Prediction of hot metal temperature based on data mining

Abstract

Accurately and continuously monitoring the hot metal temperature status of the blast furnace (BF) is a challenging job. To solve this problem, we propose a hot metal temperature prediction model based on the AdaBoost integrated algorithm using the real production data of the BF. We cleaned the raw data using the data analysis technology combined with metallurgical process theory, which mainly included data integration, outliers elimination, and missing value supplement. The redundant features were removed based on Pearson’s thermodynamic diagram analysis, and the input parameters of the model were preliminarily determined by using recursive feature elimination method. We built the hot metal temperature prediction model using the AdaBoost ensemble algorithm on a dataset with selected features as well as derived features by using K-mean clustering tags. The results show that the performance of the hot metal temperature prediction model with K-means clustering tags has been further improved, and the accurate monitoring and forecast of molten iron temperature has been achieved. The model can achieve an accuracy of more than 90% with an error of ±5°C.