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Computational Study of Hot Gas Injection (HGI) into an Ironmaking Blast Furnace (BF)

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  • Zhaoyang Li
  • Yixiong Feng
  • Aimin Wang

Abstract

Blast furnace (BF) ironmaking is the most important process that produces hot metal (HM) from iron-bearing materials continuously, rapidly, and efficiently. To date, the process is considered to have reached its limit in view of the achieved high process efficiency. In addition, the required high-quality materials are expensive and gradually getting depleted. Hot gas injection (HGI) into the shaft of the BF is an emerging technology recognized potential to solve the aforementioned problems. However, so far, limited information and studies are available, most of which are preliminary studies with regard to the feasibility and aerodynamics of the technology. This hindered the understanding and thus the effective use of this technology. This work presents a numerical study of the multiphase flow, heat, and mass transfer in a BF by a CFD-based process model. The effects of injection composition in terms of CO and CO 2 contents in HGI are studied first. The calculated results reveal that HGI of 100% CO delivers the best BF performance. Then, the effects of key variables in relation to HGI of 100% CO, including position, rate, and temperature, are systematically studied. The in-furnace states and overall performance parameters have been analysed in detail. The results show that, through appropriate control of the injection variables, it is possible to achieve improved BF performance including low fuel rate and high productivity, which are considerably affected by the HGI parameters. The BF process model is also demonstrated to be a cost-effective tool in optimizing the key variables of HGI in BF for obtaining optimum process efficiency.

Suggested Citation

  • Zhaoyang Li & Yixiong Feng & Aimin Wang, 2021. "Computational Study of Hot Gas Injection (HGI) into an Ironmaking Blast Furnace (BF)," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-16, October.
  • Handle: RePEc:hin:jnlmpe:3344143
    DOI: 10.1155/2021/3344143
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