Characterization of flow regimes in fluidized beds by information entropy analysis of pressure fluctuations

Abstract

Flow regimes in a gas‐solid cylindrical fluidized bed of 150 mm (i.d.) × 1.2 m (high) with three different sand masses (1.5, 3, and 4.5 kg) were studied via information entropy analysis of pressure fluctuations. Three classes of methods of information entropy were adopted to characterize the flow regimes. It is shown that the first‐class dimensional methods are suitable for illustrating the distinct characteristic of the flow regimes under different operating conditions, while the other two dimensionless methods are appropriate for finding the similarities or common rules. Tsallis entropy and Renyi entropy do not provide more valuable information than Shannon entropy. Most importantly, the Shannon entropy method is a good choice for characterizing the flow regimes but is weak at revealing the transition velocity (Uc) clearly. This deficiency can be made up by the method of component Shannon entropy. Shannon entropy is partitioned into 4 component Shannon entropies (named CH1CH4), which may be related with a 4‐stage fluidization process assumption. A rule is found that the transition of flow regimes transition from bubbling bed to turbulent bed takes place when the contribution proportion of CH3 is exceeded by that of CH1, and it brings new enlightenment on interpretation of the transition mechanism.

Fluidization dynamics of cohesive Geldart B particles. Part II: Pressure fluctuation analysis

“…Despite the dominant peak, some distinct peaks with less intensity were also observed. The broad band of peak frequencies corresponds to the rupture of large bubbles or the formation of small bubbles [27,35]. As the cohesive force increases, the curve shifts toward lower frequency owing to the decreasing bubble numbers.…”

“…[22]. Over the past decades, numerous algorithms have been developed to extract the specific dynamic information from the pressure signals, covering bubble behaviors [23][24][25], fluidization regime transition [26,27] and defluidization [28,29], etc. Basically, these algorithms can be classified as time-domain analysis, frequency-domain analysis and state space analysis.…”

“…By calculating the power spectral density (PSD) [33], coherent-and incoherent-output PSD [24], wavelet coefficients [34,35], etc., periodic fluidization phenomena like bubble evolution, bed mass oscillation and so on are related to different frequency bands. The state space method is to characterize the chaotic feature of fluidization system with the aid of non-linear analysis such as attractor reconstruction [36], entropy analysis [27] and correlation dimension [37,38], etc. Detailed instructions for the pressure fluctuation analysis can be found in the comprehensive reviews published by Johnsson et al [39] and van Ommen et al [40].…”

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

“…Wang et al [22] analyzed the information entropy of pressure signals to characterize the flow regime. Johnsson et al [23] compared the different methods for the analysis of pressure signals in the fluidized bed.…”

“…Hagh‐Shenas‐Lari et al 21 examined the fluidization regime at high temperature by frequency domain analysis of pressure signals. Wang et al 22 analyzed the information entropy of pressure signals to characterize the flow regime. Johnsson et al 23 compared the different methods for the analysis of pressure signals in the fluidized bed.…”