Fault Detection Based on Acoustic EmissionâEarly Agglomeration

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Fault Detection Based on Acoustic EmissionEarly Agglomeration Recognition System in Fluidized Bed Reactor Yefeng Zhou, Kezeng Dong, Huang Zhengliang, Jingdai Wang,* and Yongrong Yang State Key Laboratory of Chemical Engineering and Department of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310027, P. R. China ABSTRACT: Agglomeration is one of the most challenging problems due to overheating of the particles in fluidized bed reactors (FBRs). Therefore, it is an urgent task to develop a reliable and sensitive method, which can help accurately detect the agglomeration in an early stage. In this study, acoustic emissionearly agglomeration recognition system (AE-EARS) has been put forward for fault detection. Based on acoustic emission signals, the attractor comparison method was developed for prewarning the agglomeration in lab-scale and pilot-scale apparatus. The results concluded from this study demonstrated that the statistical characteristic S acts more sensitively to small agglomeration when compared with the malfunction coefficients CD2 and CK2, and other traditional measurement techniques (such as γ ray, temperature, and pressure difference). Besides, model optimization based on AE-EARS can help to select the criterion and improve the rate of false alarm. The analysis methods based on AE-EARS can warn the agglomeration earlier, faster, and more accurately. Especially the S value based on the attractor comparison, can be used as an indicator for “early recognition”, which enjoys a broad prospect in industrial application.

1. INTRODUCTION With its rapid development, gassolid fluidized bed reactor (FBR) technology attracts more and more attention in different kinds of chemical engineering processes,1 such as fluid catalytic cracking, catalyzed polymerization of olefin, FischerTropsch synthesis, calcining, coking, coal/biomass/waste gasification and combustion.13 On the other hand, many physical processes have also been involved in FBRs, like drying, coating, granulation, and gas purification via adsorption.4,5 Since FBRs can handle most of the operations relating to particles and effectively supply enough exchange capabilities of heat and mass to keep constant temperature and concentration, it is widely applied to chemical and physical industry. In the polymer industry, gassolid FBRs have been used for polyethylene (PE) production since late 1970s. After nearly 30 years’ development and application, gas phase polymerization has become a mainstream technology of PE production globally because of its advantages over both highpressure and slurry polymerization techniques. It deserves to be noted that condensation and supercondensation mode operations as core technologies in PE industry can be used to break through the bottleneck of the heat removal capacity and have become the focus of the world’s PE industry.6,7 More operation parameters and details for industrial gas phase reactor can be obtained in refs 8 and 9. Although fluidized beds have numerous advantages, there are still some unfavorable aspects in the related applications.1014 For example, if the reaction heat has not been removed immediately, certain changes of the fluid dynamic behavior in the reactor, such as formation of agglomeration and sintering of the bed particles, would disrupt the fluidization of the reactors. To be specific, the most puzzling problems in industrial scale FBRs for polymerization are the agglomeration and sintering of particles due to the overheating of the particles or high levels of liquid in the particle bed. The formation of chunks exerts a seriously r 2011 American Chemical Society

harmful effect on the mixing quality of solid particles. The fluid dynamic behavior within the reactor will be different according to the degree of agglomerations. For example, small agglomerations can lead to a slower bed particle movement, then inactive zones will emerge in some specific parts of the reactor, and the gas distributor will be blocked partially (Figure 1a). To go a step further, the recycling gas is distributed unevenly at the beginning, with superficial velocity decreasing (formation of hot spot) or increasing (intense fluidization) locally, which gives rise to bias flow (Figure 1b). When the agglomerations gather into larger lumps, the fluidization status of the particles bed may be deteriorated totally, which results in complete blockage of the distributor (Figure 1c). At last, the unscheduled shutdown of the industrial unit will lead to tremendous economic losses. Because the agglomeration would limit the reactor performance for PE production, it is very urgent to develop an ideal method in which agglomeration could be monitored early and accurately. The first and more important consideration is “early warning”, which means detecting the trend of agglomeration before it becomes irreversible. In addition, agglomeration must be monitored “accurately” by using this ideal method, which would be able to reflect the nature of the FBRs even when many possible environmental disturbances exist. As mentioned above, the agglomeration has been recognized as one of the most challenging problems existing in the ethylene polymerization reactor. Actually, the time scale that the defluidization process lasts in the reactor is about tens of minutes or larger, so it is possible to monitor the agglomeration in advance and take timely measures to avoid further deterioration of the Received: February 4, 2011 Accepted: June 9, 2011 Revised: May 17, 2011 Published: June 09, 2011 8476

dx.doi.org/10.1021/ie200260t | Ind. Eng. Chem. Res. 2011, 50, 8476–8484

Industrial & Engineering Chemistry Research

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Figure 1. Agglomerations in the industrial FBR.

FBRs. In industry, the agglomerations or sheetings in FBRs can be repelled by controlling reactor temperature, increasing superficial velocity, decreasing catalyst feed rate, or regulating other parameters. To detect the agglomeration as soon as possible, the process operators mainly rely on common measurements, such as average bed pressure drop, temperature of reactor, or γ ray measurement. However, it is difficult to immediately detect the agglomerations on the basis of these measurement techniques, and the best time to prevent large chunks from growing from small agglomeration is always lost. Therefore it is an urgent task to develop a reliable and sensitive method for monitoring the agglomeration in advance. Recently, many measurement techniques have been developed within various chemical engineering processes.15 Most of the popular techniques currently used for agglomeration detection are based on pressure, temperature, electrostatic, radiation, and optical measurements. However, these techniques have their limitations, for example, some techniques are intrusive to flow field, some lagging in time, some harmful to human health, and some not fit for a severe industrial environment. Therefore, acoustic measurement has been developed so rapidly in recent years. Compared to the currently used detection methods, acoustic emission (AE) can be considered as a noninvasive, environmentally friendly, and reliable technique, which can be used to monitor the movement of particles and particle size distribution.1619 A recent study shows that acoustic emission has been applied successfully to detect agglomeration in horizontal stirred bed reactors.13 Therefore, AE measurements could be also used to monitor agglomeration effectively in FBRs.10 For the AE technique, choosing a reliable signal analysis method is crucial. In the past, frequency domain analysis, such as spectral analysis, wavelet, wavelet-packet, and R/S analysis had been usually used to process the AE signals.20 The frequency domain analysis aims at selecting characteristic variables (such as average energy, percentage of energy, and peak value) in a specific frequency range to correlate with the process parameters. Moreover, the advantages of this analysis are simple and less time-consuming.

However, the application of frequency domain analysis would cause some disadvantages, such as random criteria and unfavorable universality. Thus, another type of analysis method, time domain analysis can be adopted to deal with the acoustic data in order to obtain a more useful message at different times, with the help of statistics theory or chaos theory.10,11,13 In some unknown mechanism systems, such as a complex multiphases flow system, satisfactory results will be obtained by using this analysis method. For example, some progresses for agglomeration detection have been made both in lab-scale and industrial-scale fluidized beds, which are based on mostly pressure fluctuation signals.12,21,22 Therefore, time domain analysis is gradually becoming a hot topic of research. Therefore, to detect the fault of agglomeration in advance, the acoustic emission early agglomeration recognition system (AEEARS) has been put forward. Two popular time domain analysis methods, attractor comparison method and complexity analysis method, were applied to analyze the AE signals. Both in lab-scale and pilot-scale fluidized bed polymerization reactors, a number of experiments were performed to seek a reliable and early detection method for agglomerations. The attractor S and the coefficients of malfunction C, which are based on the AE signals, were both proven to be suitable for monitoring the agglomeration. These analysis results could help us earn more time to handle the ongoing agglomeration, which has an important theoretical value as well as a great industrial significance. Moreover, in a comparison between the two analysis methods, the statistical characteristic S acts much earlier than malfunction coefficients C. More details will be further discussed in the following parts.

2. THEORY AND METHOD 2.1. Acoustic Emission Early Agglomeration Recognition System (AE-EARS). The concept of the early agglomeration

recognition system (EARS) was first introduced by J. R. van Ommen et al.11 They have made some progress toward the early detection of agglomeration, and their studies are all based on pressure measurements. On the basis of our group’s previous research 8477

dx.doi.org/10.1021/ie200260t |Ind. Eng. Chem. Res. 2011, 50, 8476–8484


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hydrodynamic behavior has a significant change. More details about the method are given in the literature.23 2.2.2. Complexity Analysis Method and Coefficients of Malfunction C. Recently, many researchers24,25 have used some chaotic coefficients, especially the correlation dimension and Kolmogorov entropy (K-entropy) to analyze signals with qualitative change. Therefore we assume that the complexity analysis method can be applied to detect the agglomeration from AE signals. The malfunction coefficients express the normalized deviation of a certain property from a chosen reference situation. Here, we define the coefficients as follows: CD2 ¼

Figure 2. Schematic diagram of the experimental apparatus: (1) fan, (2) dryer and heater, (3) flow meter, (4) mixing room, (5) distributor, (6) fluidized bed, (7) expanding section, (8) acoustic sensor, (9) data acquisition system, (10) computer.

findings,10,13 we have developed a noninvasive acoustic emission early agglomeration recognition system (AE-EARS) for fault diagnosing in FBRs. In this system, the state comparison method is essential for this system. First of all, two different states, the operation condition (evaluation time series) and normal condition (reference time series), are compared. And then, the deviation between these two states is calculated and compared with the specific threshold function. In the end, a judgment can be made based upon the comparison results on whether the abnormal situation occurred in the reactors. It is emphasized that both the attractor comparison method and complexity analysis method are based on AE-EARS. Attractor comparison method and complexity analysis, which are both based on the chaotic theory and belong to a nonlinear analysis method, were chosen to analyze the complex AE signals. 2.1.1. Attractor Comparison Method and Statistical Characteristic S. The attractor comparison method can be used to determine whether two time series are generated by the same mechanism. The two series are reference time series from normal condition and evaluation series from operation condition. Diks et al.23 proposed a statistical characteristic S for testing the null hypothesis that two multidimensional probability distributions are identical. The statistical characteristic S is defined as the ratio ^ ) shown as follows: ^ to the square root of Vc(Q of Q ^ Q S ¼ qffiffiffiffiffiffiffiffiffiffiffiffi ^Þ VC ðQ

ð1Þ

which has an expectation value of zero and a standard deviation of unity under the null hypothesis of two series being generated by ^ is an unbiased estimator of squares the same mechanism. Here, Q ^) of a distance between the two probability distributions and Vc(Q ^ . Moreover, as Diks et al.23 is the variance of the estimator Q pointed out, the null hypothesis can be rejected when the estimated value of S is larger than 3, which is supported by the fact that the probability of finding the value of S larger than 3 is smaller than 0.05. To be specific, the key problem in this paper we intended to solve is the agglomeration detection. Once the S value becomes larger than 3, we can draw a conclusion that agglomerations have formed in the fluidized bed and the corresponding

CD2, a CD2, 0 CD2, 0

CK2 ¼

CK2, a CK2, 0 CK2, 0

ð2Þ

where these symbols with subscript “a” represent AE signal results under operation condition, while the symbols with subscript “0” represent results from a normal fluidization system. In our research, the coefficients of malfunction can be obtained for comparison between the agglomeration and normal conditions. More information can be found in refs 26 and 27.

3. EXPERIMENTS AND SETTINGS 3.1. Experimental Apparatus and Materials. Figure 2 is a schematic diagram of our experimental apparatus used in this study. Because the three experimental apparatuses are mainly different in the reactor size, we just show a typical apparatus here. It consists of two main parts: a fluidized bed system and an acoustic emission measurement system. The fluidized bed without preheating is made of plexiglass, 150 mm i.d. (inner diameter), with a 1000 mm height, and the perforated distributor (with pore diameter of 2.0 mm and an open area ratio of 2.6%) is installed at the bottom. The fluidized bed with preheating is made of glass, 100 mm i.d., with a 1000 mm height, and the sinter-plate distributor (with pore diameter of 1.1 mm, open area ratio of 2%) is installed at the bottom. The fluidized gas was heated by electro-thermal wire to a settled temperature before entering the fluidized bed. The temperature in fluidized bed was controlled by a thermistor. Industrial experiments were carried out in a pilot fluidized bed with inner diameter of 420 mm, height of 4000 mm height, and perforated-plate distributor (with pore diameter of 12 mm, open area ratio of 2.5%). Parameters and operational conditions of experimental apparatus are shown in Table 1. Linear low-density polyethylene (LLDPE) and bimodal PE particles (offered by SINOPEC) were used in the cold mold experiment with physical properties as shown in Table 2. The acoustic emission signals online collection and analysis system was developed by the UNILAB Research Center of Chemical Engineering in Zhejiang University, China. It consists of an AE sensor, a preamplifier, a signal conditioning system, and a data acquisition system. The AE sensor used in this study is a piezoelectric accelerometer, which is extensively used in collecting the acceleration of vibration without the noise transferred via the air (PXR15, 150 kHz, 100300 kHz, 65 dB, China). The transducer attached noninvasively to the outside of the bed is monitored at a special location above the gas distributor with a specific sampling frequency. The height of the sensor was shown in Table 1. An acoustic coupling agent is used to transfer the acoustic emission in the vessel to the transducer. For temporary installation, silicone grease is used to hold the transducer in place. For permanent installation, adhesive grease holds the transducer 8478



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Table 1. Parameters and Operational Conditions of Experimental Apparatus apparatus

mode

Dbed (mm)

material

Hsensor (m)

Dorifice (mm)

US (m 3 s1)

orifice ratio (%)

experiment 1

cold (heated)

glass

100

0.1

0.5

1.1

2

00.5

experiment 2

cold

plexiglass

150

0.2

1

2

2.6

01.2

pilot

hot

Fe

420

1

4

12

2.5

0.6

Table 2. Particle Properties of the Polyethylene Used in the Experiments MIa (g 3 (10 min)1)

Fp (kg 3 m3)

dp (mm)

LLDPE

2.0

918

0.46

bimodal PE

6.0

946

0.365

materials

a

Hbed (m)

Table 3. Optimal Parameter Settings for Applying the Attractor Comparison Test to the Lab & Industrial FBR AE Signals Tw

MI = melting index.

in place and acts as an effective acoustic-coupling agent. The preamplifier (PXPA IV, 2500 kHz, China) supplies sufficient gain to the signal in order for it to be transmitted via a cable with length up to 200 m. The signal conditioning system used in experiments is PXMA signal conditioning equipment (PXMA, China). It provides additional gain and filters to the signals with a high-pass cutoff frequency of 2 Hz. The data acquisition system consists of a data acquisition card (NI PCI-6071E, Nation Instruments, USA) and a personal computer. The AE signals generated by the piezoelectric accelerometer are amplified and conditioned. Finally, they are transferred to a data acquisition card connected to the computer and controlled by software package Labview. The model was also applied to the industrial fluidized beds of LLDPE, high density polyethylene (HDPE), and bimodal PE. To detect agglomeration as accurately as possible, it is better to fix the AE sensor at the region in which moving lumps are often observed. With careful consideration, the AE sensor can also be located near or fixed on the distributor in order to get agglomeration information as much as possible. In this study, the AE sensor was located 0.1 and 0.2 m above the gas distributor for experiment 1 and 2, while the sensor was fixed 1 m above the gas distributor in industrial experiments. We used three different methods to obtain agglomerations. For cold model without heating, we added agglomerations of different size into the fluidized bed, and these agglomerations were collected from the industrial plant. For cold model with heating, agglomerations will be formed by heating the bimodal PE particles. For the pilot unit, excess catalysts were added to the industrial device to generate bigger chunks. 3.2. Settings for the Attractor Comparison Method. To power the monitoring ability of the attractor comparison method, five main parameters need to be optimized in pre-experiments. An optimization method is adopted to find the right parameters, which should lead to an S value close to 0, when two AE time series are measured under similar hydrodynamic conditions, and a maximally high S value under different conditions. Specifically, the reference time series were measured under good fluidization conditions, while another two time series measured were used as the evaluation time series: one was measured at the same conditions as the normal situation and the other was measured for a different bed composition as the “agglomeration” situation. Besides, the sampling frequency should be selected to ensure that no information contained in the signals is lost. The implementation details can be referenced.11,13 Here, Table 3 shows the results.

L

m

d

Fs

lab-scale

40 ms

5s

10000

0.70

500 kHz

industrial-scale

50 ms

8s

7500

0.48

300 kHz

4. RESULTS AND DISCUSSION 4.1. Preliminary Analysis for AE-EARS. 4.1.1. Sensitivity Analysis. To eliminate the negative effect of fluidized bed

parameters on detection results and reduce the sensitivity of nontarget parameters in this model, we systematically studied two main operation parameters that would influence S: the superficial gas velocity and the bed mass. The corresponding countermeasures can be used to reduce the sensitivity toward the changes in gas velocity/bed mass. On one hand, for superficial gas velocity variation, standard normalization (see eq 2) can be applied to AE signals to reduce its effects on detection results.28 On the other hand, for bed mass, AE transducers should be located on the middle point between the distributor and the dynamic bed level (0.5 h, h is the dynamic bed level) to reduce the impacts made by fluctuating bed mass. xk ¼

Ak A σA

ð3Þ

4.1.2. Reliability Verification Test. To prove the availability of AE signals, we carried out a reliability verification test for the fluidized bed cold model. Here, we define the criteria of agglomeration size. In the industrial field, we define the agglomeration whose diameter is larger than 20 mm. To make it convenient for the study in the laboratory, we assume the diameter of agglomeration is about 2 mm. In the test performed in the cold model (150 mm i.d.), the superficial gas velocity was 0.6 m 3 s1, while the quality of LLDPE particles and other factors were kept constant, and agglomerations (1 wt %) with diameters of 10, 18, 20, and 40 mm were orderly added into the fluidized bed with an average size of 0.46 mm. At the same time, AE signals were sampled and analyzed. The AE signals were analyzed by wavelet transformation,29 then the signals of mesoscale were extracted for reconstruction so as to compare the processed signals between two different states; one is for evaluation series, the other is for reference. As shown in Figure 3, AE signals are so sensitive to agglomerations that a significant difference can be observed between the agglomerationexisting condition and the normal condition. Here, more details of the method procedure are not given and you can obtain more information in the literature.16,20 4.2. Agglomeration in a Temperature-Rise Period. To simulate the complete process of the agglomeration formation 8479



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Figure 3. Comparison of acoustic signals of mesoscale between two different states:10 (a) add 1 wt % agglomerates of 18 mm, (b) normal fluidization.

Figure 4. Evolution of statistical characteristic S based on attractor comparison, using data obtained from the fluidized bed by heating the inlet gas from room temperature to 80 °C. The dotted line represents the threshold values (cold-model).

in industrial device, experiments were systematically performed in preheated fluidized bed (100 mm i.d.) by heating the bed particles. The gas was heated from 30 to 80 °C before entering the fluidized bed. AE signals were collected each time when temperature was increased by 10 °C with a sampling time of 20 s. When the temperature was above 80 °C, AE signals were sampled for 20 s every 3 min. Because of its low-molecular-weight polymer contents, small particles will fuse into bigger agglomerations when heated. As a result, the fluidization condition will be influenced by variation of the average particle size. 4.2.1. Attractor Comparison Method and Characteristic S. Based on the attractor comparison method, the attractors can be calculated from AE signals sampled at different temperatures, as shown in Figure 4. From this figure, we find that when temperature was increased from 30 to 50 °C, the attractor S kept relatively steady. After 20 min, when the temperature rose to above 50 °C, the attractor S began to increase. It is because the particles lost their water contents gradually in this period, which resulted in an even more intense movement. When gas temperature approached 70 °C, S increased rapidly and became larger than the threshold value S = 3. It could be indicated that the operation time series and the reference time series were generated by a different mechanism from then on. In other words, there were some significant fluid dynamic behavior changes in the cold model reactor, such as formation of agglomerations. By careful observation of the experimental process, the part of the bimodal PE started to agglomerate to bigger lumps and the particle size distribution of the bed had been changed appreciably, which led to a more chaotic

Figure 5. Evolution of malfunction coefficients expressed in correlation dimension (CD2). The dotted line represents the threshold values (coldmodel).

system. In the meantime, it can be found that some fine particles near the fluidized bed wall began to present an “overlappingscattering” phenomenon, while the agglomeration were reversible in this stage. When gas temperature reached 80 °C, we could witness a continuous increase of the characteristic S. At this stage, all fine particles in the fluidized bed became sticky, then agglomerated further. Correspondingly, the velocity of “scattering” was on the decrease gradually, while the obvious “overlapping” lumps could be observed near the wall and thus the size of particles was increased to larger. Notably, it could be speculated that the agglomeration became irreversible after this S-increasing stage. From the previous study results, Wang et al.10 found that two coefficients of malfunction based on complexity analysis could be used to monitor the agglomeration in FBRs. They are CD2 and CK2, expressed in correlation dimension and K-entropy, respectively. The evolution diagrams of both CD2 and CK2 are shown in Figures 5 and 6. More details are given in this literature.10 Here, it should be emphasized that when the coefficients under operation condition exceed their threshold values, we can make sure that some significant fluid dynamic behavior changes have occurred inside the fluidized bed, such as the formation of agglomerations. From Figures 4, 5 and 6, by comparison with both the attractor comparison method and the complexity analysis method, we can conclude that the coefficients of malfunction C and attractor characteristic S are suitable for monitoring the agglomeration in the cold-model reactor. But there are still two remarkable differences: (1) the slope that S value gets across the critical line is smaller than C; (2) S value precedes C in exceeding the threshold. 8480



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Figure 6. Evolution of malfunction coefficients expressed in K-entropy (CK2). The dotted line represents the threshold values (cold-model).

Figure 7. Evolution of statistical characteristic S based on attractor comparison (pilot-scale industrial reactor).

Table 4. The Catalyst Feed Rate and Agglomeration Size at Different Time time

before

10:00

catalyst feed rate (g 3 h1)

3.0

5.9

agglomeration (mm)

17:00

18:30

19:30

8.3

6.2

6.2

about 2

about 8

about 25

Overall, the agglomeration caused by heating is a dynamic process of gradual changes rather than sudden changes. From the two differences, we can assume that the value of S can reflect more suitable information when compared to C. More specifically, particles agglomerate together gradually with durative rising temperature in the fluidized bed. Therefore we can obtain actual information of hydrodynamics via S value. Difference (2) shows that the S value is more sensitive to the agglomeration process than other coefficients. Thus, we can prove that S values calculated via attractor comparison method can indicate the trend of the hydrodynamic behaviors earlier in fluidized bed when compared to the C values, and help us provide timely warning. However, there exists some certain subjective factors in setting threshold of malfunction coefficients. Here, we could not draw a conclusion just by relying on the data of lab-scale experiments. Therefore, industrial data is necessary for model verification. 4.3. Model Verification of Reaction Agglomeration Process in Pilot-Plant. To explore the detecting effects of AE-EARS and to develop ideal malfunction criterions for industrial FBRs, we produced agglomerations by adding excessive catalysts into pilot apparatus. The approach of the experiment is shown as follows: FBR was operated in a normal condition (superficial velocity, 0.6 m 3 s1; grade, HDPE; flow-rate of catalyst, 3.0 g 3 h1) at first; next, the catalyst feed rate increased to 5.9 g 3 h1 at 10:00; then, the flow-rate increased again, to 8.3 g 3 h1 at 17:00; finally, the feed rate had been regulated at 6.2 g 3 h1 since 18:30. The whole reaction process data (including ΔP and ΔT of the bed) was recorded and analyzed, while the AE signals were sampled and processed to monitor agglomerations online. At the same time, the particle size distribution of product was recorded at different moment, and agglomerations were sampled at 17:00 and 18:30. We can obtain this information more directly from Table 4. It needs to be noted that the bed height was controlled constant during the pilot experiment. With catalyst flow-rate boosted, the output of products PE was increased, so was the space-time-yield of the FBR. When the heat of the polymerization reaction is matched with the maximum capacity of heat removal, the corresponding catalyst flow-rate

Figure 8. Evolution of malfunction coefficients expressed in correlation dimension (CD2) (pilot-scale industrial reactor).

reaches its maximum allowed in this reactor. As a popular saying goes, “everything has two sides.” Since the heat of polymerization has not been removed immediately, some adverse effects, such as accumulation of heat, local temperature climbing, agglomeration, or sheeting, will occur in sequence. In the pilot experiments, the particle size distribution of product was in accordance with the above analysis, as shown in Table 4. Specifically, no agglomeration was observed when the catalyst feed rate increased at the first time (10:00). When the secondary increment of catalysts was added into the reactor, a few agglomerations had been detected since 18:20, then the discharged pipeline was blocked completely until 19:30. 4.3.1. Attractor Comparison Method and Characteristic S. Figure 7 shows the evolution of the attractor in the industrial experiments, and the characteristic S is based on AE signals. From this figure, we can obtain that the S value remained almost constant until 16:20 despite of the increase of catalyst feed rate in the primary stage (10:00). From this result, we prove that the series under operation condition was identical with that in the normal state. In other words, the two time series being compared are generated by the same mechanism. And after 17:00, the S value began to increase slightly, which indicated that the particles inside the fluidized bed started to gather together. It was probably caused by catalyst feed rate increase for the second time (17:00), and it was speculated that the catalyst mass within the bed have reached the maximum load. We can also draw the same conclusion from the increase of the mean size (0.4820.522 mm) of particles in the discharge pipeline. With the reaction continued, the attractor S exceeded its threshold S = 3 at 17:20, which 8481



Figure 9. Evolution of malfunction coefficients expressed in K-entropy (CK2) (pilot-scale industrial reactor).

Figure 10. Comparison among statistical characteristic S, pressure drop ΔP and temperature difference ΔT (pilot-scale industrial reactor).

marked a significant change in the FBR; that is, generally, when S value is greater than 3, the occurrence of an “abnormal” state can be accepted, and the confidence probability is 95%. In addition to these, from a few chunks (30 mm) in the pipeline of discharging, we obtain that the condition of the reactor was deteriorating. Some small agglomerations would assemble into larger chunks. After 18:30, S reached a much larger value because the biggest chunk formed in this reactor. Thus, from Figure 7 and Table 4, we can easily conclude that the attractor comparison method can be applied to monitor agglomeration in an industrial apparatus. Figures 8 and 9 show the evolution of malfunction coefficients C calculated through complexity analysis method, which is based on AE signals.10 In these pilot-plant experiments, it is also convenient and accurate to apply C to agglomeration detection in industry. However, S has many advantages over C values. For example, S surpassed its threshold value at 17:20, while C did at about 18:00. Besides, in setting threshold functions for C values, there is a certain degree of arbitrary criterion. 4.3.2. Comparisons of Different Measurement Techniques and Analysis Methods. On the basis of the analysis discussed above, the attractor S based on attractor comparison and the coefficients of malfunction C based on complexity analysis, are both suitable for monitoring the agglomeration. Although there exists a small difference between the two kinds of analysis methods, both detection methods can warn much earlier compared to γ radiation which monitored the agglomeration at 18:51. By contrast, the characteristic S has a response to the emergence of agglomeration 2030 min earlier than the coefficients C. Similarly, in Figure 10, we can see that the S curve can be used for agglomeration detection about 7080 min earlier than

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Figure 11. The effect of filtering on the detection results illustrated with a cold-model experiment (by using the minimum S value of the three consecutive S values).

Figure 12. The effect of filtering on the detection results illustrated with a cold-model experiment (by using the moving average method).

temperature (ΔT) and pressure (ΔP) difference signals in the industrial experiments, which could assist operators to take countermeasure to avoid the agglomeration in advance. Therefore, it can be concluded that the attractor comparison method is earlier, faster, and more accurate than the complexity analysis method, although both of them can be used to detect the agglomeration. To sum up, the preferable S value can be used as an indicator for “early recognition”, which enjoys a broad prospect in industrial application. 4.4. Optimization of Detection Model. To achieve high accuracy and rapid detection, we can optimize the AE-EARS model by some adjustment in detail, including the selection of criterion and the improvement in rate of false alarm. 4.4.1. Selection of Criterion. From these study results, either from lab-scale apparatus or from pilot-scale equipment, we obtain the same conclusion that the statistic S is better than coefficient C in monitoring the agglomeration. In other words, S is more sensitive to small changes in the particle size distribution, which means that it is more possible for S to offer “early and accurate warning” of agglomeration in FBRs. Thus, we prefer to select S as the characteristic parameter for agglomeration detection. And the critical value for agglomeration formation is S = 3. At the same time, coefficient C can be used as an alternate characteristic parameter to verify the former results. 4.4.2. Improvement in Rate of False Alarms. The sensitivity can be reduced by taking effective strategies (details in section 4.1.1), but there are some random, unrecognized disturbances in an industrial process, among which some cannot be measured, leading to an increase of the rate of false alarms. 8482


Industrial & Engineering Chemistry Research To overcome this problem, we improve the rate of false alarms by “filtering” the original S value.12 More explicitly, two filtering methods are shown here: (1) select the minimum S value of three consecutive S values (Figure 11); (2) process to smooth the curve through the moving average method (Figure 12). From these figures, we can see that the S curve after being filtered has a lower rate of false alarms than the original one. Moreover, it is pointed out in the literature12 that the confidence level of the attractor comparison method will increase from 95% to 99% or more by using the minimum value selecting method. Filtering the original S is very effective to decrease the rate of false alarms, but it is also crucial to select an ideal reference time series before sampling. When S is applied to an industrial unit, we should consider the AE signal time series in the best conditions as a reference. Once the grade transition occurs, the reference sample should be updated. With regard to long time detection, the moving average method can also be considered to filter the S value to obtain desirable results.12

5. CONCLUSIONS A noninvasive measurement method for detecting agglomerations in the fluidized bed was developed, which was based on AE signals, in lab-scale, and pilot-scale respectively. Preliminary analysis, such as sensitivity analysis and reliability verification were performed to make sure that the AE measurement can be applied to agglomeration detection. AE-EARS, developed by our study group, can be applied both in cold model and pilot-plant reactors. The attractor S based on attractor comparison and the coefficients of malfunction C based on complexity analysis, are both proven to be suitable for monitoring the agglomeration and could warn the fault early, fast, and accurately. Compared with the coefficients of malfunction CD2 and CK2, or other traditional measurement techniques (γ ray, temperature, and pressure difference), the statistical characteristic S acts much earlier and faster. As a result, the process operators will earn more time to take measures to prevent the ongoing agglomeration, which would decrease the unscheduled shutdown of the plant, thus generating great economic profit for the corporation. To sum up, the preferable S value can be used as an indicator for “early recognition”, which enjoys a promising future in industrial application. For example, this kind of method can be extended to be used in other FBRs in which the agglomeration would occur and disrupt the fluidization of the reactors. Moreover, the detection model can be optimized to improve the detection accuracy by selecting criterion more reasonably and decreasing rate of false alarm via filtering the original data. ’ AUTHOR INFORMATION Corresponding Author

*Tel.:+86-571-87951227. Fax: +86-571-87951227. E-mail address: [email protected].

’ ACKNOWLEDGMENT It is a pleasure to acknowledge the following scientists and students at Zhejiang University: Yijia Cao, Congjing Ren. The field data presented here could not have been accomplished without their assistance. The authors acknowledge the support and encouragement of the National Natural Science Foundation of China 21076180.

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’ NOMENCLATURE Ak = vector of AE values A = average of AE values, V C = coefficient of malfunction CD2 = coefficient of malfunction expressed in correlation dimension CK2 = coefficient of malfunction expressed in K-entropy, respectively CD2,a = correlation dimension of AE signals from operation condition CK2,a = Kolmogorov entropy of AE signals from operation condition CD2,0 = correlation dimension of AE signals in normal fluidization system CK2,0 = Kolmogorov entropy of AE signals in normal fluidization system d = bandwidth for smoothing of points in the state space dP = mean diameter of particles which is determined by sieving, mm Dbed = diameter of the fluidized bed, mm Dorifice = diameter of orifice in gas distributor, mm Fs = sampling frequency, Hz h = the dynamic bed level, m Hbed = height of the bed, m Hsensor = fixed position of AE sensor, m L = segment length m = embedding dimension MI = melting index, g 3 (10 min)1 ^ = estimator for Q Q S = estimator for the normalized squared distance between two attractors Tw = time window, s US = m 3 s1 ^ ) = conditional variance of Q ^ V c( Q Xk = normalized AE values in reference time series Xi = vector of normalized AE values in reference time series σA = standard deviation of AE values, V R = threshold value to determine the malfunction of agglomeration Fp = density of solid particles, kg 3 m3 ’ REFERENCES (1) Zhang, W. A Review of Techniques for the Process Intensification of Fluidized Bed Reactors. Chin. J. Chem. Eng. 2009, 17 (4), 688–702. (2) van Ommen, J. R.; Mudde, R. F. Measuring the Gas-Solids Distribution in Fluidized Beds—A Review. Int. J. Chem. React. Eng. 2008, 6. (3) Braham, R. J.; Harris, A. T. Review of Major Design and Scale-up Considerations for Solar Photocatalytic Reactors. Ind. Eng. Chem. Res. 2009, 48 (19), 8890–8905. (4) Naelap€a€a, K.; Veski, P.; Pedersen, J. G.; Anov, D.; Jørgensen, P.; Kristensen, H. G.; Bertelsen, P. Acoustic Monitoring of a Fluidized Bed Coating Process. Int. J. Pharm. 2007, 332 (12), 90–97. (5) Wormsbecker, M.; Pugsley, T.; van Ommen, J. R.; Nijenhuis, J.; Mudde, R. Effect of Distributor Design on the Bottom Zone Hydrodynamics in a Fluidized Bed Dryer Using 1-D X-ray Densitometry Imaging. Ind. Eng. Chem. Res. 2009, 48 (15), 7004–7015. (6) Jiang, Y.; McAuley, K. B.; Hsu, J. C. C. Nonequilibrium Modeling of Condensed Mode Cooling of Polyethylene Reactors. AIChE J. 1997, 43 (1), 13–24. (7) Yang, Y. R.; Yang, J. Q.; Chen, W.; Rong, S. X. Instability Analysis of the Fluidized Bed for Ethylene Polymerization with Condensed Mode Operation. Ind. Eng. Chem. Res. 2002, 41 (10), 2579–2584. 8483


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Fault Detection Based on Acoustic EmissionâEarly Agglomeration

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