Prediction model of blast furnace molten iron temperature based on GRA-DE-KELM

Abstract

Aiming at the inherent defects of the traditional blast furnace temperature model, a prediction model of blast furnace molten iron temperature based on GRA-DE-KELM is proposed. Because the blast furnace ironmaking process is extremely complex and has the characteristics of multivariable, nonlinear, and strong coupling, the traditional modeling method cannot meet the requirements of high precision prediction of molten iron temperature. Firstly, because the parameters affecting the temperature of molten iron have strong correlation, in order to reduce the complexity of modeling and improve the performance of the model, it is necessary to extract the main parameters affecting the temperature of molten iron. In this paper, the GRA (gray relation analysis) method is used to analyze the input variables and determine the input variables of the model. Then the KELM (kernel extreme learning machine) prediction model is established by combining the analyzed variables, and DE (differential evolution) algorithm is used to optimize the model kernel parameters. Finally, the model is trained and tested using field-acquired data and compared to traditional predictive models. The results show that the model can quickly and accurately predict the molten iron temperature, and has a good guiding significance for the actual regulation of blast furnace temperature.