Development of Blast Furnace Burden Distribution Process Modeling and Control
The burden distribution process is an important and efficient measure to maintain the stable operation of the blast furnace. An accurate burden distribution model will reveal the impact on the internal furnace state and help to optimize the blast furnace production index. This article reviews the recent development of the modeling and control techniques in the burden distribution process. The current modeling methods of the blast furnace burden distribution can mainly be divided into the following types: the mechanism-based method, the physical scale model-based experiments and the data-driven method. However, most of the existing modeling methods are not applicable to general blast furnaces because it depends on the specific furnace structure and parameters. Furthermore, with the advancement in measurement technology, sensors now provide rich amount of online measurement of the blast furnace iron-making process. This makes the data analysis more challenging. It is imperative to establish new modeling methods for the burden distribution process. Therefore, this paper points out the new trends in modeling and control of the blast furnace burden distribution process. First, a dynamic clustering method based on dynamic time warping and adaptive resonance theory is introduced. Second, the inverse dynamic model-based burden distribution control is developed. Furthermore, a multi-model-based switch for modeling the fluctuating blast furnace process is formulated. Finally, the reinforcement learning method for the dynamic optimization of the production index is recommended.
Y. Yang et al., "Development of Blast Furnace Burden Distribution Process Modeling and Control," ISIJ International, vol. 57, no. 8, pp. 1350-1363, Iron and Steel Institute of Japan, Aug 2017.
The definitive version is available at https://doi.org/10.2355/isijinternational.ISIJINT-2017-002
Electrical and Computer Engineering
Keywords and Phrases
Blast furnaces; Dynamic models; Dynamics; Inverse problems; Reinforcement learning; Burden distribution; Data driven; Dynamic clustering; Inverse dynamic model; Mechanism analysis; Multi-model switching control; Physical experiments; Process control; Data-driven
International Standard Serial Number (ISSN)
Article - Journal
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