Pressure Fluctuations in a Liquid-Sprayed Gas Fluidized Bed

Article pubs.acs.org/IECR

Pressure Fluctuations in a Liquid-Sprayed Gas Fluidized Bed Sandip Bhowmick,* Neetu A. Baveja, C. P. Shringi, K. T. Shenoy, and S. K. Ghosh Chemical Engineering Division, Bhabha Atomic Research Centre, Mumbai 400085, India ABSTRACT: Generally the quality of a liquid-sprayed fluidized bed is monitored by measuring the bed temperature at several axial positions, as well as the average bed pressure drop. The number of temperature measurement points increases during scaleup of the reactor. Such a large number of measuring points makes the installation very expensive. High frequency pressure fluctuation measurement in a fluidized bed is emerging as a promising technique to retrieve qualitative information on the dynamics of gas fluidized beds. Pressure fluctuation measurement at just one height is sufficient for early detection of changes in the hydrodynamics of fluidized beds when the fluidized bed height is about 1 m or less. Hence, investigation has been carried out on pressure fluctuation in a liquid-sprayed gas fluidized bed. A monitoring technique based on the pressure fluctuation measurement can be used in parallel with the conventional temperature measurement, and the number of temperature measuring points can be reduced. Also the combination of both gives more reliability in online monitoring. In the present study, differential pressure fluctuation was measured across a liquid-sprayed fluidized bed. The feed liquid was either water or ammonium nitrate solution in the experiments carried out. The measured data were analyzed using time domain, frequency domain, and state space analysis. The aim of this investigation was to find a reliable analysis technique which can be easily applied to relate the measured pressure data to the health of the liquid-sprayed fluidized bed. A drastic reduction in the standard deviation of pressure fluctuation was observed when ammonium nitrate solution was injected. The standard deviation shows the demarcation among dry, water-sprayed, and solution-sprayed fluidized beds. However, a wide spread of its value with time adds difficulty in setting the limit corresponding to desired behavior of the solution-sprayed fluidized bed. In the frequency analysis a significant difference was not observed among dry, water injected, and solution injected fluidized beds with respect to the dominant frequency and the magnitude of the peak. S-statistics shows some promising results as most of the S values were below the critical value for consistent hydrodynamics or quality of the liquid-sprayed fluidized bed. If the pressure fluctuation in a dry fluidized bed is chosen as reference, S values are significantly higher than the critical value. It was also found that the S-statistics can detect the change in bed hydrodynamics when a higher concentration solution was fed to the fluidized bed.

1. INTRODUCTION A pilot scale fluidized bed reactor of 300 mm diameter was installed at the Chemical Engineering Division of the Bhabha Atomic Research Centre. This activity was a continuation of the earlier work where experiments on thermal denitration of ammonium nitrate solution were carried out in a bench scale fluidized bed reactor of 150 mm diameter.1 During the operation of the pilot scale reactor, the bed temperature was continuously monitored at various axial locations. From the operating experience it is realized that if the axial temperature variation is within 20−25 °C, the particles are in a well-fluidized condition. Therefore, the intense mixing of particles prevents the formation of a low temperature wet zone near the point of liquid injection. There are no certain guidelines to decide the number and locations of temperature measurement points which will adequately monitor the condition of the entire fluidized bed. During scale-up, the number of measuring points will go up. Such a large number of measuring points makes the installation very expensive. Also, detection of any deviation from normal operation using such an axial temperature variation limit is a tedious job for large reactors. Hence, another continuous monitoring technique needs to be developed which uses one of the operating parameters that can be easily measured in fluidized beds. This monitoring technique can be used in parallel with the conventional temperature measurement, and the number of temperature measuring points can be reduced. Temperature and pressure © 2014 American Chemical Society

measurements are the only routine measurement techniques in industrial fluidized bed reactors.2 In general, bulk density and expanded bed height are estimated from the low frequency and time-averaged measurement of static pressure drop across the fluidized bed. The qualitative information on the dynamics of gas fluidized beds can be obtained by measuring pressure fluctuation at frequencies greater than 20 Hz.3 For temperature measurements, it is difficult to combine a high enough sample frequency with the robustness of the equipment needed for industrial measurements.4 On the other hand, the nonintrusive and robust character of pressure fluctuation measurement allows for applications in hot and reactive fluidized beds. Also, the contact between the probe and corrosive gaseous chemicals can be avoided by applying a small purge gas flow. It is also noted that when the fluidized bed height is about 1 m or less, measuring pressure fluctuations at just one height is sufficient for early detection of changes in the hydrodynamics of fluidized beds.4,5 Therefore, the feasibility of pressure fluctuation measurement as a possible choice for continuous monitoring of hydrodynamics of a newly installed pilot scale fluidized bed reactor will be explored in the study. Received: Revised: Accepted: Published: 12631

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A detailed decomposition of pressure fluctuations into global−high amplitude phenomena and local−low amplitude phenomena was proposed by van der Schaaf et al.6 High amplitude pressure fluctuations are propagated by fast compression waves (wave velocity > 10 m/s) and can simultaneously be detected throughout the bed and even in the plenum. Low amplitude pressure fluctuations are transmitted by slow pressure waves (wave velocity < 2 m/s) and can only be detected in the proximity of the pressure sensor. The global pressure fluctuations are composed of bubble/jet formation at the distributor plate,7−9 bubble coalescence and splitting,10,11 bubble eruption at the bed surface,7,12−16 mechanical bed mass oscillation,17 and gas flow fluctuation.17,18 Local pressure fluctuations are originated from bubble passage19−21 and gas turbulences.22 In the present study, differential pressure fluctuation was measured across the liquid-sprayed fluidized bed. Water and ammonium nitrate solution were used as the feed liquids. Pressure fluctuations of the dry fluidized bed were also measured at different bed temperatures. Measured data were analyzed using time domain, frequency domain, and state space analyses. The aim of this investigation was to find a reliable analysis technique which can be easily applied to relate the measured pressure data to the condition of fluidized bed. Regarding the pressure fluctuation in a continuous liquidsprayed fluidized bed, much information is not available in the published literature.

2. EXPERIMENTAL SECTION A schematic sketch of the pilot scale fluidized bed reactor is shown in Figure 1. The reactor, with an internal diameter of 300 mm and a height of 1650 mm, was made of SS304L. Fluidizing air was introduced into the plenum chamber through a drilled pipe. The drilled pipe delivers gradually expanded gas into the plenum chamber, and jetting phenomena can be avoided by this practice. A perforated plate type distributor was fitted into the bottom of the reactor. The distributor ensures a uniform distribution of air throughout the bed cross section. Silica sand was used as the bed material. The size distribution of sand particles was determined using an electromagnetic sieve shaker (EMS-8). The size analysis result is presented in Table 1. The true density and volume-surface mean diameter of particles were 2600 kg/m3 and 382 μm, respectively. The particles belong to Geldart’s group B.23 A freeboard section having approximately 2 times the diameter of the bed diameter was provided to reduce the entrainment of particles. Three arrangements have been commonly applied in the pressure fluctuation measurement, i.e., single-point absolute pressure, pressure differential between the bed and the freeboard, and double-point differential pressure.24 In the present study differential pressure fluctuations were measured across the fluidized bed using the purge method of pressure measurement. The pressure taps were mounted flush with the wall of the reactor, and their locations are shown in Figure 1. The low pressure point was well above the fluidized bed surface. An ABB 600T Series differential pressure transmitter with a pressure range of 0−2000 mmWC (0−19.62 kPa) and an accuracy of ±0.1% of the full range was used to measure differential pressure fluctuation. The sampling frequency was 40 Hz. The data were stored in a paperless recorder, Yokogawa DX 1004-3-4-2. Stainless steel tubing of 4.35 mm internal diameter was used to connect the transmitter with the fluidized bed. Initially, the minimum fluidization velocity (umf) was

Figure 1. Schematic sketch of pilot scale fluidized bed reactor. N1, solid inlet; N2, solid outlet; N3, fluidizing air inlet; N4, off-gas outlet.

Table 1. Particle Size Distribution of the Sand Used mesh range (μm)

weight fraction (xi)

500−425 425−355 355−300 300−250 250−175 ∑xi

0.520 0.200 0.136 0.104 0.040 1.00

determined from a plot of average pressure drop versus superficial air velocity as presented in Figure 2. At 30 °C, the measured umf was 0.115 m/s. In all the experiments the volumetric flow rate of fluidization air was kept constant at 800 slpm (1.8umf at 30 °C and 4umf at 380 °C). A constant bed inventory of 90 kg was maintained. The solid was continuously fed and withdrawn from the reactor at a very slow rate (6−8 kg/h) using a nonmechanical solid feeding and withdrawal system. Undesired fines were segregated from the withdrawn solid, and the coarser fraction was fed back to the reactor. This continuous solid circulation maintains proper particle size distribution inside the fluidized bed and provides makeup for entrained solids. The fluidized bed was heated using two induction heaters, and the temperature was maintained at desired level by a PID controller. Two induction heater coils (copper tubing) were wound uniformly around the reactor wall, and each coil covers almost half of the fluidized bed height. The bed temperature of 12632

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3.2. Frequency Domain Analysis. Analysis of the frequency distribution by fast Fourier transform (FFT) has been applied to the time series of pressure from a fluidized bed. For a time series pn of length N, the FFT is a length N vector P with elements N

P(k) =

∑ pn ωN(n− 1)(k− 1)

[1 ≤ k ≤ N ]

n=1

(3)

where ωN = e−i(2π / N )

(4)

The magnitude of FFT is determined by the following equation: FFT magnitude =

dry fluidized bed, i.e., with no liquid feed, was varied in the range 30−380 °C. The liquid was injected into the fluidized bed as a spray using an external mix type pneumatic nozzle. The bed temperature was maintained around 380 °C. The feed pump was reciprocating, single acting, and double diaphragm type. A dampener was installed in the feed line to provide a pulseless flow. The feed nozzles were horizontally oriented and located at 500 and 650 mm above the distributor. A constant air flow was maintained through the liquid side of the feed nozzles during dry operation. This purge air flow along with atomization air flow prevents the choking of the nozzle orifice. Initially, water was used as feed to stabilize the plant process systems. The actual feed, ammonium nitrate solution, was started after a steady condition was achieved. The concentration of ammonium nitrate in the solution was 180 g/L. The feed flow rates through the bottom and top nozzles were on the order of 18 and 12 L/h, respectively.

3. ANALYSIS TECHNIQUES Direct visual comparison of the pressure fluctuation time series is not a possible option. Statistical analysis methods are needed to retrieve relevant information from time-series data. Several comprehensive reviews on these statistical analysis techniques are available in the literature.25−27 3.1. Time Domain Analysis. The simplest approach in time domain analysis is to study the amplitude of pressure fluctuation, usually expressed as standard deviation. The standard deviation of the measured differential pressure fluctuation signal, pn, is expressed as follows: 1 N−1

S=

n=1

− p ̅ )2 (1)

with the average 1 p̅ = N

n=1

VC(Q̂ )

(6)

4. RESULTS AND DISCUSSION Figure 3 shows typical 120 s segments of differential pressures in dry and liquid-sprayed fluidized beds. A general comparison among these trends can be made from visual observation. At dry condition, it was found that higher bed temperature gives signal fluctuations with larger amplitudes for a constant volumetric fluidization air flow rate. A significant difference was not obtained between pressure signals of dry bed and water

N

∑ pn

Q̂

where Q̂ and VC(Q̂ ) are an unbiased estimator of the squared distance between the delay vector distribution and the variance of Q̂ , respectively. An S value greater than 3 represents a significant difference between reference and evaluation time series with 95% confidence level. Hence, bed hydrodynamics has considerably deviated from the desired behavior. In his thesis, Chaplin presented an S-statistics code for monitoring fluidized bed drier hydrodynamics.31 This code has been suitably modified for our use. The values of parameters, i.e., time window, embedding dimension, bandwidth, and segment length, were set at 0.5 s, 20, 0.5, and 3 s, respectively. The reference and evaluation time series had a length of 120 s.

N

∑ (pn

(5)

Kage et al. used 2048 pressure data to estimate the power spectral density function during frequency analysis of pressure fluctuation in a fluidized bed for the detection of various modes of fluidization.28 In this study, 4800 pressure data sampled for 2 min were transformed to FFT. 3.3. State Space Analysis. This approach is typical for nonlinear analysis, and different algorithms and methods are available in the literature. Most of the methods are based on the reconstruction of the data into an attractor in state space. The attractor comparison technique developed by van Ommen et al. has been adopted in the present study.29 Initially, a reference time series representing the desired fluidization state of the bed was obtained. Consecutive time series, defined as evaluation time series, was measured. Both the reference and evaluation time series were normalized to make the test less sensitive to the superficial gas velocity and bed mass. The normalized time series were converted into an attractor, i.e., a multidimensional distribution of delay vectors. Delay vectors generated from reference and evaluation time series were compared by determining a statistics S using the Dikas et al. test.30 The Sstatistics is defined as

Figure 2. Estimation of minimum fluidization velocity at 30 °C.

σp =

[Re(P)]2 + [Im(P)]2

(2)

Sasic et al. recommended that in a simple time domain analysis a pressure signal of 60 s, sampled at 20 Hz, is sufficient for qualitative interpretation of changes in the flow regime of a fluidized bed.18 12633

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on fluidized particles.35 Consequently the bed fluidity decreases. They present a method to quantify the bed fluidity using dynamic pressure measurement. Though the method was adequate for laboratory measurements, it cannot be implemented in pilot or commercial plants. Hence, here no attempt has been made to determine the bed fluidity. Figure 4 clearly shows a drastic reduction in σp when ammonium nitrate solution was injected. This difference was also significant with respect to water injection. However, the net flow of fluidizing agent increases due to the evaporation of liquid. The addition of water and ammonium nitrate solution in such a hot fluidized bed did not result in the formation of agglomerates and only modified the bed fluidity. The viscosity and surface tension of ammonium nitrate solution are greater than those of normal water. As drying progresses, the solution concentration will increase and consequently viscosity and surface tension will increase. The viscosity, surface tension, and boiling point data against solution concentration are presented in Table 2. The Figure 3. Example of differential pressure signals from dry and liquidsprayed fluidized beds. The volumetric flow rate of fluidization air was 800 slpm in all cases. Liquid was fed at the rate of 30 L/h. Concentration of ammonium nitrate in solution was 180 g/L.

Table 2. Physical Properties of Aqueous Ammonium Nitrate Solutiona

injected bed at 380 °C. The fluctuation was damped considerably after feeding of the ammonium nitrate solution. The objective of this section was to find a signal analysis method that can be reliably used for continuous monitoring the quality of the liquid-sprayed fluidized bed. 4.1. Standard Deviation of Pressure Fluctuations. Figure 4 shows the typical values of standard deviation (σp) of

concn (%)

bp at 1 atm (°C)

viscosity at 100 °C (kg/m·s)

surface tension at 100 °C (dyn/cm)

0 10 20 30 40 50 60 70 80 90 100

100.00 102.88 103.95 105.55 107.65 111.30 115.46 121.68 130.46 158.27 229.73

0.000 282 0.000 685 0.000 694 0.000 711 0.000 732 0.000 769 0.000 856 0.000 984 0.001 301 0.002 179 −

58.7 60.1 61.6 63.3 65.3 67.5 − − − 85.5 103.8

a

Concentrations of 180 g/L ≡ 16.86% and 300 g/L ≡ 26.98%.

boiling point temperature of the solution is also higher than that of water at same pressure. Also, during solution injection extra heat is required to vaporize the ammonium nitrate. Hence, in the case of solution injection the rate of evaporation was slower compared to the water injection. Combination of these factors leads to lower bed fluidity during injection of ammonium nitrate solution. Hence, the amplitude of differential pressure fluctuation or σp decreases. It has been apparent that the simple σp will be sufficient to monitor the quality of liquid-sprayed fluidized bed as it shows the demarcation among dry, water-sprayed, and solutionsprayed fluidized beds. Pressure data having a length of 90 min were recorded from the solution-sprayed fluidized bed during steady state operation. σp of differential pressure data of 2 min length was determined and plotted against time as shown in Figure 5. A wide spread of σp data in the range 50−90 Pa has been observed. The σp mainly depends on the superficial gas velocity and the static bed height. The variation in fluidization air flow rate was on the order of ±10 slpm. Though an approximately fixed quantity of particles was maintained inside the reactor, small bed mass fluctuation occurred due to continuous solid circulation through the reactor. Effort has been made to detemine other possible reasons for a wide spread in σp data. Mickley et al. measured the instantaneous heat transfer coefficient (hi) at a point on a vertical tube located along the axis of fluidized bed.36 They found a sharp variation

Figure 4. Standard deviation of differential pressure fluctuation in dry fluidized bed at different temperatures and liquid-sprayed fluidized bed.

measured pressure fluctuation signals. It has already been established that umf decreases with increase in bed temperature. Hence, operating velocity ratio (u0/umf) increases with increase in bed temperature for a constant volumetric fluidization air flow rate. It is shown in the several literatures32−34 that the σp is proportional to the (u0 − umf). Therefore, for a constant volumetric fluidization air flow the standard deviations of pressure fluctuation increases with increase in bed temperature. According to McDougall et al. the cohesive force acting among the fluidized particles increases when a liquid is sprayed 12634

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Figure 5. Plot of standard deviation versus time for solution-sprayed fluidized bed.

in hi values with time. The variation of hi with time was not periodic. A similar fluctuation in hi was also reported by Baskakov et al.37 They concluded that the exchanger surface is being contacted alternately by gas bubbles (very low hi values) and emulsion packets (high hi values). The overall heat transfer coefficient for wall to bed only represents a time-averaged value. As we are interested in high frequency phenomena, the fluctuation in hi should be considered. In the induction heated fluidized bed the heat is generated in the reactor wall due to dissipation of the eddy current. Fluidized particles take the heat from the wall and ultimately transfer it to the solution droplets deposited on the particles. Hence a relation should exist between hi and the rate of vaporization. The liquid feed rate to the fluidized bed was constant. In one small time interval high hi results in a higher rate of liquid vaporization and negligible accumulation of liquid in bed. In the next time interval low hi results in a lower rate of liquid vaporization and considerable accumulation of liquid in the bed. This leads to bed mass fluctuation and vapor flow fluctuation. This generated vapor can be considered as secondary fluidization air. The heat flow from induction heaters was controlled by PID controllers. This also caused a considerable changing of heat transport in the bed with time. It ultimately leads to fluctuation in gas velocity in the fluidized bed. Therefore, a significant variation in σp data with time was obtained in liquid-sprayed fluidized bed. The range of σp values in the liquid-sprayed fluidized bed was comparable with that in dry bed at a temperature range of 30− 75 °C. It is very difficult to set a σp limit corresponding to the desired behavior of the fluidized bed. As σp strongly depends on the superficial gas velocity, van Ommen et al. suggested that the standard deviation of pressure fluctuation is inadequate for industrial application.29 This drives us to examine other methods for pressure fluctuation analysis. 4.2. Frequency Domain Analysis of Pressure Fluctuations. Guo et al. investigated the pressure fluctuation in a bubbling fluidized bed of Geldart group B type particles at 1000 °C.38 According to their findings, the major frequencies of the pressure fluctuation signal range from 1 to 9 Hz in the fluidized state. Hence, in Figure 6 the FFT magnitude was plotted over 0−9 Hz for dry and liquid sprayed fluidized beds. A high intensity peak was noted between 0 and 0.5 Hz in all cases. In

Figure 6. Variation of FFT magnitude with frequency for different conditions of fluidized bed: (a) dry fluidized bed, (b) water-sprayed fluidized bed, and (c) solution-sprayed fluidized bed.

our case the differential pressure was measured across the fluidized bed. The low pressure tapping was located well above the bed surface. Therefore, it can be considered that the low pressure point will be unaffected by the global pressure waves 12635

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generated inside the fluidized bed. Hence, the measured data are comparable with absolute pressure fluctuation data. Zhang et al. presented a power spectral density plot of absolute pressure measured 110 mm above the distributor in a fluidized bed of FCC particles.39 They observed a high intensity peak at almost 0 Hz at higher superficial gas velocity. They reported that the high intensity peak should be related to gas flow fluctuation characterized by very low frequency. It can be identified from Figure 6 that the peak exists almost at 0 Hz. This represents the presence of gas flow fluctuation generated due to thermal fluctuation as discussed earlier. As extra flow generated due to vaporization of liquid, the gas flow rate was higher in the liquid-sprayed fluidized bed than in the dry fluidized bed. However, a slight decrease in the peak value was observed in the liquid-sprayed fluidized bed. When the liquid droplets deposited on the particles, the cohesive force acting between the particles became significant. This adds damping in the oscillation of fluidized particles. With respect to the dominant frequency and the magnitude of the peak, a significant difference was not observed among dry, water injected, and solution injected fluidized beds. Spectral analysis is rarely reported in the literature as being used for online monitoring the state of fluidization in fluidized beds.29 4.3. State Space Analysis of Pressure Fluctuations. A “static reference” or “fixed reference” method has been used to estimate the S value. Pressure fluctuations within a fixed time window were set as the reference. The monitoring feature was retrieved by shifting the evaluating window with time. The variation of S value with time in the solution-sprayed fluidized bed is presented in Figure 7. Most of the data points were

Figure 8. Run time versus bed temperature measured at different axial locations.

wetting. The locations of thermocouples are also shown in Figure 9.

Figure 9. Locations of thermocouples for measurement of bed temperature.

According to earlier discussion, bed mass fluctuation, gas velocity fluctuation, and vapor flow fluctuation alway occur in a liquid-sprayed fluidized bed. Van Ommen et al. showed that the S-statistics is insensitive to variation in gas velocity and bed mass up to 10%.29 It provides the flexibility in application of this method for our pilot scale fluidized bed. This analysis method will be an attractive option to monitor the hydrodynamics of the liquid-sprayed fluidized bed. If pressure fluctuation in a dry fluidized bed is chosen as reference, S values are significantly higher than the S = 3 as shown in Figure 10. S-statistics should detect the infrequent event such as progressive deterioration of the bed due to excessive wetting. That will define the actual sensitivity and selectivity of this analysis method. The operating pilot plant does not allow us to do such experiments as it will lead to shutdown and heavy maintenance. Small pressure data were available when the plant was operated with ammonium nitrate solution of higher concentration (300 g/L). It will produce greater cohesive forces among the particles and change the bed fluidity. Figure 11 shows that the S-statistics can detect this change.

Figure 7. S value as a function of time for ammonium nitrate run.

below the critical value of 3. There was no significant deviation of the S value from the critical value. Therefore, we can predict that there were no changes in the hydrodynamics or quality of the fluidized bed as the run progressed. As evidence of our prediction, the temperature data obtained at various axial positions of the bed have been presented in Figure 8. It indicates that there was no change in state of fluidization as all the temperatures were showing steady values. If the bed temperature decreased progressively, it represents excessive 12636

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However, further studies are required to determine the sensitivity to changes in liquid flow rate and concentration of ammonium nitrate. This investigation can be considered as the beginning of the development of a continuous monitoring technique for high temperature liquid-sprayed gas fluidized beds using pressure fluctuations.

■

AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. Notes

The authors declare no competing financial interest.

■ Figure 10. S value as a function of time for ammonium nitrate run when pressure fluctuations in dry bed used as reference.

NOMENCLATURE i = unit imaginary number k = integer number n = integer number N = number of sampling point p̅ = average of time series of differential pressure fluctuations, Pa pn = time series of differential pressure fluctuations, Pa Q̂ = estimator for squared distance between two attractors S = estimator for the normalized squared distance between two attractors umf = minimum fluidization velocity, m/s u0 = superficial gas velocity, m/s VC = conditional variance

Greek Symbols

■

σp = standard deviation of differential pressure fluctuation, Pa ωN = Nth root of unity

REFERENCES

(1) Bhowmick, S.; Rao, H.; Sathiyamoorthy, D. Thermal Denitration of Ammonium Nitrate Solution in a Fluidized-Bed Reactor. Ind. Eng. Chem. Res. 2012, 51, 8394−8403. (2) Werther, J. Measurement techniques in fluidized beds. Powder Technol. 1999, 102, 15−36. (3) Brown, R. C.; Brue, E. Resolving dynamical features of fluidized beds from pressure fluctuations. Powder Technol. 2001, 119, 68−80. (4) van Ommen, J. R. Monitoring Fluidized Bed Hydrodynamics; Delft University Press: Delft, Netherlands, 2001. (5) van Ommen, J. R.; van der Schaaf, J.; Schouten, J. C.; van Wachem, B. G. M.; Coppens, M. O.; van den Bleek, C. M. Optimal placement of probes for dynamic pressure measurements in large-scale fluidized beds. Powder Technol. 2004, 139, 264−276. (6) van der Schaaf, J.; Schouten, J. C.; van den Bleek, C. M. Origin, propagation and attenuation of pressure waves in gas-solid fluidized beds. Powder Technol. 1998, 95, 220−233. (7) Littman, H.; Homolka, G. A. J. The pressure field around a twodimensional gas bubble in a fluidized bed. Chem. Eng. Sci. 1973, 28, 2231−2243. (8) Cai, P. The transition of flow regime in dense phase gas−solid fluidized bed. Ph.D. Thesis, Tsinghua University, Beijing, China, 1989. (9) Nelson, B. H.; Briens, C. L.; Bergougnou, M. A. Pressure fluctuations at individual grid holes of a gas−solid fluidized bed. Powder Technol. 1993, 77, 95−102. (10) Fan, L. T.; Ho, T. C.; Hiraoka, S.; Walawender, W. P. Pressure fluctuations in a fluidized bed. AIChE J. 1981, 27, 388−396. (11) Fan, L. T.; Ho, T. C.; Walawender, W. P. Measurements of the rise velocities of bubbles, slugs and pressure waves in a gas−solid fluidized bed using pressure fluctuation signals. AIChE J. 1983, 29, 33−39. (12) Baskakov, A. P.; Tuponogov, V. G.; Filippovsky, N. F. A study of pressure fluctuations in a bubbling fluidized bed. Powder Technol. 1986, 45, 113−117.

Figure 11. Influence of change in solution concentration on the S value.

5. CONCLUSION Measurement of pressure fluctuation can be successfully used for continuously monitoring the hydrodynamics of a liquidsprayed fluidized bed. This will supplement the conventional axial bed temperature measurement. The combination of both gives more reliability in online monitoring and reduces the number of measurement points. The choice of a proper statistical method for pressure fluctuation analysis is the most important task. Though standard deviation is the simplest method, a wide spread of its value against run time adds difficulty in setting the limit corresponding to the desired behavior of the fluidized bed. In frequency analysis a significant difference was not observed among dry, water injected, and solution injected fluidized beds with respect to the dominant frequency and the magnitude of the peak. S-statistics shows some promising results as most of the S-values were below the critical value for consistent hydrodynamics or quality of the fluidized bed as the run progresses. It was also found that the Sstatistics can detect the change in bed hydrodynamics when a higher concentration solution was fed to the fluidized bed. 12637

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(13) Sun, J. G.; Chen, M. M.; Chao, B. T. Modeling of solids global fluctuations in bubbling fluidized beds by standing surface waves. Int. J. Multiphase Flow 1994, 20, 315−338. (14) Kok, J. B. W.; Benschop, A. A. J. Propagation velocity and rate of attenuation of surface waves on a homogeneously fluidized bed. Int. J. Multiphase Flow 1994, 20, 651−657. (15) van der Schaaf, J.; Schouten, I. C.; Johnsson, F.; van den Bleek, C. M.; Multiple modes of bed mass oscillation in gas−solid fluidized bed. Proceedings of the 15th International Conference on Fluidized Bed Combustion, Savannah, GA, 1999; American Society of Mechanical Engineers: New York, 1999. (16) Li, J. H.; Kwauk, M. Exploring complex systems in chemical engineeringthe multi-scale methodology. Chem. Eng. Sci. 2003, 58, 521−535. (17) van Ommen, J. R.; Mudde, R. F. Measuring the gas-solids distribution in fluidized bedsA review. 12th International Conference on FluidizationNew Horizons in Fluidization Engineering; Curran Associates: Red Hook, NY, 2007; pp 31−46. (18) Sasic, S.; Leckner, B.; Johnsson, F. Characterization of fluid dynamics of fluidized beds by analysis of pressure fluctuations. Prog. Energy Combust. Sci. 2007, 33, 453−496. (19) Davidson, J. F. Symposium on fluidizationdiscussion. Trans. Inst. Chem. Eng. 1961, 39, 230−232. (20) Kehoe, P. W. K.; Davidson, J. F. The fluctuation of surface height in freely slugging fluidized beds. AIChE Symp. Ser. 1972, 69, 41−48. (21) Baeyens, J.; Geldart, D. An investigation into slugging fluidized beds. Chem. Eng. Sci. 1974, 29, 255−265. (22) van der Schaaf, J.; Schouten, J. C.; Johnsson, F.; van den Bleek, C. M. Nonintrusive determination of bubble and slug length scales in fluidized beds by decomposition of the power spectral density of pressure time series. Int. J. Multiphase Flow 2002, 28, 865−880. (23) Geldart, D. Types of gas fluidization. Powder Technol. 1973, 7, 285−292. (24) Bi, H. T. A critical review of the complex pressure fluctuation phenomenon in gas−solids fluidized beds. Chem. Eng. Sci. 2007, 62, 3473−3493. (25) Johnsson, F.; Zijerveld, R. C.; Schoutenb, J. C.; van den Bleek, C. M.; Leckner, B. Characterization of fluidization regimes by timeseries analysis of pressure fluctuations. Int. J. Multiphase Flow 2000, 26, 663−715. (26) van Ommen, J. R.; Sasic, S.; van der Schaaf, J.; Gheorghiu, S.; Johnsson, F.; Coppens, M. O. Time-series analysis of pressure fluctuations in gas−solid fluidized bedsA review. Int. J. Multiphase Flow 2011, 37, 403−428. (27) Rüdisüli, M.; Schildhauer, T. J.; Biollaz, S. M. A.; van Ommen, J. R. Measurement, monitoring and control of fluidized bed combustion and gasification. Fluidized Bed Technologies for near-Zero Emission Combustion and Gasification; Scala, F., Ed.; Woodhead Publishing: Cambridge, U.K., 2013; pp 813−864. (28) Kage, H.; Agari, M.; Ogura, H.; Matsuno, Y. Frequency analysis of pressure fluctuation in fluidized bed plenum and its confidence limit for detection of various modes of fluidization. Adv. Powder Technol. 2000, 11, 459−475. (29) van Ommen, J. R.; Coppens, M. O.; van den Bleek, C. M. Early warning of agglomeration in fluidized beds by attractor comparison. AIChE J. 2000, 46, 2183−2197. (30) Diks, C.; van Zwet, W. R.; Takens, F.; DeGoede, J. Detecting differences between delay vector distributions. Phys. Rev. E 1996, 53, 2169−2176. (31) Chaplin, G. Monitoring fluidized bed dryer hydrodynamics using pressure fluctuations and electrical capacitance tomography. Ph.D. Thesis, College of Graduate Studies and Research, University of Saskatchewan, Saskatoon, Canada, 2005. (32) Bi, H.; Chen, A. Pressure fluctuations in gas-solids fluidized beds. China Particuol. 2003, 1, 139−144. (33) Puncochar, M.; Drahos, J. Origin of pressure fluctuations in fluidized beds. Chem. Eng. Sci. 2005, 60, 1193−1197.

(34) Sobrino, C.; Sánchez-Delgado, S.; García-Hernando, N.; de Vega, M. Standard deviation of absolute and differential pressure fluctuations in fluidized beds of group B particles. Chem. Eng. Res. Des. 2008, 86, 1236−1242. (35) McDougall, S.; Saberian, M.; Briens, C.; Berruti, F.; Chan, E. Using dynamic pressure signals to assess the effects of injected liquid on fluidized bed properties. Chem. Eng. Process. 2005, 44, 701−708. (36) Mickley, H. S.; Fairbanks, D. F.; Hawthorn, R. D. The relation between the heat transfer coefficient and thermal fluctuations in fluidized bed heat transfer. Chem. Eng. Prog. Symp. Ser. 1961, 57, 51− 60. (37) Baskakov, A. P.; Berg, B. V.; Vitt, O. K.; Filippovsky, N. F.; Kirakosyan, V. A.; Goldobin, J. M.; Maskaev, V. K. Heat transfer to objects immersed in fluidized beds. Powder Technol. 1973, 8, 273−282. (38) Guo, Q.; Yue, G.; Werther, J. Dynamics of pressure fluctuation in a bubbling fluidized bed at high temperature. Ind. Eng. Chem. Res. 2002, 41, 3482−3488. (39) Zhang, Y.; Bi, H. T.; Grace, J. R.; Lu, C. Comparison of decoupling methods for analyzing pressure fluctuations in gas-fluidized beds. AIChE J. 2010, 56, 869−877.

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dx.doi.org/10.1021/ie501170q | Ind. Eng. Chem. Res. 2014, 53, 12631−12638