BO-Catboost Model-based Prediction of Blast Furnace Coal Injection Rate

Abstract

As a critical parameter in blast furnace production, the coal injection rate is not only related to the stability of furnace condition, but also a vital index for evaluating production economy. In most of the blast furnaces, this parameter is often determined by the operator’s experience. This paper establishes a coal injection rate prediction model based on the Catboost (category gradient boosting algorithm), which can provide a better basis for operators to control the parameter. At first, the collected steel production data were processed, the last time operational parameters that had greater impact on the coal injection rate were selected out as the input of the model, and the current time coal injection rate was used as the single output of the model. Next, the Catboost model was quoted, and the Optuna optimization algorithm based on the Bayesian principle was used to optimize the Catboost model (BO-Catboost), enhancing the model’s capabilities and avoiding over-fitting phenomenon. Then, the effects of the Catboost model under different optimization algorithms were compared, and the prediction results of the BO-Catboost model were compared with the predictions of the ordinary Catboost, BO-Random Forest and BO-XGboost (Extreme Gradient Boosting) model. The results show that the BO-Catboost model is better than other models. Finally, a blast furnace coal injection monitoring system based on Web technology was established, which can display the coal injection prediction information on the board, the test shows that it has a certain guidance for the control of the coal injection rate.