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Modern blast furnace ironmaking technology primarily utilizes the thermal condition of the furnace belly to reflect the furnace temperature status. However, the complexity of the smelting process makes effective modeling and control extremely challenging. During the ironmaking process, the control of furnace temperature directly affects production efficiency and product quality. Since the furnace temperature is difficult to measure directly, the silicon content in molten iron is commonly used to reflect the thermal state of the blast furnace. Traditional methods for predicting the silicon content in molten iron have limitations and struggle to adapt to complex and variable production conditions. With the advancement of neural network technology, this paper constructs a prediction model for the silicon content in blast furnace molten iron by creating a hybrid of Convolutional Neural Networks (CNN), Long Short-Term Memory Networks (LSTM), and a MultiHead Attention Mechanism (MA). Through deploying and analyzing the CNN-LSTM-MA model in a real production environment, the superiority of the CNN-LSTM-MA model in silicon content prediction has been verified. © 2024 SPIE.
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ISSN: 0277-786X
年份: 2024
卷: 13259
语种: 英文
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