Deep learning assisted characterization of bubble behavior in a gas-solid fluidized bed with binary particle mixtures

被引:0
作者
Yang, Peng [1 ]
Guo, Qiankun [1 ]
Chen, Hongsheng [1 ]
机构
[1] Chongqing Univ, Coll Mat Sci & Engn, Dept Met Engn, Chongqing 400044, Peoples R China
基金
中国国家自然科学基金;
关键词
Bubbling fluidized bed; Deep learning; U-Net model; Bubble recognition; Bubble behavior; IMAGE-ANALYSIS METHOD; SIZE DISTRIBUTION; SEGMENTATION; ALGORITHM; DYNAMICS;
D O I
10.1016/j.powtec.2024.120333
中图分类号
TQ [化学工业];
学科分类号
0817 ;
摘要
Gas-solid fluidized beds are widely applied as chemical reactors, and the fluidization quality in the bed is closely related to bubble behavior. Digital image processing is a commonly used non-invasive method for bubble behavior analysis, but it is usually constrained by experimental conditions such as lighting, making identification of bubble and emulsion phases still challenging. Herein, deep learning is applied in this study to optimize traditional digital image processing techniques. By evaluating different deep learning models (FCN, DeepLab V3, U-Net), rapid and accurate identification and segmentation of bubble images can be achieved, and the U-Net model performs best, achieving an identification accuracy of 99.05 %. Further application of U-Net to analyze bubble behavior demonstrates that deep learning methods enable efficient and accurate identification of bubbles and real-time analysis of bubble behavior, highlighting the significant potential application of deep learning in the field of complex hydrodynamics in fluidized beds.
引用
收藏
页数:12
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