Prediction model of end-point phosphorus content in BOF steelmaking process based on PCA and BP neural network

Abstract A prediction model based on the principal component analysis (PCA) and back propagation (BP) neural network is proposed for BOF end-point phosphorus content, based on the characters of BOF metallurgical process and production data. PCA is used to reduce dimensionality of the factors influencing end-point phosphorus content, and eliminate the correlations among the factors, and then the obtained principal components are used as BP neural network input vectors. The combined PCA-BP neural network model is trained and tested by history data, and is further compared with multiple linear regression (MLR) model and BP neural network model. The results of the comparison show that the PCA-BP neural network model has the highest prediction accuracy and PCA improved the generalization capability. Finally, online prediction system of BOF end-point phosphorus content based on PCA and BP neural network is developed and applied in actual productive process. Field application results indicate that the hit rate of end-point phosphorus content is 96.67%, 93.33% and 86.67% respectively when prediction errors are within ±0.007%, ±0.005% and ±0.004%. The combined PCA-BP neural network model has achieved the effective prediction for end-point phosphorus content, and provided a good reference for end-point control and judgment of quick direct tapping of BOF.

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