An image processing method for feature extraction and dynamic tracking of particle clusters in CFBs--颗粒学报PARTICUOLOGY

Guo, Y., Yin, S., Lu, S., Song, T., Ge, H., & Lu, P. (2023). An image processing method for feature extraction and dynamic tracking of particle clusters in CFBs. Particuology, 77, 1-13. https://doi.org/10.1016/j.partic.2022.09.004

An image processing method for feature extraction and dynamic tracking of particle clusters in CFBs

Yue Guo, Shangyi Yin^*, Shibing Lu, Tao Song, Huijun Ge, Ping Lu

School of Energy and Mechanical Engineering, Nanjing Normal University, Nanjing, 210023, China

10.1016/j.partic.2022.09.004

Volume 77, June 2023, Pages 1-13

Received 24 May 2022, Revised 9 September 2022, Accepted 15 September 2022, Available online 24 September 2022, Version of Record 2 December 2022.

E-mail: syyin@njnu.edu.cn

Highlights

• Centroid was used to locate the clusters.

• Dynamic tracking of clusters is achieved.

• Three zones are divided laterally in the riser according to clusters' movement.

• Parameters of motion and morphology features can be obtained simultaneously.

Abstract

A new image processing method based on the high-speed camera is proposed to identify, locate, and track clusters. The instantaneous characteristic parameters of particle clusters in the riser of the circulating fluidized bed (CFB) can be acquired, such as solids holdup, vertical velocity, lateral displacement, aspect ratio and near-circularity. Experiments were carried out with glass bead particles, river sand particles and FCC particles. The time series of images of gas–solid flow in a CFB riser with a 100 mm × 25 mm cross-section and 3.2 m in length were obtained using high-speed cameras. The k-means⁺⁺ clustering algorithm is utilized to identify the clusters, centroid is applied to locate the clusters, and the cross-correlation algorithm is employed to track the specific clusters and number them to get the instantaneous characteristic parameters. The results illustrate that the shapes of clusters in the center area are closest to circle, moving upwards at a uniform speed, while the clusters in the side-wall area are mostly elongated or long chain-like, moving slowly downwards. In the transition area, the clusters are more complex, moving upwards at a constant speed, and having large lateral displacement. The results show that the image processing method used in this study is successful in acquiring the dynamic and structural parameters of the clusters simultaneously.

Graphical abstract

Keywords

Circulating fluidized bed; ClustersImage processing; Dynamic tracking

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