An Integration Model for Converter Molten Steel End Temperature Prediction Based on Bayesian Formula

Highly accurate prediction of converter molten steel end temperature is important foundation of realizing intelligent smelting in converter procedure. Single model prediction of converter end temperature has problems such as weak generalization ability and difficulty in increasing accuracy. To tackle these problems, this review proposes a modeling method based on Bayesian formula that dynamically integrate multiple models with different features. First, various data patterns and features are explored using different modeling methods and respective prediction model for converter molten steel end temperature is constructed. Then, the value range of the objective is discretized and the confidence levels of the prediction results from different models are computed based on Bayesian formula. Last, the prediction values of different models are weighted according to their confidence level and a comprehensive prediction result is obtained. Testing and comparison are carried out on the integration model and the three single models (support vector regression, random forest, and BP neural network) using actual production data. Results show that the integration model based on Bayesian formula can further increase the prediction accuracy effectively on top of single model prediction accuracy. The modeling method is proposed to evaluate the confidence level of prediction results of different models by Bayesian formula, and dynamically integrate multiple models according to the confidence level. Results show that the integration model based on Bayesian formula can further improve the prediction accuracy based on the single model prediction accuracy.