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Ind. Eng. Chem. Res. 2009, 48, 3466–3473
Agglomeration Detection by Acoustic Emission (AE) Sensors in Fluidized Beds Jingdai Wang, Yijia Cao, Xiaojing Jiang, and Yongrong Yang* State Key Laboratory of Chemical Engineering and Department of Chemical and Biochemical Engineering, Zhejiang UniVersity, Hangzhou 310027, P. R. China
The occurrence of agglomeration in fluidized-bed production of polyolefin can have negative impacts on the efficiency of the reactor operation and may lead to defluidization and unscheduled shutdown of the plant. A novel method by using acoustic emission (AE) sensors was developed to detect different types of agglomeration in fluidized bed. Chaos analysis was discussed, and coefficients of malfunction were defined to recognize agglomeration. AE signals we divided into micro-, meso-, and macroscales by wavelet transform, and on the basis of the energy of mesoscale fraction analysis of AE signals, a prediction model was developed to predict the size of moving chunks in the bed. The application effect of AE method was illustrated with experiments carried out both in laboratory and the plant. 1. Introduction Agglomeration is one of the most important problems in olefin polymerization process carried out in gas-solid fluidized bed. Accumulation of the agglomerates may finally lead to defluidization and unscheduled shutdown of the plant. So, it makes sense to detect agglomeration as early as possible, and thus there will be more time to take some necessary measures to prevent the product from worse condition and avoid the economic loss. The general cause of agglomeration in olefin polymerization processes is that the heat removing rate is less than the heat releasing rate in local area. One type of these causes is that the catalyst is too active or the distribution of catalyst is not symmetrical and some catalyst may even adhere to the wall of the fluidized bed.1 In these cases, the heat is released too fast to be removed and the local temperature will rise up quickly, which in turn will promote the reaction and make the state even worse. Another type is poor heat transfer as a result of dead zone of fluidization and electrostatics charge in the bed.2,3 In these cases, the agglomerates are often attached to the inside surface of the fluidized bed and are usually referred as wall sheeting in some literatures. The sheeting may fall off from the wall as the catalyst activity and electrostatic forces decrease or the sheeting become too heavy to be attached to the wall. The fallen sheeting and other chunks formed in the first type may move in the bed and fall down to block the pores in the distribution plate, and some agglomerates may be discharged with other production particles and the discharge system may be blocked by these agglomerates. All these cases may lead to the unscheduled shutdown of the plant. In this paper, agglomerates in fluidized bed will be considered as two types: the moving agglomerates and the wall sheeting. In most cases, agglomerates in this paper are corresponding to the moving ones. In order to detect agglomeration in fluidized bed, it is quite important to select specific signals which are sensitive to agglomerating. Some of the signals being researched are related to the cause of agglomeration, and some are influenced by agglomeration. As an “accident”, agglomeration signals are often compared with that of normal conditions, and when obvious deviation between the two is detected, agglomeration might have appeared. * To whom correspondence should be addressed. Tel.: +86-57187951227. Fax: +86-571-87951227. E-mail: address: yangyr@ zju.edu.cn.
The most popular methods to detect agglomeration are pressure,4-6 electrostatic,7,8 temperature, radiation, and optic measurements.9 Pressure drop and fluctuations are widely used to monitor the fluidization for agglomeration which may affect the movement of bubbles or particles in the bed. However, the invasion characteristics might limit their application ranges and also cause the inconvenience for the users that fine particles can easily intrude and block the probe. As referred above, electrostatic is one of the causes of agglomeration, so it is theoretically feasible to be used to detect agglomeration. Useful conclusions about the relationship between electrostatic and wall sheeting have been presented in the literature, but further research about how to detect or foresee agglomeration are still on the way. When the particles agglomerate in the fluidized bed, the temperature may rise in some cases, so some research was carried out to detect agglomeration from the analysis of temperature time series. Though some good results have been obtained from the research, there are some inherent problems. One is that it is the local temperature rather than the overall temperature that is sensitive to the formation of agglomeration, and some agglomeration may not be detected as a result of the delay of the temperature change. The radiation (γ ray) method is now being replaced by other methods because of its harm to human health. Optic measurements are not suitable to the rigorous fluidized conditions in factories though it may offer more accurate information about the shape and size of agglomerates. Compared with these methods, acoustic emission (AE) signals in gas-solid fluidized bed reactors which are mostly generated as a result of the collision between particles and the internal wall of the fluidized bed and sampled by accelerometers have affluent information about the size and movement of fluidized particles,10-12 which makes it possible to detect agglomeration, one type of unusual particles, by analyzing AE signals. Different mathematical methods have been applied to analyze signals and recognize agglomerations. Those methods include standard deviation, spectral analysis, and nonlinear time series analysis. Take pressure drop and fluctuation for example, the standard deviation is not suitable for detecting changes in industrial installations for it strongly depends on the superficial gas velocity; spectral analysis is rarely reported as method to monitoring agglomerations because it is rather insensitive to changes of particle sizes. Nevertheless, some advanced methods have been developed recently, such as early agglomeration
10.1021/ie800324m CCC: $40.75 2009 American Chemical Society Published on Web 02/12/2009
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recognition system (EARS). The core of EARS is the attractor comparison method, developed at DUT.6 The attractor comparison method compared pressure time series in a statistical way using nonlinear analysis techniques and, as a result, was able to recognize agglomerations in full-scale biomass fired fluidized beds. The mathematical analysis of EARS is quite useful and may be possible to be developed with AE signals. In this paper, AE signals are analyzed by chaos analysis and energy fraction analysis. The results show that these methods are quite suitable to detect agglomeration in fluidized bed for the process of olefin polymerization. 2. Theory 2.1. Wavelet Analysis. The wavelet transform (WT) is a time-frequency analysis method that has been shown to be a powerful tool in the areas dealing with signal analysis and processing. WT is being applied to transient signal processing, such as pressure fluctuation and acoustic signals generated in fluidized beds.14-16 WT is often referred to as a sort of mathematical microscope, because different parts of the signal under analysis can be examined by automatically adjusting the focus. In other words, the basic function of WT is narrow (has smaller level) at higher frequencies and broad (has larger level) at low frequencies. By the use of WT, time series signals can be decomposed to different scales.15,17 Take a three level WT analysis of a signal with the highest frequency 500 kHz for example. In the first detail level, the signal frequency ranges from 250 to 500 kHz, in the second detail level from 125 to 250 kHz, and 62.5 to 125 kHz as the third detail level; 0-62.5 kHz is the third approximate level. Much more detail introduction and discussion about wavelet analysis can be found in ref 18. In this paper, the AE signals were analyzed by wavelet transform, and the signals in different detail levels were corresponding to different physical scales in the fluidized bed by the use of R/S analysis which was mentioned in the literature.15 On the basis of the differences of Hurst exponents of signals in different scales, a similar criterion proposed by Zhao15 was introduced to decompose the AE signals into three characteristic scales: macroscale (with all Hurst exponent larger than 0.5), mesoscale (with some Hurst exponents less than 0.5 and some Hurst exponents larger than 0.5), and microscale (with all Hurst exponents less than 0.5). On the basis of Zhao’s contribution,15 we assume that the microscale signals (first-fifth detail wavelet decomposing levels) could reflect the action of particles; the mesoscale signals (sixth and seventh detail wavelet decomposing levels) reflect the information of bigger particles, i.e. agglomeration or bubbles; and the macroscale signals (eighth and ninth detail wavelet decomposing levels) reflect the average dynamics or much bigger chunks. Detail discussion about the relationship of physical scales and signal levels can be found in the literature.15,19 2.2. Coefficients of Malfunction Based on Correlation Dimension and Kolmogorov Entropy. The characteristic of AE signals is instantaneous and stochastic. In other words, AE signals are typically nonstationary in the sense that the frequency and statistical characteristics change with time. They are made of series of, often overlapping but sometimes separate, decaying transient bursts occurring at irregular intervals and with random amplitudes. During the processing of AE signals, a common problem is how to extract physical-meaning parameters of interest when these involve in variations of time and frequency. Recently, many researchers20,21 have found that some chaotic coefficients, especially the correlation dimension and Kolmog-
orov entropy (K-entropy) are sensitive to the signals with qualitative change, which means it might be possible to detect the generation of agglomerations by calculating these chaotic characteristic parameters form AE signals. Detail research about correlation dimension and K-entropy can be found in ref 22. Generally, the correlation dimension is a measure of the dimensionality of the space occupied by a set of random points. When the correlation dimension is relatively small, the number of parameters which can influence the signal output system is relatively small. The K-entropy is a number expressed in bit · s-1 that quantifies the unpredictability, thus the degree of organization of the system, and is one of the characteristic numbers that quantifies the attractor.23 In regular systems, K-entropy is 0; in random systems, K-entropy is infinite. In this paper, the method was used introduced by Zhao19 to calculation the correlation dimension and K-entropy at the same time by least-squares method. It is not necessary to go into the details of the calculation procedures of the method; more information can be found in ref 24. In order to know the changes of the chaotic parameters during the process of agglomeration, the coefficient of malfunction of C is defined as follows: CD2 )
|
|
CD2,a - CD2,0 , CD2,0
CK2 )
|
CK2,a - CK2,0 CK2,0
|
(1)
where CD2 and CK2 are coefficients of malfunction expressed in correlation dimension and K-entropy, respectively; CD2,a and CK2,a are the correlation dimension and K-entropy of AE signals from operation condition, and CD2,0 and CK2,0 are the correlation dimension and K-entropy of AE signals from a normal fluidization system. On the basis of the previous research, it is obvious that the coefficients of malfunction of agglomeration are much larger than that in normal fluidization. In our research, a threshold value R was defined to determine whether the system is normal or failure. When the coefficient of malfunction C was larger than R, it can be judged that the system is in malfunction that may be caused by agglomeration in most cases, and some useful measures should be taken to prevent the reaction process from even worse condition. It is much easy to recognize malfunction of agglomeration by the use of coefficient of malfunctions, especially in industry processes. In our experiments, correlation dimension and K-entropy were calculated from normal fluidization experiments, and coefficient of malfunction was calculated from the compare between agglomeration and normal condition. 3. Experiments 3.1. Experimental Apparatus. Figure 1 is a schematic diagram of the experimental apparatus used in this study. It consists of two parts: a fluidized bed and the acoustic emission measurement system. The fluidized bed without preheating is made of made of Plexiglas, 150 mm i.d., with a 1000 mm height, and the perforated-plate distributor (with a 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 1.1 mm, open area ration of 2%) is installed at the bottom. The fluidized gas was heated by electrothermal 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 an interdiameter of 420 mm, 4000 mm height, and
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distribution and may block the pore which in extreme cases will lead to defluidization. Besides being fixed on the wall of the fluidized bed, AE sensors should also be located near or fixed on the distributor if possible in order to get as affluent agglomeration information as possible. In this paper, the AE sensors were located at a distance of 1 m above the gas distributor in industrial experiments. 4. Results and Analysis
Figure 1. Schematic diagram of experimental apparatus: (1) fan, (2) flowmeter, (3) mixture field, (4) distributor, (5) bed, (6) extened field, (7) AE sensor, (8) preamplifier, (9) main amplifier, (10) computer.
perforated-plate distributor (with pore diameter 12 mm, open air ratio of 2.5%). Parameters and operational conditions of the 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 shot noise online collection and analysis system developed by the UNILAB Research Center of Chemical Engineering in Zhejiang University consists of the data collection system and the computer. The data collection system consists of an AE signal sensor, amplifier, and analog to digital (A/D) converter. The AE sensor used in this study is a piezoelectric accelerometer, which is broadly used in collecting the acceleration of vibration without the noise transferred via the air (PXR15, 150 kHz, 100-300 kHz, 65 dB, China) [parameters of the sensor used in the experiments: resonance frequency 150 kHz, bandwidth at 10 dB 100-300 kHz, sensitivity 65 dB]. The transducer is attached noninvasively to the outside of the bed being monitored at special location above the gas distributor (as shown in Table 1) with sampling frequency of 500 kHz. The height of sensor was determined by the method presented in ref 25. An acoustic coupling agent is used to transfer the acoustic emission in the vessel to the transducer. For temporary installations, silicone grease is used to hold the transducer in place. For permanent installations, an adhesive holds the transducer in place and acts as an effective acoustic-coupling agent. The preamplifier (PXPA IV, 2-500 kHz, China) supplies sufficient gain for the signal to be “driven” down a cable, which can be up to 200 m in length. The signal conditioning system used in the experiments is PXMA signal conditioning equipment (PXMA, China). It provides additional gain and filters the signals with a high-pass cutoff frequency of 2 kHz. 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 the data acquisition card connected to the computer, controlled by the software package Labview. The same model was applied to the industrial fluidized bed of LLDPE, high-density polyethylene (HDPE), and bimodal polyethylene. In order to detect agglomeration as accurate as possible, it is better to fix AE sensors at the regime that moving agglomerations often observed. For example, most agglomerations that too heavy to be well fluidized may fall onto the
4.1. Preliminary Result of Laboratory Scale Experiments. In the industrial field, we generally define the particle whose diameter is above 20 mm as agglomeration. To conveniently study in the laboratorial level, we assume the particle whose diameter is above 2 mm as agglomeration. In the cold mold fluidized-bed (Φ150) experiment, the superficial gas velocity is 0.6 m · s-1. The quality of LLDPE particles and other factors were kept constant, and the size of particles in the bed was changed in different experiments. Further, agglomerations (1 wt %) with diameters of 10, 18, 20, and 40 mm were orderly add into the particles with the average sizes of 0.46 mm. Acoustic emission signals were sampled and analyzed. In the cold mold experiment with agglomerates added into the fluidized bed, it is obvious that the AE signals are sensitive to agglomerations, as shown in Figure 2. The AE signals were analyzed by wavelet transform, and then, signals in mesoscale, which are considered to have abundant information of agglomerations as discussed in section 2, were selected to be displayed. Early research has presented the idea that different wavelet levels represent the range of diameter of particles. The main frequency will fall down with the increase of the particle size.25 Experiments show that for the agglomeration, whose concentration is 0.1 wt %, when its diameter is below 20 mm, the energy percentage in the sixth wavelet level d6 (7.82-15.63 kHz) increase apparently (as shown in Table 3). When its diameter is above 20 mm, the seventh wavelet level d7 (3.91-7.82 kHz) increase apparently. It can be predicted that when the d8 (1.95-3.91 kHz) increased apparently, the bigger agglomeration would have appeared. When the mesoscale are defined as the gather of d6 and d7, it can be concluded that when energy percentage of mesoscale increases apparently; agglomerations may have appeared, and some measures must be taken to avoid serious results such as defluidization. Further research about the relationship between AE signals and agglomeration size is presented in section 4.3. 4.2. Chaos Analysis and Coefficient of Malfunction. In the fluidized bed with preheated, the gas temperature increased from 30 to 80 °C. Acoustic emission (AE) signals were collected each time when temperature was increased by 10 °C with a sample time of 20 s. When the temperature was above 80 °C, AE signals were sampled for 20 s every 3 min. Because of the low-molecular-weight polymers contained in the bimodal PE, the particles will agglomerate with each other when heated, and the fluidization condition will be influenced as a result of changing of average particle size. On the basis of the chaos analysis, the correlation dimension and K-entropy and the corresponding coefficients of malfunction were calculated from AE signals sampled in these experiments, as shown in Figures 3 and 4. It can be found that when temperature increases, correlation dimension and K-entropy increase, and the confusion degree of the system increased. It is because some fine particles began to agglomerate and the particle size distribution has changed appreciably. At this time,
Ind. Eng. Chem. Res., Vol. 48, No. 7, 2009 3469 Table 1. Parameters and Operational Conditions of Experimental Apparatus apparatus
mode
material
Dbed/mm
Hsensor/m
Hbed/m
DOrifice/mm
orifice ratio/%
Us/m · s-1
experiment 1 experiment 2 pilot
cold with heated cold hot
glass acryl glass Fe
100 150 420
0.1 0.2 1
0.5 1 4
1.1 2 12
2 2.6 2.5
0-0.5 0-1.2 0.6
Table 2. Properties of Experimental Material LLDPE and Bimodal PE materials
MIa/g · (10 min)-1
Fp/kg · m-3
dp/mm
LLDPE bimodal PE
2.0 6.0
918 946
0.46 0.365
a
MI means melting index.
Table 3. Acoustic Energy Percentage in Mesoscale Changed with the Agglomeration Size (nine levels, Us ) 0.6 m · s-1, LLDPE) agglomeration size (mm)
normal
5
10
20
40
energy percentage (d6) energy percentage (d7) energy percentage (d8) × 100
0.033 0.017 0.354
0.041 0.019 0.309
0.049 0.017 0.372
0.065 0.020 0.311
0.063 0.031 0.357
the coefficients of malfunction, both CD2 and CK2, increased when the temperature increased, especially CD2. As the temperature increased from 70 to 80 °C, the system was even more complicated and the corresponding coefficients of malfunction stepped up evidently, which means agglomerations might have been engendered in the fluidized bed but still could not be observed obviously. At 80 °C, both coefficients of chaos and malfunction and the complexity of system kept increasing, which means the agglomerates were growing on and on. In addition, the line of coefficients of malfunction fluctuated as well as increased, which reflected that some of the agglomerations might break up while in general particles were prone to agglomerate. Agglomerations were observed in the bed, which validated the feasibility of monitoring agglomerations by coefficients of chaos and malfunctions of AE signals. Although coefficients of chaos such as correlation dimension and K-entropy also changed as the particles agglomerating, the judge rule was difficult to apply into different systems. In contrast, once a threshold value is set, agglomeration can be judged by the coefficient of malfunction. On the basis of the carefully observation of the experimental phenomenon, the agglomeration of the particles become irreversible and uncontrolled when the temperature achieves 80 °C; therefore, we are going to use limiting values of 0.3 for RD2 and 1.2 for RK2. Higher values will give a warning which indicates that the state in fluidized bed has changed and some corresponding measures need to be carried out. The increase of confusion degree of fluidized system in the experiments results from the movements of bubbles and particles in the bed. As particles agglomerate, the process of fragmentation and coalescence of bubbles become more and more irregular
and complex. As a result, the AE signals engendered from the collision of particles and wall of the bed are more complex than that of normal fluidization, and the degree of chaos will increase. In the pilot-plant experiment with HDPE production, the schedule was as follows. The origin catalyst was changed by a much more active catalyst in even larger flux (5.9 g · h-1) at 10:00 a.m. Although the production was higher, no agglomeration was observed. At 17:00 pm, the catalyst flux was increased to 8.3 g · h-1, and at 18:00 pm, agglomerations were emerged in the bed. At 18:30 pm, the catalyst flux was decreased to 6.2 g · h-1, and at 19:30 pm, the discharged tube was blocked by agglomerations. AE signals were sampled every 5 min in average. In this experiment, by increasing the catalyst flux, the production rate of polyolefin was promoted and agglomerations were produced because the heat could not be removed quickly enough. The coefficients of chaos and malfunction of AE signals were analyzed in Figures 5 and 6. As shown in Figure 5, at the beginning of experiment, although the catalyst adding rate was increased, the accumulation influence was not obvious and could not be observed from the AE signals and the correlation dimension and K-entropy did not change a lot. From 17:50 pm, as the agglomeration engendered, the complexity of system increased and the correlation dimension and K-entropy both increased rapidly. The coefficients of malfunction also increased a lot as a result of fine particles agglomerated to larger particles. At 17:59 pm, the coefficients of chaos and malfunction increased steeply, which indicated that the fraction of large particles had increased a lot and some small agglomerations had formed in the bed. At 18: 10 pm, the line in Figures 5 and 6 showed the second steep increase, and small agglomerations assembled into a larger chunk. At 18:51 pm, the coefficients of chaos and malfunction got to the peak, which indicated even larger chunk were formed. At the same time, the γ radiation fixed on the wall of pilot fluidized bed also monitored the agglomeration. For some agglomerations which might break up into fractions, the line in Figures 5 and 6 decreased a little when the catalyst adding rate decreased after 19:50 pm. From the viewpoint of threshold R for coefficient of malfunctions, it will be much more convenient to apply the agglomeration detection method in industry. An early warning will be given when the coefficient of malfunctions are larger than R. In this case, the alarm sent out by the AE method around 18:00
Figure 2. Comparison of acoustic signals in mesoscale when (a) 1 wt % agglomerated of 18 mm was added and (b) normal fluidization. The experiments were carried out in the cold without the preheated mode (Φ150). The experimental conditions were as follows: Us ) 0.6 m · s-1, Fp ) 918 kg · m-3, dp ) 0.46 mm (normal LLDPE particles).
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Figure 5. Evolvement of chaotic characteristic parameters. The experiments were carried out in a pilot industrial fluidized bed reactor (Φ420). The experimental conditions were as follows: Us ) 0.6 m · s-1 (HDPE).
Figure 3. Evolvement of chaotic characteristic parameters. The experiments were carried out in preheated mode (Φ100). The experimental conditions were as follows: Us ) 0.6 m · s-1, Fp ) 946 kg · m-3, dp ) 0.365 mm (bimodal PE).
Figure 6. Evolvement of malfunction coefficients. The experiments were carried out in a pilot industrial fluidized bed reactor (Φ420). The experimental conditions were as follows: Us ) 0.6 m · s-1 (HDPE).
Figure 4. Evolvement of malfunction coefficients. The experiments were carried out in preheated mode (Φ100). The experimental conditions were as follows: Us ) 0.6 m · s-1, Fp ) 946 kg · m-3, dp ) 0.365 mm (bimodal PE).
was much earlier than the γ radiation, which indicated that the threshold (RD2 ) 0.3, RK2 ) 1.2) gotten from the laboratoryscale experiments is a “universal” criterion and also could work out in industrial plants. After the experiment, some pieces of agglomeration were observed in the discharge system, with the size of 420 × 40 × 5 (mm). There were also some fragments of agglomeration with the diameter of 30 mm and a relatively high hardness in the
discharge system, which proved that agglomerations did break up during the experiment. From the analysis above, the coefficients of chaos and malfunction can also to be applied to monitor the agglomeration in the industry process. Moreover, from the comparison of Figures 5 and 6, although both the degree of chaos and malfunction can be used to judge the agglomeration in the system, the coefficient of malfunction may be more convenient to be applied in different systems. In the pilot experiment, when agglomerations were detected from the coefficients of malfunction, the energy fraction in mesoscale also changed as the law shows in section 4.1. It indicates that the energy fraction change is probably to be used to predict the agglomeration size even in the industry process. Details will be shown in section 4.4. 4.3. Comparison between Acoustic Measurement and Temperature Measurement. During the pilot experiment expressed above, temperature was also measured as well as the acoustic signals. The energy fraction of AE in mesoscale was presented in Figure 7, and the temperature change was illustrated in Figure 8. From the two figures, it can be observed that the temperature measurement system sent an agglomeration alarm signal at 17:15 pm when a peak was observed in Figure 8, while the acoustic emission measurement sent an alarm at 16:46 pm when the first peak was observed in Figure 7. It is illustrated that the acoustic emission measurement is earlier than the temperature measurement to detect moving agglomeration in the pilot-reactor fluidized bed.
Ind. Eng. Chem. Res., Vol. 48, No. 7, 2009 3471
Figure 7. Evolvement of acoustic emission energy fraction of mesoscale in the fluidized bed The AE signals were sampled during the pilot experiments in an industrial fluidized bed reactor (Φ420). The experimental conditions were as follows: Us ) 0.6 m · s-1 (HDPE). The energy fraction of d6 and d7 scales were defined as mesoscales in the paper.
Figure 8. Temperature evolvement in the fluidized bed. The variation of temperature was recorded during the pilot experiments in an industrial fluidized bed reactor (Φ420). The experimental conditions were as follows: Us ) 0.6 m · s-1 (HDPE).
the energy fractions in mesoscale and the size of agglomerations, and then, the linear model is as follows. S ) a(Ed6 - E0d6) + b(Ed7 - E0d7) + c(Ed8 - E0d8)
Figure 9. Relationship between acoustic energy percentage in mesoscale and the sizes of agglomeration The experiments were carried out in the cold without preheated mode (Φ150). The experimental conditions were as follows: Us ) 0.6 m · s-1, Fp ) 918 kg · m-3, dp ) 0.46 mm (normal LLDPE particles). The agglomeration with diameter of 10, 18, 20, and 40 mm were added (1 wt %) into the cold mode in an orderly manner.
4.4. Prediction of Agglomeration Size. Early research in cold molds has illustrated that the energy fraction of AE signals in the mesoscale is sensitive to the agglomeration, and a close relationship between the energy fraction in mesoscale and the size of agglomerations is expected to be found when the average mass ratio of agglomerations to the total particles is constant. In the cold mold experiment, the energy fraction of d6 and d7 is sensitive to the size of agglomerations and two approximate linear relationships can be observed in Figure 9. Thus, we assume that there is a linear relationship between the change of
(2)
0 0 0 , Ed7 , and Ed8 represent the energy fraction of d6, Where, Ed6 d7, and d8, respectively, under the normal fluidized state. Ed6, Ed7, and Ed8 represent the energy fraction of d6, d7, and d8, respectively, in the practical production. a, b, and c represent the coefficient of agglomerations of these three scales. On the basis of the experiments both in the cold mold and pilot equipment, a, b, and c can be determined. The values changed a little because of the differences of temperature and pressure between the cold and pilot experiments. In general, the coefficient c > b > a, which demonstrates that the size of agglomerations reflected by d8 is bigger than those by d6 and d7, with the same increase of the energy fraction. The prediction model of eq 2 is deduced based on the assumption of constant mass fraction of agglomerations in the fluidized bed. However, this assumption is not necessary because in the fluidized bed with not too many agglomerations, the chance of more than one agglomerations colliding with the wall nearby one acoustic sensor is quite low. Then assuming that, in one sample time, no more than one agglomeration has collided with the wall nearby the acoustic sensor, the prediction model is feasible, without the consideration of the mass fraction of agglomerations in the fluidized bed. The prediction model based on the cold mold experiment is applied to the pilot fluidized-bed reactor. The results of both of the two experiments are shown in Tables 4 and 5, respectively. In the pilot experiment where the agglomeration changed with
3472 Ind. Eng. Chem. Res., Vol. 48, No. 7, 2009 Table 4. Comparison of the Actual Value and Prediction (Φ150 Experimental Apparatus) actual value (mm) prediction (mm) error (%)
5 5.4 8
10 10.5 5
20 21.4 7.0
40 44.5 11.2
Table 5. Comparison of the Actual Value and Prediction (Φ420 Pilot Apparatus) time
17:00
17:30
18:30
19:00
19:30
prediction (mm) actual value (mm)
1.86
2.55 2.5
7.76
14.33
23.51 about 25
passing time, agglomeration was sampled at 17:30 and 19:30 pm and the predicted value showed that the prediction model is suitable to detect agglomerations in the pilot. By adjusting the coefficient in eq 2, the prediction model of agglomerations can also be applied in other product systems. For instance, in a LLDPE industrial fluidized reactor of a factory, the AE signals of agglomerations encountered on day was sampled and analyzed, as shown in Figure 10. Note that the production was stable before 13:30 pm, despite a few small fluctuations. The acoustic signals changed steeply after 13:34 pm, which, in our opinion, was corresponding to agglomerations with size larger than 120 mm. That means that some useful measures should be taken immediately to remove the agglomeration and prevent the fluidized state from being exacerbated. Unfortunately, without those methods, the agglomeration grew on and on to be even larger than 160 mm (16:30 pm) and the discharge system was seriously blocked. It should be mentioned that the early warning equipment began to send warnings 15 min later, which was too late. When repairing the reactor and removing chunks from the discharge system, agglomerations of 220 × Φ160 (mm) were observed in the discharge pipe. Even though the errors between prediction and actual values can be acceptable from an engineering viewpoint, we still believe that the real connection between the size of agglomerates and the energy ratio of AE signals is not linear, which might be nonlinear or chaotic systems. Therefore, the results we presented here are parts of the early stage of work, and many experiments should be carried out in the future. 4.5. Detection of Wall Sheeting by AE Methods. The agglomerations discussed above are all moving ones. As discussed in section 1, some moving agglomerations result from wall sheeting that has fallen off of the wall, so if wall sheeting can be detected, it may give an even earlier alarm than moving agglomeration detection in some cases. However, wall sheeting not moving in the fluidized bed cannot generate corresponding AE signals, so that they cannot be detected by the methods mentioned above. Other analysis method of AE signals should be developed to recognize wall sheeting. In the Φ150 mm laboratory scale apparatus, experiments are carried out to study the influence of wall sheeting on AE signals. PE agglomeration was attached to the inside wall of the fluidized bed by plasticene as wall sheeting. AE signals were sampled on the wall, and the axial as well as the circumferential energy distributions were compared with those without “wall sheeting”. It could be observed that by reference to the curve of the AE energy distribution in the normal fluidized bed, the anomalous drop in the axial distribution of AE energy indicated the height of the wall sheeting, and the region where the circumferential distribution of AE energy was unsymmetrical. The average energy was much lower, and the standard deviation was much larger than those in the normal fluidized bed corresponding to the circumferential location of wall sheeting. The result was
Figure 10. Agglomerate size predicated by the AE method varying with time in an industrial unit (Us ) 0.6 m · s-1, Fp ) 918 kg · m-3, LLDPE).
Figure 11. Three-dimensional diagram of the AE energy differences. The experiments were carried out in the cold without preheated mode (Φ150). The experimental conditions were as follows: Us ) 0.6 m · s-1, Fp ) 918 kg · m-3, dp ) 0.46 mm (normal LLDPE particles). The agglomeration was attached to the inside wall of the fluidized bed by plasticene as wall sheeting.
shown in Figure 8, where the wall of the fluidized bed was spread and the energy corresponded to the height of the curve. In Figure 11, the red regime where the anomaly drop was observed by reference to the normal fluidization implied the presence of wall sheeting nearby. The preliminary experiments validated the feasibility to detect wall sheeting by AE sensors. However, as described above, this method calls for quite a lot of sensors to get a whole image of the fluidized bed which is not economical for the industrial application. 5. Conclusion A noninvasive method of detecting agglomerations in fluidized beds to product polyolefin was developed based on the analysis of AE signals sampled by accelerometers. This method was based on the energy fraction analysis and chaos analysis of AE signals and by the definition of coefficients of malfunction (CD2 and CK2), and agglomerations that were moving in the fluidized bed could be detected; their size can be predicted from an empirical model. The first results of laboratory scale experiments with and without outside heating and plant experiments proved the feasibility of this new method. A preliminary experiment about the detection of wall sheeting by the AE method was also carried out, and the result showed that it is
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possible to detect both moving and attaching agglomerates in the fluidized bed. The method based on the AE signals generated mostly by the particles in the fluidized bed is much easier to understand and apply to industrial apparatuses. Moreover, the AE measurement can also be applied to detect agglomeration in other fluidized systems, such as in full-scale biomass fluidized beds and so on. But, there are still some flaws about this method. The linear model for predicting the size of agglomerates proposed here lacks both theoretical support and experimental data. Therefore, the prediction capability of the model is still a problem. The detection of moving agglomerations and wall sheeting is quite different from each other, and the method is still not a complete solution. Further research is required to modify the two parts, and some other factors that may influence the accuracy of this method should also be considered and studied. Furthermore, the detection of agglomeration discussed in this paper is still not really “early recognition”. Only when agglomerations have been formed in the fluidized bed can we detect them. Although AE measurement is sensitive to the agglomerations even when they are quite small, it may be too late to take measures to avoid worse states in some cases. In order to investigate the whole reactor from different views and detect agglomerations as early as possible, AE measurement should be combined with other measurements such as temperature and pressure fluctuation. Some other techniques such as multi-information fusion can be applied and developed in the field of agglomeration detection. Acknowledgment It is a pleasure to acknowledge the following scientists and students at Zhejiang University: Congjing Ren, Xianbo Yu, and Weijie Shu. 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 20490205. Nomenclature a, b, c ) coefficients in the function of size of agglomerations, corresponding to the energy fraction in scales d6, d7, and d8 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 CK2,a ) Kolmogorov entropy of AE signals CD2,0 ) correlation dimension of AE signals in normal fluidization system CK2,0 ) Kolmogorov entropy of AE signals in normal fluidization system dp ) mean diameter of particles which is determined by sieving, mm D2 ) correlation dimension of operation time series E0d6, E0d7, E0d8 ) energy fraction in scales d6, d7, and d8, respectively, under the normal fluidized state Ed6, Ed7, Ed8 ) energy fraction in scales d6, d7, and d8, respectively, in the practical production K2 ) Kolmogorov entropy of operation time series, bit · s-1 MI ) melting index, g · (10 min)-1 S ) size of agglomeration, mm R ) threshold value to determine the malfunction of agglomeration Fp ) density of solid particles, kg · m-3
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ReceiVed for reView February 27, 2008 ReVised manuscript receiVed November 16, 2008 Accepted January 19, 2009 IE800324M
