Analysis of wall fouling and electrostatic charging in gas-solid fluidized beds

Lu, Junyu; Fujii, Shun; Yasuda, Masatoshi; Matsusaka, Shuji

doi:10.1016/j.apt.2020.06.034

Cited by 7 publications

(2 citation statements)

References 23 publications

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“…Gas velocity u g is one of the most important operating conditions in fluidized beds and influences the movement of the particles within the bed. The relative humidity RH has an important effect on the charge of particles in fluidized beds, and it has been reported in the literature 9,11,18,21,41,42 that the particle charge decreases with increasing relative humidity. The particle size and the dielectric constant both influence the electrostatic properties of the particles.…”

Section: Methodsmentioning

confidence: 99%

“… 5 During fluidization, electrostatic charges frequently develop due to triboelectric charging 6 and gas ionization. 7 The electrostatic charge may cause wall fouling, 8 , 9 sheeting, 10 and other issues. To control the electrostatic effect in fluidized beds, it is necessary to conduct a comprehensive study of the mechanism of particle charging in fluidized beds.…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning Approach to Predict the Surface Charge Density of Monodispersed Particles in Gas–Solid Fluidized Beds

Chen

Zhao

2022

ACS Omega

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Gas–solid fluidized beds are complex particle systems, and the electrostatic behavior of particles in fluidized beds is even more complex, which is influenced by numerous factors such as particle properties and operating conditions. Current studies focus on the effect of a certain factor on particle charging without a global picture. Furthermore, there is no mathematical model that can describe the interaction of multiple factors on particle charging because it is difficult to build a model for such a complex system. Therefore, a new approach is needed. In this study, a model capable of accurately predicting the surface charge density of particles in monodispersed gas–solid fluidized beds within a certain range was developed based on the literature and experimental data through several machine learning methods including kernel ridge regression (KRR), support vector machine regression (SVR), and multilayer perceptron (MLP). SVR and MLP models gave the best results with R 2 equal to 0.980 and 0.979, respectively. However, the sensitivity analysis showed that the MLP model was more reliable than the SVR model. In conclusion, the feasibility of using machine learning to analyze the charging behavior of particles in fluidized beds is demonstrated, and the proposed MLP model can serve as an accurate correlative tool for fast and effective estimation of particle surface charge density in gas–solid fluidized beds.

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