chemical engineering research and design 1 0 4 ( 2 0 1 5 ) 156–163

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Chemical Engineering Research and Design

journa l h om epage: www.elsev ier .com/ locate /cherd

Early detection of agglomeration in a polyethylenefluidized bed at high temperature and pressure byvibration signature analysis

Fatemeh Alamolhodaa, Ahmad Shamirib, Mohd Azlan Hussainb,c,Rahmat Sotudeh-Gharebagha, Navid Mostoufia,∗

a Multiphase Systems Research Lab, School of Chemical Engineering, College of Engineering, University of Tehran,P.O. Box 11155/4563, Tehran, Iranb Department of Chemical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysiac Chemical & Petroleum Engineering Department, Faculty of Engineering, Technology & Built Environment,UCSI University, 56000 Kuala Lumpur, Malaysia

a r t i c l e i n f o

Article history:

Received 4 February 2015

Received in revised form

29 May 2015

Accepted 3 August 2015

Available online 10 August 2015

Keywords:

Fluidization

Vibration signature

S-test

a b s t r a c t

Analysis of vibration signatures was used as a reliable non-intrusive technique for detecting

the hydrodynamic state of a polyethylene fluidized bed for early detection of agglomeration.

Experiments were carried out in a pilot scale gas–solid fluidized bed at 15–25 bar and 100 ◦C.

The bed was operating in the bubbling regime of fluidization. Accelerometers were used to

record wall vibration generated by flow of particles and bubbles through the bed at various

operating pressures and initial particle sizes. Time and frequency domains and state space

methods were applied to analyze the experimental data. These methods were able to detect

the agglomeration only when the bed has become defluidized. Analyzing the experimen-

tal data by the S-test showed that changes in the particle size due to agglomeration can

be detected at least 4 ◦C before defluidization of the bed. This study confirmed that using

vibration signatures and applying S-test to the vibration data is a satisfactory approach for

Agglomeration

Defluidization

early detection of agglomeration in fluidized beds.

© 2015 The Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.

Bartels et al., 2010; Mansourpour et al., 2014) and early detec-

1. Introduction

Gas–solid fluidized beds are used in a variety of processesin chemical industries. The most significant advantages ofthis process are uniform bed temperature, high rate of heatand mass transfer and easy solid handling compared to simi-lar processes. Despite their broad applications, fluidized bedshave their own limitations in operation. One of the main prob-lems of an olefin polymerization in fluidized bed reactor isagglomerate formation caused by overheating of polymer par-ticles. This can affect the quality of solid mixing, blockageof the gas distributor, defluidization of the bed and conse-quently, reactor shutdown (van Ommen et al., 2000; Noble,

2008). Therefore, conditional monitoring of gas–solid fluidized

∗ Corresponding author. Tel.: +98 21 6696 7797; fax: +98 21 6696 7781.E-mail address: mostoufi@ut.ac.ir (N. Mostoufi).

http://dx.doi.org/10.1016/j.cherd.2015.08.0030263-8762/© 2015 The Institution of Chemical Engineers. Published by

beds is vital as agglomeration affects the hydrodynamics ofthe bed and can be one of the reasons that cause the abovementioned faults.

Early detection of agglomeration can be achieved byproper monitoring of the fluidized bed hydrodynamics. Sev-eral intrusive and non-intrusive measurement techniqueshave been utilized for monitoring the hydrodynamics offluidized beds. Measurement of pressure fluctuation havebeen used by many researchers for determining changesin the particle size (Davies and Fenton, 1997; Weinsteinet al., 2000), predicting the onset of defluidization (Mettanantet al., 2009), detecting hydrodynamic changes (Ibrehem, 2009;

tion of agglomeration (Schouten and van den Bleek, 1998;

Elsevier B.V. All rights reserved.

chemical engineering research and design 1 0 4 ( 2 0 1 5 ) 156–163 157

Nomenclature

a acceleration (m/s2)f frequency (Hz)L number of the time-series segmentsN length of the time-series signalPi

xx power-spectrum estimate of each segment(m2/s4)

Pxx averaged power spectrum (m2/s4)Q squared distance between two attractorsQ estimator for QS estimator for normalized squared distance

between two attractorsUmf minimum fluidization velocity (m/s)Vc conditional variancexi amplitude of the time-series signalx mean value

Greek symbol�a standard deviation of vibration signatures

(m/s2)

v2icebfnvflttS

tacaunTiaqm

2

SF(t(et(ouab

an Ommen et al., 2000; Nijenhuis et al., 2007; Gheorghiu et al.,004). Acoustic emissions have been applied in various flu-dized beds to characterize the fluidization state or regimehanges (Zukowski, 1999; Tsujimoto et al., 2000; Salehi-Nikt al., 2009). Temperature measurements were also proposedy Lau and Whalley (1981) and Scala and Chirone (2006)or detection of agglomeration. Analyzing the vibration sig-atures has been used for detecting minimum fluidizationelocity and transition from bubbling to slugging in gas–soliduidized beds (Abbasi et al., 2009), determining regime transi-ion (Azizpour et al., 2011; Shiea et al., 2013) and characterizinghe hydrodynamics of fluidized beds (Abbasi et al., 2010;heikhi et al., 2012).

Vibration signatures of the wall of a fluidized bed con-ain useful information about hydrodynamics of the bed. Inddition to growth and movements of bubbles, these signalsan also be originated from particle–wall collisions and areffected by the particle size. The present study focuses onsing vibration signatures of the wall of the bed as a novelon-intrusive method for early detection of agglomeration.he experiments were carried out in a pilot scale gas–solid flu-

dized bed with operating condition close to the industrial casend analyzing the bed vibration signatures using time and fre-uency domain analysis and nonlinear state space analysisethods.

. Experiments

chematic diagram of the experimental set up are shown inig. 1. The experiments were carried out in a stainless steelSS316) fluidized bed with internal diameter, height and wallhickness of 10 cm, 150 cm and 0.6 cm, respectively. A cycloneS-1) and four filters were installed at the gas exit to removentrained fine particles from the reactor. The gas distribu-or was a stainless steel perforated plate of 100-mesh sizeShamiri et al., 2014). Polyethylene particles with mean sizesf 500, 850 and 1250 �m and particle density of 950 kg/m3 weresed in experiments (Table 1). Differential pressure indicator

nd pressure transmitters were placed before and after theed (see Fig. 1) and used to record pressure (in bar gauge)

of the system. Temperature of the reactor was measured at6 different positions (T-1 to T-6), starting at 16 cm above thedistributor up to the top of the reactor. The uncertainty ofmeasured temperatures was estimated to be ±0.1 ◦C.

A heater was used to warm up the inlet nitrogen gas. Ineach experiment, the fluidized bed temperature was grad-ually increased until the solids reach their softening point(90 ◦C for the polyethylene used in this work) and formagglomerates. A compressor was used to compensate thepressure drop through the bed. Each experiment was startedat room temperature and the temperature was increasedcontinuously by the heater after the pressure of the sys-tem reached the set value in each case. Pressure of thereactor was controlled using a pressure regulator. The flowcirculating through the column was regulated by a controlvalve and measured by a flow meter located at the reactorinlet.

Two different cases were investigated in the experiments.In the first case, polyethylene particles with mean size of 500,850 and 1250 �m at 25 bar(g) were used in order to investi-gate the effect of initial particle size on agglomeration. In thesecond case, effect of pressure on the agglomeration processwas studied in which polyethylene particles with mean sizeof 500 �m were used at 15, 20 and 25 bar(g). The static bedheight in each experiment was about 25 cm which was equiv-alent to aspect ratio (L/D) of 2.5. The volumetric gas flow ratewas set to 1657 L/min (superficial gas velocity of 0.49 m/s) inall cases at ambient temperature (29 ◦C). Solid samples weretaken from a sampling probe (21 mm inside diameter) located16 cm above the distributor plate by opening a valve to acontainer. The bed was operating in the bubbling regime offluidization. The bed height during the operation was about41 cm.

A Brüel & Kjær (4394) accelerometer with 10 ± 2% mV/g sen-sitivity was used to measure the vibration signatures of thebed. This probe was mounted on the column at 16 cm abovethe distributor level. Brüel & Kjær dynamic signal analyzer(type photon +) was used for converting analog signals pro-duced by accelerometers to digital signals. It includes twoinputs with CCLD sensor power (expandable to 4 inputs), anoutput signal source and full capability signal analysis andwaveform source software. The sampling frequency was setto 65,536 Hz to prevent information loss associated with thevibration content of the signals and the measurement timewas considered 120 s.

3. Method of analysis

In order to extract reliable information from vibration signa-tures, different methods of analysis can be used. The mostcommon methods to characterize time dependent signalsfrom fluidized beds are time domain analysis (Puncochar et al.,1985; Abbasi et al., 2010), frequency domain analysis (Abbasiet al., 2009) and state space analysis (Shiea et al., 2013).

3.1. Time domain analysis

Standard deviation of signals from fluidized beds have beenused to identify regime transitions (Bi et al., 2000; Azizpouret al., 2011), determine the minimum fluidization velocity(Sobrino et al., 2008; Abbasi et al., 2009) and detect defluidiza-

tion in industrial fluidized-bed reactors as an on-line tool (vanOmmen et al., 2004). Standard deviation of time series xi shows

158 chemical engineering research and design 1 0 4 ( 2 0 1 5 ) 156–163

Fig. 1 – Schematic of the fluidized bed experimental set up.

chemical engineering research and design 1 0 4 ( 2 0 1 5 ) 156–163 159

Table 1 – Particles properties.

Particle diameter (�m) 500 500 500 850 850 850 1250 1250 1250Particle density (kg/m3) 950 950 950 950 950 950 950 950 950Pressure (bar) 15 20 25 15 20 25 15 20 25Umf (m/s) 0.061 0.056 0.052 0.107 0.096 0.088 0.147 0.129 0.117

0.8

tt

�

w

x

3

AsaScpc2

P

tb

P

3

VdmsisSDtTO

S

wdv

a

accordingly. The size of agglomerates in the sample reachesa maximum and then decreases at higher temperatures. This

0.93

0.95

0.97

0.99

1.01

0

2

4

6

8

10

10510095908580

dp

(mm

)

T (º C)

20 bar25 bar

Rela

tive b

edP

ressure d

rop

Fig. 2 – Change in mean size of sampled particles due toincrease in the bed temperature on the left side and relative

Uc (m/s) 0.696 0.594 0.537

he deviation of the distribution from a normal distribution inime interval of N and is defined as follows:

=

√√√√ 1N − 1

N∑i=1

(xi − x)2 (1)

here the mean value is evaluated from:

¯ = 1N

N∑i=1

xi (2)

.2. Frequency domain analysis

nalysis of data in the frequency domain through the powerpectral density function (PSDF) was also applied for evalu-tion of vibration signatures recorded in the fluidized bed.pectral analysis often aims at obtaining dominant frequen-ies presented in the time-series and ascribing them to varioushysical phenomena (van Ommen et al., 2011). The PSDF isalculated from the following equation (van der Schaaf et al.,002):

ixx(f ) = 1∑N

n=1w2(n)

[N∑

n=1

xi(n)w(n)e−j2�nf

]2

(3)

The Hanning window was used in the present study forhe window function. The averaged power spectrum thenecomes (Johnsson et al., 2000):

xx(f ) = 1L

L∑i=1

Pixx(f ) (4)

.3. S-test

arious nonlinear analysis methods were used for analyzingynamic changes in the hydrodynamics of fluidization. Allethods of nonlinear time series analysis are based on con-

truction of an attractor of the dynamic evolution of systemn the state space. The collection of successive states of theystem during its evolution in time is called attractor. The-statistic method is based on a statistical test proposed byiks et al. (1996) that compares two attractors by evaluating

he dimensionless squared distance S between the attractors.he S-value is calculated using the following equation (vanmmen et al., 2000):

= Q√VC(Q)

(5)

here Q is the unbiased estimator of squared distance of twoelay vector distributions in the state space and VcQ is theariance of Q .

The S-value has an expectation of 0 and a standard devi-tion of 1 for a stationary hydrodynamics under the null

43 0.719 0.650 0.968 0.826 0.747

hypothesis of two series being generated by the same mech-anism. In fact, since two signals obtained from the samehydrodynamic conditions will generate the same attractors inthe state space, the S-test can show this similarity of attractorswith an S-value close to zero. When comparing two signalsrecorded at different operating conditions, an S-value greaterthan 3 indicates, with more than 95% confidence, that thereexist two different attractors in this case. Comparison in thiscase is between two attractors. In other words, the hydrody-namics has changed significantly in these two conditions (vanOmmen et al., 2000).

4. Results and discussion

Vibration signatures were recorded for various initial meanparticle sizes at different bed pressures during gradualincreasing of bed temperature. In the followings, agglomer-ation was identified primarily through the measurement ofmean particle size. Then, analysis of vibration signatures intime and frequency domains and state space were carried outto assess the capability of these methods in early detection ofagglomeration.

4.1. Agglomeration and defluidization

As the heated nitrogen gas passes through the bed, the tem-perature increases gradually and particles become sticky attheir softening point. Fig. 2 shows the change in mean size ofsampled particles as well as relative bed pressure drop dur-ing the temperature increase. Temperatures reported in thisfigure are those measured by the thermocouple located at thetop of the dense bed (T-4 in Fig. 1). The reference pressure inthis figure is the bed pressure drop before start of agglomera-tion (normal operation of the bed). As the bed collapses due toagglomeration, the pressure drop decreases drastically. It canbe seen in this figure that by increasing the temperature tohigher than the softening point of polyethylene particles (90 ◦Cfor the polyethylene used in this work), agglomerates startto form and the mean size of particles in the bed increases

bed pressure drop change during the process on the rightside (for initial size of 500 �m).

160 chemical engineering research and design 1 0 4 ( 2 0 1 5 ) 156–163

0

0.1

0.2

0.3

0.4

105100959085

ST

D (

m/s

2)

T (ºC)

15 bar20 bar25 bar

Fig. 3 – Standard deviation of vibration signatures againsttemperature at various bed pressures for initial particle sizeof 500 �m.

0

0.1

0.2

0.3

0.4

105100959085

ST

D (

m/s

2)

T ( ºC)

500 µm

850 µm

1250 µm

Fig. 4 – Standard deviation of vibration signatures againsttemperature at bed pressure of 25 bar for various initial

PSDFs of vibration signatures at various bed pressures for500 �m initial particle size at bed temperature of 88 ◦C are

0

0.005

0.01

0.015

3000200010000

PS

DF

(m

2/s

4)

Frequency (H z)

80 ºC 89 ºC 98 ºC100 ºC

Fig. 5 – PSDFs of vibration signatures at various bed

trend can be explained by the fact that increasing the size ofparticles due to agglomeration continues with increasing thebed temperature. However, larger agglomerates settle downwhen their minimum fluidization velocity becomes greaterthan the superficial gas velocity. As a result, only smaller parti-cles pass through the sampling channel at high temperatures.Calculating minimum fluidization velocity (Wen and Yu, 1966)also confirms that large agglomerates reach their minimumfluidization velocity at the maximum points shown in Fig. 2and the superficial gas velocity becomes lower than the min-imum fluidization velocity of large agglomerates. Variation ofthe relative bed pressure drop (dp(bed)/initial weight of the bed)against bed temperature (shown in Fig. 2) also confirms thatdefluidization starts at the above specified points. The relativebed pressure drop is almost constant before the bed starts tocollapse since all particles are suspended by the flow of thegas, even though their size is increasing. The relative bed pres-sure drop decreases suddenly at the same temperature thatdefluidization of the bed starts (according to Fig. 2) due to thefact that the amount of particles being suspended by the flowof the gas becomes lower.

4.2. Time domain analysis

Evolution of standard deviations of vibration signatures (m/s2)due to increase in the bed temperature, measured at vari-ous bed pressures for initial particle size of 500 �m, is shownin Fig. 3. It can be seen in this figure that the standarddeviation exhibits a distinct maximum at all pressures. Thismaximum standard deviation takes place at the same tem-perature that the bed becomes defluidized, as shown inFig. 2. This trend can be explained by the fact that increas-ing the size of particles leads to formation of larger bubbleswhich provides greater standard deviation (Zarghami et al.,2010). After the formation of agglomerates, large agglom-erates settle down and form a packed bed at the bottomof the bed. This is the reason that the standard deviationdecreases by further increasing the temperature. Althoughagglomeration and settling of large agglomerates occurs con-tinuously during the operation, the rest of the bed wasstill in the fluidization state before the collapse of the bed.This was sensed as the normal operation by the vibrationsensor, which was mounted 16 cm above the distributor.Therefore, before complete defluidization of the bed, thestandard deviation is not affected significantly by settling oflarge agglomerates. However, at the moment of collapse ofthe bed (complete defluidization), the bed is in an unsta-

ble state which manifests itself by high amplitude signals(i.e., large standard deviation). After defluidization, the bed

particle sizes.

enters a new state which is stable (fixed bed) and the standarddeviation becomes small again in this situation. Since thisinstability occurs merely at the moment of defluidization, thetrend of standard deviation can reveal this fact only at thispoint.

Trends of standard deviation of vibration signaturesrecorded at bed pressure of 25 bar using three different initialparticle sizes are illustrated in Fig. 4. This figure also demon-strates that the standard deviation reaches a maximum at thetemperature that agglomerates become so large that the bedstarts to become defluidized and agglomerates settle at thebottom of the bed. However, both Figs. 3 and 4 demonstratethat the jump in the standard deviation occurs exactly at thedefluidization point (according to Fig. 2) and it is not possibleto detect defluidization of the bed prior to its happening bymonitoring the standard deviation of vibration signatures.

4.3. Frequency domain analysis

In order to further investigate the effect of change of parti-cle size on vibration signatures of the fluidized bed, vibrationsignals were analyzed in the frequency domain. Fig. 5 showsthe PSDF of the bed vibration signatures at various bedtemperatures for the initial mean particle size of 500 �mat the bed pressure of 20 bar. It can be seen in this fig-ure that there is a sharp peak around 1800 Hz in all PSDFs.According to Abbasi et al. (2009), these peaks in the PSDFof vibration signatures of the wall arise from movement ofbubbles in the bed. Moreover, Ghorbani et al. (2013) devel-oped a model for vibration of the shell of a cylindricalfluidized bed and showed that the dominant frequency ofvibration signatures is in the range of 1500–2000 Hz whichis related to vibration frequency of bubbles in the fluidizedbed.

temperatures for 500 �m initial particle size at bed pressureof 20 bar.

chemical engineering research and design 1 0 4 ( 2 0 1 5 ) 156–163 161

0

0.005

0.01

0.015

3000200010000

PS

DF

(m

2/s

4)

Frequ ency (Hz)

15 bar20 bar25 bar

Fig. 6 – PSDFs of vibration signatures at various bedpressures for 500 �m initial particle size at bed temperatureof 88 ◦C.

sodashto

mcapFIvw2ibfwobwa(iccud

Ftp

Table 2 – Parameters of the S-test.

Time window(ms)

Embeddingdimension

Band width Segmentlength (s)

0.3 19 0.2 1000

that the S-test is also sensitive to changes in the initialparticle size and can detect agglomeration of particles in the

hown in Fig. 6. This figure demonstrates that the intensityf the peak related to bubbles (between 1500 and 2000 Hz)ecreases by increasing the operating pressure. This trend is

result of decreasing of bubble size with increasing the pres-ure (Mansourpour et al., 2013). Therefore, in order to detectydrodynamic changes in the fluidize bed, it can be suggestedo track the changes of the peak related to bubbles in the PSDFf vibration signatures.

Since the peak between 1500 Hz and 2000 Hz is related tootion of bubbles inside the bed, the area under this peak

an represent the total energy of bubbles in the bed. Therea under the bubble peak in the PSDF as a function of tem-erature in the frequency range of 1000–4000 Hz is shown inig. 7 at various bed pressures for 500 �m initial particle size.t can be seen in this figure that the energy of the PSDF ofibration signals increases with decreasing the bed pressurehich is the result of the decrease in bubble size (Abbasi et al.,

009). A decreasing trend can be seen in each curve sincencreasing the bed temperature leads to formation of largerubbles (Sanaei et al., 2010) due to increase in interparticleorces (Lettieri et al., 2000; Shabanian and Chaouki, 2014, 2015)hich causes the decrease in the dominant peak in the PSDFf the bed vibration signatures (Abbasi et al., 2009). As cane seen in Fig. 7, each curve demonstrates a sharp decreasehich occurs at the same temperature that the standard devi-

tion exhibits a maximum in Fig. 3. According to Bartels et al.2010), a strong decrease in the power density takes place dur-ng the agglomeration. Therefore, the sharp decrease in Fig. 7an be related to start of defluidization of the bed. Like thease of standard deviation, however, it is not possible to fig-re out from Fig. 7 if and when the bed is going to becomeefluidized.

0

0.25

0.5

0.75

1

105100959085

Are

a

T (ºC)

15 bar20 bar25 bar

ig. 7 – The area under the PSDF as a function ofemperature at various bed pressures for 500 �m initialarticle size.

4.4. S-test

In order to investigate the sensitivity of the S-statistic methodfor detection of agglomeration and defluidization of the bed,the time series were first normalized as follows:

xi = ai − a

�a(6)

This normalization reduces the sensitivity of the methodto small changes in the superficial gas velocity (van Ommenet al., 2000). Performance of the S-test depends on appro-priate selection of four parameters: embedding dimension(m), bandwidth (d), segment length (l) and time window (Tw).These parameters were calculated according to the proceduredescribed by Shiea et al. (2013) and their optimum values aregiven in Table 2.

The S-value as a function of temperature, calculated forvibration signatures, using parameters listed in Table 2, atvarious bed pressures are shown in Fig. 8. It can be seenin this figure that the S-value is less than 3 at the begin-ning of the fluidization process where the bed is completelyfluidized and large agglomerates have not been formed yet.However, this value crosses the threshold value of S = 3 at allpressures in the temperature range of 91–93 ◦C. This showsthat this method can detect defluidization at the tempera-ture at least 5 ◦C less than the temperature in which completedefluidization occurs. The S-value continues to increase athigher temperatures which means that the change in thehydrodynamics of the bed is significant compared to the ref-erence state which was considered the start of the process(80 ◦C). Since the method is insensitive to the changes in thegas velocity as a result of normalization of the vibration sig-natures by ±10 (van Ommen et al., 2000; Shiea et al., 2013), itcan be concluded that the dissimilarity predicted by the S-testarises from the change in the particle size due to agglomera-tion in the bed.

Fig. 9 shows the S-values at various initial particle sizes as afunction of temperature. This value reaches the critical valueS = 3 in the temperature range of 91–92 ◦C that is at least 4 ◦Cless than the temperature at which complete defluidizationof the bed occurs in each case. Therefore, it can be concluded

0

1

2

3

4

5

10510095908580

S-v

alu

e

T ( ºC)

15 bar20 bar25 bar

Defluidiz ation

Fig. 8 – The S-value calculated for vibration signatures atvarious pressures as a function of temperature.

162 chemical engineering research and design 1 0 4 ( 2 0 1 5 ) 156–163

0

1

2

3

4

5

6

10510095908580

S-v

alu

e

T (ºC)

500 μm850 μm1250 μm

Defluidization

Fig. 9 – The S-value calculated for vibration signatures at

various initial particle sizes as a function of temperature.

bed distinctly before complete defluidization of the bed andearlier than other methods described in previous sections.

5. Conclusion

Vibration signatures of the wall of fluidized bed were mea-sured by an accelerometer. The signals were used for earlydetection of agglomeration of polyethylene particles. Theexperiments were carried out in a pilot scale gas–solid flu-idized bed with the operating condition close to the industrialcase (pressure range of 15–25 bar and temperature up to100 ◦C). The bed was operating in the bubbling regime. Timedomain, frequency domain and nonlinear state space analy-sis methods were used to analyze the vibration signatures. Itwas shown that the standard deviation of the vibration sig-natures, as a time domain analysis method, is able to detectcomplete defluidization of the bed. Defluidization of the bedwas also detected using frequency analysis of the vibrationsignatures. Results obtained from the S-test showed that thismethod is sensitive to changes in particle size and can detectagglomeration at least 4 ◦C before complete defluidization ofthe bed and earlier than the other two methods. Therefore,this method can be considered as a reliable method for earlydetection of agglomeration in fluidized beds.

Acknowledgements

The authors would like to acknowledge the University ofMalaya and the Ministry of Higher Education of Malaysiafor supporting this collaborative work via the researchgrant UM.C/625/1/HIR/MOHE/ENG/25. The authors would alsoacknowledge the supported by Iranian National Science Foun-dation (Grant No. 91001766).

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