Detecting and Counteracting Agglomeration in Fluidized Bed Biomass

Energy & Fuels 2009, 23, 157–169

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Detecting and Counteracting Agglomeration in Fluidized Bed Biomass Combustion Malte Bartels,† John Nijenhuis,† Jasper Lensselink,‡ Marcin Siedlecki,§ Wiebren de Jong,§ Freek Kapteijn,† and J. Ruud van Ommen*,† Delft UniVersity of Technology - DelftChemTech, Delft Research Centre for Sustainable Energy, Julianalaan 136, 2628 BL Delft, The Netherlands, Energy Research Centre of The Netherlands (ECN), P.O. Box 1, 1755 ZG Petten, The Netherlands, and Delft UniVersity of Technology - 3mE, Section Energy Technology, Leeghwaterstraat 44, 2628 CA Delft, The Netherlands ReceiVed July 23, 2008. ReVised Manuscript ReceiVed October 18, 2008

Agglomeration in fluidized bed combustion can be a big operational problem, leading to unwanted defluidization and shutdown of the installation. Therefore, the onset of such events has to be reliably detected in an early stage and combined with counteractions to avoid further agglomeration and defluidization. The suitability of the attractor comparison method to detect agglomeration in combination with different counteraction strategies is investigated on a laboratory-scale (∼1 kWth) and a small commercial-scale (∼1 MWth) fluidized bed combustor. The agglomeration characteristics and the time until defluidization occurs can vary considerably depending on scale and process conditions, but also for similar operating conditions on the same scale. In all cases, attractor comparison has shown to detect the approach of defluidization early enough to prevent defluidization if a suitable counteraction strategy is applied. A temporary increase of fluidization velocity to promote agglomerate break-up is not a useful method to avoid agglomeration on laboratory-scale. A decrease in operating temperature below the melting points of potassium silicates can be a successful emergency strategy to ensure continued trouble-free operation. However, a subsequent temperature increase leads to further agglomeration, potentially very rapid, with alkali still present in the bed; therefore, some strategy to replace the bed content or neutralize the alkali is necessary. Semicontinuous replacement of bed material is shown to be a successful permanent solution to avoid defluidization. Yet, its application requires careful economic consideration. For larger reactor scales, agglomeration can occur more localized, which justifies several measuring positions. Especially for monitoring transition regions, for example, start-up, the application of a moving reference that has a constant negative time offset to the evaluation window is advantageous.

Introduction Fluidized Bed Energy Conversion and Agglomeration. Fluidized bed conversion (combustion and gasification) of carbonaceous solid material (e.g., coal, biomass, and waste) at high temperatures is an industrial practice to generate steam, electricity, and syngas. The solid fuel is added to the fluidized bed of inert solid material, which acts as a heat reservoir. Silica sand is most commonly used as bed material. The actual amount of the fuel itself in the bed as compared to the inert bed material is relatively low, in the order of a few percent. The fluidized bed ensures good mixing of the fuel, which is continuously fed, as well as a good distribution of the produced heat. The resulting homogeneous temperature distribution is one of the important advantages of fluidized beds over other reactor concepts. Moreover, fluidized beds have the advantage of being flexible for a variety of fuels; see, for example, ref 1. Despite its broad application, solid fuel conversion in fluidized bed processes is still struggling with technical difficulties. Agglomeration can be a major operational problem; its mechanisms are reviewed in ref 2. Inorganic alkali components from the fuel, mainly potassium (K) and sodium (Na), can be a source * To whom correspondence should be addressed. Telephone: +31-1527-82133. Fax: +31-15-27-85006. E-mail: [email protected]. † Delft University of Technology - DelftChemTech. ‡ Energy Research Centre of The Netherlands. § Delft University of Technology - 3mE.

for agglomeration due to the formation of low-melting silicates with the silica from the bed material. At common operating temperatures, often around 850 °C, the sand/ash particles can then get covered with an adhesive layer of molten alkali silicates and subsequently form larger agglomerates due to the formation of permanent bonds upon collisions. If this process is not recognized, it eventually propagates to partial or total defluidization of the bed, which in turn results in a lengthy and expensive unscheduled shutdown of the installation. Biomass as a feedstock for energy conversion has received considerable attention as it is CO2-neutral and often widely available. However, the alkali content can vary considerably between different fuels. Especially for certain types of biomass, often cheap agricultural residues, as well as some low-rank coal types, the alkali content is often rather high. The wider application of biomass fuels in fluidized beds can be jeopardized for this reason. Reliable operation has to be ensured in industrial practice to make the use of biomass more attractive and acceptable. Agglomeration Detection. Detection methods for agglomeration can be based on different types of measurements, but especially for industrial fluidized beds the measurement of pressure is one of the most suitable options.3 Relatively simple (1) McKendry, P. Bioresour. Technol. 2002, 83, 55–63. (2) Bartels, M.; Lin, W.; Nijenhuis, J.; Kapteijn, F.; van Ommen, J. R. Prog. Energy Combust. Sci. 2008, 34, 633–666.

10.1021/ef8005788 CCC: $40.75  2009 American Chemical Society Published on Web 12/15/2008

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analysis methods such as the average pressure drop over the bed4 or standard deviation and variance5 have been applied. However, they are not considered suitable as they often react ambiguously, only detect agglomeration at a very late stage before actual defluidization, or are too cross-sensitive to other process changes, that is, producing false alarms. High-frequency pressure fluctuation measurements have been shown to be viable for further analysis, as they contain information about the hydrodynamics of the bed.6 They can therefore be useful for the detection of events, such as agglomeration, via the detection of their precursors. Attractor comparison7 has been developed specifically for agglomeration detection and is based on pressure fluctuation measurements in the bed. The principle of this method consists of reconstructing and comparing attractors. An attractor is the collection of points that results from projection of consecutive points from a pressure-time series into an n-dimensional state space. One first records a reference (well-fluidized) operating state of the bed from which a reference attractor is generated. Subsequently, the attractor of the current operating state is reconstructed and compared to the reference attractor. This comparison is based on a statistical test8 that evaluates the dimensionless squared distance S between two attractors. S has an expectation of 0 and a standard deviation of 1 for stationary hydrodynamics. S-values larger than 3 indicate with more than 95% confidence that the hydrodynamics have significantly changed, for example, due to agglomeration. Attractor comparison has been applied for early agglomeration detection on laboratory-scale and industrial-scale and has been shown to be suitable as an early warning tool in fluidized bed agglomeration.9 The method has been shown to be insensitive (S-value < 3) for relative changes of less than approximately 10% in gas flow and bed mass, which is important in light of avoiding false alarms due to common process fluctuations. Agglomeration Counteractions. Methods for counteracting agglomeration are often designed for implementation in existing processes. They can be distinguished into operational actions, utilization of additives fed to the process, and alternative bed materials. Operational strategies can involve drastic measures to avoid shutdowns in urgent cases, such as a strong temperature decrease10 or stopping the fuel feed.11 More gradual methods involve a moderate temperature decrease, increased recycle of bed material via a sieving installation,12 changing the ratio between different fuels, or switching between fuels. These methods are already practiced in industry, but usually not published. Various different additives or alternative bed materials are also used to prevent agglomeration. The underlying motivation for their application is to avoid formation of a sticky layer of liquid-phase silicates around the particles by preferred (3) Werther, J. Powder Technol. 1999, 102, 15–36. (4) Rehmat, A. G.; Patel, J. G. Inst. Gas Technology (IGTE); Controlling and maintaining fluidised beds - under non-steady state conditions in ash agglomerating fluidised beds; Patent US4544375-A, 1985. (5) Chirone, R.; Miccio, F.; Scala, F. Chem. Eng. J. 2006, 123, 71–80. (6) Johnsson, F.; Zijerveld, R. C.; Schouten, J. C.; van den Bleek, C. M.; Leckner, B. Int. J. Multiphase Flow 2000, 26, 663–715. (7) van Ommen, J. R.; Coppens, M.-O.; van den Bleek, C. M.; Schouten, J. C. AIChE J. 2000, 46, 2183–2197. (8) Diks, C.; van Zwet, W. R.; Takens, F.; DeGoede, J. Phys. ReV. E 1996, 53, 2169–2176. (9) Nijenhuis, J.; Korbee, R.; Lensselink, J.; Kiel, J. H. A.; van Ommen, J. R. Chem. Eng. Sci. 2007, 62, 644–654. (10) van der Drift, A.; Olsen, A. Conversion of biomass, prediction and solution methods for ash agglomeration and related problems. ECN-Report (Energy Research Centre of the Netherlands) ECN-C--99-090., 1999. (11) Ergu¨denler, A.; Ghaly, A. E. Biomass Bioenergy 1993, 4, 135– 147.

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formation of other components with higher melting points or to reduce/avoid siliceous bed material altogether. The most suitable additives contain Ca, Al, Mg, Fe, or mixtures thereof. Kaolin consists mainly of kaolinite (Al2O3 · 2SiO2 · 2H2O); see, for example, refs 10 and 13. For all counteraction strategies, one can distinguish between measures that are related to short time-scales that can be considered “emergency” strategies, and methods that are related to longer time-scales that can be considered long-term strategies. Avoiding defluidization and shutdown is the major goal of emergency strategies. Although very important in urgent cases, they also generally have the disadvantage that one has to move away from optimal operating conditions for a certain time, which reduces the load and/or increases emissions. A decrease in operating temperature below the temperature of liquid-phase silicates (∼750 °C) is one example. Long-term counteractions aim at stable and optimal operation conditions, for example, using additives to prevent agglomeration or even using a different bed material. A review of the various agglomeration detection and counteraction methods can be found in ref 2. Goal. The goal of this paper is 2-fold. First, it is investigated whether attractor comparison can be successfully applied to prevent further agglomeration and defluidization in fluidized beds by using different counteraction measures. This implies that the moment to initiate the counteraction has to be determined reliably. Moreover, the effect of the counteraction has to be monitored, as it is necessary to determine when to stop or reduce the counteraction again. Second, it is investigated how effective different counteraction strategies are to stop or to even reverse agglomeration, once agglomeration is detected by attractor comparison. This second goal is therefore specifically related to the combination of detection and counteraction and does not aim at a complete assessment of the various possible counteraction strategies. This investigation has been carried out on laboratory-scale and small commercial-scale combustion installations to explore the applicability of attractor comparison-based counteractions on different scales. With the scope being on the coupling between detection and counteraction, we only focus on operational counteraction strategies here and are not concerned with structural solutions to avoid agglomeration (e.g., using alternative bed materials or adapted reactor design). Experimental Section Two different experimental bubbling fluidized bed setups have been used in this investigation: a cylindrical laboratory-scale setup with a diameter of 7.4 cm and a small commercial-scale setup with a square cross-section of 1 × 1 m. The laboratory-scale installation “WOB” is located in the laboratory of the “Biomass, Coal & Environmental Research” group at the Energy Centre of The Netherlands (ECN) in The Netherlands. The small commercialscale installation is a 1 MWth fluidized bed boiler manufactured by the company Crone (The Netherlands) and located in the (12) Korbee, R.; Lensselink, J.; van Ommen, J. R.; Nijenhuis, J.; van Gemert, M.; Haasnoot, K. ECN-Report (Energy Research Centre of the Netherlands) ECN-C--04-052, 2004. ¨ hman, M.; Nordin, A. Energy Fuels 2000, 14, 618–624. (13) O (14) Khan, A. A.; de Jong, W.; Spliethoff, H. Energy Fuels 2007, 21, 3709–3717. (15) van Ommen, J. R.; Schouten, J. C.; van der Stappen, M. L. M.; van den Bleek, C. M. Powder Technol. 1999, 106, 199–218. (16) Visser, H. J. M. ECN-Report (Energy Research Centre of the Netherlands) ECN-C--04-054, 2004. (17) Tortosa Masia´, A. A.; Buhre, B. J. P.; Gupta, R. P.; Wall, T. F. Fuel Process. Technol. 2007, 88, 1071–1081.

Agglomeration in Fluidized Bed Biomass Combustion

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Table 1. Fluidized Bed Characteristics and Typical Operating Conditions laboratory-scale: WOB diameter max. load [kW] typ. load [kW] fluidization velocity [m/s] bed material bed mass [kg] fuels

typical fuel feed rate [kg/h] feed location

small commercial-scale: Crone boiler

7.4 cm (cylindrical) 3 ∼1 0.59

1 × 1 m (square) 1000 600-900 0.70

silica sand, range 400-600 µm (d50 ) 530 µm) 1.06 milled demolition wood milled olive pits (typically a 75%25% ratio) ∼0.25 (80%/20% ratio wood/olive pits)

silica sand, range 400-630 µm

bottom bed via screw feeder

480 pelletized demolition wood pelletized pepper plant residue (PPR) ∼150 (wood only) ∼250 (50%/50% ratio wood/paprika) falling onto top of the bed

laboratory of Energy Technology (3mE) at Delft University of Technology; further details can be found in ref 14. Table 1 gives an overview of both setups and the main operating conditions. We operated both setups at temperatures of 800-850 °C for the normal process conditions, comparable with the temperature range of industrial combustion. The minimum fluidization velocity of the WOB bed material has been determined experimentally to be roughly 0.27 m/s (room temperature). Pressure fluctuations have been measured using piezo-electric pressure transducers (Kistler type 7261). In the WOB, one sensor was connected to a 4 mm internal diameter tube of ∼10 cm length in the middle of the dense bed. The tube dimensions guarantee undisturbed measurements according to the guidelines in ref 15. A nitrogen purge flow of 2 m/s at normal conditions has been applied to prevent blocking of the tubes from the bed. The pressure fluctuations have been low-pass filtered at 200 Hz and sampled with 400 Hz to avoid aliasing effects according to the Nyquist criterion (sampling frequency g2 times the lowest frequency present). The pressure drop has been measured over a section of ∼1/3 of the fluidized bed height in the middle of the bed. Temperature has been measured at three different heights in the center of the bed (T2, T4, T5 - 45, 95, 145 mm) and at one position at the wall (T3, at the same height as T2). Pressure drop and temperature measurements were sampled at 1/10 Hz. For the Crone boiler tests, we have applied four different pressure fluctuation measurements in total, from which we found only two to be reliable (for an unknown reason, the signals from the other two positions did not always yield a picture consistent with the first two sensors as well as the process variables). The measuring tubes are located on the same wall, each having a ∼20 cm distance from their adjacent wall, and they penetrate the bed about 10 cm at ∼40% bed height; see Figure 1. The tubes were 8 mm in internal diameter and purged with nitrogen at 1.65 m/s at normal conditions; also, here the tube dimensions guarantee undisturbed measurements according to the guidelines in ref 15. The pressure fluctuations have been low-pass filtered at 200 Hz and sampled with 400 Hz to avoid aliasing effects according to the Nyquist criterion. Furthermore, we used existing measuring points for pressure drop over the bed and three different in-bed temperature measurements. The thermocouples have a relatively thick shielding of ∼15 mm and penetrate the bed ∼40% from the side. For both setups, we carried out two reference experiments in which we let the bed agglomerate without applying any counteraction. For the WOB tests, the bed defluidized in both cases. In case of the Crone boiler tests, it defluidized in one case; in the other case, operation had to be stopped due to strongly changing temperature gradients and difficult operation, although the bed did not fully defluidize. As counteraction, we applied the following

Figure 1. Top-view of the positions of the pressure fluctuation measurement probes and thermocouples in the Crone boiler. Table 2. Overview of Experiments in the WOB

experiment

type of counteraction

total operating operating temperature time >800 [°C] °C [hh:mm] defluidization 850-880

∼06:20

yes

840-880

∼03:00

yes

825-860

∼09:00

no

820-860

∼04:30

no

WOB5

- (reference case) - (reference case) sand replacement (via reactor top) sand replacement (via fuel screw) gas velocity increase

825-855

∼04:45

WOB6

temperature reduction

725-875

∼05:35

stopped (cyclone blockage) stopped (strong temperature gradients)

WOB1 WOB2 WOB3 WOB4

strategies in the WOB: bed replacement in small batches with addition via the top of the reactor or via the fuel feed screw, fluidization velocity increase, and temperature decrease via a decreased fuel feed rate. For the Crone boiler, the replacement of bed material during operation was not possible; therefore, we restricted our strategy to a temperature decrease via reduced fuel feed rate (two experiments) and using kaolin as an additive. An overview of the experiments in the WOB is given in Table 2 and in the Crone boiler in Table 3. For both setups, we started with a fresh (i.e., not previously used) batch of bed material for each experiment to avoid agglomeration by accumulated alkali on the bed material from previous experiments. The general strategy was to start up the bed with demolition wood only, as this contains relatively small amounts of alkalis. The second fuel, containing significantly higher alkali amounts, has been cofed after a certain time of stable operation in most cases; sometimes the bed had shown agglomeration-related problems before. The fuel ash, alkali, and chlorine contents are shown in Table 4.

Results and Discussion WOB Laboratory-Scale Setup. Reference Cases. To demonstrate that the counteractions are indeed beneficial, we first ran two controlled agglomeration cases where we let the fluidized bed agglomerate and defluidize without interfering. Those reference tests also served to determine the time until defluidization and the reproducibility of the agglomeration process. Table 5 shows an overview of the duration and total amount of fuel added during both reference experiments. The total increase in bed mass was ∼22 wt % for experiment WOB1 and ∼3 wt % for experiment WOB2, as determined by weighing the total bed mass after the experiment. This difference


Bartels et al. Table 3. Overview of Experiments in the Crone Boiler

type of counteraction

operating temperature [°C]

- (reference case) - (reference case) temperature reduction temperature reduction kaolin addition

700-840 780-870 670-870 715-860 720-820

experiment Crone1 Crone2 Crone3 Crone4 Crone5 (continuation of Crone4)

Table 4. Ash, Alkali, and Chlorine Content in the Fuel in wt % of the Fuel (As Received)a WOB

ash content [wt %] Si content [wt %] Na content [wt %] K content [wt %] Cl content [wt %] Al content [wt %] Ca content [wt %] Mg content [wt %] Fe content [wt %] P content [wt %] ratio K/Cl/Na/Cl ratio K/Si

Crone boiler

milled demolition wood

milled olive pits

pelletized demolition wood

pelletized pepper plant residue (PPR)

3 0.477 0.087 0.102 0.04 0.081 0.423 0.06 0.069 0.012 2.6/2.2 0.21

6.2 0.275 0.012 0.340 0.13 0.02 0.55 0.14 0.02 0.015 2.6/0.09 0.24

1.7 0.162 0.061 0.147 0.064 0.032 0.33 0.077 0.026 0.082 2.3/0.95 0.91

13.5 0.795 0.090 2.76 0.12 0.35 3.10 0.60 0.19 0.31 23/0.76 3.47

a For a detailed fuel analysis of the milled olive pit fuel, see ref 16, and for the fuels used in the Crone boiler, see ref 17.

cannot be attributed to the different total ash inputs in both cases. One therefore has to conclude that the agglomeration process and/or ash elutriation can vary significantly between experiments. The results from experiment WOB1 are shown in Figure 2. Here, the S-value is shown as well as the AAD (the average absolute deviation, a measure for the intensity of the signal) of the pressure fluctuations and the pressure drop over the bed. The S-value continuously increases along the course of the experiment. It is interesting to note that as S is increasing, the AAD is slowly decreasing, indicating a continuous decrease in signal amplitude. This effect is thought to originate from continuously increasing particle stickiness. The decreasing AAD also correlates with a decreasing peak at ∼2 Hz in the power spectrum. Starting around 13:30 h, a slight decrease in the pressure drop is observed and the fluctuations of the pressure drop also slowly decrease, until defluidization when the pressure drop sharply decreases. This decrease in pressure drop clearly accelerates as the bed further approaches the point of defluidization. The bed might even get partially defluidized, and the gas then rises upward within the bed through channels. The results for the second reference (WOB2) are qualitative very similar to the first reference (WOB1) and are not shown here. Both reference experiments exhibit in principle a qualitatively similar behavior for the gradual decrease in AAD and pressure drop, accompanied by an increasing S-value. The second reference case shows a less stationary behavior, with the S-value staying below 3 for a shorter period. More important, the quantitative behavior between both greatly varies; the defluidization occurs after less than 1.5 h after the fuel switch in experiment WOB2 and is therefore much quicker than in case of WOB1. In both cases, there was some shift in the particle size toward larger sizes as compared to the fresh sand (obtained by sieving). For reference experiment WOB1, we observed that this shift was larger and that the resulting particle size distribution was slightly bimodal, while it remained unimodal

total operating time >650 °C after start-up [hh:mm] ∼07:10 ∼08:10 ∼04:00 ∼10:05

defluidization yes yes (partial only) no (stopped due to bad fluidization) no no (stopped at slightly worsened fluidization)

for reference experiment WOB2. The reason for this difference appears to lie in a differing agglomeration process: with longer operation times it is thought that more material can be deposited on the sand, which would also enhance agglomerate formation. The operating temperatures in the two experiments were similar; the second reference experiment was operated starting at ∼865 °C as compared to the first reference experiment starting at ∼855 °C. Both slowly increased over time, roughly 20 °C until shortly before defluidization. This difference of 10 °C at these temperature levels seems unlikely to be solely responsible for the difference in time until defluidization, but could be the case if the alkali-silica ratio in the bed exceeds a specific silicate melting point. Note some differences in the in-bed temperatures between experiments WOB1 and WOB2: whereas during WOB1 the in-bed temperatures T2, T4, and T5 differ less than 5 °C, for WOB2 the lower in-bed temperature T2 is 5-10 °C lower than T4 and T5. This would point at some (smaller) agglomerates already being present in the lower part of the bed, thereby worsening mixing with a resulting lower temperature. This observation is also consistent with the much faster agglomeration during experiment WOB2. Also, the slightly different fluidization velocity during the start-up phase with only wood (see Table 5) would not explain this difference in operating time until defluidization. In any case, the difference in defluidization times shows that there can be a rather large variability in the agglomeration process and time until defluidization occurs. This also implies that the available time for taking counteractions can vary and one should consider the shortest times for the choice of a suitable counteraction strategy. In these two reference cases, the average absolute deviation (AAD) also seems to indicate the approaching defluidization well. However, the intensity of the pressure fluctuations, measured by either AAD or variance/standard deviation, is also dependent on fluctuations in the gas flow,18 and therefore this is not considered a reliable method for early agglomeration detection in an industrial process. Moreover, the AAD (or standard deviation) is not considered suitable because its absolute value has shown to vary between experiments and it does not directly give a statistical significant indication of a hydrodynamic change, as in case of the S-value. Such a statistically significant indicator is necessary to reliably determine the moment to start the counteraction. The agglomeration phenomena for the reference runs WOB1 and WOB2 have been rather gradual. For the subsequent counteraction experiments, we decreased the sensitivity by tuning the parametrization of the attractor comparison method such that there is some significant agglomeration taking place before starting the counteraction. By decreasing the evaluation time window rather than decreasing sensitivity via other parameters of the method, we still have the option of a quick response in case the agglomeration process would occur faster. In experiment WOB2, the bed defluidized much earlier; therefore, we have taken this experiment as the “worst case” reference. We chose a necessary early warning time of at least 20 min between three consecutive S-values above 3 and



Table 5. Operating Times and Added Fuel for Reference Experiments WOB1 and WOB2 experiment

time from fuel start (wood only) until switch to cofeeding olive pits [hh:mm]

time from switch to cofeeding olive pits until defluidization [hh:mm]

total fuel added up until defluidization [g]

WOB1 WOB2

02:09a (08:26-10:35) 01:56 (08:30-10:26)

04:22 (10:35-14:57) 01:22 (10:26-11:48)

1380 (wood) + 260 (olive pits) 775 (wood) + 80 (olive pits)

a

The bed was started up with a fluidizing velocity of 0.45 m/s for 01:30 h, then switched to 0.59 m/s.

Figure 2. S-value (reference at 11:30 h, reference time window ) 10 min, evaluation time window ) 5 min, embedding dimension ) 707), average absolute deviation AAD, and pressure drop during experiment WOB1 at a fluidization velocity of 0.59 m/s. Defluidization of the bed takes place directly after the last S-value shown at 14:57 h.

defluidization to determine the time window size for the method. The reference window size has been adjusted to 5 min, the evaluation time window to 3 min, and the embedding dimension to 40. In the following, the counteractions are initiated when three consecutive S-values exceeded 3. This strategy makes the method more robust against occasional peaks in the S-value, which can originate from temporary effects in the bed hydrodynamics not related to agglomeration. Replacement of Bed Material. The replacement of bed material has been realized by batch-wise removal of bed material with subsequent batch-wise addition of the same amount of fresh bed material. The S-value was calculated online, with a reference taken within a stable region after the start of cofeeding the olive pit fuel. The goal is to continue to replace material, and potentially adapt the replacement rate, until the S-value decreased below the value of 3 again. In the first experiment, we started replacing bed material by first removing a batch of 100 g (∼10%) of bed mass via the bottom of the bed and subsequently added a batch of the same amount of fresh bed material via the top of the reactor. This procedure is repeated every 10 min. The actual removal and addition each take about 1-2 min. The resulting S-values are shown in Figure 3. The S-value first increased due to the ongoing agglomeration; upon three consecutive S-values above 3, the replacement was started. After replacement was started, the S-value initially increased further. There can be two reasons for this effect: the delay of the counteraction during which agglomeration was continuing, but also the mass removal and addition that influenced the hydrodynamics. Around 11:30-12:00 h, the S-value decreased again; this means that the bed replacement showed a beneficial effect. After some time, however, the S-value started to increase again. Simultaneously, a sudden slight decrease in pressure drop occurred, accompanied by a temperature increase (see also Figure 4); it is not certain what the source of this effect was. A partially (temporary) defluidized area could be a possibility in line with the observations, keeping in mind that the alkali input via the fuel is still continuing. It was therefore decided to double the bed replacement rate by

Figure 3. S-value for bed replacement as counteraction method after three consecutive S-values rose above 3 (experiment WOB3). Two different replacement rates have been applied.

Figure 4. Pressure drop and in-bed temperatures T3 and T4 during experiment WOB3.

increasing the batch size to 200 g every 10 min. With this higher replacement rate the S-value decreased again. This shows that the replacement rate has to be adapted to the agglomeration process; ultimately, the control action would have to be dependent on the S-value to control the replacement rate. Upon stopping the replacement, the S-value first slightly decreased below 3, probably as now the influence of addition and removal on the bed is absent again. It might be necessary to take such an effect into account when implementing the method based on a reference without the influence of the counteraction. After the counteraction was stopped, the S-value again started to increase as agglomeration continued, and the bed was still operated more than 2.5 h after stopping the bed replacement. The total operating time with the combination of demolition wood and olive pits was roughly 8 h, which is considerably longer than the 1:22 and 4:22 h of the reference experiments. The cumulative amount of fuel introduced in the bed until the final regular stop was 1875 g of demolition wood and 445 g of olive pits, which is also much more than in the reference experiments (Table 1). Besides the S-value, the development of the pressure drop and temperatures, important “common” process parameters, is shown in Figure 4.


Figure 5. S-value for the replacement of bed material, addition via the fuel screw (experiment WOB4).

The pressure drop remained relatively constant during the course of the experiment, except for temporary fluctuations. As compared to the decreasing trend in the reference cases, this underlines the beneficial effect of the bed replacement. The temperature strongly fluctuated during the bed replacement. This effect is expected, as a share of 10% of the hot bed material is replaced by the cold fresh sand. One more phenomenon is striking, however: the difference in temperature between two adjacent measuring points in the dense bed T4 and T3 continuously increases during the course of the experiment. This difference is mainly due to a decrease in T3; the other three in-bed positions T4, T5, and T6 remain relatively constant. Such a decrease in T3 has also been observed in several other experiments. It is thought that the positioning of T3 is responsible for this decrease. Being located at the wall, already small amounts of agglomerated material can build up at the probe and provide some insulation, whereas the rest of the bed can be well-fluidized. This proposed mechanism is consistent with earlier observations of agglomerate build-up at the wall in this setup. Although the bed replacement is effective in returning to normal combustion conditions, it apparently cannot prevent some build-up on the wall. For the second experiment with bed material replacement (WOB4), we have chosen to realize the addition of bed material via the fuel feed screw. Herewith the fresh, cold bed material is introduced more gradually (the actual addition takes about 3-4 min), and the temperature fluctuations can therefore be kept smaller than in the previous experiment (WOB3). The resulting S-values are shown in Figure 5. In this case, the bed replacement results, with comparably short delay, in a decrease of the S-value and therefore clearly is successful in counteracting agglomeration. As compared to the first experiment with bed replacement (WOB3), here the replacement rate of 100 g each 10 min is already sufficient to reduce S below 3 again. Less disturbances of the bed due to the changed additions strategy and smaller fluctuations in bed temperature could be responsible for this effect. Here, the pressure drop slightly decreased up until the start of bed replacement, whereas during the actual replacement it remains constant; this also points at a beneficial effect of the replacement. The temperature T3 is decreasing here as well, similarly to the previous experiment WOB3. Besides the success of the bed replacement as counteraction measure, it is remarked that one needs to carefully consider the economics of replacing bed material to decide if and to which degree of replacement rate this strategy is suitable in industrial practice. The removal of heat from the bed, but also the generated “waste” bed material, has to be considered here.

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Figure 6. S-value and absolute windbox pressure for the application of temporary fluidization gas velocity as counteraction strategy (experiment WOB5). Periods of increased gas velocity are indicated with horizontal bars at the bottom of the graph.

Temporary Increase in Fluidization Velocity. Temporarily increasing the fluidization velocity is another strategy to counteract agglomeration. With the same parametrization of the attractor comparison method as above, we have increased the fluidization velocity each time three consecutive S-values exceeded the value of 3 (experiment WOB5). The regular velocity was 0.59 m/s and the increased velocity 0.74 m/s; this is estimated to correspond to roughly 3.5 and 4.5 times the minimum fluidization velocity of the fresh bed material at this operating temperature. After a period of usually 6-8 min, we decreased the velocity again back to 0.59 m/s; in total, we applied this cycle five times. During this experiment, we encountered a continuously increasing total pressure in the setup; operation eventually had to be stopped for safety reasons. The reason for the pressure increase was identified to be dust depositions partially blocking the cyclone inlet. The resulting S-value as well as the total pressure in the windbox are shown in Figure 6. We have chosen the windbox pressure as representative here, but all other absolute pressure indicators above and in the bed show the same qualitative picture. The absolute pressure in the windbox shows a clear steplike behavior that corresponds to the applied changes in fluidization velocity; the lower baseline refers to 0.59 m/s, the upper plateau to 0.74 m/s. Each increase in fluidization velocity results in an increase in the S-value, which is to be expected given the fact that the reference is set at a velocity of 0.59 m/s and that the method is only insensitive to relative changes in gas flow up to ∼10%. For the first two times of applying the gas velocity increase, one can see that the S-value returns to below 3 afterward; yet, there already is an offset from 0 remaining. This implies that the gas velocity increase was successful in returning closer to the reference conditions again, considering that in the reference cases without any counteraction the S-value would continuously increase. For the third and fourth time the counteraction is applied, the S-value does not return to below 3, and for the remaining time until the stop of the experiment it monotonically increases further. Overall, there is a clear correlation between the increasing total pressure in the system and the S-value. It could therefore be that this effect masks agglomeration effects in a later stage, in which a gas velocity increase might not be sufficient to break up agglomerates. The pressure drop over the bed has decreased slightly (∼10%) during the course of the experiment; the bed mass at the end of the experiment has even slightly increased (∼4%). Moreover, the particle size distribution at the end of the experiment was very similar to the one of reference experiment


Figure 7. Effect of temporary temperature decrease on the S-value (experiment WOB6). The solid vertical lines indicate the start of period with reduced fuel feed rate, while the dashed vertical lines indicate the return to regular fuel feed rate.

WOB1 after defluidization; this confirms that some agglomeration has taken place despite the temporary gas velocity increase. Temporary Temperature Decrease. We also investigated temperature decrease as a possible counteraction. The decrease of operating temperature has been realized through lowering the total fuel feed rate by ∼45% (∼50% reduction in demolition wood and ∼25% reduction in olive pit fuel). As in the previous experiments, each time three consecutive S-values exceeded the value of 3 the counteraction was started. Upon stabilization of the S-value, the temperature has been increased again. The resulting S-values together with the temperatures T3 and T4 are shown in Figure 7. After the first start of the counteraction at 11:42 h, the temperatures decreased to ∼750 °C. The S-value first remained somewhat stable above 3, but then further increased and stabilized. This stabilization at a higher S-value can be explained by two effects. First, the decreased temperature leads to a ∼10% decreased fluidizing gas velocity. Second, the combustion process continues at lower fuel feed rate and lower temperature. This will influence the combustion process, for example, via a slower volatile release from the fuel pellets and presence of less fuel pellets in the bed, leading to hydrodynamics different from the reference state. Upon stabilization of the S-value at around 20, the temperature is increased again by increasing the fuel feed rate; the S-value subsequently returns to around 3-4. This indicates that the hydrodynamics have changed as compared to in the reference state. Next, the counteraction cycle basically starts over again once more. However, one can observe an overall increase in the S-values with time. At 15:43 h, the experiment was stopped. The difference of the temperature at T3 and the other temperatures is strongly increasing when returning to the higher operating temperature of ∼850 °C. Such a continuous decrease in T3 relative to the other bed temperatures has already been observed during the previous experiments and has been related to build-up of material around this thermocouple at the wall. When switching to the lower operating temperature of ∼750 °C, however, this difference decreases again. This indicates that the agglomeration is reversible to some degree, most probably because at this temperature the particles are not sticky anymore. As a consequence, agglomerates in the bed can break and agglomerates adhering to the thermocouple can be (partially) removed. Overall, it appears that the temperature decrease to some degree counteracts the agglomeration process. The agglomeration process is not completely reversed, as there is a growing offset in S-value as compared to the reference state. Moreover,


the longer the setup operates, the quicker the S-value rises upon temperature increase (compare 12:00-12:30 h and 14:00-14: 30 h in Figure 7). This effect is expected considering that the ash is not removed. The increasing amount of potassiumcontaining ash in the bed can then relatively quickly form new agglomerates when the operating temperature exceeds the melting point of low-melting silicates. The effect of temperature reduction is therefore only temporary. A permanent solution for this issue could only be realized with additional effort, for example, a system to refresh the sand bed. Whether or not further agglomeration and defluidization can actually be avoided by decreasing the temperature will depend on the time scale during which the bed can be cooled down as compared to the time scale for the bed to further agglomerate and defluidize. With the most important melting temperatures of potassium silicates starting around 750 °C and typical operating temperatures of 850 °C, one therefore has to realize roughly a 100 °C temperature decrease. In this case, we only applied a reduction in fuel feed rate of about 20%, taking about 30 min for a reduction of 100 °C. With a quicker strategy, for example, temporarily stopping the fuel feed, possibly in combination with increased gas flow, we consider it realistic to achieve such a temperature decrease within 15 min here. Crone Boiler Pilot-Scale Setup. The suitability of the attractor comparison method has also been tested on a larger scale in a 1 MWth bubbling fluidized bed combustor. This installation does not have the possibility of bed replacement during operation. The counteractions applied here have been limited to reducing the operating temperature via a reduction in fuel feed rate as well as using an additive. Results of five experiments are presented. The first two experiments serve as a reference case in which we let the bed agglomerate and do not apply any counteraction. The third experiment comprises reducing the operating temperature via decreasing fuel feed rate, with consecutive efforts to continue operation at higher temperatures. The fourth experiment also applies temperature reduction as counteraction strategy, in this case with continued operation at a low temperature level. In the fifth experiment, the addition of small amounts of kaolin to prevent agglomeration is tested. Reference Cases. The first reference experiment has been started with demolition wood (DW) as fuel with typical operating temperatures of 830-840 °C. After starting to cofeed ∼70 wt % pepper plant residue (PPR), the temperature greatly decreased to levels of 700-750 °C. The PPR has a roughly 30% smaller heating value than DW, but the total fuel feed rate was increased to make sure the total energy input is the same as before the cofeeding. The reason for this strong temperature reduction is therefore thought to originate from the properties of the PPR, which appears to burn in a very nonoptimal manner, that is, having slow mass and energy transfer within the pellet and difficult disintegration. Visual observations confirmed that burning pellets maintain their structure quite well during combustion. This phenomenon is attributed to the rather strong fibrous structure of the pellets. In addition, the operation with PPR also yielded very high CO-concentrations in the flue gas, confirming its far from optimal combustion. For the operation of the boiler, this means that to realize an increase in temperature an increase in total fuel feed rate is not an option, but rather an increase of the share of wood in the fuel feed. Doing this, we returned to the desired operating conditions of 800-850 °C. Although this strategy did in principle work, the


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Figure 8. Agglomerates from reference experiment Crone1. Agglomerates of various different sizes were present, with characteristic holes of ∼6 mm fitting the PPR-fuel (in the upper picture, fresh PPR pellets were inserted into the holes to illustrate this).

temperature change was surprisingly slow, taking more than 2 h to increase from 720 to 750 °C after starting to decrease the PPR-share. When the operating temperature reached about 760 °C, the temperature rose rapidly and the bed defluidized. After the termination of the experiment, we found many white agglomerates of different sizes in the bed (Figure 8). Figure 9 shows the temperature history in the middle of the bed (T2) during this reference experiment (top) and the pressure drop (bottom), together with the S-values from both measuring positions. The position of the reference state can be discerned by a strongly negative S-value due to a comparison of the reference attractor with itself. During the course of the temperature increase, the S-values of both measuring positions slightly increased; around 17:30 h, both have clearly increased above 3. At this point, neither pressure drop nor temperature or temperature difference unambiguously indicates the approaching defluidization, which illustrates the advantage of attractor comparison above such commonly applied process variables. The bed has been operated at relatively low temperatures of ∼730 °C for quite some time, so a large amount of ash is already present in the bed as there is no bed refreshment. Subsequently, in the temperature range of ∼750 °C the melting points of some silicates are exceeded, so that bed material can become sticky and agglomerate. It is most likely that the mechanism of agglomeration here originates in potassium silicate ash particles or droplets that become increasingly sticky due to their melting around 750 °C and subsequently result in direct interparticle adhesion between bed and ash particles.21 Furthermore, gaseous alkali chlorides can condense onto bed particles and thereby form sticky bed particle layers rich in K and Si. In addition, the condensation process of alkali chlorides is enhanced at lower temperature, although this effect appears less important here. For this reason, the agglomeration process is different from a bed continuously operated at a high temperature of ∼800-850 °C. At a higher temperature, many potassium silicates are above their melting point already. As a consequence, the properties of the bed, for example, particle stickiness and present agglomerates, can change more gradually. Also, homogenization of condensed material on the particle surface and further reaction

Figure 9. Development of the S-value from probes 1 and 2, temperature T2 (top), and pressure drop (bottom) during reference experiment Crone1, in which agglomeration with subsequent defluidization took place (only last period of experiment shown). Fixed reference for attractor comparison: 15:00-15:15 h.

with other elements can have an influence in making the agglomeration process more gradual here. The detection of agglomeration is therefore expected to be somewhat better at a higher operating temperature. More importantly, the available time to apply any counteraction that still avoids defluidization is significantly shorter if the agglomeration process itself is faster; the time scale of any given counteraction method therefore is of crucial importance for its success. For the second reference experiment (Crone2), we decided to operate at higher temperatures to avoid the rapid agglomeration process previously observed. To avoid a too strong temperature drop when cofeeding PPR, we decided to only operate at low shares of PPR of about 20 wt %. To ensure sufficient introduction of potassium into the bed within reasonable times, we also manually added ∼13 kg of potassium chloride over a time period of ∼3 h in small batches via a pneumatic transport system into the dense bed. Upon cofeeding PPR, the temperature in this case only decreased down to about 800 °C. However, it was rather difficult to maintain a constant operating temperature via controlling the fuel feed rate, and we observed quite strong temperature gradients; this generally points toward poor fluidization and agglomeration, although the bed did not completely defluidize. Figure 10 shows the temperature history in the middle of the bed (T2) and the freeboard temperature, together with the S-values of both measuring positions. The S-value overall increases relative to the chosen reference and also strongly fluctuates, together with fluctuating temperatures. Around 18:45 h, the temperature in the bed decreases to about 780 °C and the S-value returns to a comparably low level. In addition, the freeboard temperature has increased



Figure 10. Development of the S-value from probes 1 and 2, temperature T2 in the middle of the bed, and freeboard temperature (top) and pressure drop (bottom) during reference experiment Crone2, in which agglomeration with subsequent partial defluidization took place (only last period of experiment shown). Fixed reference for attractor comparison: 15:00-15:15 h.

considerably from typically 550-600 °C during normal operating conditions to over 650 °C (Figure 10), at a relatively constant thermal load of the boiler. This means that the combustion process has shifted more toward the upper bed and freeboard. We therefore suspect that the bed was partially defluidized in the late stages, which was confirmed by a large agglomerate resting on the bottom and leaning on the thermocouples penetrating the bed at the end of the run (Figure 11). The other fluctuations in temperature and S-value could originate from larger agglomerates formed in combination with partial break-up and movement within the (bottom) bed. The pressure drop over the bed also fluctuates and occasionally changes rather quickly (Figure 10, bottom), which is suspected to originate from this formation and break-up or movement of agglomerates in the vicinity of the high-pressure side pressure drop measurement in the bottom bed. Temporary Temperature Decrease. With the previous two experiments, we have adapted the parametrization of attractor comparison to this process to ensure that any agglomeration is detected reliably and early enough. The optimal settings (reference time 15 min, evaluation time 5 min, embedding dimension ) 70) have already been used in the previous two experiments.

Figure 11. Large agglomerate leaning onto the thermocouples in the bottom of the bed after experiment Crone2.

As in the previous reference experiments, we started up the boiler with demolition wood for the first counteraction experiment. The boiler start-up was carried out in two temperature steps, first operating at ∼720 °C and then increasing to ∼850 °C, to obtain reference data for both temperature levels. As the bed was only operated with a fresh sand bed and demolition


Figure 12. Development of the bottom temperature T1, middle temperature T2, together with their difference (top) and the S-value from probes 1 and 2 together with temperature T2 (bottom) during experiment Crone3. Temporary fuel reduction is applied as counteraction. Fixed reference for attractor comparison: 10:00-10:15 h.

wood, containing very little potassium, no agglomeration was expected at this point. However, we did experience some temperature run-away due to agglomeration quite early. During the whole experiment, the process was monitored online using attractor comparison, based on measurement probe 1 and with a moving reference 30 min before the current online evaluation. The reference window is continuously shifted together with the evaluation window to maintain a constant offset in time between both; this is called “moving reference”. Figure 12 (top) shows the temperature of the lower and middle thermocouple as well as their difference (the points of the fuel stops are located directly before the high temperature peaks at 11:30 and 12:15 h). Figure 12 (bottom) shows the S-values with a fixed reference for this case together with the temperature T2 in the middle of the bed. Around 10:50 h, the moving reference for probe 1 suddenly increased above 3, but this was thought to be due to the increase in operating temperature, that is, the hydrodynamics at lower operating temperature being different from those at higher temperature. However, at the same time, we experienced increasing temperature differences between the middle and the lower thermocouple and eventually also a quickly rising temperature exceeding 870 °C; at this point, the fuel feed was stopped to avoid too high temperatures (11:26 h). This resulted in a strong temperature decrease. Shortly after this, the combustion process was started again (11:30 h), which is confirmed by the S-value returning to below 3, a decreasing temperature difference T2-T1, and a quickly increasing pressure drop (not shown here). Upon further increase in temperature, however, soon the same phenomenon occurred again. In this case, only the S-value from probe 2 returned to below 3, whereas the S-value from probe 1 stayed above 3. This indicates that

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probably some irreversible changes have taken place in the bed, assuming that the hydrodynamics have already stabilized in this period. Although it was possible to maintain fluidization after this second event, only unstable operation could be achieved with strong temperature differences between the middle and the lower thermocouple and a significantly reduced pressure drop. The S-value also remained high and confirmed the operational problems; eventually the experiment had to be stopped. After opening the bed, we found several smaller white agglomerates. It is important to notice that the S-value stays below 3 within a temperature range from ∼720 to ∼840 °C in the beginning. This shows that the subsequent hydrodynamic changes are not due to a temperature change, but indeed due to agglomeration. For this transition and the subsequent operation at higher temperatures of ∼840 °C, pressure drop did not indicate the approaching defluidization. The temperature difference between lower (T1) and middle (T2) probe, however, is already continuously increasing in this stage and correlates well with the S-value in this case. The fact that agglomeration occurred in this case has not been expected because the potassium content of the demolition wood is rather low. Comparing the total potassium input in form of K2O up until stopping the experiment, one can see a large difference of 25-28 kg of K2O in the reference experiments Crone1 and Crone2 as compared to only about 1 kg of K2O from the demolition wood in this case. The agglomerates we found after opening the boiler were white and could be clearly distinguished from the light brown fresh sand. Few of the larger agglomerates had some characteristic holes, which were the size of the 6 mm PPR pellets, indicating that some PPR has been present. Although we did not feed any PPR, there must have been some PPR pellets remaining in the fuel bunker. We estimated this additional input of K2O from the remaining PPR to 0.5 kg, which together with the K2O from the wood is still much smaller than both reference cases. As mentioned previously, the quick temperature increase with subsequent melting of potassium silicate particles could be responsible for this fast agglomeration process. The observed phenomenon of a relatively quickly agglomerating bed basically seems to be the same as in experiment Crone1. It can therefore be concluded that a situation in which potassium is introduced at temperatures below the first melting points of common eutectics (∼750 °C) with consecutive temperature increase bears the danger of rapid agglomeration. Very small amounts of potassium are apparently already sufficient to yield this phenomenon. For the fourth experiment, the start-up procedure has been changed to prevent any significant operation periods at temperatures around or below 750 °C. During this start-up, we ran an online attractor comparison calculation with moving reference, based on probe 1. As shown in Figure 13, this moving reference S-value slightly increased between 10:00 and 10:30 h to values of almost 3. After two consecutive S-values above 3 (10:46 h), we started the counteraction. Shortly after the fuel reduction the temperature in the bed quickly increased, but with the already decreased fuel input it was possible to keep this increase in reasonable bounds. At this point, the S-value sharply rose. Subsequently, we continued operation with a lower fuel input to demonstrate that the counteraction was successful in returning to stable operation again. As one can see, the S-value for this moving reference also returns to zero for continued operation at 720-730 °C. This shows that the counteraction based on the moving reference S-value is indeed successful in ensuring continued safe operation. Note the second peak for the S-value shifted 30 min from the first peak. The occurrence



Figure 13. Temperature T2 in the middle of the bed and S-value of probes 1 and 2 for the temperature decrease as agglomeration counteraction strategy (experiment Crone4). A moving reference with 30 min time difference between reference and evaluation was used.

Figure 14. Temperature T2 in the middle of the bed and S-value of probes 1 and 2 for the temperature decrease as agglomeration counteraction strategy (experiment Crone4). Fixed reference for attractor comparison: 10:05-10:20 h.

of this second peak is expected and originates from the fact that the reference is continuously moving with time and is therefore eventually situated in the area of the hydrodynamic change itself.9 In practice, one has to account for this effect, that is, disregard the second peak in this case. For probe 2 an intermediate peak in the S-value appears around 10:15-10:20 h. This peak already indicates some agglomeration-related problems, considering that the agglomeration process can occur locally in the bed and that during this process agglomerates can form, break up, and potentially move within the bed. It has been shown that the use of several pressure fluctuation sensors is important as the detection distance of each sensor within the fluidized (dense) bed is limited to ∼0.5 m.19 One possible explanation in this case is that agglomerates have been formed in the vicinity of probe 2, but then either broke up again or moved within the bed. If the location of the reference is chosen within the time window of such an effect, the reference already includes agglomeration effects and therefore becomes less sensitive for additional agglomeration. This is confirmed by the lower peak in S for the rapid temperature increase (∼10: 50 h) in Figure 13. The S-values for probes 1 and 2 for a fixed reference are shown in Figure 14. Here, the S-value for probe 1 is strongly rising shortly before the rapid temperature increase. As this increase occurs very suddenly, this could mean that generally the available time could possibly not be sufficient to take counteractions before a temperature run-away. The S-value after the counteraction decreases again to a level of around 3, which shows that the bed has undergone some irreversible changes. When using a fixed reference, the position of this reference can have a significant influence on the resulting trend of the S-value. This becomes also clear from the trend of the S-value during the whole period before the rapid temperature increase. The identification of the reason for this transient process is not straightforward: it could be related to the stabilization of the combustion process as the temperature is also still increasing,

but it could also be related to increased particle stickiness and agglomerate formation. The moving reference (Figure 13) has a clear advantage during the start-up phase. It indicates a change in the hydrodynamics relative to a reference with constant offset and therefore does not require a suitable choice of a single reference location; especially during start-up and other transient periods choosing a suitable reference is difficult. For stationary operation, both a fixed and a moving reference are in principle suitable. If a high sensitivity toward hydrodynamic changes is desired, a fixed reference is of advantage assuming that a suitable reference can be defined. If the method should be more robust against gradual process changes with relatively long time scales, a moving reference is of advantage. However, one needs to choose a suitable time difference between reference and evaluation windows. Longer time differences behave more like a fixed reference; shorter time differences make the method less sensitive. For this case, we found 30 min a good choice. For larger industrial installations, much longer time differences can be suitable, for example, 24 h,9 to also compensate day/night gradients. An optimal choice should be obtained for each specific installation. Another relevant observation is that the S-value from position 2 of the fixed reference does not respond until just before the actual temperature increase. This effect is related to the choice of the reference location and becomes clear when considering the moving reference for position 2 (Figure 13): an intermediate peak appears around 10:15-10:20 h, which indicates some intermediate agglomeration problems as already stated for the moving reference case. As compared to the WOB, where we achieved 100 °C in about 30 min, we here achieved a ∼140 °C decrease within 15 min only by regulating fuel feed rate. The time to reduce the operating temperature ∼100 °C is expected to be in the same order of magnitude for larger-scale industrial fluidized beds. In practice, a strong reduction in fuel feed rate will have a stronger influence on the boiler load and production than, for example, refreshing bed material or increasing gas velocity, and will therefore probably only be considered suitable for emergency cases. Overall, one can conclude that during start-up the application of a fixed reference has limitations and a moving reference is strongly recommended. Especially in combination with larger beds, the utilization of several sensors is important because of potentially very localized agglomeration phenomena. Addition of Kaolin. The available kaolin had a comparably small particle size in the range well below 100 µm. As the kaolin had to be introduced onto the top of the bed, we wet-milled it

(18) van Ommen, J. R.; Schouten, J. C.; van den Bleek, C. M. In An Early-Warning-Method for Detecting Bed agglomeration in Fluidized Bed Combustors. Paper No. FBC99-0150; Reuther, R. B., Ed.; Proc. 15th Int. Conf. on Fluidized Bed Combustion; ASME: New York, 1999. (19) van Ommen, J. R.; van der Schaaf, J.; Schouten, J. C.; van Wachem, B. G. M.; Coppens, M.-O.; van den Bleek, C. M. Powder Technol. 2004, 139, 264–276. (20) Risnes, H.; Fjellerup, J.; Henriksen, U.; Moilanen, A.; Norby, P.; Papadakis, K.; Posselt, D.; Sørensen, L. H. Fuel 2003, 82, 641–651. ¨ hman, M.; Nordin, A. Energy Fuels 2005, 19, 825– (21) Brus, E.; O 832.


Figure 15. Development of the temperature T2 in the middle of the bed, the temperature in the freeboard, and the S-value from probes 1 and 2 for the addition of kaolin as agglomeration counteraction strategy (experiment Crone5). Fixed reference for attractor comparison: 11: 30-11:45 h.

to a moisture content of about 1 wt % and a broad particle size range into the millimeter-range to avoid that it would be elutriated before penetrating the bed. We have implemented the addition of kaolin as counteraction after the previous experiment Crone4 on the same day and upon subsequent temperature increase (Figure 15). Upon gradual temperature increase, the S-value increases (∼14:45 h) and the addition of kaolin is started after three consecutive S-values (probe 2) exceeded 3. The average kaolin feed rate during this ∼2 h period was about 22 kg/h, that is, about 4.5% of the total bed weight (480 kg) per hour or about 10% of the fuel feed rate (∼230 kg/h). The addition of kaolin appears to be beneficial in the beginning, as the S-values remain more or less constant until about 16:00 h. Afterward, however, S further increases. This could mean at least two things: The hydrodynamics changed due to the presence of kaolin (kaolin particles were found homogeneously distributed over the whole bed after opening the boiler), but also that the kaolin did indeed help to limit agglomeration. However, either the amount is not enough or the particle size is too large, and therefore the contact area for the reaction is limited. After further continued increase of the S-value, it was decided to eventually stop the addition of kaolin. Toward the end of the experiment, the temperature in the freeboard was increasing (Figure 15), together with a slight decrease of the bed temperature; this is basically the same effect as in reference experiment Crone2, where agglomeration took place. After the experiment, we found many kaolin particles in the millimeter-range in the bed (Figure 16). The kaolin was distributed homogeneously in the bed as larger particles, potentially a result of the wet-milling pretreatment, which implies only limited contact area between the kaolin and the sand. Some larger agglomerates were also found at the walls and in one corner of the bed. Kaolin addition in this form is therefore considered to be not efficient to prevent agglomeration. Previous positive experiences in the literature, however, suggest this additive to be generally suitable.10,13 The kaolin should be introduced into the bed in such a way that it homogeneously penetrates the bed, but also offers large contact area for the reaction with the alkalis. A large contact area and good distribution is suggested to be achieved by introducing the additive into the pellet by treatment with a liquid solution (e.g., ref 20, for investigating the catalytic effect of Ca during pellet

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Figure 16. Bed material after experiment Crone5. White kaolin particles are distributed homogeneously throughout the bed.

gasification) or by mechanically pressing some additive together with the fuel into pellets during the pellet production. Conclusions Attractor comparison was applied for early agglomeration detection in a laboratory-scale and small commercial-scale fluidized bed combustor, where it was used to determine the starting point for taking counteractions against the agglomeration. Together with a suitable counteraction strategy, defluidization of the bed can be successfully prevented or delayed. In the laboratory-scale setup (WOB), the agglomeration process was very gradual if no counteractions are taken. Although the two reference cases were qualitatively similar, there was a large difference in the operation time until defluidization. The semicontinuous replacement of bed material as counteraction measure was successful to prevent defluidization and return to a well-fluidized bed. This strategy can be considered as a permanent solution as ash and potassium silicates are removed from the bed, although the process economics (heat loss, waste bed material) have to be carefully considered. A temporary increase in gas velocity as counteraction measure resulted in a blocked cyclone inlet in this case and did not prevent agglomerate formation, indicated by an increase in the bed particle size. A temporary decrease in temperature as counteraction measure did help to reduce the S-value, but upon subsequent temperature increase the S-value rose again and reached even higher values; that is, bed material properties have irreversibly changed and agglomeration continued. Both increased gas velocity and decreased temperature can therefore only be considered as a temporary strategy, as they do not remove or convert the accumulated alkali species in the bed. For the small commercial-scale setup (Crone), the agglomeration events were different from those of the laboratory-scale setup: temperature run-away and defluidization of the bed occurred much faster. Agglomeration-related problems specifically occurred at transitions in operating temperature. Still, attractor comparison was able to detect the approaching defluidization in an early stage. In such an early stage, pressure drop did not indicate any approaching defluidization, whereas temperature differences often already showed a continuously increasing trend. A subsequent temperature decrease to below 750 °C has been shown to be a successful strategy to prevent defluidization and continue trouble-free operation. However, operation at such low temperatures in combination with subsequent temperature increase can lead to rapid agglomeration, even for small amounts of alkali in the bed. Before the


temperature increase, the present alkali should therefore be removed or neutralized (reacted). A positive effect of using kaolin as additive is not demonstrated, as some agglomeration was observed in this case. Agglomeration can occur localized in the bed, indicating that for larger units several measurement positions might be necessary for reliable monitoring. The results of the attractor comparison have often been shown to be quite sensitive to the position of a fixed reference. During transient operation, the application of a moving reference is advantageous,


as no fixed reference bed condition has to be determined. Agglomeration events outside transition regions are considered to be detected and counteracted in an earlier stage, as they progress more gradual as compared to the proven cases here. Acknowledgment. Funding from the Delft Research Centre for Sustainable Energy is gratefully acknowledged. EF8005788

Detecting and Counteracting Agglomeration in Fluidized Bed Biomass

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