PREDICTION OF FeO CONTENT IN SINTER BASED ON TCN-DENSENET
The FeO content in sinter is an important quality and energy consumption indicator of the sintering process,and also has a direct impact on blast furnace smelting.In response to the current situation of long time lag in chemical detection methods for detecting FeO content in sinter,this paper proposes a method for predicting FeO content in sinter by mixing Temporal Convolutional Network(TCN)and Dense Connected Convolutional Network(DenseNet).Firstly,TCN is used to establish a time series prediction model for FeO content in sinter,and infrared images of the tail section of the sintering machine are collected.DenseNet is used to establish a FeO prediction model for sinter,and the output results of the two are integrated through adaptive weighted averaging method to obtain the final FeO content prediction value.Based on the characteristics of infrared images of sinter layers,DenseNet was improved by adding attention layers,modifying the convolutional block structure,and adjusting the size and step size of shallow convolutional layers.The model was validated on the actual production data of a large sintering machine in a domestic steel company.After data processing,model parameter optimization,and other operations,the TCN DenseNet hybrid model proposed in this paper achieved a hit rate of 94.34％and a root mean square error of 0.21 for predicting the FeO content of sinter within an absolute error of±0.4％in the test set,which is better than the prediction effect of using TCN or DenseNet alone for modeling.This method has a significant effect on improving the accuracy and stability of FeO content prediction in sinter,and can provide data support for production operations in sintering sites.
万方数据
维普
