The endpoint 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 phosphorus content were selected,and then the input parameters were obtained by grey correlation analysis and principal component analysis(PCA).The number of hidden layer nodes was 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 phosphorus content was established based on PCA-BP neural network,and the actual production data of Q235 steel was substituted into the model for simulation.Compared with the results of the model established by the traditional BP,PCA-BP neural networks and wavelet neural network,it is indicated that the endpoint hit rate of the optimized PCA-BP algorithm neural network is higher,and the hit rate of endpoint phosphorus content is 44％,86％and 96％respectively when prediction errors are within±0.004％,±0.008％and±0.01％.
维普
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