Due to the many uncertainties in the sintering process， the reliability of mechanism analysis and point prediction results is insufficient. Therefore， a random forest-extreme tree-kernel density estimation （RF-ET-KDE） algorithm is proposed to realize interval predictions for physical indicators， such as particle size and moisture. Firstly， data preprocessing and feature selection operations are adopted to screen out the most suitable feature variables for modeling. Secondly， the RF-ET algorithm based on Stacking is utilized to realize point predictions for the indicators. This algorithm makes the model with higher accuracy and generalization， and then the KDE algorithm is adopted to calculate the prediction error of the indicator. The distribution interval and interval prediction results under a certain confidence level are obtained. Finally， the proposed model is compared with the other combined models. The results show that the RF-ET algorithm has higher point prediction accuracy， and the KDE algorithm can quantify the error of the indicator very well， so that a higher credibility interval prediction result can be obtained.
