Core temperature prediction model based on AdaBoost algorithm
WANG Kun,LIU Xiao-jie,LIU Er-hao,LI Hong-yang,LIU Song,LV Qing
1.College of Metallurgy and Energy， Key Laboratory for Advanced Metallurgy Technology of Ministry of Education， North China University of Science and Technology， Tangshan 063009， Hebei， China; 2.Technical Centre， Chengde Iron and Steel Group Co.， Ltd.， Chengde 067000， Hebei， China
Abstract：Abstract： In order to better detect changes of the working condition of hearth, the AdaBoost integrated algorithm was used to establish a model for predicting the furnace core temperature in advance. By predicting the core temperature in advance, the trend of the state of the hearth is captured, which is convenient for the operator to adjust in time. The realtime data of the relevant parameters of Chenggang No.4 blast furnace were collected, and the time was used as the axis to integrate and match the parameters. The PauTa criterion was used to test the large outliers, and the linear interpolation method was used to fill the gaps, and the input parameters were redundantly eliminated by the correlation coefficient. Using the AdaBoost model for prediction, it is found that the prediction effect is more accurate than that of the single decision tree model.
王坤1，刘小杰1，刘二浩2，李宏杨1，刘颂1，吕庆1. 基于AdaBoost算法的炉芯温度预测模型[J]. 钢铁研究学报, 2020, 32(5): 363-369.
WANG Kun1,LIU Xiao-jie1,LIU Er-hao2,LI Hong-yang1,LIU Song1,LV Qing1. Core temperature prediction model based on AdaBoost algorithm. JOURNAL OF IRON AND STEEL RESEARCH , 2020, 32(5): 363-369.