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Flow analysis on carbonaceous deposition of heavy oil droplets and catalyst particles for coking formation process

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  • Gao, Zhuwei
  • Li, Chengxin
  • Qi, Xinyu
  • Wei, Yaodong
  • Liu, Zhongxin

Abstract

The flow state has an important influence on the carbonaceous deposition for coking formation process. To study the flow of heavy oil droplets and catalyst particles in FCC process, we used the scanning-electron microscope (SEM) to analyze the microstructure of coke and simulated the flow by computational fluid dynamics (CFD) method. A new iso-vortex surface method according to swirling strength was used to analyze the fluid motion. The results showed that the dissolution of powdered coke sample in ethanol was more uniform than that in water. Swirling strength on the outer wall of vortex finder was large, and there was a depression in the flow layer at about 270°. Under the swirling strength of 654.59 s−1, the flow layer in the outer wall of vortex finder was thick in the range of 0–180°. A stable stagnant layer in the barotropic gradient boundary layer was easier to form a deposition layer of fine particles or droplets. When the fluid velocity outside the boundary layer increased, the shear force increased. The thickness of sedimentary layer and the size of sedimentary particles in the boundary layer tended to decrease. In addition, the backflow also affected the deposition of particles and droplets.

Suggested Citation

  • Gao, Zhuwei & Li, Chengxin & Qi, Xinyu & Wei, Yaodong & Liu, Zhongxin, 2022. "Flow analysis on carbonaceous deposition of heavy oil droplets and catalyst particles for coking formation process," Energy, Elsevier, vol. 260(C).
    • Handle: RePEc:eee:energy:v:260:y:2022:i:c:s0360544222018862
    DOI: 10.1016/j.energy.2022.124988
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    References listed on IDEAS

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    1. Wang, Lulu & Feng, Xuan & Shen, Laihong & Jiang, Shouxi & Gu, Haiming, 2019. "Carbon and sulfur conversion of petroleum coke in the chemical looping gasification process," Energy, Elsevier, vol. 179(C), pages 1205-1216.
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    1. Qiu, Yuhang & Hui, Yunze & Zhao, Pengxiang & Cai, Cheng-Hao & Dai, Baiqian & Dou, Jinxiao & Bhattacharya, Sankar & Yu, Jianglong, 2024. "A novel image expression-driven modeling strategy for coke quality prediction in the smart cokemaking process," Energy, Elsevier, vol. 294(C).

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