Fusion prediction of blast furnace temperature based on combination of knowledge and data
In view of the large lag in the complex blast furnace smelting process,in order to improve the accuracy of blast furnace temperature prediction,a furnace temperature fusion prediction method combining empirical knowledge and data was proposed.First,this paper analyzed experience of the blast furnace,obtained the hysteresis relationship of each variable in the blast furnace and each variable had a cumulative relationship within the lag time in the blast furnace,and the accumulations affected the current blast furnace temperature.Selecting the input variable reasonably by correlation analysis of the accumulations of each variable.Then a method of fusing the temperature of the molten iron with the silicon content of the molten iron was proposed to better characterize the temperature of the blast furnace.Finally,based on combining knowledge of experience and data,a neural network was used to establish the fusion model for prediction of the blast furnace temperature by accumulations as inputs.In the experiment,the data came from blast furnace production in a steel plant and this data is used for simulation,the model has good performance and provides new ideas for prediction of the blast furnace temperature.
blast furnace ironmakingknowledge of experienceneural networkblast furnace temperature
NETL
NSTL
万方数据
