Synthetically predicting the quality index of sinter using machine learning model

ABSTRACT

A comprehensive prediction model of sinter quality based on machine learning algorithm was proposed. First of all, the mass historical data of actual sintering production was collected, cleaned and integrated. On this basis, the drum index and screening index of sinter were analysed by cluster analysis, and the quality of sinter was evaluated synthetically by clustering results and iron grade of sinter. Then, the important characteristic variables related to sinter quality index were screened by Recursive feature elimination, stability selection and random forest selection. The comprehensive classification model of sinter quality and the regression model of sinter’s total iron content were established by using various machine learning algorithms. The results show that the prediction accuracy of the classification model and regression model established by the extra tree is the best, and the application effect of the model is verified by using the testing set. The F1-score of the comprehensive quality index classification model is 0.92 and the R² of the total iron content regression model is 0.882. This model has good learning and generalization ability, and the accurate prediction of sinter quality index is realized.

KEYWORDS:

• Sinter
• quality prediction
• machine learning
• feature extraction
• ensemble learning

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

Thanks are given to the financial supports from the Hebei Province Graduate Innovation Project [grant number CXZZBS2017120]; the key Program of National Nature Science Foundation of China [grant number U1360205].