Nonlinear characteristics analyses of particle motion for predicting flow regimes--颗粒学报PARTICUOLOGY

Kang, P., Lu, Y., Yang, L., Liu, L., Hu, X., Luo, X., . . . Zhang, R. (2020). Nonlinear characteristics analyses of particle motion for predicting flow regimes. Particuology, 53, 134-141. https://doi.org/10.1016/j.partic.2020.03.008

Nonlinear characteristics analyses of particle motion for predicting flow regimes

Panxing Kang ^{a 1}, Yujian Lu ^{a 1}, Lei Yang ^a, Libin Liu ^a, Xiayi (Eric) Hu ^a, Xiao Luo ^b, Hongbo Chen ^a, Yefeng Zhou ^a *, Rui Zhang ^a *

a National and Local United Engineering Research Centre for Chemical Process Simulation and Intensification, Chemical Process Simulation and Optimization Engineering Research Center of the Ministry of Education, Xiangtan University, Xiangtan 411105, China
b College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, China

10.1016/j.partic.2020.03.008

Volume 53, December 2020, Pages 134-141

Received 15 February 2019, Revised 24 September 2019, Accepted 23 March 2020, Available online 30 May 2020, Version of Record 16 December 2020.

E-mail: zhouyf@xtu.edu.cn; ruizhang@xtu.edu.cn

Highlights

• Signal analyses of acoustic emission (AE) and pressure fluctuation were compared.

• AE signals were analyzed using complexity and Shannon entropy.

• Nonlinear characteristics were studied based on AE signals.

• Flow regime transitions could be identified using AE signal analysis.

Abstract

Gas–solid flow regimes have a significant impact on particle transport and separation in a fluidized bed reactor. In this study, to determine flow regime transitions in gas–solid fluidized beds, an acoustic technique was used to detect and analyze the behavior of gas and solids. Algorithm complexity, fluctuation complexity, and Shannon entropy analyses of acoustic emission signals were performed to examine nonlinear system characteristics, and to determine the flow regime transition velocities uc, uk, and uFD. Moreover, using the standard deviation of pressure signals, pressure measurements and acoustic measurements were compared. The relative deviations (RDs) between the experimental and empirical values of uk were 8.8%, 13.7%, 8.8%, and 30.4% for the algorithm complexity, fluctuation complexity, Shannon entropy, and pressure signal standard deviation, respectively, while the respective RDs for uFD were 15.7%, 23.9%, 15.7%, and 97.8%. The RDs between the experimental and empirical values of uc were all 6.4%. The experimental values obtained from acoustic signal measurements were therefore closer to the empirical values. In summary, the integration of non-intrusive acoustic measurements, complexity analysis, and Shannon entropy analysis is suitable for identifying flow regime transitions.

Graphical abstract

Keywords

Acoustic emission; Nonlinear characteristics; Complexity analysis; Shannon entropy; Flow regime transition

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