Data‐driven modelling methods in sintering process: Current research status and perspectives

• Published in: Canadian journal of chemical engineering Vol. 101; no. 8; pp. 4506 - 4522
• Main Authors: Yan, Feng, Zhang, Xinmin, Yang, Chunjie, Hu, Bing, Qian, Weidong, Song, Zhihuan
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.08.2023; Wiley Subscription Services, Inc
• Subjects: Artificial intelligence; Big Data; control and optimization; data‐driven modelling; deep learning; Environmental protection; intelligent sintering; Iron and steel industry; Ironmaking; Modelling; Optimization; prediction; Sintering
• ISSN: 0008-4034; 1939-019X
• DOI: 10.1002/cjce.24790

The sintering process, as a primary modus of the blast furnace ironmaking industry, has enormous economic value and environmental protection significance for the iron and steel enterprises. Recently, with the emergence of artificial intelligence and big data, data‐driven modelling methods in the sintering process have increasingly received the researchers' attention. But now, there is still no systematic review of the data‐driven modelling approaches in the sintering process. Therefore, in this article, we conduct a comprehensive overview and prospects on the data‐driven models for the purpose of intelligent sintering. First, the mechanism and characteristics of the sintering process are introduced and analyzed elaborately. Second, the detailed research status of the sintering process is illustrated from four aspects: key parameters prediction, control, optimization, and others. Finally, several challenges and promising modelling methods such as deep learning in the sintering process are outlined and discussed for future research.