A Multi-Head Self-Attention-based on GRU Encoder-Decoder Framework for Predicting Molten Iron Silicon Content

Silicon content is a significant index in the process of blast furnace ironmaking. It is used to measure the quality of molten iron produced.It only meets the requirements if it is too high or too low. In the production process,the silicon content in molten iron needs to be controlled within a stabl...

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Vydáno v:Data Driven Control and Learning Systems Conference (Online) s. 1190 - 1194
Hlavní autoři: Cai, Yu, Yang, Chunjie, Lou, Siwei, Zeng, Zhenyu, Liao, Huanyu, Zhang, Bing
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 12.05.2023
Témata:
Blast Furnace
Blast furnaces
Encoder-Decoder
Feature extraction
Gate Recurrent Unit
Learning systems
Logic gates
Multi-Head Attention Mechanism
Process control
Production
Si Content Prediction
Silicon
ISSN:2767-9861
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Shrnutí:Silicon content is a significant index in the process of blast furnace ironmaking. It is used to measure the quality of molten iron produced.It only meets the requirements if it is too high or too low. In the production process,the silicon content in molten iron needs to be controlled within a stable range.At the same time,due to the time lag, nonlinear and dynamic characteristics of blast furnace itself, it is difficult to predict the silicon content accurately. This paper proposes a multi-head self-attention-based gate recurrent unit encoder-decoder framework that can better extract global dynamic features and local features, improve prediction accuracy and pass the experimental verification.
ISSN:2767-9861
DOI:10.1109/DDCLS58216.2023.10167277