Leimbach, Steffen
[Author];
Lukas, Johannes
[Author];
Kolb, Sebastian
[Author];
Yang, Lei
[Author];
Plankenbühler, Thomas
[Author];
Sega, Marcello
[Author];
Harting, Jens
[Author];
Karl, Jürgen
[Author]
Early Detection of Agglomeration in Fluidized Beds by Means of Frequency Analysis of Pressure Fluctuations
You can manage bookmarks using lists, please log in to your user account for this.
Media type:
E-Article
Title:
Early Detection of Agglomeration in Fluidized Beds by Means of Frequency Analysis of Pressure Fluctuations
Contributor:
Leimbach, Steffen
[Author];
Lukas, Johannes
[Author];
Kolb, Sebastian
[Author];
Yang, Lei
[Author];
Plankenbühler, Thomas
[Author];
Sega, Marcello
[Author];
Harting, Jens
[Author];
Karl, Jürgen
[Author]
imprint:
American Chemical Society, 2022
Published in:Energy & fuels 36(9), 4924 - 4932 (2022). doi:10.1021/acs.energyfuels.1c04356
Footnote:
Diese Datenquelle enthält auch Bestandsnachweise, die nicht zu einem Volltext führen.
Description:
For description of the fluidization state of fluidized beds, both time-domain and frequency-domain analyses of high-frequency pressure fluctuations are established approaches. Common methods for the detection of agglomeration or defluidization in fluidized beds use the variance or the standard deviation of the pressure signal or the maximum in its frequency spectrum. These methods are used, for example, in biomass combustion or gasification. However, these approaches lack the reliability for applications as an early agglomeration warning system in industrial applications. To address this issue, the present study introduces a robust methodology by means of extracting a characteristic frequency from the power spectral density of the pressure signal. A comparison of our developed approach with the commonly used frequency maximum and standard deviation for predicting the onset of agglomeration in laboratory experiments shows promising sensitivity on agglomeration formation. In order to evaluate the general applicability of this method on an industrial scale, this work investigates dependencies of possible influences, such as gas velocity, sand quantity, and temperature, on the characteristic frequency. The results indicate that the characteristic frequency can be a promising and robust method for the early detection of the onset of agglomeration in industrial plants.