Detection of agglomeration and gradual particle size changes in circulating fluidized beds

Bartels, Malte; Nijenhuis, John; Kapteijn, Freek; Ommen, J. Ruud van

doi:10.1016/j.powtec.2010.03.035

Cited by 35 publications

(17 citation statements)

References 9 publications

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“…The content of Alkali and alkali earth metals in the fuel, such as K, Na, Ca, Mg can increase agglomeration potential significantly, and if Cl is present this can facilitate the progression even further. Furthermore, modes for agglomeration prediction have been presented by Gatterning [47], agglomeration detection methods have been presented by Bartels et al [48,49], and the mitigation of agglomeration have been presented by Morris et al [50]. Proven methods for predicting agglomeration, controlled agglomeration test as well as fuel and ash analysis, work only for fuels that are relatively homogeneous.…”

Section: Agglomerationmentioning

confidence: 99%

Waste Fuel Combustion: Dynamic Modeling and Control

2018

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The focus of this study is to present the adherent transients that accompany the combustion of waste derived fuels. This is accomplished, in large, by developing a dynamic model of the process, which can then be used for control purposes. Traditional control measures typically applied in the heat and power industry, i.e., PI (proportional-integral) controllers, might not be robust enough to handle the the accompanied transients associated with new fuels. Therefore, model predictive control is introduced as a means to achieve better combustion stability under transient conditions. The transient behavior of refuse derived fuel is addressed by developing a dynamic modeling library. Within the library, there are two models. The first is for assessing the performance of the heat exchangers to provide operational assistance for maintenance scheduling. The second model is of a circulating fluidized bed block, which includes combustion and steam (thermal) networks. The library has been validated using data from a 160 MW industrial installation located in Västerås, Sweden. The model can predict, with satisfactory accuracy, the boiler bed and riser temperatures, live steam temperature, and boiler load. This has been achieved by using process sensors for the feed-in streams. Based on this model three different control schemes are presented: a PI control scheme, model predictive control with feedforward, and model predictive control without feedforward. The model predictive control with feedforward has proven to give the best performance as it can maintain stable temperature profiles throughout the process when a measured disturbance is initiated. Furthermore, the implemented control incorporates the introduction of a soft-sensor for measuring the minimum fluidization velocity to maintain a consistent level of fluidization in the boiler for deterring bed material agglomeration.

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Section: Agglomerationmentioning

confidence: 99%

Waste Fuel Combustion: Dynamic Modeling and Control

2018

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“…Measurements are easy to assess; they only require transmitters installed at appropriate locations, usually in the plenum chamber, as this location provides an overview of bubbles and particles movement throughout fluidization. Researches focusing on the hydrodynamic monitoring of fluidized beds and which are based on the processing of pressure fluctuation signals have been widely accepted by the international scientific community due to their great potential of application in the identification of agglomeration in the processes, such as: gasification and biomass combustion [9][10][11][12][13], coke production [14] and coating and granulation of solid particles in a fluidized bed [15][16][17][18][19][20]. The papers of Johnsson et al [4] and van Ommen et al [21] report many results on the monitoring of fluidization regimes employing various methods of analysis of pressure fluctuation signals, besides describing in detail such analysis.…”

Section: Nomenclaturementioning

confidence: 99%

Monitoring and control of coating and granulation processes in fluidized beds – A review

Silva

Butzge

Nitz

et al. 2014

Advanced Powder Technology

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“…The method has been used to detect the agglomeration of particles during biomass combustion in bubbling (nijenhuis et al, 2007;Bartels, 2008b) and circulating fluidized beds (Bartels et al, 2010a). Figure 18.15 gives an example of a typical result obtained with attractor comparison: while the average pressure drop only indicates defluidization, the s-statistic obtained from attractor comparison gives an early warning for agglomeration.…”

confidence: 99%