Prediction Model for Vanadium Content in Vanadium and Titanium Blast Furnace Smelting Iron Based on Big Data Mining

Abstract

A model for predicting the vanadium content in a molten iron blast furnace (BF) was developed to solve the problem of late iron detection during the smelting process of a vanadium and titanium BF. First, based on the whole process data platform of BF ironmaking, the standardized data warehouse of BF smelting was established, and the variables related to vanadium content in molten iron are selected in the model. Clean data were obtained by processing the original data. Afterward, the feature extraction of variables was achieved by feature construction and PCA dimensionality reduction, and the final input feature variables were determined using a combination of multiple feature selection algorithms and production process experience. Finally, the CatBoost model was selected for prediction. The results show that CatBoost achieved better results than XGBoost and long short-term memory (LSTM) models, and all indicators were higher than in these two models. The R2 of CatBoost reached 0.773, and the index of prediction error within ±0.020% reached 89.65%, which met the actual production requirement of a vanadium and titanium commercial BF in China.