to adjust the hidden node parameters as well as the weights of the adaptive neural networks. Simulations in a multi-object mode have been carried out to verify the effectivity of new neural optimization system. Calculation results confirm the feasibility of this approach and show a good agreement with experimental values obtained from a steel plant.