Investigation of fluidized bed behaviour using electrical capacitance tomography

Abstract

The temporal and cross-sectional distributions of particles in a 127 mm diameter fluidized bed have been obtained using a new generation, high-speed Electrical Capacitance Tomography. Two planes of eight electrodes were used and mounted at 160 and 660 mm from the gas distributor which was a 3 mm thick porous plastic plate made (maximum pore size of 50-70 m). 3 mm diameter, nearly-spherical polyethylene granules made up the bed. Experiments at sampling frequencies of 200-2000 cross-sections per second and gas superficial velocities from just below the minimum fluidization to 83% above minimum fluidization velocities were used. The time series of the cross-sectional average void fractions have been examined both directly and in amplitude and frequency space. The last two used Probability Density Functions and Power Spectral Densities. The information generated show that the fluidized bed is operating in the slugging mode, not surprising given the size of the particles. It has been found that an increase of the excess gas velocity above the minimum fluidization velocity resulted in the increase of the mean void fraction, length and velocity of the slug bubbles as well as the bed height, and in a slight decrease of the slug frequency. The results are presented in a level of detail suitable for comparison with later numerical simulation.

Three-dimensional computational fluid dynamics investigation of hydrodynamics behaviour of gas–solid flow in fluidized bed of Geldart D particles

“…The minimum fluidization velocity predicted by the CFD simulation is found to be around 0.79 m/s, which is close to the value of 0.78 m/s. reported in Azzi et al 53 Therefore, the computed results agree quite well with experimental measurements of Azzi et al 53 with an error of 1%. Consequently, there is no significant difference between the experimental and simulated results based on the prediction of pressure drop.…”

Design and Optimization of a Finite Element Model for Electrical Resistance Tomography of Human Lungs

“…This greatly reduces the time to compute the effective boundary voltage of the sensitivity field, enhancing the real-time performance of the algorithm [33]. As shown in Figure 5, the of the preliminary uniformly distributed finite element model was 21.1003%, and the conditional number of the corresponding sensitivity matrix was 1.2367×10 7 , according to the structural model of human lungs (Figure 5) and the theoretical boundary voltage solved by the model in Figure 6. From the boundary voltage of the sensitivity field obtained by the preliminary model (Figure 7), it can be seen that the optimization of node number does not affect the calculation accuracy of the forward problem.…”

“…Based on the different electrical features of the object in the sensitivity field, the ET techniques could be divided into electromagnetic tomography (EMT), electrical capacitance tomography (ECT), electrical impedance tomography (EIT), and electrical resistance tomography (ERT). Based on the principle of electromagnetic induction, the EMT can reconstruct the distribution state of the magnetic permeability for the medium in the sensitivity field [1][2][3][4][5][6]; Based on the principle of capacitance sensitivity, the ECT can reconstruct the distribution state of the dielectric constant for the medium in the sensitivity field [7][8][9][10][11][12][13][14][15][16][17]; Based on the principle of impedance sensitivity, the EIT can reconstruct the distribution state of the complex admittance for the medium in the sensitivity field [18][19][20][21][22][23][24]; Based on the principle of resistance sensing, the ERT can reconstruct the distribution state of the dielectric resistivity/conductivity for the medium in the sensitivity field [25][26][27][28][29][30].…”

Bubble Behaviors in a Dense Gas‐Solids Fluidized Bed: Analysis of Pressure Fluctuation

“…The bubble property is one of the most important subjects in the hydrodynamics of dense fluidized beds. The bubble behaviors could be identified by visual observation 6–8, electrical capacitance tomography 9, 10, optical fiber probe 11, 12, and analysis of pressure fluctuation 13–15.…”