Title: Measuring binary fluidization of nonspherical and spherical particles using machine learning aided image processing

Journal Article · · AIChE Journal
DOI: https://doi.org/10.1002/aic.17693 · OSTI ID:1855869
ORCiD logo [1]; ORCiD logo [2];  [3];  [3];  [4]
  1. Department of Mechanical Engineering Guangdong Technion‐Israel Institute of Technology Shantou Guangdong China
  2. Department of Chemical Engineering Guangdong Technion‐Israel Institute of Technology Shantou Guangdong China
  3. National Energy Technology Laboratory Morgantown West Virginia USA, Leidos Research Support Team Morgantown West Virginia USA
  4. National Energy Technology Laboratory Morgantown West Virginia USA
+ Show Author Affiliations

Abstract The binary fluidization of Geldart D type nonspherical wood particles and spherical low density polyethylene (LDPE) particles was investigated in a laboratory‐scale bed. The experiment was performed for varying static bed height, wood particles count, as well as superficial gas velocity. The LDPE velocity field were quantified using particle image velocimetry (PIV). The wood particles orientation and velocity are measured using particle tracking velocimetry (PTV). A machine learning pixel‐wise classification model was trained and applied to acquire wood and LDPE particle masks for PIV and PTV processing, respectively. The results show significant differences in the fluidization behavior between LDPE only case and binary fluidization case. The effects of wood particles on the slugging frequency, mean, and variation of bed height, and characteristics of the particle velocities/orientations were quantified and compared. This comprehensive experimental dataset serves as a benchmark for validating numerical models.

Sponsoring Organization:
USDOE
OSTI ID:
1855869
Journal Information:
AIChE Journal, Journal Name: AIChE Journal Journal Issue: 7 Vol. 68; ISSN 0001-1541
Publisher:
Wiley Blackwell (John Wiley & Sons)Copyright Statement
Country of Publication:
United States
Language:
English

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