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Acoustic Emission Detection of Particle Movement in a Cross-Flow Moving Bed Yuntao Jiang, Conjing Ren, Zhengliang Huang, Jingdai Wang,* Binbo Jiang, Jian Yang, and Yongrong Yang State Key Laboratory of Chemical Engineering, Department of Chemical and Biochemical Engineering, Zhejiang University, Hangzhou 310027, P. R. China ABSTRACT: A passive acoustic emission (AE) technique has been applied to monitor and characterize particle movement in a cross-flow moving bed. Experimental results show that AE signals are very sensitive to particles’ movement. By using spectrum analysis, both the relationship between particle discharging rate and the average signal energy and the relationship between particle moving velocity and frequency shift of the characteristic peak of acoustic signals have been found to be linear. Therefore, two correlations have been proposed to characterize the particle discharging rate and the particle moving velocity. The correlations have been validated using experimental data. The AE signal analysis was further used to detect the two adverse particle movement status: cavity and pinning. Convenient and accurate detection of particle movement is of important significance in the improvement of heat and mass transfer efficiency of the bed, thereby providing guidelines in the design of the radial flow moving bed reactor.

1. INTRODUCTION While fixed-bed operations are inherently unsteady-state, ending after a certain time, moving beds can operate continuously. In particular, a cross-flow moving bed (CMB) possesses a number of advantages, such as low fluid pressure drop, low particle attrition rate, uniform contact of fluid and solid phases, and controllable particle residence time. Due to the aforementioned advantages, CMB reactors are widely applied in the chemical industry,1−4 including the well-known continuous catalytic reforming process. In a CMB the compressive stress in the solid phase increases along the direction of the gas flow, because of the gas drag exerted on the solids.5 Consequently, the radial solid flow exhibits a velocity gradient. The operating efficiency of a CMB largely depends on the narrow distribution of axial gas flow and radial particle velocity. The solid velocity gradient strongly affects uniformity of catalyst coking and products quality. In addition, if the gas flow is large enough, the resulting frictional force is sufficient to support the weight of the bed and downward movement ceases in some region adjacent to the downstream wall; the bed is pinned. Pinned catalyst becomes completely deactivated by coking, preventing continued operation of the reactor. Near the gas inlet, particles deviate from the upstream wall; a cavity appears. The emergence of a cavity reduces gas uniformity along the axial direction, causing gas short cut. Pinning and cavity are the two undesired adverse particle flow status. In order to enhance the efficiency of CMB, it is of great significance to understand the particle movement in it. Numerous studies have been conducted to investigate the movement of both solid and fluid phases in a rectangular and annular cross-flow moving bed, respectively. Song et al.6 investigated the relationship between the axial nonuniformity of the gas flow distribution and the mode of the gas flow. Ginestra and Jackson7 gave a qualitative account of pinning. Doyle et al.8 extended these ideas to the practical use of the annular crossflow moving bed. Pilcher and Bridgwater9 provided a full © 2014 American Chemical Society

description of the development and collapse of cavities in rectangular moving beds. MacDonald and Bridgwater10 found that the cavity behavior can be categorized in a manner similar to that provided by Geldart for fluidization. According to the above theoretical and experimental study results, the particle movement directly affects axial gas distribution, and therefore, a rapid and accurate detection of particle movement is necessary for stable reactor operation. Some researchers have proposed advanced experimental means11,12 and computational methods13−15 to study the movement of the particles, such as electrostatic,16,17 radiation, and optical methods. The radiation technique is harmful to human health, and the optical method is only effective in the regions near to the sensors and thus not suitable for a real industrial plant. Besides being healthy and convenient, the acoustic emission (AE) technique is also nonintrusive to the reactor and has been successfully applied in industry.18−25 By analyzing the time series of the AE signals that are generated by particle friction and collision, affluent information about particle movement can be extracted. The present paper focuses on the online detection of particle movement in a rectangular cross-flow moving bed, and for the first time, the AE technique and the image method have been combined to realize the online detection of the discharging rate, particle moving velocity, and adverse particle flow status. Most importantly, this work gives a valuable insight into the particle movement behavior in a cross-flow moving bed and develops a new detection method for adverse operating status.

2. EXPERIMENTAL SECTION 2.1. Material. The material used in present work is ZSM-5 catalyst particle, whose repose angle, loose bulk density, Received: Revised: Accepted: Published: 4075

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Table 1. Physical Properties of the Catalyst Particles particle type

ΦRa

Φib

Φwc

ζd

ρse

ρf

σg

εh

Dpi

ZSM-5

34.9

24.7

22.5

0.931

930

1970

47.5

0.528

1.62

Angle of repose (deg). bAngle of internal friction (deg). cAngle of wall friction (deg). dDegree of sphericity. eBulk density (kg·m−3). fParticle true density (kg·m−3). gCompressive strength (N/particle). hVoid ratio. iAverage particle size (mm). a

compressive strength, particle size distribution, and other physical parameters are shown in Table 1. The catalyst particle is spherical. The tracer particle used for the image method is ZSM-5 catalyst coated with red paint, so the variation of the tracer particle in particle size and density can be negligible. As a result, the tracer particle can be used to reflect the real flow condition of particles in the moving bed. 2.2. Moving Bed. The experimental setup consists of two parts: a rectangular moving-bed cold model and an acoustic emission measurement system. The rectangular moving-bed cold model is made of transparent Plexiglas, as shown in Figure 1c. The gas−solid Figure 2. Discharging pipe of the moving bed reactor.

Figure 3. AE signal measurement system: (1) AE sensor, (2) preamplifier, (3) signal conditioning, and (4) computer.

AE sensor is a piezoelectric accelerometer with a resonance frequency of 150 kHz and a peak sensitivity of 69 dB, which is widely used in collecting vibration signals (Physical Acoustic Corp., Type R15). The sampling frequency was 400 kHz, the sampling time was 10 s, and the magnification time was 10. The digital resolution of the capture card was 16-bit. The signal was first amplified locally, transmitted by the A/D module, and digitized at a sampling rate of 100 kHz. The distribution of the measurement locations is shown in Figure 4. The values in parentheses in the figure denote the coordinates of the measurement points. The abscissa value represents the horizontal distance between the upstream side and the measurement point; the positive direction is along the gas flow direction. The ordinate value represents the vertical distance between the measurement point and the bottom of the bed; the positive direction is along the bed height direction. In this experimental work, with a constant gas flow rate of 60 m3/s and different discharging rates of 0.17, 1.40, 1.60, 3.00, 5.00, and 6.59 mm/s, the tracer particles were injected into the reactor. After injection the instantaneous positions of the tracer particles were recorded by camera, meanwhile the AE signals at the seven measurement locations were acquired. With a constant discharging rate of 2.93 mm/s, we collected the cavity and pinning AE signals at gas flow rates of 180, 200, 220, 250, 280, and 300 m3/h. The AE sensor was mounted on the top of the upstream wall and downstream wall, respectively.

Figure 1. Rectangular cross-flow moving bed.

contacting zone has a height of 1130 mm, width of 400 mm, and thickness of 175 mm. The upstream wall and downstream wall have a 515 mm length symmetrical opening area (see the shaded area in Figure 1b). Ordinary stainless steel mesh is mounted on the opening area to avoid particle leakage, and the opening ratio is around 22.0% according to the fan-shaped tube design data of the industrial catalytic reforming process. In the bed, catalyst pellets migrate vertically downward under gravity, while air horizontally flows across the bed and through the porous plates that form the vertical retaining walls. The gas flow pattern is as shown in Figure 1a. The discharging pipe of the moving bed is shown in Figure 2. It is made of stainless steel. The discharging slot is rectangular, and the inner diameter of vertical pipe is 40 mm, while the distance between the slot and the reactor wall is about 10 mm. With the aid of a conveying system, the particles return back to the feeding mouth to establish a stable particle circulatory system. 2.3. AE Measurement and Data Acquisition. The acoustic emission online collection and analysis system26 was developed by UNILAB Research Center of Chemical Engineering at Zhejiang University, Hangzhou, China. As shown in Figure 3, it is mainly composed of an AE sensor, a preamplifier, a signal conditioning system, and a data acquisition system. The

3. METHOD In order to enhance the resolution and signal-to-noise ratio, we introduced an improved power spectrum density algorithm19 to 4076

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discharging slot and the friction between interparticles and particles and the reactor wall. To be specific, the discharging slot is near to the downstream side (at the right side of the reactor in Figure 5). As a result, the particles just above the discharging slot move down first. Meanwhile, regular downward movement of the particles at the wall zone is retarded due to the frictional forces between the catalyst particles and the wall of the bed. Under the above conditions, the particle movement of catalysts displays an inverted triangle flow distribution, as shown in Figure 5. The movement distance of tracer particles at different times is determined on the basis of the imaging results shown in Figure 6. The selected measurement positions and the

Figure 4. Distribution of AE measurement locations on the cross-flow moving-bed wall.

analyze and process our AE signals. In this work we divided the raw data into n groups, after conducting the transformation (the fast Fourier transform) for each group, respectively, the final power spectral diagram (PSD) could be obtained by averaging the n groups’ results. The number of divided data groups, n, was the ratio of sampling points to power spectral points. The average spectral energy was the sum of the AE signal power spectra processed according to the algorithm above. Besides the PSD analysis the global properties of the signal, such as the standard deviation and average absolute deviation, were used to characterize the particle movement.

Figure 6. Trend of tracer particle belt with time.

upstream side of the moving bed are at distances of 2, 6, 12, 19, 26, 32, 38 cm. Considering the particle moving velocity in each time period to be constant, the particle time-average moving velocity at each time period for every measurement position is calculated from the measurement results and shown in Figure 7. Figure 7 shows the moving velocity at different time periods for every measurement position. By fitting, the curve is used to represent the average particle moving velocity at different position based on the instantaneous velocity results. 4.2. Online Detection of Particle Moving Velocity. Because of the gas drag exerted on the solids, particle moving velocity changes with the horizontal position.5 In order to detect the particle moving velocity at a certain position, the improved algorithm19 was used to analyze AE signals collected at the sampling locations (2, 90), (19, 90), (37, 90) with a discharging rate of 6.59 mm/s.

4. RESULTS AND DISCUSSION 4.1. Particle Moving Velocity Characterization by Image Method. With a constant discharging rate of 1.6 mm/s, red tracer particles were injected into the reactor. After injection, the instantaneous positions of the tracer particles in the moving bed at different times were recorded by camera. Figure 5 shows the variation of the tracer particles distribution with time. It can be observed from the figure that near the upstream side of the reactor, particles move downward slower than those near the downstream side. This uneven particle velocity distribution can be attributed to the position of the

Figure 5. Tracer particles flow pattern at different times. 4077

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Figure 7. Tracer particle belt moving speed.

Figure 9. Close-up diagram of the second characteristic peak in Figure 8.

By analyzing the signal, we extracted useful information to characterize the particle movement. Figure 8 shows the signal

sampling location a specific ΔF value was obtained. By linearly associating the particle movement velocity determined by the image method with ΔF, we obtained a correlation, and the relationship is shown in Figure 10.

Figure 8. Power spectrum of AE signal at different horizontal locations.

power spectra. It can be obtained that all the three spectra exhibit three characteristic peaks of certain frequencies. The frequencies of signals are different at different positions; however, it is interesting that the frequency of the second characteristic peak (30−35 kHz) changes with the sampling location, in other words, changes with particle moving velocity. It can be clearly obtained that the frequency of the second characteristic peak increases with the distance between the sampling point and the upstream side. In the measurement range, the longer the distance between the sampling point and the upstream side, the faster the particles migrate. In other words, the faster the particles migrate, the lower the second characteristic peak frequency is (Figure 9). Acoustic signal comes from the friction and impact between the particles and the wall. While the intensity of friction and impact changes with the particle moving velocity, the acoustic signal changes with the particle moving velocity. Through acoustic signal spectrum analysis, it is possible to extract the relationship between acoustic signal and particle moving velocity. The aforementioned analysis revealed that the frequency position of the signal shifts toward lower frequency gradually with the increase in the particle moving velocity. Therefore, we extracted the frequencies of the second characteristic peaks and considered the minimum frequency value of 31 738 Hz obtained at (37, 90) to be the standard frequency. The difference between each second characteristic peak frequency and the standard frequency was denoted as ΔF, so at each

Figure 10. Relationship between particle moving velocity and frequency difference of the second characteristic peak on the power spectral diagram.

Figure 10 reveals that the frequency difference of the second characteristic peak shows a relatively high linear relationship with the particle moving velocity (R = 0.964). Consequently, at any time (t), for a specific sampling location of vertical and horizontal coordinates (i, j), the particle moving velocity Vij correlation can be established as follows:

Vij = a1ΔFij + b1

(1)

In eq 1, a1 and b1 are the fitting parameters. In order to verify the above correlation, the predicted value was compared with the actual value at a discharging rate of 1.6 mm/s. The average relative deviation of the correlation is shown in Table 2. The average relative deviation of this model is less than 9.24%, indicating that the model is capable of describing the signal characteristic peak frequency shift with the particle moving velocity at different locations. The standard deviation and the average absolute deviation of the original AE signals at the sampling positions (2, 90), (19, 90), and (37, 90) were also correlated with the particle movement velocities. As shown in Figure 11, neither of the correlation coefficients are so high. 4078

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Table 2. AARD between Predicted and Experimental Values for Particle Moving Velocities at Different Horizontal Locations (cm) Vpre/(mm·s−1) Vexp/(mm·s−1) AARD/%

(2, 90)

(19, 90)

(37, 90)

(2, 35)

(19, 35)

(37, 35)

(37, 3)

0.25 0.23 8.70

0.88 0.81 8.64

1.30 1.19 9.24

0.22 0.23 4.35

0.74 0.81 8.64

1.25 1.19 5.04

1.28 1.19 7.56

Figure 11. Relationships between the particle movement velocity with the standard deviation and average absolute deviation of the AE signal.

Figure 13. Relationship between average energy of AE signal and discharging rate.

4.3. Online Detection of Discharging Rate. At the same gas flow rate, the interparticle friction and collision become more intense with the discharging rate increase. Finally, the change of the particle movement status was reflected in the acoustic signal. Figure 12 shows the relationship between AE

where the subscripts i and j denote transverse and longitudinal coordinates of the sampling location, respectively. The correlation equation is shown as below: Dij = a 2Eij + b2

(2)

In eq 2, a2 and b2 are the fitting parameters. AE signals were collected at different measurement locations, and the analysis results of AE signals are shown in Figure 14.

Figure 12. The relationship between AE signal power spectra and particle discharging rates. Figure 14. The relation between average energy of AE signal and discharging rate at different sampling locations.

signal power spectra at the sampling position (37, 3) and particle discharging rates. Under different discharging rates, the shapes of AE signal power spectra are roughly the same. All the power spectra show some cumulative energy peaks at specific frequencies; meanwhile, the characteristic peak intensity increases gradually with the discharging rate of the catalysts, indicating that the average energy of AE signal power spectrum increases gradually, too. Figure 13 shows the relationship between the average energy of AE signal and the discharging rate of the particles. Figure 13 shows a relatively high degree of linearity (R = 0.969) between the average energy of the AE signal and the particle discharging rate. From the linear relationship, a correlation equation can be established to calculate particle discharging rate Dij from AE signal energy at an arbitrary time t,

The figure shows that the range of variation of the AE signal average energy is different for different measurement locations, but for a certain position, the average energy maintains a relatively good linear relationship with the discharging rate of particles. At the seven sampling locations, we established seven correlations, respectively. In order to validate the above correlations, under a discharging rate of 5 mm·s−1, the AE average energy values at the seven measurement locations were obtained and substituted into the prediction equations to calculate the theoretical discharging rate. The average relative errors of the equations are reported in Table 3. The average relative errors are less than 4079

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Table 3. Average Absolute Relative Deviation (AARD) between Calculated and Experimental Results for a Certain Discharging Rate at Different Locations (cm) Dcal/(mm·s−1) Dexp/(mm·s−1) AARD%

(2, 90)

(19, 90)

(37, 90)

(2, 35)

(19, 35)

(37, 35)

(37, 3)

5.24 5.00 4.80

4.78 5.00 4.40

5.35 5.00 7.00

5.23 5.00 4.60

4.59 5.00 8.20

4.85 5.00 3.00

5.12 5.00 2.40

8.20%, indicating that the AE-based correlation equation is capable of predicting the particle discharging rate. The standard deviation and average absolute deviation of the original AE signal at the sampling position (37, 3) were correlated with discharging rate. As shown in Figure 15, the signal global properties correlated with the discharging rate agree quite well, as can be observed from the correlation coefficients.

Figure 16. The installation location of AE sensor for cavity detection.

became smaller and smaller. When the gas flow was larger than 300 m3/h, the cavity was not as stable as before, and a slight disturbance led to the collapse of the cavity, causing the particles near the upstream wall to be close to fluidization. Thus, the particle movement became much more intense. The power spectra of cavity AE signals at different gas flow are shown in Figure 17. According to the shape of the spectra, the spectra are divided into two classes. In order to distinguish the difference between the two kinds of spectra, the spectra are put into two categories: one is shown in Figure 17b, where the power spectra of AE signals are standing for the conditions for which the cavity has not formed, and the other is shown in Figure 17c, where the spectra are standing for conditions for which the cavity has already emerged. The results show that when the gas flow is less than 250 m3· −1 h , the cavity has not yet been formed, and all the spectra have a similar shape. Once the gas flow is greater than 250 m3·h−1, the cavity appears, and thus, the power spectra display several new characteristic peaks, which are shown in Figure 17c. Compared to Figure 17a, the shape of the AE signal power spectrum of a cavity has a relatively smooth peak shape. Accordingly, we set up the criterion of cavity determination by observing the changing peak shape of the AE signal power spectrum from rough to smooth. On the other hand, as shown in Figure 18, the global properties of the AE signal at the cavity detection site increase with an increase in the gas flow rate. In our opinion, the cavity AE signal global properties represented the particle movement activity. The higher the values of standard deviation and average absolute deviation, the higher the particle movement activity, and so was the cavity size. The global properties of the acoustic signal could reflect the active degree of particle movement, but from their relationship, we still found it difficult to determine the appearance of the cavity using the standard deviation and average absolute deviation of the acoustic signal, because when the cavity appeared, there was no obvious transition of the global properties of the AE signal, as shown in Figure 18. 4.4.2. Pinning Detection. The AE sensor was mounted on the reactor wall opposite to the pinning. The power spectra of

Figure 15. Relationships between the discharging rate with the standard deviation and average absolute deviation of the AE signal.

4.4. Online Detection of Cavity and Pinning. Cavity and pinning are two typical adverse operating conditions in a CMB. A cavity usually appears near the upstream side because of excessive gas flow. Once the cavity has emerged, the only way to eliminate it is by decreasing the gas flow. The cavity reduces the gas distribution uniformity, which accelerates catalyst deactivation where the gas flow is larger. Consequently, a fluctuation of catalyst coke content occurs at the discharging duct. The coke content variation will change the operating conditions of the regeneration unit, which is seriously detrimental to the continuous operation of the regenerator. On the downstream side, particles would be pinned, when the gas flow is high enough. Therefore, hot spots, runaway, severe coking, and even the permanent deactivation of catalysts occur, and side reactions are intensified. In addition, pinning lessens the available reactor volume. From the above analysis, an accurate and rapid detection technique for cavity and pinning is of great significance in maintaining steady and continuous operation of the CMB. The AE technique has been successfully used for agglomeration detection in the fluidized bed reactors27 and has been adapted in the study of the moving-bed systems. 4.4.1. Cavity Detection. The AE sensor was mounted on the reactor wall where the cavity emerged, as shown in Figure 16. Under a certain discharging rate, along with the increase of the gas flow, the interaction between the particles in the cavity site was gradually replaced with the interaction between particles and air, and the solid holdup near the upstream wall 4080

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Figure 18. The standard deviation and average absolute deviation of the AE signal at different operation gas flow rates at the cavity detection site.

particles became motionless. As a result, the standard deviation and average absolute deviation values of the AE signal at the pinning site gradually decreased, as shown in Figure 20, until the pinning appeared, and then the two values increased sharply. The authors think that when pinning appeared, the collected AE signal came from the particle movement in the main stream instead of the pinned particles. Due to the appearance of pinning, the particle flow sectional area became smaller; under the same discharging rate, the particle movement velocity became much larger than the former particle movement velocity near the downstream wall. Consequently, the signal standard deviation and average absolute deviation increased sharply. This result is consistent with the results in Figures 11 and 15. As a result the sudden transition of AE signal, standard deviation and average absolute deviation could be used to characterize the appearance of pinning.

5. CONCLUSIONS The AE technique has been applied for the first time to detect online particle movement in a cross-flow moving bed. Correlation equations of discharging rate and particle moving velocity with AE signal average energy and the signal frequency difference have been proposed. The two correlation equations have proved to be accurate. Comparisons of model results with experimental data show that the maximum average relative deviation of discharging rate and particle moving velocity is less than 8.20% and 9.24%, respectively. It is worth noting that the frequency domain of the raw signal depended on the movingbed material, operating conditions, and device geometry;28 consequently, the parameters of the correlations that we conducted to characterize the particle discharging rate and the particle moving velocity are specific for our device. As a result, the AE signal global properties do not reflect both the particle movement velocity and discharging rate well, while the spectral analysis does. The signal global properties indicate the particle movement activity. The authors think that through the spectral analysis of the AE signal, an ample amount of information about the particle movement in the gas−solid system can be extracted, especially information pertaining to particle collision and friction. The authors hope to report work on this in a future publication. It also has been verified via PSD analysis that the AE technique is capable of qualitatively detecting the emergence of

Figure 17. AE signal power spectra at different gas flow rates where the cavity emerges.

the AE signals of pinning are shown in Figure 19. Similar to the results for the cavity, the power spectra of pinning AE signals can also be classified into two categories, one is the spectra with less characteristic peaks, as shown in Figure 19b, and the other is with more characteristic peaks, as shown in Figure 19c. The results clearly show that once the pinning has occurred, the acoustic signal power spectrum has a high probability to move from double peak to multipeak. On the basis of this observation, a criteria of pinning can be set up by observing the shape of the AE signal power spectrum switching from double peak to multipeak. Near the downstream wall, along with the increase of the gas flow rate, the gas drag force exerted on the particle increased, and particle movement activity gradually reduced until the 4081

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Figure 20. The standard deviation and average absolute deviation of the AE signal at different operation gas flow rates at the pinning detection site.

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AUTHOR INFORMATION

Corresponding Author

*Tel.: +86-0571-87951227. Fax: +86-0571-87951227. E-mail: [email protected]. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS The authors gratefully acknowledge the support and encouragement of the National Natural Science Foundation of China (21236007), the National High Technology Research and Development Program of China (2012AA030304), the Specialized Research Fund for the Doctoral Program of Higher Education of China (20110101120020), the National Basic Research Program of China (2012CB720500), Zhejiang Provincial Natural Science Foundation of China (LQ13B060002) on this work.

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REFERENCES

(1) Qi, G. Z. A Study on the Process of Methanol to Olefins Reaction. Ph.D. thesis, 2006, 86−90 (in Chinese). (2) Zhang, H. M. Advances in Process Research of Methanol to Light Olefins. Chem. React. Eng. Technol. 2008, 24 (2), 178−182 (in Chinese). (3) Vora, B. V.; Pujado, P. R.; Andersen, J. M.; et al. Production of Polyolefins from Natural Gas and Integration with Gas-to-liquids Facilities. 6th World Congress of Chemical Engineering; Melbourne, Australia, Sep 2001; pp 23−27. (4) Yao, B. Z.; Xu, Z. H. Technical Advance and Economic Analysis on Methanol to Propylene. Techno-econ. Petrochem. 2010, 26 (2), 7− 11 (in Chinese). (5) Tsubaki, J.; Tien, C. Solid Velocity in Cross-flow Moving Beds. Powder Technol. 1987, 53 (2), 105−112. (6) Song, X. Q.; Wang, Z. W.; Jin, Y.; Gong, M. S. Hydrodynamics of Radial Flow Moving-bed Reactor. CIESC J. 1993, 44 (4), 1−10 (in Chinese). (7) Ginestra, J. C.; Jackson, R. Pinning of a Bed of Particles in a Vertical Channel by a Cross Flow of Gas. Ind. Eng. Chem. Fund. 1985, 24 (2), 121−128. (8) Doyle, F. J.; Jackson, R.; Ginestra, J. C. The Phenomenon of Pinning in an Annular Moving Bed Reactor with Cross Flow of Gas. Chem. Eng. Sci. 1986, 41 (6), 1485−1495. (9) Pilcher, K. A.; Bridgewater, J. Pinning in a Rectangular Moving Bed Reactor with Gas Cross-flow. Chem. Eng. Sci. 1990, 45 (8), 2523− 2542.

Figure 19. AE signal power spectra at the pinning site over different gas flow rates.

a cavity and pinning, while the global properties analysis can only quantitatively characterize the emergence of pinning, but not a cavity. The obvious change of AE signal power spectrum can be used as a judgment criterion of cavity and pinning in a CMB. At the upstream, the peak shape of AE signal power spectrum switched from rough to smooth, indicating the appearance of a cavity. At the downstream, the shape of the AE signal power spectrum switched from double peak to multipeak, revealing the emergence of pinning. The sharp increase of the pinning signal standard deviation and average absolute deviation indicates the emergence of pinning. 4082

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dx.doi.org/10.1021/ie401631d | Ind. Eng. Chem. Res. 2014, 53, 4075−4083