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Carbon value online measurement is one of the most important tasks of the AOD furnace ferroalloy production process. Being the smelting process is complex in this paper promote the wavelet neural network algorithm in order to predict the endpoint of carbon value and using online data to training the wavelet neural network. The simulation results showed that the prediction relative error between the...
According to Jilin Ferroalloy Factory 10-ton AOD furnace actual smelting condition, analyzes the impact factor of AOD furnace molten iron endpoint temperature, by optimizing the neural network connection weights and structure, design prediction model of molten iron endpoint temperature based on RBF neural network, using LM algorithm and 50 furnaces actual production data to train the model, and predicts...
Radial basis function (RBF) neural network is used to predict the blast furnace hot metal based on its characteristics such as fast convergence and global optimization. As hot metal silicon content had close relationship with furnace temperature, the change of temperature in furnace was reflected indirectly by hot metal silicon content. Newrbe function in Matlab was applied for function approximation...
Accurate prediction of the end-point temperature and carbon content of AOD furnace is of great significance to raise the hitting rate of the end-point. Based on AOD refining practice, the predictive model of end-point temperature and carbon content of AOD furnace low carbon Chromium iron making based on BP neural network was put forward. The results showed that the model is much accurate and applicable.
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