Temperature prediction of submerged arc furnace in ironmaking industry based on residual spatial-temporal convolutional neural network

被引:1
|
作者
Liu, Hong-Xuan [1 ]
Li, Ming-Jia [2 ]
Guo, Jia-Qi [1 ]
Zhang, Xuan-Kai [3 ]
Hung, Tzu-Chen [4 ]
机构
[1] Xi An Jiao Tong Univ, Key Lab Thermo Fluid Sci & Engn, Sch Energy & Power Engn, Minist Educ, Xian 710049, Peoples R China
[2] Beijing Inst Technol, Sch Mech Engn, Beijing 100081, Peoples R China
[3] Shandong Univ, Sch Energy & Power Engn, Jinan 250061, Peoples R China
[4] Natl Taipei Univ Technol, Dept Mech Engn, Taipei, Taiwan
关键词
Submerged arc furnace; Heat transfer; Electric field; Joule heat; Spatial-temporal convolutional neural; networks; ELECTROSLAG REMELTING PROCESS; SLAG BATH; MODEL; SHAPE;
D O I
10.1016/j.energy.2024.133024
中图分类号
O414.1 [热力学];
学科分类号
摘要
The submerged arc furnace is widely regarded as one of the most promising ore smelting technologies. However, the real-time monitoring of the multiple physical fields including electric, thermal, and mas, through computational fluid dynamics demands significant computational resources. This paper introduces the spatial-temporal convolutional neural network algorithm to address this challenge. Initially, the influences of various working conditions on these physical fields are analyzed. Subsequently, a prediction model is developed based on the coupling of these multiple physical fields model. The spatial-temporal convolutional neural network algorithm is then employed to elucidate the main parameter distributions, enabling the automatic real-time detection of temperature variation trends and providing a theoretical foundation for intelligent furnace operation. The findings indicate that the electric field is the predominant factor causing non-uniform heat distribution, with localized overheating primarily occurring at the electrode ends. The application of the proposed model facilitates dynamic prediction of the temperature distribution, establishing relationships between historical and future time steps as well as local and global temperature variations. The reliability of the temperature prediction model is confirmed, with the model achieving an accuracy of 99.76 %, surpassing the 98.18 % accuracy of the traditional multi-layer perceptron model.
引用
收藏
页数:16
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