Predictive model of sinter drum index based on multi-category production status
ZHANG Zhen1, LI Xin1, LIU Song2, LI Fu-min1, LIU Xiao-jie1, LÜ Qing1
1. School of Metallurgy and Energy, North China University of Science and Technology, Tangshan 063210, Hebei, China; 2. Department of Computer Science and Technology, Tangshan College, Tangshan 063000, Hebei, China
Abstract：This paper combined big data of sintering production with machine learning algorithms, and proposed a research method for predicting sinter drum index under multi-category production conditions. Through collection, integration and pre-processing operations of data, a total of 65 characteristic parameters were obtained. Based on the sintering end point (BTP), the drum index was divided into two categories used experimental research and visual analysis. Based on the classification of drum index data set, the feature selection algorithm was used to calculate the important ranking of feature parameters, and the best combination of feature parameters was determined as the model input parameters. The LightGBM and CatBoost algorithms were used to establish the prediction models of drum index respectively. The test results showed that the CatBoost prediction model had the best comprehensive prediction effect. Compared with drum index prediction model constructed by all data sets, the prediction effects of abnormal and normal drum index prediction models constructed by categories had been improved to a certain extent. The R2 fit degree could reach 88.09% and 90.69%. In addition, the prediction model of sinter drum index under multi-category production state could achieve a hit rate of 95% within the error range of 0.25%.
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2022, Vol. 32 
