Ternary semantic segmentation of hybrid particles in fluidized beds via dual CBAM-DeepLabV3+: Enhanced characterization of nanoparticle agglomeration--颗粒学报PARTICUOLOGY

Juhui Chen^{a b *}, Yongxin Zhu^{a b}, Dan Li^{a b}, Buyang Peng^{a b}, Yongen Zhao^{a b}, Wenqing Du^{a b}, Xifeng Cao^{a b}, Siarhei Lapatsin^{c d}, Michael Zhuravkov^d

a School of Mechanical and Power Engineering, Harbin University of Science and Technology, Harbin, 150080, China
b Key Laboratory of Advanced Manufacturing and Intelligent Technology, Ministry of Education, Harbin University of Science and Technology, Harbin 150080, China
c School of Mechatronics Engineering, Harbin Institute of Technology, Harbin 150001, China
d Chongqing Research Institute, Harbin Institute of Technology, Chongqing 401135, China

10.1016/j.partic.2026.06.022

Volume 116, September 2026, Pages 279-296

Received 11 May 2026, Revised 15 June 2026, Accepted 21 June 2026, Available online 27 June 2026, Version of Record 11 July 2026.

E-mail: chenjuhui@hrbust.edu.cn

Highlights

• Proposing a three-class semantic segmentation model for mixed particle systems.

• Developed a DeepLabV3+ model with dual CBAM for enhanced particle recognition.

• Achieved high-precision pixel-level differentiation of mixed particles.

• Geldart B particles suppress excessive agglomerate growth and irregularity.

Abstract

Accurate characterization of nanoparticle agglomeration in mixed-particle micro fluidized beds remains challenging because conventional binary semantic segmentation cannot effectively distinguish nanoparticles from Geldart B particles. To address this issue, a ternary semantic segmentation framework based on an improved DeepLabV3+ architecture is developed, in which pixels are classified into background, nanoparticles, and Geldart B particles. Dual convolutional block attention modules (CBAM) are incorporated to enhance multi-scale feature extraction and boundary recognition under complex fluidization conditions. Compared with baseline models, the proposed framework improves the mean intersection over union (mIoU) by 4.00%, enabling reliable pixel-level separation of mixed particles. Based on the high-precision segmentation results, the evolution of nanoparticle agglomeration characteristics and radial distribution behavior under different superficial gas velocities is quantitatively analyzed. The results show that introducing Geldart B particles suppresses excessive agglomerate growth, reduces the average equivalent diameter, and promotes the formation of more compact and morphologically regular agglomerates. Enhanced local hydrodynamic disturbance and heterogeneous particle interactions further promote agglomerate restructuring and modify the competition between aggregation and breakup processes during fluidization. Overall, the proposed framework provides an effective tool for quantitative investigation of agglomeration behavior and mesoscale flow structure evolution in complex gas–solid fluidized systems.

Graphical abstract

Keywords

Semantic segmentation; DeepLabV3+; Dual CBAM; Geldart B particles; Nanoparticle agglomeration

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