| Issue |
EPJ Web Conf.
Volume 332, 2025
The 8th International Conference on Physics, Mathematics and Statistics (ICPMS2025)
|
|
|---|---|---|
| Article Number | 01009 | |
| Number of page(s) | 12 | |
| DOI | https://doi.org/10.1051/epjconf/202533201009 | |
| Published online | 09 July 2025 | |
https://doi.org/10.1051/epjconf/202533201009
Research on 6.5MPa multi-nozzle opposed coal-water-slurry gasifier based on CFD
1 Inner Mongolia University of Science and Technology, Baotou 014010, Inner Mongolia, China
2 Inner Mongolia Rongxin Chemical Co., Ltd., 014300, Ordos, Inner Mongolia Autonomous Region, China
* Corresponding author's email:1339364010@qq.com
Published online: 9 July 2025
This paper takes the multi-nozzle opposed-type coal-water slurry gasifier in industrial operation as the research object, and constructs a full- process dynamic model of the gasifier based on the Ansys platform. The Euler-Euler multiphase flow model is selected, combined with various equations such as the continuity equation, momentum equation, and energy equation, as well as the P-1 radiation model, gas-solid heterogeneous and homogeneous reaction models. Under specific boundary condition assumptions, a numerical simulation of the gasification process in the gasifier is carried out. The research focuses on exploring the influence of changes in the oxygen-to-coal ratio and the concentration of the coal-water slurry on the composition of the effective gas (CO+H₂) at the gasifier outlet and the temperature field of the outlet gas. The simulation results show that within a certain operating range, increasing the oxygen-to-coal ratio will strengthen the combustion heat release, increase the temperature of the outlet syngas. When the oxygen-to-coal ratio reaches 1.3, the effective components of the syngas reach a peak value, and after that, increasing the oxygen-to- coal ratio will cause a slight decrease in the effective components of the syngas. Increasing the concentration of the coal-water slurry can improve the composition of the effective gas and the gasification efficiency, and at the same time increase the temperature of the outlet syngas. By comparing with the actual industrial production data, the reliability of the model is verified. This research provides a design basis and a dynamic control paradigm for the long-term and high-load industrial operation of the gasifier, and provides data support for the combination of machine learning and traditional computational fluid dynamics in the later stage.
© The Authors, published by EDP Sciences, 2025
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