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Predicting the Endpoint Phosphorus Content of Molten Steel in BOF by Two-stage Hybrid Method
Affiliation:1. School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China;2. Beijing Key Laboratory of Knowledge Engineering for Materials Science, Beijing 100083, China;3. School of Metallurgical and Ecological Engineering, University of Science and Technology Beijing, Beijing 100083, China;1. State Key Laboratory of Advanced Metallurgy, University of Science and Technology Beijing, Beijing 100083, China;2. School of Metallurgy and Ecology Engineering, University of Science and Technology Beijing, Beijing 100083, China;3. Tangshan Iron and Steel Company of Hebei Iron and Steel Group, Tangshan 063016, Hebei, China;1. Physics Department, Nelson Mandela Metropolitan University, PO Box 77000, Port Elizabeth 6031, South Africa;2. Fuel Performance and Design Department, Idaho National Laboratory, PO Box 1625, Idaho Falls, ID 83415-6188, USA;3. Department of Physics, Chemistry and Biology, Linköping University, SE-581 83 Linköping, Sweden;1. State Key Laboratory of Advanced Metallurgy, University of Science and Technology Beijing, Beijing 100083, China;2. School of Metallurgy and Ecology Engineering, University of Science and Technology Beijing, Beijing 100083, China;3. Tangshan Iron and Steel Company of Hebei Iron and Steel Group, Tangshan 063016, Hebei, China;1. School of Metallurgical and Ecological Engineering, University of Science and Technology Beijing, Beijing 100083, China;2. Research and Development Center, WISCO, Wuhan 430080, Hubei, China;1. Inner Mongolia Key Laboratory for Utilization of Bayan Obo Multi-Metallic Resources: Elected State Key Laboratory, Inner Mongolia University of Science and Technology, Baotou 014010, Nei Mongol, China;2. School of Materials Science and Engineering, Shanghai University, Shanghai 200444, China;1. Jiuquan Iron and Steel Co., Jiayuguan 735100, Gansu, China;2. State Key Laboratory of Advanced Metallurgy, University of Science and Technology Beijing, Beijing 100083, China
Abstract:A two-stage hybrid method is proposed to predict the phosphorus content of molten steel at the endpoint of steelmaking in BOF (Basic Oxygen Furnace). At the first clustering stage, the weighted K-means is performed to produce clusters with homogeneous data. At the second predicting stage, each fuzzy neural network is carried out on each cluster and the results from all fuzzy neural networks are combined to be the final result of the hybrid method. The hybrid method and single fuzzy neural network are compared and the results show that the hybrid method outperforms single fuzzy neural network.
Keywords:K-means clustering  fuzzy neural network  hybrid method  predicting  endpoint phosphorus content
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