Online measurement method of FeO content in sinter based on infrared machine vision and convolutional neural network

Abstract

Sintering is an important process in iron and steel manufacturing. FeO content of sinter is a key index for both sintering and blast furnace ironmaking, but it needs manual sampling and testing in practical production. In this research, an online measurement method based on machine-tailed infrared images and convolutional neural network (CNN) for the FeO content is proposed. Firstly, a target detection model of sinter bed cross-section (SBC) is established, to obtain the key frame image (KFI) of SBC in each unloading cycle. Then, the KFI is labeled with the offline chemical analysis of FeO content, and a modified CNN is proposed to build the online measurement model for FeO content. The experimental results show that the proportion of samples with detection error of FeO content within ± 0.5 % reaches 91.2 % and the RMSE is less than 0.3 %. It provides a novel measurement approach for online detection of FeO content.