Endpoint Temperature Prediction of Molten Steel in VD Furnace Based on AdaBoost.RT-ELM
Chen Zeng, Jianguo Wang, Guoqiang Zhao, Yuan Yao, Chao Xu · 2020 IEEE 9th Data Driven Control and Learning Systems Conference (DDCLS) · 2020
The endpoint temperature of the molten steel in the VD (Vacuum Degassing) furnace is an important parameter determining the quality of the finished steel products. Based on the in-depth analysis of the vacuum refining process of the VD furnace, combined with the field data, the effective preprocessing of the data was completed. Then the NNG (Non-Negative Garrote) variable selection algorithm is used to determine the input variables. An integrated ELM (Extreme Learning Machine) molten steel endpoint temperature modeling method based on AdaBoost.RT is proposed. Experimental simulation results show that the AdaBoost.RT-ELM prediction model has significantly improved prediction accuracy than a single ELM.