- Email: [email protected]
Investigation of wetting and drying process in a gas-solid fluidized bed by electrical capacitance tomography and pressure measurement
Powder Technology 301 (2016) 1148–1158
Contents lists available at ScienceDirect
Powder Technology journal homepage: www.elsevier.com/locate/powtec
Investigation of wetting and drying process in a gas-solid fluidized bed by electrical capacitance tomography and pressure measurement J.L. Zhang a,b, M.X. Mao a,b, J.M. Ye a, H.G. Wang a,⁎, W.Q. Yang c a b c
Institute of Engineering Thermophysics, Chinese Academy of Sciences, Beijing 100190, China University of Chinese Academy of Sciences, Beijing 100049, China School of Electrical and Electronic Engineering, University of Manchester, Manchester M13 9PL, UK
a r t i c l e
i n f o
Article history: Received 26 February 2016 Received in revised form 13 May 2016 Accepted 29 July 2016 Available online 30 July 2016 Keywords: Fluidized bed Electrical capacitance tomography Top spraying Gas-solid flow
a b s t r a c t Top-spray configurations are widely used in pharmaceutical fluidized beds for particle coating and granulation. The process is complex due to the change of particle moisture as well as particle density. It is important to investigate the flow hydrodynamics in the process. In this paper, electrical capacitance tomography (ECT) combined with pressure transducers are used to investigate the flow hydrodynamics of gas-solids flow in a top-spray fluidized bed for wetting and drying. The flow characteristics investigated include bubble size, dominant frequency and standard deviation. Discrete wavelet transforms (DWT) and Fast Fourier Transform (FFT) are used to analyze the capacitance and pressure signals. The effects of key process parameters, including superficial velocity, particle size and spraying conduction on the flow hydrodynamics, are analyzed. The result indicates that both the material moisture and particle size influence the minimum fluidized velocity and bubble characteristics during the wetting and drying process. The result also indicates that the moisture content of particles and superficial fluidization velocity are two main factors, which determine the bubble characteristics in the top-spray process. The bubble size presents a complex trend, which is influenced by the spray pressure. The fluctuation frequency of bubbles presents a different trend in the wetting and drying stage. © 2016 Elsevier B.V. All rights reserved.
1. Introduction Top-spray fluidized bed drying, granulation and coating are important processes in chemical, pharmaceutical, energy and many other industries for solid dose formulation. The main advantage of using topspray fluidized beds is that they provide efficient heat and mass transfer and can mix and agglomerate excipients and active material ingredients to produce uniform blends of particles [1]. In a top-spray fluidized bed granulation process, water or water-based binder solution is sprayed on the particle surface to increase adhesion forces so that agglomerating excipients can be formed. A drying process is the inverse process of granulation. During a drying process, water is extracted and other solvents are removed from particles to provide a sufficient shelf life. In most cases, the top-spray fluidized bed process involves the growth of the particles, the increase in the moisture content of the particles as well as the change of particles physical properties, such as permittivity and conductivity [2,3]. All these parameters would affect the hydrodynamic behavior in a fluidized bed process [4,5]. In the top-spray fluidized bed process, the quality of end-point product strongly depends on the proper setting of operational parameters and conditions [6]. Due to the complex gas-solids-droplets multi⁎ Corresponding author. E-mail address: [email protected] (H.G. Wang).
http://dx.doi.org/10.1016/j.powtec.2016.07.069 0032-5910/© 2016 Elsevier B.V. All rights reserved.
phase flows behavior in the process, however, it is difficult to measure the processes and difficult to diagnose fault of the processes using the conventional measurement tools, which can normally provide ingle point measurement only, such as pressure and optical fiber sensors [7]. Research has been carried out to investigate the gas-solids flows in top-spray fluidized beds by experiment [2,8,9] and simulation [10– 14]. Several techniques have been developed for measuring the gassolids process, including computer-based imaging [15], optical fiber probe [16], fluorescent tracer technique [17] and positron emission particle tracking (PEPT) [18]. However, most of the previous work was focused on the end-point product quality not the flow hydrodynamics, which affect the product quality in the top-spray process, especially in the wetting process. Compared with other process tomography techniques and considering the non-conductive nature of the top-spray fluidized bed process, electrical capacitance tomography (ECT) is a suitable imaging technique for measuring the top-spray fluidized bed wetting and drying processes [19,20]. It is fast (N100 frames per second) and can reconstruct permittivity distribution, which is a function of the particles concentration and moisture content. Research has been carried out to investigate the hydrodynamic behavior in a fluidized bed for drying and coating process based on ECT measurement [3,19,20]. Chaplin and Pugsley [19] revealed the gas-solids characteristics based on the capacitance and pressure signals in a conical shape fluidized bed drying. Takei et al. [21] measured
J.L. Zhang et al. / Powder Technology 301 (2016) 1148–1158
1149
Fig. 1. Fluidized bed with measurement instruments and spraying system.
the solids concentration in a Wurster fluidized bed coater based on ECT and wavelet analysis. However, most of the studies deal with a drying process and the application of ECT both in wetting and drying process has not been reported. The objective of this research is to investigate the flow hydrodynamic behavior of top-spray wetting and drying processes in a gas-solid fluidized bed by ECT and pressure sensors. An ECT sensor with 12 electrodes combined with pressure transducers were used to measure the top-spray fluidized bed processes with different operational conditions. Both the wetting and drying stages in a top-spray fluidized bed were investigated based on the capacitance and pressure signals. Discrete wavelet transforms (DWT) and Fast Fourier Transform (FFT) are used to analyze the measurement results. Key process parameters including cross-sectional area solids concentration, time-average raw capacitance, average solids concentration in the chamber and the frequency spectra characteristics are analyzed. 2. Experimental setup and measurement methods 2.1. Experimental setup and measurement sensors Fig. 1 shows the experimental setup of a lab-scale fluidized bed including measurement sensors and top-spray system, with temperature, pressure, humidity sensors and ECT sensor. The top-spray system includes a peristaltic pump and a dual-fluid nozzle. The chamber of the fluidized bed is made of cast acrylic with conical shape in the bottom
and cylindrical shape on the top. The diameter of the chamber is 150 mm in the bottom and 260 mm on the top respectively. Heated airflow generated from a roots blower and heater is introduced to fluidize the bed through an air gauze distributor. To provide a uniform air distribution and keep a turbulent-free airflow, a plenum chamber is designed below the air distributor. The spraying system consists of a dual-fluid nozzle, a compressor and a peristaltic pump. The dual-fluid spraying nozzle is used to atomize the solution, which is distilled water with low conductivity, i.e. b1 μS/cm. The compressed air provided by the air compressor is manually controlled by a relief valve located on the upstream air pipe of the nozzle. A peristaltic pump is used to provide and maintain a constant water flow. A humidity sensor (HMP110) is installed on the cylinder part at 700 mm above the air distributor to measure the outlet air temperature and relatively humidity (RH). Differential pressure transducers (Setra 268110KLD11FF2NN) are installed at 50 mm and 250 mm respectively above the air distribution to measure the pressure drop. The measurement system also includes NI-9203 USB DAQ modules for data acquisition. The pressure taps are connected to the differential pressure transducers, which are interfaced with a PC via the NI-9203 modules. The data acquisition rate for pressure is set as 100 samples per second. 2.2. ECT sensor and image reconstruction Fig. 1(e) shows the designed ECT sensor with twelve electrodes made of self-adhesive copper tape and mounted on the bottom of the
1150
J.L. Zhang et al. / Powder Technology 301 (2016) 1148–1158
fluidized bed. The electrodes are stuck to the outside wall of the fluidized bed covered by shielding copper to eliminate external interference. An AC-based ECT system with 16 channels is used to acquire capacitance signals [22]. The excitation frequency can be programmed in the range of 10 kHz and 500 kHz and voltage amplitude up to 20 V. The ECT sensing area is divided into 64 × 64 virtual pixels, which results in 3001 effective pixels used to reconstruct the image, representing the cross-sectional solids permittivity distribution, by certain image reconstructed algorithms. Normally, two algorithms are used to reconstruct the image, i.e. Linear Back Projection (LBP) and Landweber iteration [23]. The LBP is given as follows G ¼ ST C N
ð1Þ
where S is the sensitivity map, CN is the normalized capacitance and G indicates the gray level of the pixels. To improve the image quality and measurement accuracy, an optimized iterative algorithm based on the Landweber iteration is used to reconstruct the image [24]: G jþ1 ¼ G j ð1:0 þ τÞ þ αST λ−SG j ð1−τÞ
ð2Þ
where α is the step length, τ is the relaxation factor introduced to ensure convergence and λ is the normalized capacitance calculated by λ¼
C M −C L C H −C L
ð3Þ
where CM is the measured capacitance vector, CH and CL represent the capacitance measured in a packed and empty bed respectively. To normalize the measured capacitance CM according Eq. (3), low and high calibrations need to be done before the experiment to obtain the data of CH and CL. In this research, the ECT measured zone filled with bed materials and air are saved as CH and air as CL respectively. For wetting and drying process, the change of particle moisture content will result in the change in the measurement capacitance. In this research, dynamic calibration is adopted to reduce the moisture content effect on the capacitance. More details on the dynamic calibration are given in references [7,20]. 2.3. Experimental conditions and materials Sugar pellets with different diameters and corn powder with different water content are used in this research. The bulk density of pellets is 1550 kg/m3 and the closed packing volume is 0.5. The bulk density of corn powder is 1200 kg/m3 and the closed packing volume is 0.53. The effects of superficial velocity and pellets diameter on flow hydrodynamics have been investigated. The results of top-spray processes with different spray pressure and flow rate are also given to demonstrate the possibility of optimization of operating parameters. In the top-pray wetting and drying processes, the inlet air temperature is set to 35 °C and 65 °C respectively. 3. Results and discussion 3.1. Effect of particle moisture and diameter on the minimum fluidization velocity Corn powder with three different moisture contents are used to determine the minimum fluidization velocity and the result is shown in Fig. 2(a). As can be seen, there are three critical points, which represent the minimum fluidization velocities for the corn powder with different moisture content, i.e. 5%, 15%, and 25% respectively. The main reason for the increase of the minimum velocity is due to the change of particle density which is a function of process moisture content [7]. In theoretical, there are many factors which affect the minimum velocity in
Fig. 2. Pressure drop with fluidization velocity for pellets with different diameter.
fluidization, for example, particle size, particle shape, density, and physical properties [25]. In this research, we only focused on the particle moisture and size effect on the minimum fluidization velocity. Fig. 2(b) shows the minimum fluidization velocity for pellets with different particle size. It can be seen that particle size has a big influence on the minimum fluidization velocity. Small particles can be fluidized more easily than large particles. Normally, the minimum fluidization velocity can be calculated based on theoretical equations as follows [25].
U mf ¼
2 dp ρs −ρg g ε3mf ϕ2s 1−ε mf
150μ g
;
Rep;mf b20
ð4Þ
where the minimum voidage of εmf is given by 2 ε mf ¼
4 0:586φ−0:72 s
μ 2g
30:029
5 n o 3 ρg ρs −ρg gdp
0:021 ρg ρs
ð5Þ
Table 1 Minimum fluidization velocity. Bed material
Theoretical data (m/s) Experimental data (m/s)
Pellet 1300 μm
800 μm
500 μm
0.508 0.491
0.267 0.281
0.2 0.229
J.L. Zhang et al. / Powder Technology 301 (2016) 1148–1158
1151
Fig. 3. Image reconstruction for pellets and corn powder.
Fig. 4. Capacitance changes for pellets and corn powder.
where dp is the diameter or equivalent diameter of the bed material, ρg is the density of air, μg is the dynamic viscosity of air, and ρs is the true density of the bed material, φs is the sphericity factor of the particle used in the test. It can be seen from the Eq. (4) that there are three main factors which affect the minimum fluidization velocity, namely diameter, density and voidage. For a certain particle, the voidage is fixed. Therefore, the minimum fluidization velocity increases with the particle diameter and density, just as the results presented in Fig. 2 which particle with high moisture content has high minimum fluidization velocity. Table 1 gives a comparison results between the theoretical calculation based on Eqs. (4)–(5) and experimental results for the minimum fluidization velocity for different particles. The measurement results
Fig. 6. Bubble size with different pellets diameter.
are in a good agreement with theoretical results with a maximum error of 14.5%. 3.2. Image reconstruction for solids concentration in the top-spray process Fig. 3 shows the reconstructed ECT images in different times for the top-spray fluidized bed for sugar pellets and corn powder respectively. In those images, blue color means air bubble with relative low
Fig. 5. Bubble size and capacitance changes for pellets.
1152
J.L. Zhang et al. / Powder Technology 301 (2016) 1148–1158
Fig. 8. Solids concentration profile.
3.3. Capacitance change in the top-spray fluidized bed process
Fig. 7. Bubble size change in the top-spray process.
permittivity and red color solids phase with relative high permittivity. From Fig. 3, it can be seen that the bubble size varies over a small range for pellets and wide range for corn powder. Meanwhile, the blue and red areas present core-annular distribution for both particles, indicating high density particles in the near wall region and low density particles in the central area of the chamber.
Fig. 4 shows the changes of average capacitance of all the measurement capacitance values in the top-spray process for sugar pellets and corn powder respectively. It can be seen that the capacitance increases with continuous spraying solution onto the bed within the first 15 min for both materials. However, the change of pellets is much smaller than that for corn powder. For pellets, the smooth surface results in poor water absorption performance and most of the droplets are evaporated directly by the fluidized air, leading to little change in the moisture contents. Alternatively, most of liquid was absorbed by corn powder, resulting in quick increase in moisture content. Fig. 5 shows more details for the top-spray process with corn powder as bed materials. In this figure, the bubble size is calculated based on the ECT images. In this research, the bubble defined as a surface area where the solids concentration is below a certain value and the value is taken as 0.2 according to references [26,27]. In the time period of 15 and 20 min, the
J.L. Zhang et al. / Powder Technology 301 (2016) 1148–1158
process is in drying and there is a sharp decrease in the moisture resulting in the decrease in capacitance. In the time period of 20 and 45 min, the capacitance remains nearly constant. With further spraying solution into the bed, there is another peak for the capacitance, which can be clearly seen in Fig. 5.
3.4. Bubble size in the top-spray process Bubble size is one of the most important parameters, which indicates the gas-solids flow hydrodynamics. Figs. 6 and 7 show the average bubble size in the top-spray process for different powders with different operation conditions. Fig. 7 also gives the cubic-fitting curves for different conditions. Fig. 6 shows the average bubble size for the process without top-spray. It can be seen that the bubble size increases with superficial velocity and particle size. Fig. 7 shows the bubble size profiles for top-spray processes with different bed material, spray pressure and flow rate. Generally, it can be seen that the change of the bubble size is more complex than that without top-spray. The bubble size decreases in the wetting stage and increases in the drying stage due to the change in moisture which results in the change in particle density. In addition, the change in bubble size for corn powder is wider than that for pellets, which is consistent with the results in Fig. 4. For the corn powder, the condition of 0.4 MPa has the smallest particle size compared that with other two conditions as shown in Fig. 7(b). However, the bubble size change little when pressure changes from 0.4 MPa to 0.5 MPa, which indicates that the effect of atomization pressure can be neglected with relatively high pressure i.e. N 0.4 MPa. Fig. 7(c) shows the bubble size profiles for top-spray with corn as bed material with different spray flow rate. It can be seen that there is a small bubble with high flow rate, for that the particles absorbing more water in this condition are more difficult to be fluidized. In summary, the bubble size is influenced by the wetting and drying conditions in the top spray fluidized bed.
1153
for scattered signals is given as follows vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi u N u 1 X ðxðnÞ−xÞ2 ; SD ¼ t N−1 n¼1
x¼
N 1X xðnÞ N n¼1
where x(n) is the original scattered signals, x is the time average value of the signals and N is the total sample number. Fig. 9 shows the dominant frequency and the corresponding SD value for pressure signal during the top-spray process for sugar pellets and corn powder respectively. It can be seen that the variation of dominant frequency with time is nearly the same for pellets and corn powder in the whole process. For sugar, the variation of dominant frequency with time correlates strongly with corresponding changes in the SD as shown in Fig. 9(a). Both decrease with wetting and increase sharply in the beginning of drying and then reach a relative stable value. However Fig. 9(b) shows that in the case of corn, the dominant frequency and SD present varied trend in the whole process. The results above illustrate that different type of bed particle show similar flow character but with different fluctuation. Fig. 10(a) shows the dominant frequency and SD values for pressure signals with different spray pressure for pellets. It can be seen that the top-spray process can be divided into three stages with different character of dominant frequency. The dominant frequency decreases in Stage-I due to the increased moisture and then sharply increases in Stage-II because there is a rapid decrease of bed material moisture in the beginning of drying. While the dominant frequency floats around some value in Stage-III due to the little change of bed material moisture in the rest
3.5. Solids concentration Fig. 8(a) shows the average solids concentration along the radial direction with different superficial velocity for pellets. As can be seen, the concentration nearly keeps constants when the superficial velocity is lower than 1 m/s. With high superficial velocity, the concentration increases from the center to the near wall region and the flow is typically core-annular distribution. Fig. 8(b) shows the average solids concentration profiles with different particle diameters with a superficial velocity of 3 m/s. For large particles, the solids concentration presents a more uniform distribution along the radial direction than that for small particles with a same fluidization velocity. Fig. 8(c) shows the solids concentration profiles at two time step, i.e. t = 5 min and t = 30 min. Due to high density in the wetting stage, the particles are difficult to be fluidized which results in a relative uniform solid concentration. However, in the second time stage, due to low moisture content, the density of particles reduced and it is easy to be fluidized with the same superficial velocity.
3.6. Dominant frequency and standard deviation analysis Fast Fourier Transformation (FFT) is a commonly used technique for fluidization analysis and it has been employed extensively to analyze the dynamic behavior of fluidized beds with pressure measurement [28]. To quantify the information obtained both from pressure and ECT measurement in time series, FFT is used to analyze the measured signals for different process conditions. In this research, the dominant frequency based on FFT and standard deviation (SD) is used to quantify the flow characteristics in the top-spray process. The equation defining the SD
ð6Þ
Fig. 9. Dominant frequency and standard deviation.
1154
J.L. Zhang et al. / Powder Technology 301 (2016) 1148–1158
Fig. 10. Dominant frequency and standard deviation for pellets.
time of drying. The SD value for pressure has a larger value in the wetting stage than that in the drying stage, indicating that the fluctuation is stronger in the wetting stage than that in drying stage. Fig. 10(b) shows the dominant frequency and SD values for the pressure signals with different spray flow rate for pellets. It can be seen that the dominant frequency decreases in the wetting stage due to the increase in moisture and then increases in the drying stage. Furthermore, there is a lower dominant frequency with higher spray flow rate in the Stage-I, for that the pellets can absorb more water in this condition and the larger density weaken the flow. Meanwhile, the standard deviation has a similar trend to that in Fig. 10(a).
Fig. 10(c) shows the dominant frequency and SD value of the normalized capacitance with different spray pressure for pellets. In the wetting stage, the dominant frequencies calculated based on pressure and capacitance have the same characteristics, but with different value. The SD value of normalized capacitance has an obviously different trend compared with that of pressure signal as shown in Fig. 10(a). The SD value decreases in the wetting stage and then increases in the drying stage. Furthermore, there is a lower SD value with higher spray pressure in the wetting stage. The main reason for the difference between pressure and capacitance is that the pressure only represents the local fluctuation and the
J.L. Zhang et al. / Powder Technology 301 (2016) 1148–1158
ECT represents the fluctuation of whole cross section, which provides more comprehensive information on the flow hydrodynamics. 3.7. Discrete wavelet transforms (DWT) analysis DWT is an effective analytical tool for time series signal analysis because DWT can re-express a time series in terms of coefficients, which are associated with particular scales and different frequency bands [6]. Normally, DWT decomposes a raw signal into details and approximations part respectively. The details part represents the high frequency component which is the lower half of the frequency range of the raw signal and the approximations part for the low frequency component which is the upper half of the frequency range of the raw signal. The approximation part can be further decomposed into a new scale of approximation and detail signals. Thus, the decomposition of a given signal S into different scales can be obtained by the application of the DWT to S into N levels, i.e. aN and dN, and the signal of S can be finally written as s ¼ d1 þ d2 þ ⋯dN þ aN
ð7Þ
There are many types of DWT decomposition models and they are including Harr, Daubechies, Mexican hat, and Spline wavelet [29]. In
1155
this research, Daubechies wavelets with order of three (db3) were used for decomposing the capacitance and pressure signals since these wavelets are good enough to have an engineering application [29–31]. Details on how to choose the number of Daubechies wavelets and level of decomposition has been given in reference [32]. Fig. 11 shows the 6-scale wavelet decomposition for pressure signal in a selected case. Fig. 12 shows the dominant frequency spectra for the approximation and detail parts for the pressure and capacitance signals in the scale levels of 1 based on FFT analysis. It can be seen that the pressure and capacitance measurements give nearly same dominant frequency, i.e. 10 Hz both for the approximation and detail parts in the scale level of 1. However, the frequency spectra calculated from capacitance is more complex than that by pressure. The difference will be discussed further in the following. As is well known, different scales of wavelet decomposition associated with different phenomena in a fluidized bed, i.e. the highest frequency for noise, the intermediate frequency for fluidization characteristics and the lowest frequency for systemic effects [33]. The noise, fluidization characteristics and systemic effects are also termed micro-scale with high frequency, meso-scale with intermediate frequency and macro-scale with low frequency, respectively [33,34]. The energy contained in the wavelet-decomposed detail signal at different scales of decomposition can be used to quantify the significance for each of
Fig. 11. Decomposition of pressure signals by wavelet analysis (a represents approximations and d represents details).
1156
J.L. Zhang et al. / Powder Technology 301 (2016) 1148–1158
Fig. 12. Dominant frequency for pressure and capacitance for the approximation and detail part of signals at level j = 1.
the components. The energy of a detail signal at decomposition scale j is given by [35] EDj ¼
XN 2 D j ðt Þ 1
ð8Þ
Fig. 13 shows a sample of the energies of the wavelet-decomposed detail signals in different levels for pressure and capacitance signals in the spraying and drying process. Fig. 13(a) shows that the highest EDj value is in the intermediate scale of 4both for spraying and drying process for pressure signal. The EDj result indicates that the fluidization characteristics changes little for the two processes for pressure signal. Fig. 13(b) shows that the highest value of EDj for capacitance is in the intermediate scale of 4 for spraying and 3 for drying process respectively, which means that different fluidization characteristics occurs in the spraying and drying process. The main reason for the difference in EDj between pressure and capacitance is in that the ECT measurement represents the information on the whole cross section while the pressure measurement only represents the near-wall region [35]. Fig. 14 shows the highest value of EDj for pressure and capacitance signals in a top-spraying process for two cases at the scale level selected according to the result from Fig. 13. From Fig. 14(a), it can be seen that the value of EDj in drying process is bigger than that in spraying process, which is consistent with the results in Fig. 13(a). It can also be seen from Fig. 14(b) that the value of EDj values increase with spraying process and drop sharply in the beginning of drying process. The main reason for such decrease is related to the change in particle moisture as capacitance is more sensitive to moisture than pressure. From the above DWT and FFT analysis, it can be seen that both pressure and capacitance have similar dominant frequency while they represent slightly different detailed information on flow characteristics. Capacitance contains higher proportion of fluidization information in the spraying process and pressure contains higher proportion of fluidization information in the drying process. The results indicate that it is good to combine pressure and capacitance to investigate the gas-solids
flow characteristics in terms of local and whole cross-section area measurement. 4. Conclusion and discussion This paper presents an approach to investigation of the gas-solids flow in a top-spray fluidized bed for particle wetting and drying, by a combination of ECT and pressure. DWT and FFT are used to analyze the capacitance and pressure signals. The effects of key process parameters including superficial velocity, particle size and spraying conduction on the flow hydrodynamics are analyzed based on measurement. The main conclusion can be summarized as follows. In the top-spray fluidized bed process, both the particles moisture and diameter influence the minimum fluidized velocity. The moisture content of particle and superficial fluidization velocity are the two main factors, which affect the bubble characteristics in the top-spray process. The bubble presents a complex trend in the top-spray fluidized bed process, which is influenced by the wetting and drying conditions. The dominant frequency presents different trends in the wetting and drying stage both from capacitance and pressure measurements. Capacitance signals can give more information for investigation of the gassolids flow in terms of SD and DWT. It is good to combine pressure and capacitance to investigate the gas-solids flow characteristics in terms of local and whole cross-section area measurement. Nomenclature a approximant part in discrete wavelet transforms (–) C measured capacitance (pF) details in discrete wavelet transforms (–) dj mean particle diameter (m) dp energy of a detail signal at decomposition scale j (–) EDj FFT Fast Fourier Transformation (−) g acceleration due to gravity (m/s2) G gray level (–) Reynolds number of particle (–) Rep
J.L. Zhang et al. / Powder Technology 301 (2016) 1148–1158
Fig. 13. Energies of the wavelet-decomposed detail signals from levels 1 to 6.
1157
Fig. 14. EDj distributions of pressure and ECT for the top-spraying process.
Acknowledgements s SD Umf x
signals (–) standard deviation (–) minimum fluidization air velocity (m/s) time series of signals for capacitance (pF)
The authors are grateful to the support from the National Natural Science Foundation of China (No. 61320106004) and Chinese Academy of Sciences (CAS) Interdisciplinary Innovation Team.
References Greek symbols α step length in Landweber iteration (–) β solids concentration (–) ε voidage fraction (–) μ viscosity (kg/m·s) ρ density (kg/m3) λ normalized capacitance (–) τ relaxation factor (–)
Subscripts g gas phase H high calibration L low calibration M measurement N sample number for measurement s solid phase
[1] G.M. Walker, S.E.J. Bell, K. Green, D.S. Jones, G.P. Andrews, Characterisation of fluidised bed granulation processes using in-situ Raman spectroscopy, Chem. Eng. Sci. 64 (2009) 91–98. [2] M. Schmidt, C. Rieck, A. Bück, E. Tsotsas, Experimental investigation of process stability of continuous spray fluidized bed layering with external product separation, Chem. Eng. Sci. 137 (2015) 466–475. [3] R.H. Ge, J.M. Ye, H.G. Wang, W.Q. Yang, Measurement of particle concentration in a Wurster fluidized bed by electrical capacitance tomography sensors, AICHE J. 60 (2014) 4051–4064. [4] D. Geldart, Types of gas fluidization, Powder Technol. 7 (1973) 285–292. [5] D. Gidaspow, R. Bezburuah, J. Ding, Hydrodynamics of circulating fluidised bed, kinetic theory approachFluidization VII. Proc. of 7th Eng. Foundation Conference on Fluidization, 3–8 May, Brisbane, Australia 1992, pp. 75–82. [6] H.G. Wang, W.Q. Yang, Measurement of fluidized bed dryer by different frequency and different normalisation methods with electrical capacitance tomography, Powder Technol. 199 (2010) 60–69. [7] H.G. Wang, P. Senior, R. Mann, W.Q. Yang, Online solids moisture measurement and optimum control of fluidized bed dryer, Chem. Eng. Sci. 64 (2009) 2893–2902. [8] P.D. Hede, P. Bach, A.D. Jensen, Small-scale top-spray fluidized bed coating: granule impact strength, agglomeration tendency and coating layer morphology, Powder Technol. 176 (2007) 157–167. [9] T. Sheahan, L. Briens, Passive acoustic emission monitoring of pellet coat thickness in a fluidized bed, Powder Technol. 286 (2015) 172–180. [10] L. Fries, S. Antonyuk, S. Heinrich, S. Palzer, DEM-CFD modelling of a fluidized bed spray granulator, Chem. Eng. Sci. 66 (2011) 2340–2355.
1158
J.L. Zhang et al. / Powder Technology 301 (2016) 1148–1158
[11] M. Vanderrost, F. Ronsse, K. Dewettinck, J.G. Pieters, Modelling the bed characteristics in fluidized-beds for top-spray coating processes, Particuology 10 (2012) 649–662. [12] J.E. Hilton, D.Y. Ying, P.W. Cleary, Modelling spray coating using a combined CFDDEM and spherical harmonic formulation, Chem. Eng. Sci. 99 (2013) 141–160. [13] L. Fries, S. Antonyuk, S. Heinrich, D. Dopfer, S. Palzer, Collison dynamics in fluidized bed granulators: a DEM-CFD study, Chem. Eng. Sci. 86 (2013) 108–123. [14] W. Duangkhamchan, F. Ronsse, S. Siriamornpun, J.G. Pieters, Numerical study of air humidity and temperature distribution in a top-spray fluidized bed coating process, J. Food Eng. 146 (2015) 81–91. [15] B.A. Saadevandi, R. Turton, Particle velocity and voidage profiles in a draft tube equipped spouted-fluidized bed coating device, Chem. Eng. Commun. 191 (2004) 1379–1400. [16] Y.L. He, C.J. Lim, J.R. Grace, J.X. Zhu, S.Z. Qin, Measurements of voidage profiles in spouted beds, Can. J. Chem. Eng. 72 (1994) 229–234. [17] S. Karlsson, I.N. Björn, S. Folestad, A. Rasmuson, Measurement of the particle movement in the fountain region of a Wurster type bed, Powder Technol. 165 (2006) 22–29. [18] F. Depypere, J.G. Pieters, K. Dewettinck, PEPT visualisation of particle motion in a tapered fluidized bed coater, J. Food Eng. 93 (2009) 324–336. [19] G. Chaplin, T. Pugsley, Application of electrical capacitance tomography to the fluidized bed drying of pharmaceutical granule, Chem. Eng. Sci. 60 (2005) 7022–7033. [20] H.G. Wang, W.Q. Yang, P. Senior, R.S. Raghavan, R.S. Duncan, Investigation of batch fluidized bed drying by mathematical modelling, CFD simulation and ECT measurement, AICHE J. 54 (2008) 427–444. [21] M. Takei, T. Zhao, K. Yamane, Measurement of particle concentration in powder coating process using capacitance computed tomography and wavelet analysis, Powder Technol. 193 (2009) 93–100. [22] W.Q. Yangand, T.A. York, New AC-based capacitance tomography system, IEE Proc.Sci. Meas. Technol. 146 (1999) 47–53. [23] W.Q. Yang, L.H. Peng, Image reconstruction algorithms for electrical capacitance tomography, Meas. Sci. Technol. 14 (2003) R1–R13.
[24] W.Q. Yang, D.M. Spink, T.A. York, H. McCann, An image-reconstruction algorithm based on Landweber's iteration method for electrical-capacitance tomography, Meas. Sci. Technol. 10 (1999) 1065–1069. [25] D. Kunii, O. Levenspiel, Fluidization Engineering, Butterworth-Heinemann, Boston, 1991. [26] B.G.M. Wachem, J.C. Schouten, R. Kirshnaand, C.M. van den Bleek, Eulerian simulation of bubbling behaviour in gas-solid fluidised beds, Comput. Chem. Eng. 22 (1998) S299–S306. [27] R.C. Darton, R.D. LaNauze, J. Davidsonand, D. Harrison, Bubble growth due to coalescence in fluidised beds, Trans IChemE. 55 (1977) 274–280. [28] A. Svensson, F. Johnsson, B. Leckner, Fluidization regimes in non-slugging fluidized bed: influence of pressure drop across the air distributors, Powder Technol. 86 (1996) 229–312. [29] M. Tahmasebpoor, R. Zarghami, R. Sotudeh-Gharebagh, N. Mostoufi, Characterization of fluidized beds hydrodynamics by recurrence qualification analysis and wavelet transform, Int. J. Multiphase Flow 69 (2015) 31–41. [30] J.W. Chew, R. Hays, J.G. Findlay, T.M. Knowlton, S.B.R. Karri, R.A. Cocco, C.M. Hrenya, Cluster characteristics of Geldart Group B particles in a pilot-scale CFB riser. I. Monodisperse systems, Chem. Eng. Sci. 68 (2012) 72–81. [31] C. Guenther, R. Breault, Wavelet analysis to characterize cluster dynamics in a circulating fluidized bed, Powder Technol. 173 (2007) 163–173. [32] M. Tahmasebpour, R. Zarghami, R. Sotudeh-Gharebagh, N. Mostoufi, Characterization of various structures in gas-solid fluidized beds by recurrence qualification analysis, Particuology 11 (2013) 647–656. [33] T.Y. Yang, L.P. Leu, Multiresolution analysis on identification and dynamics of clusters in a circulating fluidized bed, AICHE J. 55 (2009) 612–629. [34] J.W. Chew, D.M. Parker, C.M. Hrenya, Elutriation and species segregation characteristics of polydisperse mixtures of group B particles in a dilute CFB riser, AICHE J. 59 (2013) 84–95. [35] G.Z. Qiu, J.M. Ye, H.G. Wangand, W.Q. Yang, Investigation of flow hydrodynamics and regime transition in a gas-solids fluidized bed with different riser diameters, Chem. Eng. Sci. 116 (2014) 195–207.
Contents lists available at ScienceDirect
Powder Technology journal homepage: www.elsevier.com/locate/powtec
Investigation of wetting and drying process in a gas-solid fluidized bed by electrical capacitance tomography and pressure measurement J.L. Zhang a,b, M.X. Mao a,b, J.M. Ye a, H.G. Wang a,⁎, W.Q. Yang c a b c
Institute of Engineering Thermophysics, Chinese Academy of Sciences, Beijing 100190, China University of Chinese Academy of Sciences, Beijing 100049, China School of Electrical and Electronic Engineering, University of Manchester, Manchester M13 9PL, UK
a r t i c l e
i n f o
Article history: Received 26 February 2016 Received in revised form 13 May 2016 Accepted 29 July 2016 Available online 30 July 2016 Keywords: Fluidized bed Electrical capacitance tomography Top spraying Gas-solid flow
a b s t r a c t Top-spray configurations are widely used in pharmaceutical fluidized beds for particle coating and granulation. The process is complex due to the change of particle moisture as well as particle density. It is important to investigate the flow hydrodynamics in the process. In this paper, electrical capacitance tomography (ECT) combined with pressure transducers are used to investigate the flow hydrodynamics of gas-solids flow in a top-spray fluidized bed for wetting and drying. The flow characteristics investigated include bubble size, dominant frequency and standard deviation. Discrete wavelet transforms (DWT) and Fast Fourier Transform (FFT) are used to analyze the capacitance and pressure signals. The effects of key process parameters, including superficial velocity, particle size and spraying conduction on the flow hydrodynamics, are analyzed. The result indicates that both the material moisture and particle size influence the minimum fluidized velocity and bubble characteristics during the wetting and drying process. The result also indicates that the moisture content of particles and superficial fluidization velocity are two main factors, which determine the bubble characteristics in the top-spray process. The bubble size presents a complex trend, which is influenced by the spray pressure. The fluctuation frequency of bubbles presents a different trend in the wetting and drying stage. © 2016 Elsevier B.V. All rights reserved.
1. Introduction Top-spray fluidized bed drying, granulation and coating are important processes in chemical, pharmaceutical, energy and many other industries for solid dose formulation. The main advantage of using topspray fluidized beds is that they provide efficient heat and mass transfer and can mix and agglomerate excipients and active material ingredients to produce uniform blends of particles [1]. In a top-spray fluidized bed granulation process, water or water-based binder solution is sprayed on the particle surface to increase adhesion forces so that agglomerating excipients can be formed. A drying process is the inverse process of granulation. During a drying process, water is extracted and other solvents are removed from particles to provide a sufficient shelf life. In most cases, the top-spray fluidized bed process involves the growth of the particles, the increase in the moisture content of the particles as well as the change of particles physical properties, such as permittivity and conductivity [2,3]. All these parameters would affect the hydrodynamic behavior in a fluidized bed process [4,5]. In the top-spray fluidized bed process, the quality of end-point product strongly depends on the proper setting of operational parameters and conditions [6]. Due to the complex gas-solids-droplets multi⁎ Corresponding author. E-mail address: [email protected] (H.G. Wang).
http://dx.doi.org/10.1016/j.powtec.2016.07.069 0032-5910/© 2016 Elsevier B.V. All rights reserved.
phase flows behavior in the process, however, it is difficult to measure the processes and difficult to diagnose fault of the processes using the conventional measurement tools, which can normally provide ingle point measurement only, such as pressure and optical fiber sensors [7]. Research has been carried out to investigate the gas-solids flows in top-spray fluidized beds by experiment [2,8,9] and simulation [10– 14]. Several techniques have been developed for measuring the gassolids process, including computer-based imaging [15], optical fiber probe [16], fluorescent tracer technique [17] and positron emission particle tracking (PEPT) [18]. However, most of the previous work was focused on the end-point product quality not the flow hydrodynamics, which affect the product quality in the top-spray process, especially in the wetting process. Compared with other process tomography techniques and considering the non-conductive nature of the top-spray fluidized bed process, electrical capacitance tomography (ECT) is a suitable imaging technique for measuring the top-spray fluidized bed wetting and drying processes [19,20]. It is fast (N100 frames per second) and can reconstruct permittivity distribution, which is a function of the particles concentration and moisture content. Research has been carried out to investigate the hydrodynamic behavior in a fluidized bed for drying and coating process based on ECT measurement [3,19,20]. Chaplin and Pugsley [19] revealed the gas-solids characteristics based on the capacitance and pressure signals in a conical shape fluidized bed drying. Takei et al. [21] measured
J.L. Zhang et al. / Powder Technology 301 (2016) 1148–1158
1149
Fig. 1. Fluidized bed with measurement instruments and spraying system.
the solids concentration in a Wurster fluidized bed coater based on ECT and wavelet analysis. However, most of the studies deal with a drying process and the application of ECT both in wetting and drying process has not been reported. The objective of this research is to investigate the flow hydrodynamic behavior of top-spray wetting and drying processes in a gas-solid fluidized bed by ECT and pressure sensors. An ECT sensor with 12 electrodes combined with pressure transducers were used to measure the top-spray fluidized bed processes with different operational conditions. Both the wetting and drying stages in a top-spray fluidized bed were investigated based on the capacitance and pressure signals. Discrete wavelet transforms (DWT) and Fast Fourier Transform (FFT) are used to analyze the measurement results. Key process parameters including cross-sectional area solids concentration, time-average raw capacitance, average solids concentration in the chamber and the frequency spectra characteristics are analyzed. 2. Experimental setup and measurement methods 2.1. Experimental setup and measurement sensors Fig. 1 shows the experimental setup of a lab-scale fluidized bed including measurement sensors and top-spray system, with temperature, pressure, humidity sensors and ECT sensor. The top-spray system includes a peristaltic pump and a dual-fluid nozzle. The chamber of the fluidized bed is made of cast acrylic with conical shape in the bottom
and cylindrical shape on the top. The diameter of the chamber is 150 mm in the bottom and 260 mm on the top respectively. Heated airflow generated from a roots blower and heater is introduced to fluidize the bed through an air gauze distributor. To provide a uniform air distribution and keep a turbulent-free airflow, a plenum chamber is designed below the air distributor. The spraying system consists of a dual-fluid nozzle, a compressor and a peristaltic pump. The dual-fluid spraying nozzle is used to atomize the solution, which is distilled water with low conductivity, i.e. b1 μS/cm. The compressed air provided by the air compressor is manually controlled by a relief valve located on the upstream air pipe of the nozzle. A peristaltic pump is used to provide and maintain a constant water flow. A humidity sensor (HMP110) is installed on the cylinder part at 700 mm above the air distributor to measure the outlet air temperature and relatively humidity (RH). Differential pressure transducers (Setra 268110KLD11FF2NN) are installed at 50 mm and 250 mm respectively above the air distribution to measure the pressure drop. The measurement system also includes NI-9203 USB DAQ modules for data acquisition. The pressure taps are connected to the differential pressure transducers, which are interfaced with a PC via the NI-9203 modules. The data acquisition rate for pressure is set as 100 samples per second. 2.2. ECT sensor and image reconstruction Fig. 1(e) shows the designed ECT sensor with twelve electrodes made of self-adhesive copper tape and mounted on the bottom of the
1150
J.L. Zhang et al. / Powder Technology 301 (2016) 1148–1158
fluidized bed. The electrodes are stuck to the outside wall of the fluidized bed covered by shielding copper to eliminate external interference. An AC-based ECT system with 16 channels is used to acquire capacitance signals [22]. The excitation frequency can be programmed in the range of 10 kHz and 500 kHz and voltage amplitude up to 20 V. The ECT sensing area is divided into 64 × 64 virtual pixels, which results in 3001 effective pixels used to reconstruct the image, representing the cross-sectional solids permittivity distribution, by certain image reconstructed algorithms. Normally, two algorithms are used to reconstruct the image, i.e. Linear Back Projection (LBP) and Landweber iteration [23]. The LBP is given as follows G ¼ ST C N
ð1Þ
where S is the sensitivity map, CN is the normalized capacitance and G indicates the gray level of the pixels. To improve the image quality and measurement accuracy, an optimized iterative algorithm based on the Landweber iteration is used to reconstruct the image [24]: G jþ1 ¼ G j ð1:0 þ τÞ þ αST λ−SG j ð1−τÞ
ð2Þ
where α is the step length, τ is the relaxation factor introduced to ensure convergence and λ is the normalized capacitance calculated by λ¼
C M −C L C H −C L
ð3Þ
where CM is the measured capacitance vector, CH and CL represent the capacitance measured in a packed and empty bed respectively. To normalize the measured capacitance CM according Eq. (3), low and high calibrations need to be done before the experiment to obtain the data of CH and CL. In this research, the ECT measured zone filled with bed materials and air are saved as CH and air as CL respectively. For wetting and drying process, the change of particle moisture content will result in the change in the measurement capacitance. In this research, dynamic calibration is adopted to reduce the moisture content effect on the capacitance. More details on the dynamic calibration are given in references [7,20]. 2.3. Experimental conditions and materials Sugar pellets with different diameters and corn powder with different water content are used in this research. The bulk density of pellets is 1550 kg/m3 and the closed packing volume is 0.5. The bulk density of corn powder is 1200 kg/m3 and the closed packing volume is 0.53. The effects of superficial velocity and pellets diameter on flow hydrodynamics have been investigated. The results of top-spray processes with different spray pressure and flow rate are also given to demonstrate the possibility of optimization of operating parameters. In the top-pray wetting and drying processes, the inlet air temperature is set to 35 °C and 65 °C respectively. 3. Results and discussion 3.1. Effect of particle moisture and diameter on the minimum fluidization velocity Corn powder with three different moisture contents are used to determine the minimum fluidization velocity and the result is shown in Fig. 2(a). As can be seen, there are three critical points, which represent the minimum fluidization velocities for the corn powder with different moisture content, i.e. 5%, 15%, and 25% respectively. The main reason for the increase of the minimum velocity is due to the change of particle density which is a function of process moisture content [7]. In theoretical, there are many factors which affect the minimum velocity in
Fig. 2. Pressure drop with fluidization velocity for pellets with different diameter.
fluidization, for example, particle size, particle shape, density, and physical properties [25]. In this research, we only focused on the particle moisture and size effect on the minimum fluidization velocity. Fig. 2(b) shows the minimum fluidization velocity for pellets with different particle size. It can be seen that particle size has a big influence on the minimum fluidization velocity. Small particles can be fluidized more easily than large particles. Normally, the minimum fluidization velocity can be calculated based on theoretical equations as follows [25].
U mf ¼
2 dp ρs −ρg g ε3mf ϕ2s 1−ε mf
150μ g
;
Rep;mf b20
ð4Þ
where the minimum voidage of εmf is given by 2 ε mf ¼
4 0:586φ−0:72 s
μ 2g
30:029
5 n o 3 ρg ρs −ρg gdp
0:021 ρg ρs
ð5Þ
Table 1 Minimum fluidization velocity. Bed material
Theoretical data (m/s) Experimental data (m/s)
Pellet 1300 μm
800 μm
500 μm
0.508 0.491
0.267 0.281
0.2 0.229
J.L. Zhang et al. / Powder Technology 301 (2016) 1148–1158
1151
Fig. 3. Image reconstruction for pellets and corn powder.
Fig. 4. Capacitance changes for pellets and corn powder.
where dp is the diameter or equivalent diameter of the bed material, ρg is the density of air, μg is the dynamic viscosity of air, and ρs is the true density of the bed material, φs is the sphericity factor of the particle used in the test. It can be seen from the Eq. (4) that there are three main factors which affect the minimum fluidization velocity, namely diameter, density and voidage. For a certain particle, the voidage is fixed. Therefore, the minimum fluidization velocity increases with the particle diameter and density, just as the results presented in Fig. 2 which particle with high moisture content has high minimum fluidization velocity. Table 1 gives a comparison results between the theoretical calculation based on Eqs. (4)–(5) and experimental results for the minimum fluidization velocity for different particles. The measurement results
Fig. 6. Bubble size with different pellets diameter.
are in a good agreement with theoretical results with a maximum error of 14.5%. 3.2. Image reconstruction for solids concentration in the top-spray process Fig. 3 shows the reconstructed ECT images in different times for the top-spray fluidized bed for sugar pellets and corn powder respectively. In those images, blue color means air bubble with relative low
Fig. 5. Bubble size and capacitance changes for pellets.
1152
J.L. Zhang et al. / Powder Technology 301 (2016) 1148–1158
Fig. 8. Solids concentration profile.
3.3. Capacitance change in the top-spray fluidized bed process
Fig. 7. Bubble size change in the top-spray process.
permittivity and red color solids phase with relative high permittivity. From Fig. 3, it can be seen that the bubble size varies over a small range for pellets and wide range for corn powder. Meanwhile, the blue and red areas present core-annular distribution for both particles, indicating high density particles in the near wall region and low density particles in the central area of the chamber.
Fig. 4 shows the changes of average capacitance of all the measurement capacitance values in the top-spray process for sugar pellets and corn powder respectively. It can be seen that the capacitance increases with continuous spraying solution onto the bed within the first 15 min for both materials. However, the change of pellets is much smaller than that for corn powder. For pellets, the smooth surface results in poor water absorption performance and most of the droplets are evaporated directly by the fluidized air, leading to little change in the moisture contents. Alternatively, most of liquid was absorbed by corn powder, resulting in quick increase in moisture content. Fig. 5 shows more details for the top-spray process with corn powder as bed materials. In this figure, the bubble size is calculated based on the ECT images. In this research, the bubble defined as a surface area where the solids concentration is below a certain value and the value is taken as 0.2 according to references [26,27]. In the time period of 15 and 20 min, the
J.L. Zhang et al. / Powder Technology 301 (2016) 1148–1158
process is in drying and there is a sharp decrease in the moisture resulting in the decrease in capacitance. In the time period of 20 and 45 min, the capacitance remains nearly constant. With further spraying solution into the bed, there is another peak for the capacitance, which can be clearly seen in Fig. 5.
3.4. Bubble size in the top-spray process Bubble size is one of the most important parameters, which indicates the gas-solids flow hydrodynamics. Figs. 6 and 7 show the average bubble size in the top-spray process for different powders with different operation conditions. Fig. 7 also gives the cubic-fitting curves for different conditions. Fig. 6 shows the average bubble size for the process without top-spray. It can be seen that the bubble size increases with superficial velocity and particle size. Fig. 7 shows the bubble size profiles for top-spray processes with different bed material, spray pressure and flow rate. Generally, it can be seen that the change of the bubble size is more complex than that without top-spray. The bubble size decreases in the wetting stage and increases in the drying stage due to the change in moisture which results in the change in particle density. In addition, the change in bubble size for corn powder is wider than that for pellets, which is consistent with the results in Fig. 4. For the corn powder, the condition of 0.4 MPa has the smallest particle size compared that with other two conditions as shown in Fig. 7(b). However, the bubble size change little when pressure changes from 0.4 MPa to 0.5 MPa, which indicates that the effect of atomization pressure can be neglected with relatively high pressure i.e. N 0.4 MPa. Fig. 7(c) shows the bubble size profiles for top-spray with corn as bed material with different spray flow rate. It can be seen that there is a small bubble with high flow rate, for that the particles absorbing more water in this condition are more difficult to be fluidized. In summary, the bubble size is influenced by the wetting and drying conditions in the top spray fluidized bed.
1153
for scattered signals is given as follows vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi u N u 1 X ðxðnÞ−xÞ2 ; SD ¼ t N−1 n¼1
x¼
N 1X xðnÞ N n¼1
where x(n) is the original scattered signals, x is the time average value of the signals and N is the total sample number. Fig. 9 shows the dominant frequency and the corresponding SD value for pressure signal during the top-spray process for sugar pellets and corn powder respectively. It can be seen that the variation of dominant frequency with time is nearly the same for pellets and corn powder in the whole process. For sugar, the variation of dominant frequency with time correlates strongly with corresponding changes in the SD as shown in Fig. 9(a). Both decrease with wetting and increase sharply in the beginning of drying and then reach a relative stable value. However Fig. 9(b) shows that in the case of corn, the dominant frequency and SD present varied trend in the whole process. The results above illustrate that different type of bed particle show similar flow character but with different fluctuation. Fig. 10(a) shows the dominant frequency and SD values for pressure signals with different spray pressure for pellets. It can be seen that the top-spray process can be divided into three stages with different character of dominant frequency. The dominant frequency decreases in Stage-I due to the increased moisture and then sharply increases in Stage-II because there is a rapid decrease of bed material moisture in the beginning of drying. While the dominant frequency floats around some value in Stage-III due to the little change of bed material moisture in the rest
3.5. Solids concentration Fig. 8(a) shows the average solids concentration along the radial direction with different superficial velocity for pellets. As can be seen, the concentration nearly keeps constants when the superficial velocity is lower than 1 m/s. With high superficial velocity, the concentration increases from the center to the near wall region and the flow is typically core-annular distribution. Fig. 8(b) shows the average solids concentration profiles with different particle diameters with a superficial velocity of 3 m/s. For large particles, the solids concentration presents a more uniform distribution along the radial direction than that for small particles with a same fluidization velocity. Fig. 8(c) shows the solids concentration profiles at two time step, i.e. t = 5 min and t = 30 min. Due to high density in the wetting stage, the particles are difficult to be fluidized which results in a relative uniform solid concentration. However, in the second time stage, due to low moisture content, the density of particles reduced and it is easy to be fluidized with the same superficial velocity.
3.6. Dominant frequency and standard deviation analysis Fast Fourier Transformation (FFT) is a commonly used technique for fluidization analysis and it has been employed extensively to analyze the dynamic behavior of fluidized beds with pressure measurement [28]. To quantify the information obtained both from pressure and ECT measurement in time series, FFT is used to analyze the measured signals for different process conditions. In this research, the dominant frequency based on FFT and standard deviation (SD) is used to quantify the flow characteristics in the top-spray process. The equation defining the SD
ð6Þ
Fig. 9. Dominant frequency and standard deviation.
1154
J.L. Zhang et al. / Powder Technology 301 (2016) 1148–1158
Fig. 10. Dominant frequency and standard deviation for pellets.
time of drying. The SD value for pressure has a larger value in the wetting stage than that in the drying stage, indicating that the fluctuation is stronger in the wetting stage than that in drying stage. Fig. 10(b) shows the dominant frequency and SD values for the pressure signals with different spray flow rate for pellets. It can be seen that the dominant frequency decreases in the wetting stage due to the increase in moisture and then increases in the drying stage. Furthermore, there is a lower dominant frequency with higher spray flow rate in the Stage-I, for that the pellets can absorb more water in this condition and the larger density weaken the flow. Meanwhile, the standard deviation has a similar trend to that in Fig. 10(a).
Fig. 10(c) shows the dominant frequency and SD value of the normalized capacitance with different spray pressure for pellets. In the wetting stage, the dominant frequencies calculated based on pressure and capacitance have the same characteristics, but with different value. The SD value of normalized capacitance has an obviously different trend compared with that of pressure signal as shown in Fig. 10(a). The SD value decreases in the wetting stage and then increases in the drying stage. Furthermore, there is a lower SD value with higher spray pressure in the wetting stage. The main reason for the difference between pressure and capacitance is that the pressure only represents the local fluctuation and the
J.L. Zhang et al. / Powder Technology 301 (2016) 1148–1158
ECT represents the fluctuation of whole cross section, which provides more comprehensive information on the flow hydrodynamics. 3.7. Discrete wavelet transforms (DWT) analysis DWT is an effective analytical tool for time series signal analysis because DWT can re-express a time series in terms of coefficients, which are associated with particular scales and different frequency bands [6]. Normally, DWT decomposes a raw signal into details and approximations part respectively. The details part represents the high frequency component which is the lower half of the frequency range of the raw signal and the approximations part for the low frequency component which is the upper half of the frequency range of the raw signal. The approximation part can be further decomposed into a new scale of approximation and detail signals. Thus, the decomposition of a given signal S into different scales can be obtained by the application of the DWT to S into N levels, i.e. aN and dN, and the signal of S can be finally written as s ¼ d1 þ d2 þ ⋯dN þ aN
ð7Þ
There are many types of DWT decomposition models and they are including Harr, Daubechies, Mexican hat, and Spline wavelet [29]. In
1155
this research, Daubechies wavelets with order of three (db3) were used for decomposing the capacitance and pressure signals since these wavelets are good enough to have an engineering application [29–31]. Details on how to choose the number of Daubechies wavelets and level of decomposition has been given in reference [32]. Fig. 11 shows the 6-scale wavelet decomposition for pressure signal in a selected case. Fig. 12 shows the dominant frequency spectra for the approximation and detail parts for the pressure and capacitance signals in the scale levels of 1 based on FFT analysis. It can be seen that the pressure and capacitance measurements give nearly same dominant frequency, i.e. 10 Hz both for the approximation and detail parts in the scale level of 1. However, the frequency spectra calculated from capacitance is more complex than that by pressure. The difference will be discussed further in the following. As is well known, different scales of wavelet decomposition associated with different phenomena in a fluidized bed, i.e. the highest frequency for noise, the intermediate frequency for fluidization characteristics and the lowest frequency for systemic effects [33]. The noise, fluidization characteristics and systemic effects are also termed micro-scale with high frequency, meso-scale with intermediate frequency and macro-scale with low frequency, respectively [33,34]. The energy contained in the wavelet-decomposed detail signal at different scales of decomposition can be used to quantify the significance for each of
Fig. 11. Decomposition of pressure signals by wavelet analysis (a represents approximations and d represents details).
1156
J.L. Zhang et al. / Powder Technology 301 (2016) 1148–1158
Fig. 12. Dominant frequency for pressure and capacitance for the approximation and detail part of signals at level j = 1.
the components. The energy of a detail signal at decomposition scale j is given by [35] EDj ¼
XN 2 D j ðt Þ 1
ð8Þ
Fig. 13 shows a sample of the energies of the wavelet-decomposed detail signals in different levels for pressure and capacitance signals in the spraying and drying process. Fig. 13(a) shows that the highest EDj value is in the intermediate scale of 4both for spraying and drying process for pressure signal. The EDj result indicates that the fluidization characteristics changes little for the two processes for pressure signal. Fig. 13(b) shows that the highest value of EDj for capacitance is in the intermediate scale of 4 for spraying and 3 for drying process respectively, which means that different fluidization characteristics occurs in the spraying and drying process. The main reason for the difference in EDj between pressure and capacitance is in that the ECT measurement represents the information on the whole cross section while the pressure measurement only represents the near-wall region [35]. Fig. 14 shows the highest value of EDj for pressure and capacitance signals in a top-spraying process for two cases at the scale level selected according to the result from Fig. 13. From Fig. 14(a), it can be seen that the value of EDj in drying process is bigger than that in spraying process, which is consistent with the results in Fig. 13(a). It can also be seen from Fig. 14(b) that the value of EDj values increase with spraying process and drop sharply in the beginning of drying process. The main reason for such decrease is related to the change in particle moisture as capacitance is more sensitive to moisture than pressure. From the above DWT and FFT analysis, it can be seen that both pressure and capacitance have similar dominant frequency while they represent slightly different detailed information on flow characteristics. Capacitance contains higher proportion of fluidization information in the spraying process and pressure contains higher proportion of fluidization information in the drying process. The results indicate that it is good to combine pressure and capacitance to investigate the gas-solids
flow characteristics in terms of local and whole cross-section area measurement. 4. Conclusion and discussion This paper presents an approach to investigation of the gas-solids flow in a top-spray fluidized bed for particle wetting and drying, by a combination of ECT and pressure. DWT and FFT are used to analyze the capacitance and pressure signals. The effects of key process parameters including superficial velocity, particle size and spraying conduction on the flow hydrodynamics are analyzed based on measurement. The main conclusion can be summarized as follows. In the top-spray fluidized bed process, both the particles moisture and diameter influence the minimum fluidized velocity. The moisture content of particle and superficial fluidization velocity are the two main factors, which affect the bubble characteristics in the top-spray process. The bubble presents a complex trend in the top-spray fluidized bed process, which is influenced by the wetting and drying conditions. The dominant frequency presents different trends in the wetting and drying stage both from capacitance and pressure measurements. Capacitance signals can give more information for investigation of the gassolids flow in terms of SD and DWT. It is good to combine pressure and capacitance to investigate the gas-solids flow characteristics in terms of local and whole cross-section area measurement. Nomenclature a approximant part in discrete wavelet transforms (–) C measured capacitance (pF) details in discrete wavelet transforms (–) dj mean particle diameter (m) dp energy of a detail signal at decomposition scale j (–) EDj FFT Fast Fourier Transformation (−) g acceleration due to gravity (m/s2) G gray level (–) Reynolds number of particle (–) Rep
J.L. Zhang et al. / Powder Technology 301 (2016) 1148–1158
Fig. 13. Energies of the wavelet-decomposed detail signals from levels 1 to 6.
1157
Fig. 14. EDj distributions of pressure and ECT for the top-spraying process.
Acknowledgements s SD Umf x
signals (–) standard deviation (–) minimum fluidization air velocity (m/s) time series of signals for capacitance (pF)
The authors are grateful to the support from the National Natural Science Foundation of China (No. 61320106004) and Chinese Academy of Sciences (CAS) Interdisciplinary Innovation Team.
References Greek symbols α step length in Landweber iteration (–) β solids concentration (–) ε voidage fraction (–) μ viscosity (kg/m·s) ρ density (kg/m3) λ normalized capacitance (–) τ relaxation factor (–)
Subscripts g gas phase H high calibration L low calibration M measurement N sample number for measurement s solid phase
[1] G.M. Walker, S.E.J. Bell, K. Green, D.S. Jones, G.P. Andrews, Characterisation of fluidised bed granulation processes using in-situ Raman spectroscopy, Chem. Eng. Sci. 64 (2009) 91–98. [2] M. Schmidt, C. Rieck, A. Bück, E. Tsotsas, Experimental investigation of process stability of continuous spray fluidized bed layering with external product separation, Chem. Eng. Sci. 137 (2015) 466–475. [3] R.H. Ge, J.M. Ye, H.G. Wang, W.Q. Yang, Measurement of particle concentration in a Wurster fluidized bed by electrical capacitance tomography sensors, AICHE J. 60 (2014) 4051–4064. [4] D. Geldart, Types of gas fluidization, Powder Technol. 7 (1973) 285–292. [5] D. Gidaspow, R. Bezburuah, J. Ding, Hydrodynamics of circulating fluidised bed, kinetic theory approachFluidization VII. Proc. of 7th Eng. Foundation Conference on Fluidization, 3–8 May, Brisbane, Australia 1992, pp. 75–82. [6] H.G. Wang, W.Q. Yang, Measurement of fluidized bed dryer by different frequency and different normalisation methods with electrical capacitance tomography, Powder Technol. 199 (2010) 60–69. [7] H.G. Wang, P. Senior, R. Mann, W.Q. Yang, Online solids moisture measurement and optimum control of fluidized bed dryer, Chem. Eng. Sci. 64 (2009) 2893–2902. [8] P.D. Hede, P. Bach, A.D. Jensen, Small-scale top-spray fluidized bed coating: granule impact strength, agglomeration tendency and coating layer morphology, Powder Technol. 176 (2007) 157–167. [9] T. Sheahan, L. Briens, Passive acoustic emission monitoring of pellet coat thickness in a fluidized bed, Powder Technol. 286 (2015) 172–180. [10] L. Fries, S. Antonyuk, S. Heinrich, S. Palzer, DEM-CFD modelling of a fluidized bed spray granulator, Chem. Eng. Sci. 66 (2011) 2340–2355.
1158
J.L. Zhang et al. / Powder Technology 301 (2016) 1148–1158
[11] M. Vanderrost, F. Ronsse, K. Dewettinck, J.G. Pieters, Modelling the bed characteristics in fluidized-beds for top-spray coating processes, Particuology 10 (2012) 649–662. [12] J.E. Hilton, D.Y. Ying, P.W. Cleary, Modelling spray coating using a combined CFDDEM and spherical harmonic formulation, Chem. Eng. Sci. 99 (2013) 141–160. [13] L. Fries, S. Antonyuk, S. Heinrich, D. Dopfer, S. Palzer, Collison dynamics in fluidized bed granulators: a DEM-CFD study, Chem. Eng. Sci. 86 (2013) 108–123. [14] W. Duangkhamchan, F. Ronsse, S. Siriamornpun, J.G. Pieters, Numerical study of air humidity and temperature distribution in a top-spray fluidized bed coating process, J. Food Eng. 146 (2015) 81–91. [15] B.A. Saadevandi, R. Turton, Particle velocity and voidage profiles in a draft tube equipped spouted-fluidized bed coating device, Chem. Eng. Commun. 191 (2004) 1379–1400. [16] Y.L. He, C.J. Lim, J.R. Grace, J.X. Zhu, S.Z. Qin, Measurements of voidage profiles in spouted beds, Can. J. Chem. Eng. 72 (1994) 229–234. [17] S. Karlsson, I.N. Björn, S. Folestad, A. Rasmuson, Measurement of the particle movement in the fountain region of a Wurster type bed, Powder Technol. 165 (2006) 22–29. [18] F. Depypere, J.G. Pieters, K. Dewettinck, PEPT visualisation of particle motion in a tapered fluidized bed coater, J. Food Eng. 93 (2009) 324–336. [19] G. Chaplin, T. Pugsley, Application of electrical capacitance tomography to the fluidized bed drying of pharmaceutical granule, Chem. Eng. Sci. 60 (2005) 7022–7033. [20] H.G. Wang, W.Q. Yang, P. Senior, R.S. Raghavan, R.S. Duncan, Investigation of batch fluidized bed drying by mathematical modelling, CFD simulation and ECT measurement, AICHE J. 54 (2008) 427–444. [21] M. Takei, T. Zhao, K. Yamane, Measurement of particle concentration in powder coating process using capacitance computed tomography and wavelet analysis, Powder Technol. 193 (2009) 93–100. [22] W.Q. Yangand, T.A. York, New AC-based capacitance tomography system, IEE Proc.Sci. Meas. Technol. 146 (1999) 47–53. [23] W.Q. Yang, L.H. Peng, Image reconstruction algorithms for electrical capacitance tomography, Meas. Sci. Technol. 14 (2003) R1–R13.
[24] W.Q. Yang, D.M. Spink, T.A. York, H. McCann, An image-reconstruction algorithm based on Landweber's iteration method for electrical-capacitance tomography, Meas. Sci. Technol. 10 (1999) 1065–1069. [25] D. Kunii, O. Levenspiel, Fluidization Engineering, Butterworth-Heinemann, Boston, 1991. [26] B.G.M. Wachem, J.C. Schouten, R. Kirshnaand, C.M. van den Bleek, Eulerian simulation of bubbling behaviour in gas-solid fluidised beds, Comput. Chem. Eng. 22 (1998) S299–S306. [27] R.C. Darton, R.D. LaNauze, J. Davidsonand, D. Harrison, Bubble growth due to coalescence in fluidised beds, Trans IChemE. 55 (1977) 274–280. [28] A. Svensson, F. Johnsson, B. Leckner, Fluidization regimes in non-slugging fluidized bed: influence of pressure drop across the air distributors, Powder Technol. 86 (1996) 229–312. [29] M. Tahmasebpoor, R. Zarghami, R. Sotudeh-Gharebagh, N. Mostoufi, Characterization of fluidized beds hydrodynamics by recurrence qualification analysis and wavelet transform, Int. J. Multiphase Flow 69 (2015) 31–41. [30] J.W. Chew, R. Hays, J.G. Findlay, T.M. Knowlton, S.B.R. Karri, R.A. Cocco, C.M. Hrenya, Cluster characteristics of Geldart Group B particles in a pilot-scale CFB riser. I. Monodisperse systems, Chem. Eng. Sci. 68 (2012) 72–81. [31] C. Guenther, R. Breault, Wavelet analysis to characterize cluster dynamics in a circulating fluidized bed, Powder Technol. 173 (2007) 163–173. [32] M. Tahmasebpour, R. Zarghami, R. Sotudeh-Gharebagh, N. Mostoufi, Characterization of various structures in gas-solid fluidized beds by recurrence qualification analysis, Particuology 11 (2013) 647–656. [33] T.Y. Yang, L.P. Leu, Multiresolution analysis on identification and dynamics of clusters in a circulating fluidized bed, AICHE J. 55 (2009) 612–629. [34] J.W. Chew, D.M. Parker, C.M. Hrenya, Elutriation and species segregation characteristics of polydisperse mixtures of group B particles in a dilute CFB riser, AICHE J. 59 (2013) 84–95. [35] G.Z. Qiu, J.M. Ye, H.G. Wangand, W.Q. Yang, Investigation of flow hydrodynamics and regime transition in a gas-solids fluidized bed with different riser diameters, Chem. Eng. Sci. 116 (2014) 195–207.