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Analysis of spouted bed pressure fluctuations during particle coating
Chemical Engineering and Processing 48 (2009) 1129–1134
Contents lists available at ScienceDirect
Chemical Engineering and Processing: Process Intensification journal homepage: www.elsevier.com/locate/cep
Analysis of spouted bed pressure fluctuations during particle coating N.E.C. Lopes ∗ , V.A.S. Moris, O.P. Taranto School of Chemical Engineering, University of Campinas, UNICAMP, P.O. Box 6066, 13083-970, Campinas, SP, Brazil
a r t i c l e
i n f o
Article history: Received 10 June 2008 Received in revised form 13 November 2008 Accepted 12 March 2009 Available online 24 March 2009 Keywords: Spouted bed Coating Analysis spectral
a b s t r a c t The purpose of this study was to identify changes occurred during the process of particle coating in a spouted bed, by comparing data from visual observation along with the statistic and spectral analysis of data from pressure drop in real time. The experiments were carried out in a cone-cylindrical spouted bed, with ABS and polystyrene particles and with a polymeric Eudragit® based suspension. The pressure drop time series were collected with Labview 7.0 and turned into the frequency domain by using a fast Fourier transform routine. The dynamic changes observed during the coating process were paired with the power spectra, in order to create an identification procedure for the situations of low particles circulation rate, internal spout and bed collapse. Hopefully, an objective identification of instabilities may lead to a future application of process control. © 2009 Elsevier B.V. All rights reserved.
1. Introduction Particle coating in a spouted bed has proved to be very promising, in view of its potential to be applied to several products, such as pills, cosmetics, seeds and fertilizers. However, the use of spouted beds in industries is still limited, since it is very difficult to maintain a stable fluid-dynamic regime. Therefore, it is highly important to pursue regime stability for any kind of use, since stability would result in more efficient processes. Many works mentioned in the literature, use the online pressure data acquisition as a tool to identify regime transition in spouted and fluidized beds [1–8]. These pressure data may be analysed in different ways: statistical analysis in time domain, frequency spectral analysis or Fourier’s domain, and analysis based on the chaos theory [1]. Studies about spouted beds aiming to identify and monitor the gas–solid contact regimes have been developed, and some important results have already been reported. Lourenc¸o [2] concluded that the use of fast Fourier transformation technique, over the pressure drop signals, resulted in a well-defined power spectrum, such as for a stable spouting flow which presented peaks between 6 and 7 Hz, a fixed bed from 40 to 45 Hz, and an internal spout characterized by two frequency bands in the range of 7–8 Hz and another one at 40–16 Hz, allowing clear identification of all flow regimes. Piskova and Mörl [3] studied regime transition for different spouted bed configurations through signal analyses. The statistical analysis of data showed that in the beginning, the standard deviation of the pressure fluctuation increased with fluid velocity, up to a maximum
∗ Corresponding author. Tel.: +55 019 35213901; fax: +55 019 35213922. E-mail address: nadia [email protected] (N.E.C. Lopes). 0255-2701/$ – see front matter © 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.cep.2009.03.003
value corresponding to the transition from fixed to moving bed. With the fluid velocity increase during the stable spouting regime, the standard deviation remained constant. Therefore, the objective of this work is to carry out online pressure data acquisition in a process of spouted bed particle coating, so as to evaluate the sensitivity in identifying changes along the process, which may lead to instabilities that may result in products out of market specifications. 2. Materials and methods 2.1. Materials The formulation chosen for coating was based on studies developed by [4], Table 1, who managed to get a plain and uniform film by using this suspension for particle coating. Coating was done in a Plexiglas cone-cylindrical spouted bed. The column was 70 cm high, with a 14.3 cm diameter, a 4 cm diameter hole, and a 60◦ base angle. Fig. 1 shows the experimental system, composed by a spouted bed and its peripheral devices. Air flow was controlled by a frequency inverter. Above the particle bed was the spray nozzle, which was fed with the coating suspension by a peristaltic pump, and with air, by centrifugal blower. A thermocouple was installed at the bed inlet, in order to measure the air temperature during intake. The differential pressure sensor has two ports: one connected to the bed internal wall, above the particles bed, and the other connected below the particles bed. The pressure drop data were obtained with the use of the LabView 7.0 software, to a sample rate of 400 Hz and 4096 points. The pressure drop data, in function of time, were converted into the frequency domain by means of a fast Fourier transform routine.
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N.E.C. Lopes et al. / Chemical Engineering and Processing 48 (2009) 1129–1134 Table 2 Particle properties.
Table 1 Formulation suspension of coating. Reagents
(%) in weight ®
Eudragit L30-D55 Polyethylene glycol (PEG 6000) Talc Magnesium sterate Titanium dioxide Corante Triethyl-citrate Distilled water
16.70 0.75 2.75 1.00 1.20 0.80 0.50 76.30
The experiments used ABS and polystyrene particles coated by a Eudragit® based polymeric suspension. The particle properties are in Table 2. The coating tests operational conditions were fixed during the whole coating process. They are presented in Table 3. The particle load and the coating suspension flow rate were defined in preliminary tests. The experiments characteristics are presented in Table 4.
Material
Shape
dp (mm)
s (g/cm3 )
εp (%)
ABS Polystyrene
Round Lenticular
3.03 3.81
1.0372 ± 0.0016 1.0610 ± 0.0016
0.83 1.03
Table 3 Coating tests operational conditions. Spouting flow air speed (m/s) Spouting flow air temperature (◦ C) Spraying pressure (psig) Atomizer nozzle height from fixed particle bed (cm)
1.20 × Ujm 60 15 10
Table 4 Tests carried out. Test
Particle
Load (g)
Suspension of coating flow rate (ml/min)
1 2 3 4 5 6 7 8
ABS ABS ABS ABS Polystyrene Polystyrene Polystyrene Polystyrene
300 300 600 600 300 300 600 600
8 10 8 10 5 8 8 10
The experiments began by allowing a pre-determined gas flow rate into to bed, obtaining steady spouting, with no coating suspension spraying. At this moment a first collection of pressure drop was carried out. Then, the coating suspension feeding device was turned on; time count was started and several data acquisition collections were done, until the bed collapse could be visually observed. At
Fig. 1. Equipment sketch.
Fig. 2. Standard deviation of the pressure fluctuation according to time.
N.E.C. Lopes et al. / Chemical Engineering and Processing 48 (2009) 1129–1134
1131
Fig. 3. Fluctuation of the pressure and probability distribution signals, polystyrene (600 g and 10 ml/min), for the different regimes studied: (a) spouting flow with coating, (b) internal spouting flow and (c) collapse.
this point the coating suspension device was turned off until the bed regained spouting movement. After that, the coating suspension was fed to bed again. The tests were finished when the particles were not able to come back to the spouting flow. With the purpose of checking instability spots and the tendency to a spouting flow collapse, notes were taken about the visual observation of the particles dynamic behavior in the bed during all collection steps.
spouting flow with fountain established and good circulation rate. The collapse or fixed bed is characterized by air flow through the interparticle interstices, with no particle circulation rate; no source of movement. The internal spouting flow differs from the fixed bed due to a central preferential channel, which causes particle displacement, especially in the conical part of the bed. The statistic analysis (standard deviation and probability distribution) and spectral analysis were used to characterize the collected signals.
3. Results and discussion 3.1. Pressure signal standard deviation The visually identified regimes in this study were the spouting flow before coating, spouting flow during coating, internal spouting flow and collapse (fixed bed). Spouting flow during coating is
Standard deviation is used to study the signal amplitude. The amplitude variation due to operational conditions has been stud-
1132
N.E.C. Lopes et al. / Chemical Engineering and Processing 48 (2009) 1129–1134
ied by many researchers, not only to identify fluidization regime transitions, but also to check fluidization quality [1–3] and [9]. For a determined signal, xi , the standard deviation can be obtained from Eq. (1).
n 1 = (xi − x¯ )2 n−1
n
(1)
i=1
x i=1 i
x¯ =
(2)
n
In this equation, x = P, x¯ = Pm, and n = 4096 (points). The standard deviation of the pressure fluctuation during coating made it possible to monitor the regimes observed in the bed. The internal spout (JI) presented the maximum standard deviation, which can be clearly observed by the peaks in Fig. 2, due to pressure drop and regime shift. A similar result was found by [3,5] and [9], who, after studying the fluid-dynamic regimes of different spouted bed construction, concluded that the maximum standard deviation corresponds to the transition from a fixed bed to a moving one. With the bed collapse (C), Fig. 2b, the standard deviation decreased due to the absence of particle movement, and the pressure drop changes were smaller. When spray nozzle was stopped (B), Fig. 2a, the standard deviation increased. 3.2. Probability distribution and fluctuation of the pressure In order to have the probability distribution curve the minimum and maximum values of pressure drop were taken. The interval was divided in 100 parts and paired with their correspondent frequency. The probability distribution curve is given by the relationship between the percentile frequency and pressure drop. A significant difference was observed between the probability distribution and the fluid-dynamic regimes studied. At collapse, the curve was narrower at its basis and sharper at its peak, when compared to the curve for the spouting flow with coating, as seen in Fig. 3. Freitas et al. [5] identified the regimes in rectangular spouted beds and also observed this probability distribution difference for the fixed bed. Some differences could be observed between the pressure fluctuation graphs for some tests. Basically, there were differences between the fluctuations amplitude, oscillation gaps and the pressure average, as shown in Fig. 3. These results are consonant with the ones presented by some authors, such as [6], [7], [1], and [2], who identified the spouted bed regimes in a study without particle coating. No references were found with data about coating. It was not possible, though, to identify a quantifying variable useful to interrupt the process. 3.3. Spectral analysis Spectral analysis can be used to characterize different spouted bed regimes, either fluidized or with other multi-phase systems. The spectral density function changes information from the time domain to the frequency domain [5]. In mathematical terms, spectral density (SD) can be defined as:
SD = lim
2
E X(f )
T →∞
(3)
T
In which X(f) is the Fourier change of the signal, given by Eq. (4).
∞
X(f ) =
x(t) e−j2ft dt
−∞
Fig. 4. Power spectrum for ABS (600 g and 10 ml/min), at the different regimes.
The dominating frequency was not a proper parameter to identify the spouting flow instability in this work, due to the existence of similar values in different dynamic situations observed. A similar result was found by [1], [3] and [5]. Different kinds of behavior were noticed for ABS and polystyrene in the spectral analysis, so they were discussed separately for better understanding of the results. 3.4. Spectra for ABS
(4)
−∞
The power spectra obtained identified the different regimes in a convincing way from the dominating peak maximum amplitude.
It could be noticed that all the spectra presented an only and well-defined peak in the range of 6–7.5 Hz, for the spouting flow regime. Before coating started, as seen in Fig. 4a, the spectrum
N.E.C. Lopes et al. / Chemical Engineering and Processing 48 (2009) 1129–1134
1133
Fig. 5. Power spectrum for polystyrene (600 g and 10 ml/min), at the different regimes.
presented a maximum amplitude of 246 Pa2 /Hz. After the coating suspension feed was turned on, this amplitude had a falling trend and remained in the range of 75–180 Pa2 /Hz, as shown in Fig. 4b, presenting a good particle circulation rate. The internal spout spectrum presented a considerable raise in amplitude, as seen in Fig. 4c. The internal spout can be visually described as a very intense particle vibration at the bed inlet, then weaker and weaker towards the upper part of the bed, where there was no vibration at all. This highrate vibration reflected significant raise in amplitude (407 Pa2 /Hz). Other authors, such as [7] and [5], also found this relation between particle vibration and spectrum amplitude. The same kind of behavior repeats for others operational conditions. 3.5. Spectra for polystyrene The spouting regime for polystyrene was not as stable as for ABS, the circulation rate was visually smaller in the annular region, with a low fountain and a pulsating movement at the bed inlet. At the spectrum, multiple peaks appeared. It is interesting to notice that there is a dominating peak in the range of 1–3 Hz, and another one in the same range for ABS, between 5.5 and 7.5 Hz. For the spectral analysis, the second peak variation was considered, with a stable spouting flow range, which made it possible to identify instability during coating. The same kind of behavior observed for the polystyrene spectrum was also observed in [7] study. A greater peak multiplicity was observed during particle coating, as seen in Fig. 5b. A high particle circulation rate was observed at the region of 10–55 Pa2 /Hz, and a low one was noticed for amplitudes lower than 10 Pa2 /Hz, at which the suspension coating atomization should be turned off. At the internal spout, the spectrum was observed as having an only dominating peak, as shown in Fig. 5c,
and a considerable amplitude raise, with amplitude higher than 136 Pa2 /Hz. At collapse, no dominating frequency was found for the spectrum, as shown in Fig. 5d. 4. Conclusions The results obtained in this study made it possible to conclude that real time pressure drop data acquisition during particle coating allowed identification and monitoring of the process. The pressure fluctuation standard deviation tends to increase when it changes from the spouting regime to the internal spout regime, and a reduction with total bed collapse, which can be used as an alarm to control the spouting regime stability. The probability distribution could be used to visually differentiate the regimes observed during coating, especially bed collapse, which presented a curve with a sharp peak and a narrow basis. The spectral analysis proved to be an objective method to identify spouting regimes during particle coating. According to the spectra obtained in this study, the amplitude, at the dominant peak, showed determined ranges for each regime and particle. However, it showed that each particle should be studied separately, so that the spout stability range during coating can be observed. The dominant frequency has not proved to be an adequate parameter for the identification of the spout instability in this work, due to the proximity of values in the different dynamic situations observed. Appendix A. Nomenclature W dp
amplitude (Pa2 /Hz) particle diameter (mm)
1134
εp s F SD T Ujm X(f) JI B C FFT P
N.E.C. Lopes et al. / Chemical Engineering and Processing 48 (2009) 1129–1134
particle porosity (%) particle density (g/cm3 ) frequency (Hz) spectral density (Pa2 /Hz) total time (s) minimal spouting flow speed (m/s) Fourier’s change of pressure signal internal spout spray nozzle stopped bed collapse fast Fourier transform mean pressure fluctuation (Pa)
References [1] J. Xu, X. Bao, W. Wei, G. Shi, S. Shen, H.T. Bi, J.R. Grace, C.J. Jim, Statistical and frequency analysis of pressure fluctuations in spouted beds, Powder Technology 140 (2004) 141–154.
[2] R.M. Lourenc¸o, Identification and Monitoring of Flow Regimes in Spouted Beds, master’s degree thesis paper. Federal University of Uberlândia, Uberlândia, MG, Brazil, 2006, 115p. [3] E. Piskova, L. Mörl, Fluidization regimes in different spouted bed apparatus constructions, Chemical Engineering Processing 46 (2006) 695–702. [4] M.W. Donida, S.C.S. Rocha, Coating of urea with an aqueous polymeric solution in a two-dimensional spouted bed, Dry Technology 20 (3) (2002) 685–704. [5] L.A.P. Freitas, O.M. Dogan, C.J. Lim, J.R. Grace, D. Bai, Identification of flow regimes in slot-rectangular spouted beds using pressure fluctuations, The Canadian Journal of Chemical Engineering 82 (2004) 60–73. [6] V.A. Silva, S.C.S. Rocha, O.P. Taranto, G.S.V. Raghavan, Analysis of stability of spouted bed fluid dynamic regime through bed drop fluctuations measurements, Science & Engineering Journal 8 (2) (1999) 129–137. [7] V.A. Silva, S.C.S. Rocha, O. Taranto, G.S.V. Raghavan, Pressure drop analysis as a toll for obtaining the fluid-dynamic regimes in spouted bed, in: Drying 98, Proceedings of the 11th International Drying Symposium, Halkidiki, Greece, 1998, pp. 512–518. [8] S. Sasic, B. Leckner, F. Johnsson, Characterization of fluid dynamics of fluidized by analysis of pressure fluctuations, Progress in Energy and Combustion Science 33 (2007) 453–496. [9] M.S. Bacelos, J.T. Freire, Stability of spouting regimes in conical spouted beds with inert particle mixture, Industry Engineering Chemical 45 (2006) 808–817.
Contents lists available at ScienceDirect
Chemical Engineering and Processing: Process Intensification journal homepage: www.elsevier.com/locate/cep
Analysis of spouted bed pressure fluctuations during particle coating N.E.C. Lopes ∗ , V.A.S. Moris, O.P. Taranto School of Chemical Engineering, University of Campinas, UNICAMP, P.O. Box 6066, 13083-970, Campinas, SP, Brazil
a r t i c l e
i n f o
Article history: Received 10 June 2008 Received in revised form 13 November 2008 Accepted 12 March 2009 Available online 24 March 2009 Keywords: Spouted bed Coating Analysis spectral
a b s t r a c t The purpose of this study was to identify changes occurred during the process of particle coating in a spouted bed, by comparing data from visual observation along with the statistic and spectral analysis of data from pressure drop in real time. The experiments were carried out in a cone-cylindrical spouted bed, with ABS and polystyrene particles and with a polymeric Eudragit® based suspension. The pressure drop time series were collected with Labview 7.0 and turned into the frequency domain by using a fast Fourier transform routine. The dynamic changes observed during the coating process were paired with the power spectra, in order to create an identification procedure for the situations of low particles circulation rate, internal spout and bed collapse. Hopefully, an objective identification of instabilities may lead to a future application of process control. © 2009 Elsevier B.V. All rights reserved.
1. Introduction Particle coating in a spouted bed has proved to be very promising, in view of its potential to be applied to several products, such as pills, cosmetics, seeds and fertilizers. However, the use of spouted beds in industries is still limited, since it is very difficult to maintain a stable fluid-dynamic regime. Therefore, it is highly important to pursue regime stability for any kind of use, since stability would result in more efficient processes. Many works mentioned in the literature, use the online pressure data acquisition as a tool to identify regime transition in spouted and fluidized beds [1–8]. These pressure data may be analysed in different ways: statistical analysis in time domain, frequency spectral analysis or Fourier’s domain, and analysis based on the chaos theory [1]. Studies about spouted beds aiming to identify and monitor the gas–solid contact regimes have been developed, and some important results have already been reported. Lourenc¸o [2] concluded that the use of fast Fourier transformation technique, over the pressure drop signals, resulted in a well-defined power spectrum, such as for a stable spouting flow which presented peaks between 6 and 7 Hz, a fixed bed from 40 to 45 Hz, and an internal spout characterized by two frequency bands in the range of 7–8 Hz and another one at 40–16 Hz, allowing clear identification of all flow regimes. Piskova and Mörl [3] studied regime transition for different spouted bed configurations through signal analyses. The statistical analysis of data showed that in the beginning, the standard deviation of the pressure fluctuation increased with fluid velocity, up to a maximum
∗ Corresponding author. Tel.: +55 019 35213901; fax: +55 019 35213922. E-mail address: nadia [email protected] (N.E.C. Lopes). 0255-2701/$ – see front matter © 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.cep.2009.03.003
value corresponding to the transition from fixed to moving bed. With the fluid velocity increase during the stable spouting regime, the standard deviation remained constant. Therefore, the objective of this work is to carry out online pressure data acquisition in a process of spouted bed particle coating, so as to evaluate the sensitivity in identifying changes along the process, which may lead to instabilities that may result in products out of market specifications. 2. Materials and methods 2.1. Materials The formulation chosen for coating was based on studies developed by [4], Table 1, who managed to get a plain and uniform film by using this suspension for particle coating. Coating was done in a Plexiglas cone-cylindrical spouted bed. The column was 70 cm high, with a 14.3 cm diameter, a 4 cm diameter hole, and a 60◦ base angle. Fig. 1 shows the experimental system, composed by a spouted bed and its peripheral devices. Air flow was controlled by a frequency inverter. Above the particle bed was the spray nozzle, which was fed with the coating suspension by a peristaltic pump, and with air, by centrifugal blower. A thermocouple was installed at the bed inlet, in order to measure the air temperature during intake. The differential pressure sensor has two ports: one connected to the bed internal wall, above the particles bed, and the other connected below the particles bed. The pressure drop data were obtained with the use of the LabView 7.0 software, to a sample rate of 400 Hz and 4096 points. The pressure drop data, in function of time, were converted into the frequency domain by means of a fast Fourier transform routine.
1130
N.E.C. Lopes et al. / Chemical Engineering and Processing 48 (2009) 1129–1134 Table 2 Particle properties.
Table 1 Formulation suspension of coating. Reagents
(%) in weight ®
Eudragit L30-D55 Polyethylene glycol (PEG 6000) Talc Magnesium sterate Titanium dioxide Corante Triethyl-citrate Distilled water
16.70 0.75 2.75 1.00 1.20 0.80 0.50 76.30
The experiments used ABS and polystyrene particles coated by a Eudragit® based polymeric suspension. The particle properties are in Table 2. The coating tests operational conditions were fixed during the whole coating process. They are presented in Table 3. The particle load and the coating suspension flow rate were defined in preliminary tests. The experiments characteristics are presented in Table 4.
Material
Shape
dp (mm)
s (g/cm3 )
εp (%)
ABS Polystyrene
Round Lenticular
3.03 3.81
1.0372 ± 0.0016 1.0610 ± 0.0016
0.83 1.03
Table 3 Coating tests operational conditions. Spouting flow air speed (m/s) Spouting flow air temperature (◦ C) Spraying pressure (psig) Atomizer nozzle height from fixed particle bed (cm)
1.20 × Ujm 60 15 10
Table 4 Tests carried out. Test
Particle
Load (g)
Suspension of coating flow rate (ml/min)
1 2 3 4 5 6 7 8
ABS ABS ABS ABS Polystyrene Polystyrene Polystyrene Polystyrene
300 300 600 600 300 300 600 600
8 10 8 10 5 8 8 10
The experiments began by allowing a pre-determined gas flow rate into to bed, obtaining steady spouting, with no coating suspension spraying. At this moment a first collection of pressure drop was carried out. Then, the coating suspension feeding device was turned on; time count was started and several data acquisition collections were done, until the bed collapse could be visually observed. At
Fig. 1. Equipment sketch.
Fig. 2. Standard deviation of the pressure fluctuation according to time.
N.E.C. Lopes et al. / Chemical Engineering and Processing 48 (2009) 1129–1134
1131
Fig. 3. Fluctuation of the pressure and probability distribution signals, polystyrene (600 g and 10 ml/min), for the different regimes studied: (a) spouting flow with coating, (b) internal spouting flow and (c) collapse.
this point the coating suspension device was turned off until the bed regained spouting movement. After that, the coating suspension was fed to bed again. The tests were finished when the particles were not able to come back to the spouting flow. With the purpose of checking instability spots and the tendency to a spouting flow collapse, notes were taken about the visual observation of the particles dynamic behavior in the bed during all collection steps.
spouting flow with fountain established and good circulation rate. The collapse or fixed bed is characterized by air flow through the interparticle interstices, with no particle circulation rate; no source of movement. The internal spouting flow differs from the fixed bed due to a central preferential channel, which causes particle displacement, especially in the conical part of the bed. The statistic analysis (standard deviation and probability distribution) and spectral analysis were used to characterize the collected signals.
3. Results and discussion 3.1. Pressure signal standard deviation The visually identified regimes in this study were the spouting flow before coating, spouting flow during coating, internal spouting flow and collapse (fixed bed). Spouting flow during coating is
Standard deviation is used to study the signal amplitude. The amplitude variation due to operational conditions has been stud-
1132
N.E.C. Lopes et al. / Chemical Engineering and Processing 48 (2009) 1129–1134
ied by many researchers, not only to identify fluidization regime transitions, but also to check fluidization quality [1–3] and [9]. For a determined signal, xi , the standard deviation can be obtained from Eq. (1).
n 1 = (xi − x¯ )2 n−1
n
(1)
i=1
x i=1 i
x¯ =
(2)
n
In this equation, x = P, x¯ = Pm, and n = 4096 (points). The standard deviation of the pressure fluctuation during coating made it possible to monitor the regimes observed in the bed. The internal spout (JI) presented the maximum standard deviation, which can be clearly observed by the peaks in Fig. 2, due to pressure drop and regime shift. A similar result was found by [3,5] and [9], who, after studying the fluid-dynamic regimes of different spouted bed construction, concluded that the maximum standard deviation corresponds to the transition from a fixed bed to a moving one. With the bed collapse (C), Fig. 2b, the standard deviation decreased due to the absence of particle movement, and the pressure drop changes were smaller. When spray nozzle was stopped (B), Fig. 2a, the standard deviation increased. 3.2. Probability distribution and fluctuation of the pressure In order to have the probability distribution curve the minimum and maximum values of pressure drop were taken. The interval was divided in 100 parts and paired with their correspondent frequency. The probability distribution curve is given by the relationship between the percentile frequency and pressure drop. A significant difference was observed between the probability distribution and the fluid-dynamic regimes studied. At collapse, the curve was narrower at its basis and sharper at its peak, when compared to the curve for the spouting flow with coating, as seen in Fig. 3. Freitas et al. [5] identified the regimes in rectangular spouted beds and also observed this probability distribution difference for the fixed bed. Some differences could be observed between the pressure fluctuation graphs for some tests. Basically, there were differences between the fluctuations amplitude, oscillation gaps and the pressure average, as shown in Fig. 3. These results are consonant with the ones presented by some authors, such as [6], [7], [1], and [2], who identified the spouted bed regimes in a study without particle coating. No references were found with data about coating. It was not possible, though, to identify a quantifying variable useful to interrupt the process. 3.3. Spectral analysis Spectral analysis can be used to characterize different spouted bed regimes, either fluidized or with other multi-phase systems. The spectral density function changes information from the time domain to the frequency domain [5]. In mathematical terms, spectral density (SD) can be defined as:
SD = lim
2
E X(f )
T →∞
(3)
T
In which X(f) is the Fourier change of the signal, given by Eq. (4).
∞
X(f ) =
x(t) e−j2ft dt
−∞
Fig. 4. Power spectrum for ABS (600 g and 10 ml/min), at the different regimes.
The dominating frequency was not a proper parameter to identify the spouting flow instability in this work, due to the existence of similar values in different dynamic situations observed. A similar result was found by [1], [3] and [5]. Different kinds of behavior were noticed for ABS and polystyrene in the spectral analysis, so they were discussed separately for better understanding of the results. 3.4. Spectra for ABS
(4)
−∞
The power spectra obtained identified the different regimes in a convincing way from the dominating peak maximum amplitude.
It could be noticed that all the spectra presented an only and well-defined peak in the range of 6–7.5 Hz, for the spouting flow regime. Before coating started, as seen in Fig. 4a, the spectrum
N.E.C. Lopes et al. / Chemical Engineering and Processing 48 (2009) 1129–1134
1133
Fig. 5. Power spectrum for polystyrene (600 g and 10 ml/min), at the different regimes.
presented a maximum amplitude of 246 Pa2 /Hz. After the coating suspension feed was turned on, this amplitude had a falling trend and remained in the range of 75–180 Pa2 /Hz, as shown in Fig. 4b, presenting a good particle circulation rate. The internal spout spectrum presented a considerable raise in amplitude, as seen in Fig. 4c. The internal spout can be visually described as a very intense particle vibration at the bed inlet, then weaker and weaker towards the upper part of the bed, where there was no vibration at all. This highrate vibration reflected significant raise in amplitude (407 Pa2 /Hz). Other authors, such as [7] and [5], also found this relation between particle vibration and spectrum amplitude. The same kind of behavior repeats for others operational conditions. 3.5. Spectra for polystyrene The spouting regime for polystyrene was not as stable as for ABS, the circulation rate was visually smaller in the annular region, with a low fountain and a pulsating movement at the bed inlet. At the spectrum, multiple peaks appeared. It is interesting to notice that there is a dominating peak in the range of 1–3 Hz, and another one in the same range for ABS, between 5.5 and 7.5 Hz. For the spectral analysis, the second peak variation was considered, with a stable spouting flow range, which made it possible to identify instability during coating. The same kind of behavior observed for the polystyrene spectrum was also observed in [7] study. A greater peak multiplicity was observed during particle coating, as seen in Fig. 5b. A high particle circulation rate was observed at the region of 10–55 Pa2 /Hz, and a low one was noticed for amplitudes lower than 10 Pa2 /Hz, at which the suspension coating atomization should be turned off. At the internal spout, the spectrum was observed as having an only dominating peak, as shown in Fig. 5c,
and a considerable amplitude raise, with amplitude higher than 136 Pa2 /Hz. At collapse, no dominating frequency was found for the spectrum, as shown in Fig. 5d. 4. Conclusions The results obtained in this study made it possible to conclude that real time pressure drop data acquisition during particle coating allowed identification and monitoring of the process. The pressure fluctuation standard deviation tends to increase when it changes from the spouting regime to the internal spout regime, and a reduction with total bed collapse, which can be used as an alarm to control the spouting regime stability. The probability distribution could be used to visually differentiate the regimes observed during coating, especially bed collapse, which presented a curve with a sharp peak and a narrow basis. The spectral analysis proved to be an objective method to identify spouting regimes during particle coating. According to the spectra obtained in this study, the amplitude, at the dominant peak, showed determined ranges for each regime and particle. However, it showed that each particle should be studied separately, so that the spout stability range during coating can be observed. The dominant frequency has not proved to be an adequate parameter for the identification of the spout instability in this work, due to the proximity of values in the different dynamic situations observed. Appendix A. Nomenclature W dp
amplitude (Pa2 /Hz) particle diameter (mm)
1134
εp s F SD T Ujm X(f) JI B C FFT P
N.E.C. Lopes et al. / Chemical Engineering and Processing 48 (2009) 1129–1134
particle porosity (%) particle density (g/cm3 ) frequency (Hz) spectral density (Pa2 /Hz) total time (s) minimal spouting flow speed (m/s) Fourier’s change of pressure signal internal spout spray nozzle stopped bed collapse fast Fourier transform mean pressure fluctuation (Pa)
References [1] J. Xu, X. Bao, W. Wei, G. Shi, S. Shen, H.T. Bi, J.R. Grace, C.J. Jim, Statistical and frequency analysis of pressure fluctuations in spouted beds, Powder Technology 140 (2004) 141–154.
[2] R.M. Lourenc¸o, Identification and Monitoring of Flow Regimes in Spouted Beds, master’s degree thesis paper. Federal University of Uberlândia, Uberlândia, MG, Brazil, 2006, 115p. [3] E. Piskova, L. Mörl, Fluidization regimes in different spouted bed apparatus constructions, Chemical Engineering Processing 46 (2006) 695–702. [4] M.W. Donida, S.C.S. Rocha, Coating of urea with an aqueous polymeric solution in a two-dimensional spouted bed, Dry Technology 20 (3) (2002) 685–704. [5] L.A.P. Freitas, O.M. Dogan, C.J. Lim, J.R. Grace, D. Bai, Identification of flow regimes in slot-rectangular spouted beds using pressure fluctuations, The Canadian Journal of Chemical Engineering 82 (2004) 60–73. [6] V.A. Silva, S.C.S. Rocha, O.P. Taranto, G.S.V. Raghavan, Analysis of stability of spouted bed fluid dynamic regime through bed drop fluctuations measurements, Science & Engineering Journal 8 (2) (1999) 129–137. [7] V.A. Silva, S.C.S. Rocha, O. Taranto, G.S.V. Raghavan, Pressure drop analysis as a toll for obtaining the fluid-dynamic regimes in spouted bed, in: Drying 98, Proceedings of the 11th International Drying Symposium, Halkidiki, Greece, 1998, pp. 512–518. [8] S. Sasic, B. Leckner, F. Johnsson, Characterization of fluid dynamics of fluidized by analysis of pressure fluctuations, Progress in Energy and Combustion Science 33 (2007) 453–496. [9] M.S. Bacelos, J.T. Freire, Stability of spouting regimes in conical spouted beds with inert particle mixture, Industry Engineering Chemical 45 (2006) 808–817.