Application of neural network to prediction of plate finish cooling temperature |
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Authors: | Wang Bing-xing Zhang Dian-hua Wang Jun Yu Ming Zhou Na and Cao Guang-ming |
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Affiliation: | (1) State Key Laboratory of Rolling and Automation, Northeastern University, Shenyang, 110004, China |
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Abstract: | To improve the deficiency of the control system of finish cooling temperature (FCT), a new model developed from a combination of a multilayer perception neural network as the self-learning system and traditional mathematical model were brought forward to predict the plate FCT. The relationship between the self-learning factor of heat transfer coefficient and its influencing parameters such as plate thickness, start cooling temperature, was investigated. Simulative calculation indicates that the deficiency of FCT control system is overcome completely, the accuracy of FCT is obviously improved and the difference between the calculated and target FCT is controlled between-15 ℃ and 15 ℃. |
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Keywords: | plate heat transfer coefficient mathematical model back propagation (BP) neural network |
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