Study on the relation of bubble behavior and bed density in gas–solid separation fluidized bed using electrical capacitance tomography

Abstract

Gas–solid Fluidized bed technology has a pivotal role in coal separation. Bubble movement behavior is an important factor affecting the fluidization stability. Fluidized bed measurement is an essential link in the bubble behavior study. As the main evaluation parameters, the concentration distribution and density distribution can reflect the bubble movement behavior. This work uses a noninvasive method of electrical capacitance tomography (ECT) for fluidized bed measurements, combined with COMSOL simulation validation for real-time imaging of bubbles in Geldart Group B magnetite powder particles. Meanwhile, the most suitable reconstruction algorithm for gas–solid separation fluidized bed is selected from three image reconstruction algorithms. And then concentration distribution and density distribution are analyzed. The results show that under reasonable gas velocity conditions (U–U_mf =2.28 and 3.17 cm/s), the central region ([0, 1/4]) concentrations of [0.43–0.45] and [0.39–0.42] and densities of [1.98–2.06 g/cm³] and [1.86–1.96 g/cm³] are obtained by ECT measurements, respectively. Finally, the bed density obtained from the ECT sensors in the experiment was validated using three different bed density models. The error can be controlled to within 20%, which indicates that the ECT measurement method has a fairly high reliability and accuracy in dry coal beneficiation field.

Keywords:

• Gas–solid fluidized bed
• electrical capacitance tomography (ECT)
• bubble movement behavior
• bed density

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The research work is financially supported by China National Funds for Distinguished Young Scientists (52125403), Natural Science Foundation of Jiangsu Province (BK20200651), National Natural Science Foundation of China (52104276, 52261135540, 52220105008, 51974306), the Postgraduate Research & Practice Innovation Program of Jiangsu Province (KYCX23_2815); the Graduate Innovation Program of China University of Mining and Technology (2023WLKXJ065), the Fundamental Research Funds for the Central Universities (2023XSCX020).