End Temperature Prediction of Molten Steel in LF Based on CBR |
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| Authors: | Fei He Anjun Xu Hongbing Wang Dongfeng He Naiyuan Tian |
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| Affiliation: | 1. State Key Laboratory of Advanced Metallurgy, University of Science and Technology Beijing, Beijing, 100083, China;2. School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, 100083, China |
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| Abstract: | In order to improve the temperature control level of molten steel in ladle furnace (LF), a case‐based reasoning (CBR) method has been proposed for predicting end temperature of molten steel in LF. To predict the temperature accurately and efficiently, this paper develops two‐step retrieval approach and the correlation based feature weighting (CFW) method for CBR. And, the study evaluates the prediction effect of CBR method by the experiment of comparison with back propagation neural network (BPNN) model and CBR model. Experimental results show that CBR model achieves better accuracy than BPNN model and the CBR method is effective to predict end temperature of molten steel in LF. |
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| Keywords: | back propagation neural network case‐based reasoning end temperature prediction ladle furnace molten steel |
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