Hang Ouyang, Jiusun Zeng
It is of critical importance to keep a steady operation in the blast furnace to facilitate the production of high quality hot metal. In order to monitor the state of blast furnace, this article proposes a fault detection and identification method based on the multidimensional Gated Recurrent Unit (GRU) network, which is a kind of recurrent neural network and is highly effective in handling process dynamics. Comparing to conventional recurrent neural networks, GRU has a simpler structure and involves fewer parameters. In fault detection, a moving window approach is applied and a GRU model is constructed for each process variable to generate a series of residuals, which is further monitored using the support vector data description (SVDD) method. Once a fault is detected, fault identification is performed using the contribution analysis. Application to a real blast furnace fault shows that the proposed method is effective.
@article{47f91926-9f62-4c1e-bca6-f3b48045d0ee,
title={139 Fault Detection and Identification of Blast Furnace Ironmaking Process Using the Gated Recurrent Unit Network},
author={Hang Ouyang and Jiusun Zeng},
year={2026},
language={en}
}TY - JOUR TI - 139 Fault Detection and Identification of Blast Furnace Ironmaking Process Using the Gated Recurrent Unit Network AU - Hang Ouyang AU - Jiusun Zeng PY - 2026 LA - en ER -
Ian Cameron, Mitren Sukhram
This book delves into the intricate processes involved in blast furnace ironmaking, emphasizing the analysis, control, and optimization of operations.
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