A Deep Learning Method for the Endpoint Carbon Prediction in BOF Steelmaking Process
Author:
Affiliation:
1. Beijing University of Technology,Faculty of Information Technology,Beijing,100124
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx8/10606543/10606546/10606817.pdf?arnumber=10606817
Reference8 articles.
1. Comparative Study of Estimation Methods of the Endpoint Temperature in Basic Oxygen Furnace Steelmaking Process with Selection of Input Parameters
2. Real-Time Dynamic Carbon Content Prediction Model for Second Blowing Stage in BOF Based on CBR and LSTM
3. A Data-driven Self-supervised LSTM-DeepFM Model for Industrial Soft Sensor
4. Deep Learning With Spatiotemporal Attention-Based LSTM for Industrial Soft Sensor Model Development
5. Theory of the backpropagation neural network;Robert,1992
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