Cluster identification using image processing--颗粒学报PARTICUOLOGY

Particle Technology Research Centre, Department of Chemical and Biochemical Engineering, University of Western Ontario, London, Ontario N6A 5B9, Canada

10.1016/j.partic.2014.12.004

Volume 23, December 2015, Pages 16-24

Received 23 June 2014, Revised 30 November 2014, Accepted 11 December 2014, Available online 6 April 2015, Version of Record 2 December 2015.

E-mail: jzhu@uwo.ca; zhu@uwo.ca

Highlights

• Solids holdup distribution in the riser was closely examined in HSV image.

• A cluster was regarded as a "compound" of core clusters and cluster clouds.

• A threshold was selected by maximizing the inter-class variance of the two classes.

• Clusters were identified clearly by a systematic process.

• The core cluster fraction was calculated by image processing method.

Abstract

By closely examining hue, saturation and value (HSV) images of the solids holdup distribution in a riser, it can be seen that a "cluster" is the combination of a relatively stable core cluster of the highest solids holdups and constantly changing cluster clouds of solids holdups that are higher than the dilute phase. Based on this analysis, a threshold selection method maximizing the inter-class variance between the background and foreground classes is introduced. A systematic cluster identification process is therefore proposed that: (1) applies the threshold selection method to obtain the critical solids holdup threshold ɛ_sc to discriminate dense and dilute phases and (2) applies the method again in the dense phase regions to obtain the cluster solids holdup threshold ɛ_sct that identifies the core clusters. Using this systematic process, clusters of different shapes and sizes and a relatively clear boundary can be visualized clearly and identified accurately. Using ɛ_sct, the core cluster fraction is calculated by dividing the total number of pixels in the core cluster by the total number of image pixels. The variation of the core cluster fraction according to operating conditions is also discussed.

Graphical abstract

Keywords

Cluster identification; Image processing; Cluster fraction; Rectangular circulating fluidized bed riser

文章导读