Journal
AICHE JOURNAL
Volume 68, Issue 7, Pages -Publisher
WILEY
DOI: 10.1002/aic.17693
Keywords
biomass utilization; fluidization; image processing; machine learning; nonspherical particles
Categories
Funding
- Natural Science Foundation of Guangdong Province [2214050009012]
- National Natural Science Foundation of China [21FAA02728]
- US Department of Energy's EERE Bioenergy Technologies Office (BETO)
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The binary fluidization behavior of nonspherical wood particles and spherical low density polyethylene (LDPE) particles was investigated in this laboratory-scale study. The experimental results demonstrate significant differences between the fluidization behavior of LDPE particles alone and the binary fluidization case, providing valuable data for validating numerical models.
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.
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