Development of an improved CBR model for predicting steel temperature in ladle furnace refining

• 作者: Fei Yuan An-jun Xu Mao-qiang Gu
• 来源期刊: 矿物冶金与材料学报
• 年，卷（期）: 2021,（8）
• 所属期刊栏目: Special Issue: Metallurgical Process Engineering and Intelligent Manufacturing
• 页码范围: 1321-1331
• 页数: 11页
• 语种: 英文

摘要

In the prediction of the end-point molten steel temperature of the ladle furnace, the influence of some factors is nonlinear. The pre-diction accuracy will be affected by directly inputting these nonlinear factors into the data-driven model. To solve this problem, an improved case-based reasoning model based on heat transfer calculation (CBR-HTC) was established through the nonlinear processing of these factors with software Ansys. The results showed that the CBR-HTC model improves the prediction accuracy of end-point molten steel temperature by 5.33％ and 7.00％ compared with the original CBR model and 6.66％ and 5.33％ compared with the back propagation neural network (BPNN) model in the ranges of [?3, 3] and [?7, 7], respectively. It was found that the mean absolute error (MAE) and root-mean-square error (RMSE) values of the CBR-HTC model are also lower. It was verified that the prediction accuracy of the data-driven model can be improved by com-bining the mechanism model with the data-driven model.