A sliding-window based signal processing method for characterizing particle clusters in gas-solids high-density CFB reactor

Chengxiu Wang, Mengjie Luo, Xin Su, Xingying Lan, Zeneng Sun, Jinsen Gao, Mao Ye, Jesse Zhu

Research output: Journal PublicationArticlepeer-review

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 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 by the bisection method on the entire piece of signals. Using the proposed method, a more realistic characterization of the particle clusters in both HDCFB and LDCFB was obtained by considering the bulk fluctuation of the gas-solids flow. The clusters in 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.

Original languageEnglish
Article number139141
JournalChemical Engineering Journal
Volume452
DOIs
Publication statusPublished - 15 Jan 2023
Externally publishedYes

Keywords

  • Circulating fluidized bed
  • Cluster
  • High-density
  • Signal processing
  • Sliding window algorithm

ASJC Scopus subject areas

  • Chemistry (all)
  • Environmental Chemistry
  • Chemical Engineering (all)
  • Industrial and Manufacturing Engineering

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