首页|A model for predicting the segregation directions of binary Geldart B particle mixtures in bubbling fluidized beds

A model for predicting the segregation directions of binary Geldart B particle mixtures in bubbling fluidized beds

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In gas fluidization processes involving different types of particles,the mixing or segregation behavior of the solid mixture is crucial to the overall outcome of the process.This study develops a model to predict the segregation directions of binary mixtures of Geldart B particles with density and size differences in bubbling fluidized beds.The proposed model was established by combining the particle segregation model,a previous particle segregation model,with a derived bed voidage equation of the bubbling fluidization based on the two-phase theory.The model was then analyzed with different function graphs of the model equations under various conditions.The results indicated that an increase in gas velocity or volume fraction of larger particles would strengthen size segregation,causing the larger and less dense components to descend.To validate the model,42 sets of data collected from 6 independent literature sources were compared with the predictions of the model.When the gas velocities were below 3.2 times the minimum gas velocity,the predictions were consistent with experimental results.This study has shed new light on the mechanisms of particle segregation in binary fluidized systems and provides a theoretical foundation for designing and manipulating gas-solid fluidized reactors.

Particle segregationBinary mixtureBubbling fluidizationGeldart B particlesTwo-phase theory

Chenmin Liu、Yuemin Zhao、Yanjiao Li、Yuqing Feng、Chenlong Duan、Chenyang Zhou、Liang Dong

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Key Laboratory of Coal Processing and Efficient Utilization,Ministry of Education,China University of Mining and Technology,Xuzhou,221116,China

School of Chemical Engineering and Technology,China University of Mining and Technology,Xuzhou,221116,China

Mineral Resources Business Unit,CSIRO,Clayton,VIC 3169,Australia

National Natural Science Foundation of ChinaGraduate Research and Innovation Projects of Jiangsu ProvinceGraduate Innovation Program of China University of Mining and Technology

52274275KYCX22_26402022WLKXJ065

2024

10.1016/j.partic.2024.01.006

颗粒学报(英文版)
中国颗粒学会　中国科学院过程工程研究所

颗粒学报(英文版)

CSTPCD
影响因子：0.632
ISSN：1674-2001
年,卷(期)：2024.90(7)