Prediction of LF Refining Endpoint Temperature Based on PCA-BP Neural Network

• Published in: Teshugang
• Main Authors: Su Chunyang, Chen Jun, Jiang Yaqing
• Format: Article
• Language: Chinese
• Published: Editorial Office of Special Steel 2023-12-01
• Subjects: Principal Component Analysis; BP Neural Network; Endpoint Temperature Prediction
• Online Access: http://www.specialsteeljournal.com/thesisDetails#10.20057/j.1003-8620.2023-00150
• ISSN: 1003-8620

In order to improve the end point temperature control level of molten steel in LF refining， a combined method based on principal component analysis （ PCA ） and BP neural network was proposed to predict the end-point temperature of molten steel in LF ladle furnace.Based on the metallurgical theory and practical production practices， 10 factors that have significant influence on the endpoint temperature of 42CrMo steel production process were selected as the index system of the prediction model.Then the data were processed by principle component analysis， and seven principal component variables were obtained. The cumulative variance contribution rate was 87.24 %， and the correlation between the data was eliminated.Based on this， a prediction model of end point temperature of LF furnace based on PCA-BP neural network was established. When the prediction error of the model is within ± 25 °C， the hit rate of the model is 98.71 %. The model has good recognition ability and can achieve the purpose of predicting the end point temperature of LF furnace production process.