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EFFECT OF OPERATING CONDITIONS IN ATMOSPHERIC FREEZE-DRYING
Reyes A., Vega R. and Bruna R.
Department of Chemical Engineering, Universidad de Santiago de Chile
E-mail: [email protected]
Abstract: Moisture content of carrot particles during atmospheric freeze-drying in a pulsed
fluidized bed was studied, as a function of particle size (6mm 6mm 1.5mm and 6mm
6mm 3mm), freezing rate (slow and quick), air temperature (–15°C and –5°C) and type of
energy supply (convective or convective + infra-red).
The experimentation was carried out through a factorial design 24 and the statistical analysis
of the results shows that the factor levels that increase the rate of moisture removal are the
following: small particle size, slow freezing rate, high temperature, and convective + infrared energy supply.
The Page empirical model and the Simplified Constant Diffusivity Model (SCDM) were
used to fit the experimental data, exhibiting the Page model the best fit. The SCDM model
permitted to calculate effective diffusivity values which are in agreement with those reported
by other authors.
Keywords: freeze-drying, carrot, pulsed fluidized bed
1.
INTRODUCTION
1.1. Freeze-drying process
The convective drying impairs sensory and functional properties of foods, due to protein and vitamin degradation
caused by high temperature. Besides, the structure of the solid is heavily damaged, affecting rehydration. Freezedrying represents a good drying alternative for foods (Di Matteo et al., 2003; Venir et al., 2007; Reyes et al., 2008),
pharmaceutical (Abdelwahed et al., 2006a, b) and nuclear products (Branger et al., 2008; De Saniot et al., 2004).
Freeze-drying consists of three stages: i) freezing of solid, ii) a primary drying stage, where sublimation of free
water occurs (between 65-90% of water content) and iii) a secondary drying stage, where bound water is eliminated
by desorption (between 10-35% of the initial moisture content). To remove moisture at temperature below 0°C,
freeze-drying uses high vacuum, thus increasing the mass transfer gradient between sublimation front and drying
medium, but the process is slow and involves high investment and operating costs due to the need of producing and
maintaining high vacuum (Ratti, 2001). To reduce costs, two alternatives have been studied: i) modify conventional
freeze-drying operating conditions, and ii) implement alternative freeze-drying processes, among which stands out
atmospheric freeze-drying (Izcara et al., 1994).
Atmospheric freeze-drying has been studied in a tunnel dryer, also by spray freezing and subsequent freeze-drying
of particles (Claussen et al., 2007), and freeze-drying of potato particles, green peas and red pepper in a fluidized
bed (Di Matteo et al., 2003; Alves-Filho et al., 2004; Alves-Filho et al., 2007). Di Matteo et al. (2003), Alves-Filho
et al. (2007) and Tomova et al. (2004) informed that, for small particles, a faster moisture removal was obtained for
a shorter diffusional path, also a slow freezing rate improved the moisture removal rate because it produced a highly
porous solid matrix which favored vapor diffusion, concluding that the higher the temperature the lower the time
required for moisture removal. Di Matteo et al. (2003) mixed adsorbent particles with particles to be freeze-dried in
a fluidized bed, which improved drying rate, but made it difficult to separate the adsorbent agent (Kudra and
Mujumdar, 2009).
To reduce the atmospheric freeze-drying time of carrot particles in a pulsed fluidized bed, the present work studied
the influence of particle size, freezing rate, air temperature and type of energy supply, on the moisture content
reached. The mass transfer gradient was maximized by circulating the air through a fixed bed of silica gel.
1.2. Fitting drying curves
Drying curves can be described by Page empirical model (Reyes et al., 2008; Sablani et al., 2000):
Xt
exp k t m
X0
where X is the moisture content on dry weight basis, k and m are model parameters and t is drying time.
(1)
Another way to describe drying curves is the Simplified Constant Diffusivity Model (SCDM) (Reyes et al., 2008),
whose application for 6mm × 6mm × 3mm and 6mm × 6mm × 1.5mm particles is presented in equations (2) and
(3).
Xt
X0
exp
2
3
D eff t
8 Lo 2
(2)
2
9
D eff t
8 Lo 2
where Lo is the half-thickness of particle (1.5mm or 0.75mm) and Deff is the effective diffusivity.
Xt
X0
2.
exp
(3)
EQUIPMENT, MATERIALS AND METHODS
2.1 Equipment
Figure 1 shows the scheme of the Atmospheric Freeze Dryer, where air is circulated by a centrifugal blower (A), the
temperature is adjusted in a cooling system (B) and dried in a fixed bed of silica gel (C). Then, it passes through a
horizontal rotating cylinder with two slots which generate pulsating air flow (D), passing then through carrot
particles bed (E). The system is provided with an IR lamp for runs that require it. (F).
G
-5ºC
Air
temperature
F
E
ON OFF
Pulse generator
Centrifugal blower
D
C
A
B
Figure 1. Atmospheric Freeze Dryer. A: centrifugal blower, B: air cooling system, C: silica gel, D: pulse generator,
E: freeze drying chamber, F: IR lamp, G: control panel.
2.2. Freezing procedure
Freezing of samples was carried out in a household freezer for 15 hours at –18ºC (slow freezing) or by immersion in
liquid nitrogen for 5 minutes (quick freezing).
2.3. Experimental design
Experimentation was carried out according to a 24 factorial design whose factor levels are shown in Table 1. The
random order of experimental runs is given in Table 2. The statistical analysis was carried out using the software
Statgraphics Plus 5.1 (Statistical Graphics Corp., USA, 2000).
Table 1. Factor levels used in the present study.
Factor
Solid size (mm)
Freezing rate
Air temperature (°C)
Type of energy supply
Lower level (–1)
6 × 6 × 1.5
Slow (household freezer)
–15
Convection
Upper lever (+1)
6×6×3
Quick (liquid nitrogen)
–5
Convection + IR
Table 2. Variable values for each experiment.
Run
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
A: Particle Size (mm)
6×6×3
6 × 6 × 1.5
6×6×3
6 × 6 × 1.5
6 × 6 × 1.5
6×6×3
6×6×3
6×6×3
6 × 6 × 1.5
6 × 6 × 1.5
6 × 6 × 1.5
6×6×3
6 × 6 × 1.5
6×6×3
6 × 6 × 1.5
6×6×3
B: Freezing Rate
Fast
Slow
Slow
Slow
Fast
Slow
Fast
Fast
Fast
Slow
Slow
Slow
Fast
Slow
Fast
Fast
C: Air Temperature (°C)
–15
–15
–5
–15
–15
–15
–15
–5
–5
–5
–5
–5
–15
–15
–5
–5
D: Type of energy supply
Convection + IR
Convection + IR
Convection
Convection
Convection + IR
Convection + IR
Convection
Convection
Convection
Convection + IR
Convection
Convection + IR
Convection
Convection
Convection + IR
Convection + IR
2.4. Experimental procedure
Silica gel (2 kg) was loaded in the corresponding compartment (Fig. 1-C), the desired temperature value was set on
the cooling system (Fig. 1-B) and the pulse generator and centrifugal blower were turned on. After the system
reached steady state, 200grams of carrot particles were loaded in the drying chamber (a 9.5cm diameter glass tube,
with a perforated plate at the bottom to allow air pass through), the initial mass was measured with a Boeco
Germany balance (Boecktel Co., BBL62, Germany). After 30 minutes, the glass tube was taken out from the
equipment, it was weighed and replaced in the equipment. This procedure was repeated until 690 minutes of
operation. The moisture content was determined in a vacuum oven until constant weight according to AOAC
920.151 (Anon., 1990). Silica gel was replaced every 2 hours to ensure the fluidization air was dry.
3.
RESULTS AND DISCUSSIONS
3.1. Drying curves
Figure 2 shows drying curves corresponding to experimental runs carried out according to Table 2, where it is
observed large differences in moisture content after 11.5 hours of operation, depending on operating conditions.
3.2. Analysis of significant effects on moisture content
Table 3 shows the estimated effects on moisture content after each hour during the process. It can be seen that
freezing rate (B), air temperature (C) and type of energy supply (D) are statistically significant effects on moisture
content during the whole period of drying. Particle size (A) was not significant during the first three hours, which
might be attributed mainly to removal of ice formed on particle surface, being the mass transfer controlled only by
external resistance. Since the fourth hour, the particle size becomes important due to diffusion of vapor generated by
sublimation and desorption through the porous layer, then the diffusion path turns important.
1.0
1.0
Run 1
Run 2
Run 3
Run 4
Run 5
Run 6
Run 7
Run 8
0.8
0.7
X/X0
0.6
Run 9
Run 10
Run 11
Run 12
Run 13
Run 14
Run 15
Run 16
0.9
0.8
0.7
0.6
X/X0
0.9
0.5
0.5
0.4
0.4
0.3
0.3
0.2
0.2
0.1
0.1
0.0
0.0
0
100
200
300
400
500
600
700
800
0
100
200
t (min)
300
400
500
600
700
800
t (min)
Figure 2. Drying curves obtained with operating conditions showed in Table 2.
Table 3. Estimated effects on moisture content at each hour and standard deviations (sef). Values in bold correspond
to significant effects at 95% confidence level.
t (h)
1
2
3
4
5
6
7
8
9
10
11
11.5
Mean
0.874
0.693
0.554
0.442
0.363
0.303
0.259
0.224
0.196
0.173
0.153
0.137
A
0.000
0.014
0.036
0.067
0.086
0.095
0.101
0.103
0.104
0.104
0.104
0.102
B
0.021
0.046
0.065
0.079
0.086
0.089
0.086
0.081
0.075
0.070
0.066
0.061
C
–0.066
–0.174
–0.252
–0.304
–0.328
–0.337
–0.326
–0.308
–0.287
–0.264
–0.242
–0.222
D
–0.021
–0.050
–0.065
–0.079
–0.083
–0.076
–0.069
–0.058
–0.049
–0.041
–0.036
–0.030
AB
0.009
0.015
0.017
0.015
0.012
0.009
0.012
0.015
0.018
0.020
0.023
0.023
AC
–0.005
–0.009
–0.011
–0.012
–0.016
–0.024
–0.035
–0.046
–0.055
–0.063
–0.069
–0.074
AD
0.001
0.000
0.003
–0.002
–0.004
–0.007
–0.009
–0.009
–0.008
–0.006
–0.005
–0.004
BC
–0.010
–0.017
–0.028
–0.034
–0.032
–0.032
–0.034
–0.035
–0.038
–0.039
–0.039
–0.039
BD
0.007
–0.007
–0.020
–0.035
–0.039
–0.035
–0.032
–0.030
–0.026
–0.023
–0.021
–0.019
CD
–0.008
–0.023
–0.036
–0.047
–0.050
–0.041
–0.030
–0.020
–0.012
–0.008
–0.004
–0.002
sef
0.005
0.012
0.017
0.022
0.021
0.018
0.016
0.014
0.013
0.012
0.011
0.011
Figure 3 shows the evolution of factor effect values through the whole process, where it can be seen that air
temperature is the factor with great impact on moisture content. It is also seen that although the particle size (A) has
no significant effect at the beginning of the process, since the sixth hour it takes the second place in importance,
followed by the freezing rate (B) and by the type of energy supply (D). Optimum factor levels that minimize the
moisture content at each hour of the process are given in Table 4, where optimum level of particle size is the smaller
diffusion path, which together with slow freezing rate leads to larger pores in the solid matrix thus improving the
diffusion process. Finally, higher temperature and convective + IR energy supply provide a grater heat flux to the
solid, favoring sublimation or desorption process.
0.4
A: Particle size
B: Freezing rate
C: Air temperature
D: Type of energy supply
AC
BC
0.3
Estimated effect
0.2
0.1
0.0
-0.1
-0.2
-0.3
-0.4
0
1
2
3
4
5
6
7
8
9
10
11
12
t (h)
Figure 3. Evolution of estimated effects with 95% confidence level on moisture content
Table 4. Optimum factor levels that minimize the moisture content at each hour of the process
Factor
Particle size
Freezing rate
Air temperature
Type of energy supply
Optimum value
6 mm × 6 mm × 1.5 mm
Slow
–5°C
Convection + IR
Table 5. Effective diffusivity and parameters of Page’s model
Deff Model
Run N° Deff × 1011 (m2/s)
1
1.62
2
0.29
3
5.13
4
0.24
5
0.21
6
2.23
7
1.37
8
3.80
9
0.40
10
0.56
11
0.55
12
7.12
13
0.19
14
2.28
15
0.65
16
6.35
RMSE
n
1
n
RMSE
0.0467
0.0157
0.0064
0.0109
0.0081
0.0611
0.0394
0.0321
0.0202
0.0334
0.0355
0.0092
0.0102
0.0467
0.0366
0.0171
p
y exp,i
i 1
y calc,i
2
Page model
k
m
RMSE
0.0055 0.7950 0.0023
0.0055 0.9175 0.0038
0.0058 0.9750 0.0041
0.0037 0.9546 0.0046
0.0029 0.9780 0.0056
0.0123 0.7098 0.0043
0.0059 0.7567 0.0019
0.0085 0.8572 0.0055
0.0031 1.0764 0.0137
0.0025 1.1857 0.0126
0.0020 1.2222 0.0087
0.0059 1.0346 0.0062
0.0027 0.9686 0.0068
0.0084 0.7777 0.0063
0.0024 1.2331 0.0129
0.0039 1.0885 0.0066
n: data number
p: number of parameters of the model
3.3. Fitting of models
Table 5 shows effective diffusivity and Page model parameters obtained by fitting experimental data to equations
(1), (2) and (3). It can be seen that Page model gives a better fit of the experimental data (lower RMSE for all runs)
than SCDM. Effective diffusivity values obtained in this work vary between 1.87×10–12 and 7.12×10–11 which are of
the same order of magnitude that those reported by other authors (Sablani et al., 2000; Reyes et al., 2008).
4.
CONCLUSIONS
The air temperature was the most important factor that affected the moisture content attained at each moment during
the drying process, followed by particle size, freezing rate and type of energy supply. Moisture removal rate was
favored by a smaller particle size, slow freezing rate, higher air temperature and convective + IR energy supply.
The best fit of the drying curves was obtained with Page empirical model, and the levels of the factors that maximize
the value of the effective diffusivity are the same that minimize the moisture content, except for particle size
ACKNOWLEDGEMENTS. The authors wish to thank the financial support of project FONDECYT Nº 1070019
and DICYT - Usach.
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