Artificial intelligence-based approach for cluster identification in a CFB riser

Cluster evolution dominates the gas–solid interaction and heat and mass transfer in risers, but cluster identification is still a challenge. Based on the unsupervised artificial intelligence approach, a two-step K-means cluster identification method is proposed to distinguish the clusters in a riser. With the consideration of the instantaneous particle motion and position information, it is able to capture the phenomenon of particles attaching and detaching a cluster. The estimated solid fraction threshold is located in the region where the slope of the solid fraction is the smallest, and approximately makes the numbers of particles detaching and attaching the clusters equal. The solid fraction threshold is about 0.204. It is found that the particles penetrating a cluster are in a more violent turbulent movement. Larger gas velocity and solid flux result in stronger interactions between clusters. The proposed method provides a new way to identify the cluster.