Details

Autor(en) / Beteiligte

• Liu, Jie
• Chen, Fuzhen
• Ma, Xianguo
• Gong, Zuoyan
• Zhang, Jianliang
• Liu, Zhengjian
• Wang, Yaozu
• Ma, YunFei

Titel

Summarization of Sinter Quality Prediction Algorithms

Ist Teil von

• 2022 37th Youth Academic Annual Conference of Chinese Association of Automation (YAC), 2022, p.1099-1105

Ort / Verlag

IEEE

Erscheinungsjahr

2022

Quelle

IEEE Xplore

Beschreibungen/Notizen

• The sintering of sinter is a dynamic process with a cumbersome mechanism, many influencing factors, and a long process. The traditional sinter quality inspection method uses a sinter cup experiment, which has a long experiment period and a lack of timely feedback of results. The sintering production process is a complex system with significant nonlinearity, strong coupling and large hysteresis, the sintering process involves many intermediate parameters and physical and chemical changes. At present, scholars have done a lot of research on the data-based sinter quality prediction algorithm, and successfully use the algorithm to guide the sintering production. This article summarizes that domestic and foreign researchers have done a lot of work on sinter quality prediction. The current quality prediction of sinter is mainly divided into data preprocessing (data dimensionality reduction), quality prediction algorithm based on neural network, and quality prediction algorithm based on multi-model. This paper analyzes the prediction principles, effects, advantages and disadvantages of the three algorithms. And the future development of this field is considered and prospected.