Proposing a machine learning approach to analyze and predict basic high-temperature properties of iron ore fines and its factors

The basic high-temperature properties of iron ore play a crucial role in optimizing sintering and ore blending, but the testing process for these properties is complex and has significant lag time, which cannot meet the actual needs of ore blending. A prediction model for the basic high-temperature properties of iron ore fines was thus proposed based on a combination of machine learning algorithms and genetic algorithms. First, the prediction accuracy of different machine learning models for the basic high-temperature properties of iron ore fines was compared. Then, a random forest model optimized by genetic algorithms was built, further improving the prediction accuracy of the model. The test results show that the random forest model optimized by genetic algorithms has the highest prediction accuracy for the lowest assimilation temperature and liquid phase fluidity of iron ore, with a determination coefficient of 0.903 for the lowest assimilation temperature and 0.927 for the liquid phase fluidity after optimization. The trained model meets the fluctuation requirements of on-site testing and has been successfully applied to actual production on site.