Prediction Model of Endpoint Temperature of Converter Steelmaking Based on PCA-BP Neural Network

The end point control of converter steelmaking is an important operation in the later stage of converter blowing. In order to predict the end point temperature of converter steelmaking more accurately, 13 process parameters that affect the endpoint temperature were selected, and then the input parameters were obtained by grey correlation analysis and principal component analysis (PCA). The number of hidden layer nodes is determined by comparing the mean square error of the prediction results of different number of hidden layer nodes. Combined the BP algorithm with variable learning rate, the prediction model of converter endpoint temperature is established based on PCA-BP neural network, and the actual production data of Q235 steel is substituted into the model for simulation. Compared with the results of the model established by the traditional BP, PCA-BP and wavelet neural network, it is indicated that the endpoint hit rate of the optimized PCA-BP algorithm neural network is higher. The hit rate of endpoint temperature is 30.00%, 62.00% and 92.00% respectively when prediction errors are within +/- 5 degrees C, +/- 10 degrees C and +/- 15 degrees C.