Burden Surface Temperature Field Estimation of the Blast Furnace

The blast furnace (BF) gas flow distribution is one of the major influences on BF production situation. While burden surface temperature field of BF is a direct reflection of the BF gas flow distribution. In this paper, a prediction model of burden surface temperature field is established based on the online sequential extreme learning machine algorithm with forgetting factor (WOS-ELM). Firstly, the BF condition data and temperature data of the cross temperature measurement are used to establish a temperature prediction model to predict the temperature of the gas flow of the throat. Secondly, the burden surface temperature is obtained by the predicted temperature and heat transfer principle. Finally, the burden surface temperature field is established using Newton interpolation and angle weight. The large amount of data and comparative experiments show that the prediction model of burden surface temperature field has a high prediction accuracy. It can provide the theoretical guidance for the actual operation of the BF.