Data-Driven Prediction of Sinter Composition Based on Multi-Source Information and LSTM Network

The quality of sinter ore is a key part in ironmaking industry, and the content of FeO of sinter ore is one of its significant quality indexes. In order to predict the content of FeO of ore agglomerate, a data-driven prediction scheme based on multi-source information and long short-term memory (Long Short-Term Memory, LSTM) network is proposed. In the proposed method, the multi-source information is firstly extracted and then they are input to the LSTM, whose target output is the corresponding Reference-FeO. The multi-source information used in this paper includes image, vibration and temperature information. And for the feature extraction of the images, a method is developed to preprocess and extract features from images. Besides, the information of vibration and temperature are selected on account of the correlation analysis and mechanism of sintering. Moreover, the corresponding Reference-FeO at the end of the sintering pallets is obtained as the target output of the LSTM network. Finally, a LSTM network is utilized to predict the target output. The experimental results show that the data-driven prediction scheme using multi-source features can reach a good performance with absolute error within 0.05 and can meet the needs of practical engineering.