1. Introduction
The utilization of both plastic particles and biomass in a fluidized bed
system has become
increasingly popular and is investigated in many studies due to their
great potential for sustainable energy conversion processes such as
combustion, gasification, and pyrolysis, to reduce greenhouse gas
emissions and abundant supply of the raw material1–3.
Most of these recycled raw materials are physically processed into
granular materials of non-spherical shape. This poses additional
complications in establishing accurate predictions of their behavior
during applications since the non-spherical particle-particle
interaction and particle-fluid interaction behave rather differently
compared to spherical particles4. Such difference is
mainly related to particle orientation and corresponding differences in
particle stresses. For example, non-spherical particles with high
surface-to-volume ratios can have 20 % - 120 % higher average shear
particle stress than that of spheroidal paricles5; a
free-falling cylinder will align itself with the axis parallel to the
flow for moderate Reynolds numbers6; non-spherical
particles of large aspect ratio are difficult to be fluidized due to
interlocking between particles7. Spherical particles
are usually used to assist the fluidization of non-spherical particles
in typical industrial applications8. Due to the
difference in particle physical properties, such as shape, size, and
density, the non-spherical particle can affect the fluidization of
spherical particles, such as the transition of fluidization
regimes9, changes of fluctuation
frequency10, and so on. Moreover, the non-spherical
particles can separate from spherical particles, which leads to
segregation. However, segregation is not preferable because non-uniform
mixing can significantly decrease the bed performance. Understanding the
dynamics of non-spherical particles and the effects of non-spherical
particles on binary fluidization is critical for the understanding of
fundamental physics and the associated industrial applications.
In situ experimental studies on non-spherical particle dynamics
in fluidized beds were seldomly reported in the literature as related
experiments to resolve particle-scale information such as position,
orientation, velocity, and size are rather costly and complicated. Buist
et al.11 measured the translational and rotational
velocities of cylindrical particles with varying elongation ratios using
magnetic particle tracking (MPT) in a cylindrical fluidized bed with
17.4 cm in diameter. Fotovat et al.12 investigated the
biomass particle shape factor on the biomass distribution and velocity
profiles in a spherical-particles-assisted binary fluidization system
using the radioactive particle tracking (RPT) method. Chen et
al.13 measured the 3D particle position and velocity
of a single tagged cylindrical particle over a long period in the binary
fluidized bed using X-ray particle tracking velocimetry (XPTV). Vollmari
et al.14 investigated the distribution and orientation
statistics of non-spherical particles in a rectangular bed using the
in-house image analysis algorithms. Studying the dynamics of particles
through processing images obtained from a high-speed and high-resolution
camera is a relatively straightforward method of acquiring quantitative
data compared to other complex measurement techniques discussed above.
Efforts have been made to increase the capability of particle scale
imaging techniques to higher particle volume fractions by improving the
particle detection algorithms or limiting the sample depth. In the
processing of the image for non-spherical particle and spherical
particle binary fluidization, the main challenge is the segmentation of
the non-spherical particles from the spherical particle and background.
Segmentation is a process to partition an image into multiple parts or
segments. Classical image segmentation methods include histogram
thresholding, edge detection based, relaxation, and semantic and
syntactic approaches15. Each approach has its
advantages and limitations. For example, region-based segmentation
separates the objects into a different region based on an automatically
or manually determined threshold value. It is simple, fast, and performs
well when the target object and background have high contrast. However,
the accuracy of this method becomes very low when the contrast is low
and there is a large overlap. Some studies employing classical image
segmentation are available in the literature. Yin et
al.16 applied an image multilevel thresholding
approach using the k-means algorithm to identify clusters in a fluidized
bed riser. Jiang et al.17 employed a particle-mask
correlation segmentation approach to detect the particle geometric
center in a 2D fluidized bed. In recent years, the machine learning
approach demonstrates great promise in the field of particle-scale data
extraction from images in multiphase flow
research18–24. For example, Yevick et
al.18 measured particle size and positions from
analyzing the holographic video microscopy data using machine learning
techniques based on support vector machines (SVMs) in real-time on
low-power computers. Shao et al.24 developed a
convolutional neural network (CNN)-based approach for measuring the 3D
particle distributions using digital in-line holography.
The aim of this study is to experimentally investigate the cylindrical
particle dynamics and their impact on binary fluidization using image
processing and pressure signal analysis. In section 2, the experiment
setups and the methodology of the machine learning-enabled image
processing methods are introduced. In section 3, the results and
discussion of pure LDPE sphere fluidization behavior, effects of
cylindrical particles mass fraction, superficial gas velocity, and
sphere inventory on the cylinder dynamics and fluidization behavior were
presented. In section 4, the results were summarized and further
discussion was presented.