(585a) Advanced Signal Analysis Methods For Selective Agglomeration Detection In Fluidized Bed Energy Conversion

Bartels, M. - Presenter, Delft University of Technology
Nijenhuis, J. - Presenter, Delft University of Technology
van Ommen, J. R. - Presenter, Delft University of Technology
Verheijen, P. - Presenter, Delft University of Technology

The utilization of coal in fluidized bed conversion (combustion and gasification) is well researched and an established technology in industrial practice. Especially when utilizing biomass, however, agglomeration of bed/ash particles often forms a major operational problem. In industrial practice, typically measurements of average pressure drop over the bed and average temperatures are used to evaluate the fluidization quality. However, in many cases these measurements do not detect agglomeration problems in an early stage of the agglomeration process; they are therefore not suitable to indicate when corrective measures against poor fluidization have to be taken.

The attractor comparison method [AIChE Jour. 2000;46:2183-2197] has been developed in order to solve this problem. This method is based on high-frequent pressure fluctuation measurements and has been successfully applied on lab- and industrial scale bubbling fluidized beds. Yet, the source of the changed hydrodynamics cannot be unambiguously determined. We have therefore developed a new screening methodology to assess the capability of many other signal analysis methods in addition to attractor comparison. First, the raw pressure fluctuation data is pre-treated (filtered) with a method out of a pool of m methods. By filtering one can potentially extract specific signal properties relevant to agglomeration and therefore increase the sensitivity of an analysis method. Consecutively, the pre-treated data is analyzed with an analysis method out of a pool of n methods. For each of the (m x n) combinations of pre-treatment / analysis method, the screening methodology then calculates a quantitative measure for the sensitivity towards agglomeration as compared to other effects, such as changes in bed mass and fluidizing gas velocity. Results show that this methodology is indeed capable to reject inappropriate methods and to highlight methods specifically sensitive towards agglomeration as compared to other effects. Besides the application in bubbling fluidized beds, special attention is given to circulating fluidized beds in light of their frequent application in the energy sector.

In addition to the agglomeration detection, we are presenting some first results of counteracting agglomeration during biomass combustion on lab- and bench scale. Our monitoring approach is applied to determine the appropriate time to impose different strategies to counteract the agglomeration process. Hereby it is possible to successfully maintain stable fluidization.