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A sliding-window based signal processing method for characterizing clusters in gas-solids high-density CFB reactor
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  • Chengxiu Wang,
  • Mengjie Luo,
  • Xin Su,
  • Xingying Lan,
  • Zeneng Sun,
  • Jinsen Gao,
  • Mao Ye,
  • Jesse Zhu
Chengxiu Wang
China University of Petroleum Beijing State Key Laboratory of Heavy Oil Processing

Corresponding Author:[email protected]

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Mengjie Luo
China University of Petroleum Beijing State Key Laboratory of Heavy Oil Processing
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Xin Su
China University of Petroleum Beijing State Key Laboratory of Heavy Oil Processing
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Xingying Lan
China University of Petroleum Beijing State Key Laboratory of Heavy Oil Processing
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Zeneng Sun
The University of Western Ontario
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Jinsen Gao
China University of Petroleum Beijing State Key Laboratory of Heavy Oil Processing
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Mao Ye
Dalian Institute of Chemical Physics, Chinese Academy of Sciences
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Jesse Zhu
The University of Western Ontario
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Abstract

Particle clusters in CFB risers were identified from the instantaneous solids holdup signals by a new sliding-window based signal processing method. By shifting the sliding time window and calculating the mean and the standard deviation within it, a non-linear threshold curve for identifying the clusters was derived instead of the conventional constant threshold. The optimal sliding window size was determined as Wb = 1024 data points based on the bisection method on the entire piece of signals. Using the proposed method, a more realistic characterization of the clusters in both the HDCFB and LDCFB was obtained by considering the bulk fluctuation of the gas-solids flow. The clusters in the HDCFB have higher solids holdup and lower velocity than that in the LDCFB. The HDCFB is also found to have a greater number of loose clusters for better gas-solids contacting and exchanges in the center of the riser.