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Measuring binary fluidization of non-spherical and spherical particles using machine learning aided image processing
  • +2
  • Cheng Li,
  • Xi Gao,
  • Steven Rowan,
  • Bryan Hughes,
  • William Rogers
Cheng Li
National Energy Technology Laboratory Morgantown
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Xi Gao
Guangdong Technion-Israel Institute of Technology
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Steven Rowan
National Energy Technology Laboratory Morgantown
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Bryan Hughes
National Energy Technology Laboratory
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William Rogers
National Energy Technology Laboratory Morgantown
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Abstract

The binary fluidization of Geldart-D type non-spherical wood particles and spherical 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.

Peer review status:UNDER REVIEW

05 Jul 2021Submitted to AIChE Journal
06 Jul 2021Assigned to Editor
06 Jul 2021Submission Checks Completed
17 Aug 2021Reviewer(s) Assigned