About this Abstract |
Meeting |
2022 TMS Annual Meeting & Exhibition
|
Symposium
|
Algorithm Development in Materials Science and Engineering
|
Presentation Title |
M-14: Smoke Detection in Ladle Hot Repair Process Based on Convolution Neural Network |
Author(s) |
Yanming Zhang, Jialu Wu, Mujun Long, Wei Guo, Huamei Duan, Dengfu Chen |
On-Site Speaker (Planned) |
Yanming Zhang |
Abstract Scope |
The intelligent control of fog gun dust removal is one of the significant means of energy saving and environmental protection during the process of ladle hot repair. However, the feature extraction method with poor adaptability is mostly applied to the online intelligent identification model of smoke. In order to improve the accuracy of the model, a novel smoke detection method based on the convolution neural network(CNN) is proposed. According to the VGG-net network structure, we construct a new network and optimize its parameters well. Experimental analysis show that the method achieves over 95% smoke detection accuracy on the dataset, which is obviously better than the feature extraction. As a result, the improved CNN can effectively reduce the water consumption of the fog gun intelligent control model. Thus, it provides a basis for the improvement of the intelligence and greenization in iron and steel industry. |
Proceedings Inclusion? |
Planned: |