Improved algorithm of extreme gradient boosting for predicting silicon content in large proportion pellet smelting process

Abstract

The silicon content in molten iron is an important indicator, to characterize the temperature of blast furnace (BF) and the quality of molten iron, which is of great significance to the stable operation of large proportion pellets in the BF smelting. Aiming at the problem of poor prediction performance and insufficient accuracy of silicon content, a prediction model of silicon content in molten iron was established based on KMeans++ and improved XGBoost algorithm to divide the information from different BF conditions in the smelting process, The genetic algorithm(GA) was adopted to optimize the model iteratively, which improved the accuracy of the results and reduced the training time for optimal results. The experimental result showed that the prediction hit of the model was improved by clustering the data and reached above 90% on average, and the accurate prediction of silicon content in molten iron in case of a large proportion of pellets of BF smelting was realized.