1. Operating performance assessment based on multi-source heterogeneous information with deep learning for smelting process of electro-fused magnesium furnace
- Author
-
Fuli Wang, Kaiqing Bu, and Yan Liu
- Subjects
Computer science ,business.industry ,Applied Mathematics ,Deep learning ,Process (computing) ,Smelting process ,computer.software_genre ,Neural network classifier ,Convolutional neural network ,Computer Science Applications ,Control and Systems Engineering ,Softmax function ,Heterogeneous information ,Artificial intelligence ,Data mining ,Electrical and Electronic Engineering ,business ,Instrumentation ,computer ,Multi-source - Abstract
The process operating performance assessment is critical for the smelting process of electro-fused magnesium furnaces to improve quality of the magnesia product and pursue optimal comprehensive economic benefit. This paper proposes a new method of multi-source heterogeneous information deep feature fusion (MSHIDFF) to achieve higher accuracy operating performance assessment in the electro-fused magnesium smelting process. Firstly, we utilize convolutional neural network, bidirectional long short-term memory network and stacked auto-encoder to extract deep features from raw image, sound and current of different performance grades. Furthermore, those multi-source deep features are fused and the softmax regression with attention mechanism is employed to train a neural network classifier for the fused deep features of different performance grades. The simulation results show that the proposed MSHIDFF method obtains the superior assessment accuracy.
- Published
- 2022