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A Desulfuration Prediction Model Research Based on RBFNN

Author: ZhangXu
Tutor: WuJunJi；RenZhenHua
School: Nanjing University of Technology and Engineering
Course: Electrical Engineering
Keywords: Desulfurization Fuzzy Neural Networks Forecast
CLC: TF704.3
Type: Master's thesis
Year: 2008
Downloads: 30
Quote: 0
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Abstract


Sulfur is the main harmful elements in the iron and steel , to remove sulfur specification requirements in order to achieve an important task for the steel production , to improve desulfurization indicators reduce the waste of desulfurizer desulphurization in the production process are two main objectives . Magnesium and sulfur at high temperatures have a great affinity . Single spray magnesium hot metal desulphurization with high desulfurization efficiency , fast response , less consumption , short processing time , processing after the the iron temperature drop characteristics are widely used in hot metal pre - desulfurization process . The hot metal pretreatment, the desulfurization effect , good or bad , is directly related to the quality of steelmaking production . The desulfurization process control key is how much of the amount of spray magnesium , currently reliably determined little spray magnesium amount forecast model . This paper as the background to the new Fushun Iron and Steel Co., Ltd. of hot metal desulphurization project , production samples and experience rules established MAMDANI fuzzy neural network , the network can not only guarantee generalization reasoning basic rules trends , but also in the memories of history has a good the accuracy of the approximation . The reasoning of the test data results show that the error within 25% hit rate of 85.7% , the error is less than 10% hit rate of 78.6% , and can effectively guide the production .

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CLC: > Industrial Technology > Metallurgical Industry > Steelmaking > General issues > Smelting process and operations > Desulfurization
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