Particle clusters for FCC particles
in a gas–solid circulating
fluidized bed with a 12.4 m high riser and a 5 m high downer were
identified from the images of the gas–solid flow by a k-means machine learning algorithm-assisted processing method.
An optimal k value of 3 was determined and justified
by several evaluation criteria for the k-means algorithm.
The solid holdup obtained from the processed images agrees well with
that from the optical fiber method. The particle cluster characteristics
between the riser and downer, such as the cluster solid holdup, equivalent
diameter, velocity, and frequency, were extracted from the processed
images and then compared in detail for the first time. The cluster
solid holdup and the cluster velocity in the riser (εcl = 0.05–0.20, V
cl = 4–10
m/s) are much higher than those in the downer (εcl = 0.005–0.020, V
cl = 2–5
m/s). The cluster equivalent diameter and the cluster frequency in
the riser and downer are similar (d
cl =
2–10 mm, f
cl = 100–400 Hz).
Empirical correlations of the cluster characteristics with the local
flow conditions and the operating parameters in both the riser and
downer are further studied.