Considering the 1–4.7 kW possible power settings for generators
3 and 4, the operating frequency bandwidth for generators 3
and 4 was 901–909 MHz and 898–905 MHz, respectively. The consistency
of generators from operating at a certain frequency might
be related to the differences in the designs and components of the
magnetrons of the generators.
In general, the operating frequency was directly proportional to
the power setting (i.e., the higher the power setting of the generator,
the higher the operating frequency). With generators 1 and 2,
for every 0.5 kW increase in power, the operating frequency
increased by 0.25 MHz. At the same power setting, generator 2,
on average, operated at 4.8 MHz higher than generator 1. For generator
3 and 4, the operating frequency was increased by 0.75 MHz
for every 0.5 kW increase in the power setting. Also, at the same
power setting, generator 3, on average, operated 2.7 MHz higher
than generator 4.
Although a general trend between operating frequency and
power setting has been established, generators manufactured by
Ferrite™ (Model GET-2024) are less consistent in achieving a
certain value of operating frequency than those manufactured by
Microdry™ (Model IV-74) (Fig. 4). Generators 1 and 2 (i.e.,
Ferrite™) produced an up and down trend of operating frequency
with power and a relative high standard deviation (i.e., approximately
±1 MHz) among measurement trials. For generators 3 and
4 (i.e., Microdry™), the curve was relatively smooth and the standard
deviation among trials was much lower (i.e., approximately
±0.3 MHz). A possible explanation was that the Ferrite™ generators
were originally designed to operate at a full power of 75 kW.
But they were modified to operate at much lower power (15 kW)
needed for WSU MATS system.
The occupied frequency bandwidth (OFBW), however, seems to
be independent from the power setting of the generator. When
looking at 80% of the total power measured by the spectrum
analyzer, the average OFBWs for generators 1, 2, 3, and 4 were
7.58, 8.35, 7.36, and 8.50 MHz, respectively.
3.2. Frequencies at operation power settings
Generators 1 and 2 were relatively close to the FCC allocated
mean frequency of 915 MHz, but generator 3 and 4 were slightly
lower (Fig. 4). This might be due to the differences in the design
and the age of the generators. Generators 1 and 2 (built in 2008)
were relatively newer than generators 3 (built in 1991) and 4 (built
in 1996). Furthermore, considering the OFBW at 80% total power of
the generators, the measured operating frequencies in Table 3
were within the experimental design in Table 2. The chosen
900–920 MHz range of frequency roughly covered the lowest and
highest possible operating frequencies of the generators.
3.3. Heating pattern validation through chemical marker method
Fig. 5 shows that the heating pattern generated from computer
simulation (Case 6) is comparable to the heating pattern identified
through the chemical marker method using WPG as the model
food. Both images were on the x–y plane and in the middle with
respect to the sample’s thickness (i.e., z axis). The heating pattern
was symmetrical in the x–y plane and can be summarized into
three areas: Cold Area 1, Cold Area 2, and Hot Area (Resurreccion
et al., 2013). The temperature distribution within a given area
was relatively uniform. The top and bottom areas in y direction
were at a lower temperature and qualitatively described by the
green/bluish color, corresponding to Cold Area 1. The central area
was also at a lower temperature which corresponds to Cold Area
2. The areas above and below Cold Area 2 qualitatively described
by the red color corresponds to the Hot Area. The heating pattern
of experimental result was correctly predicted by computer simulation.
The validated simulation model will be applied to study the influence of frequency shifts on heating pattern of food processed
in MATS system. However, the simulation results overestimated
temperature rises during the thermal processes within the MATS
system. Thus, in this study our main interest in using this model
was the prediction of heating patterns.
3.4. Influence of frequency shifts on heating patterns
The simulation results of the heating patterns in the x–y plane
at the middle layer (z direction) of the sample are summarized in
Fig. 6. The general heating pattern was not affected by frequency
shift of the microwave generator within the range of 900–
920 MHz. However, the temperature (i.e., associated to heating
rate) increased with an increase in frequency. The difference in
overall sample temperature at different frequencies was most significant
at the exit of the second cavity (Fig. 6a-iii and b-iii).
At lower frequency (e.g., Case 1 at 900 MHz), there was a clear
distinction between cold and hot areas. But at a higher frequency
(e.g., Case 5 at 920 MHz), the hot areas expanded. At 900 MHz, in
Fig. 6a – Case 1, the cold area between two hot areas is mostly
green, corresponding to a temperature range of 110–112 C; and
in Fig. 6b – Case1, the cold area is red corresponding to a temperature
range of 170–180 C. However, at 920 MHz in Fig. 6a – Case
5, the cold spot area between two hot areas is mostly yellow corresponding
to a temperature of about 120 C; and in Fig. 6b – Case
5, everything is pink which corresponds to a temperature >200 C.
Dissipation of microwave power into heat was higher at higher frequency
causing the sample to increase in temperature, the hot
areas to occupy a bigger region, and the cold area sandwiched
between the two hot areas to be a reduced region (Incropera
et al., 2007). As stated earlier, the simulation model used in this
study over-estimated sample temperatures (Resurreccion et al.,
2013). Thus, the above discussion of sample temperatures only
helped to compare relative heating intensities of different simulated
cases.
In Case 6 which simulates the process with actual operating frequencies
(Tables 2 and 3), the final heating pattern was similar to
the result between those of the simulation for Case 3 and Case 4
(Fig. 6). Although the average frequency for Case 6 is 909.34 MHz
which fell between Case 2 and Case 3 (i.e., 905 MHz and
910 MHz, respectively), generator 1 and generator 2 were operating at higher frequencies and powers (Table 3), and should therefore
have a larger contribution to heating pattern.
Comparing group (a) and group (b) in Fig. 6, the loss factor of
the circulating water inside the cavity had a significant influence
on the intensity of temperature in the sample. Although the patterns
were similar for both groups, reducing the loss factor of circulating
water by half (from e00
r ¼ 2:70 for tap water to e00
r ¼ 1:35
for deionized water) would result in a 23–37% increase in temperature.
The reason was that the amount of microwave energy dissipated
to heat is different in different circulating waters. For higher
lossy circulating water such as tap water, a relatively large amount
of the microwave energy was absorbed by the water reducing the
amount of energy that may be absorbed by the food. For relatively
lower lossy water such as deionized water, less microwave energy
was absorbed by the circulating water. This made most of the incident
microwave energy available to food material, producing
higher heating rate and final temperature of the food. In actual processing
in the MATS system, during the application of microwave
power to the heating cavities, there is an average of 2–3 C increase
in temperature of the circulating water (i.e., from 122 C to 124–
125 C) showing that water indeed absorbed microwave energy.
The microwave energy absorbed by the water is then subsequently
taken off by the chilling water through a heat exchanger attached
to the MATS system, which then reduces the temperature back to
122 C before entering the microwave heating section.
4. Conclusions
From above discussion, the following conclusions could be
derived:
The operating frequencies of four generators powering the
MATS system were influenced by the power settings. Every
0.5 kW increase in power caused an operating frequency
increase by a 0.25 MHz for generators 1 and 2, and by
0.75 MHz for generators 3 and 4. However, within a period of
one year there was no significant frequency shift for each generator
at a fixed power setting.
Both the simulation and the chemical marker method suggested
that the heating pattern was symmetrical in x–y plane and
could be summarized into three areas (i.e., Cold Area 1, Cold
Area 2, and Hot Area). The temperature distribution within a
given area was relatively uniform.
The operating frequency of the microwave generators ranged
from 900 MHz to 920 MHz did not affect the heating pattern
inside the food. But higher operating frequency resulted in an
increase of food temperature.
The overall effect of reducing the loss factor of circulating water
in the microwave heating cavities was an increase in temperature
of the food. Compared with tap water, using deionized
water as the circulating water caused a 23–37% increase in
the overall temperature of WPG.
Acknowledgements
We acknowledge the support of the Agriculture and Food
research Initiative of the USDA National Institute of Food and
Agriculture, grant number #2011-68003-20096 and Agriculture
Research center of Washington State University. The authors also
thank the Chinese Scholarship Council for providing a scholarship
to Donglei Luan for his Ph.D. studies
Considering the 1–4.7 kW possible power settings for generators3 and 4, the operating frequency bandwidth for generators 3and 4 was 901–909 MHz and 898–905 MHz, respectively. The consistencyof generators from operating at a certain frequency mightbe related to the differences in the designs and components of themagnetrons of the generators.In general, the operating frequency was directly proportional tothe power setting (i.e., the higher the power setting of the generator,the higher the operating frequency). With generators 1 and 2,for every 0.5 kW increase in power, the operating frequencyincreased by 0.25 MHz. At the same power setting, generator 2,on average, operated at 4.8 MHz higher than generator 1. For generator3 and 4, the operating frequency was increased by 0.75 MHzfor every 0.5 kW increase in the power setting. Also, at the samepower setting, generator 3, on average, operated 2.7 MHz higherthan generator 4.Although a general trend between operating frequency andpower setting has been established, generators manufactured byFerrite™ (Model GET-2024) are less consistent in achieving acertain value of operating frequency than those manufactured byMicrodry™ (Model IV-74) (Fig. 4). Generators 1 and 2 (i.e.,Ferrite™) produced an up and down trend of operating frequencywith power and a relative high standard deviation (i.e., approximately±1 MHz) among measurement trials. For generators 3 and4 (i.e., Microdry™), the curve was relatively smooth and the standarddeviation among trials was much lower (i.e., approximately±0.3 MHz). A possible explanation was that the Ferrite™ generatorswere originally designed to operate at a full power of 75 kW.But they were modified to operate at much lower power (15 kW)needed for WSU MATS system.The occupied frequency bandwidth (OFBW), however, seems tobe independent from the power setting of the generator. Whenlooking at 80% of the total power measured by the spectrumanalyzer, the average OFBWs for generators 1, 2, 3, and 4 were7.58, 8.35, 7.36, and 8.50 MHz, respectively.3.2. Frequencies at operation power settingsGenerators 1 and 2 were relatively close to the FCC allocatedmean frequency of 915 MHz, but generator 3 and 4 were slightlylower (Fig. 4). This might be due to the differences in the designand the age of the generators. Generators 1 and 2 (built in 2008)were relatively newer than generators 3 (built in 1991) and 4 (builtin 1996). Furthermore, considering the OFBW at 80% total power ofthe generators, the measured operating frequencies in Table 3were within the experimental design in Table 2. The chosen900–920 MHz range of frequency roughly covered the lowest andhighest possible operating frequencies of the generators.3.3. Heating pattern validation through chemical marker methodFig. 5 shows that the heating pattern generated from computersimulation (Case 6) is comparable to the heating pattern identified
through the chemical marker method using WPG as the model
food. Both images were on the x–y plane and in the middle with
respect to the sample’s thickness (i.e., z axis). The heating pattern
was symmetrical in the x–y plane and can be summarized into
three areas: Cold Area 1, Cold Area 2, and Hot Area (Resurreccion
et al., 2013). The temperature distribution within a given area
was relatively uniform. The top and bottom areas in y direction
were at a lower temperature and qualitatively described by the
green/bluish color, corresponding to Cold Area 1. The central area
was also at a lower temperature which corresponds to Cold Area
2. The areas above and below Cold Area 2 qualitatively described
by the red color corresponds to the Hot Area. The heating pattern
of experimental result was correctly predicted by computer simulation.
The validated simulation model will be applied to study the influence of frequency shifts on heating pattern of food processed
in MATS system. However, the simulation results overestimated
temperature rises during the thermal processes within the MATS
system. Thus, in this study our main interest in using this model
was the prediction of heating patterns.
3.4. Influence of frequency shifts on heating patterns
The simulation results of the heating patterns in the x–y plane
at the middle layer (z direction) of the sample are summarized in
Fig. 6. The general heating pattern was not affected by frequency
shift of the microwave generator within the range of 900–
920 MHz. However, the temperature (i.e., associated to heating
rate) increased with an increase in frequency. The difference in
overall sample temperature at different frequencies was most significant
at the exit of the second cavity (Fig. 6a-iii and b-iii).
At lower frequency (e.g., Case 1 at 900 MHz), there was a clear
distinction between cold and hot areas. But at a higher frequency
(e.g., Case 5 at 920 MHz), the hot areas expanded. At 900 MHz, in
Fig. 6a – Case 1, the cold area between two hot areas is mostly
green, corresponding to a temperature range of 110–112 C; and
in Fig. 6b – Case1, the cold area is red corresponding to a temperature
range of 170–180 C. However, at 920 MHz in Fig. 6a – Case
5, the cold spot area between two hot areas is mostly yellow corresponding
to a temperature of about 120 C; and in Fig. 6b – Case
5, everything is pink which corresponds to a temperature >200 C.
Dissipation of microwave power into heat was higher at higher frequency
causing the sample to increase in temperature, the hot
areas to occupy a bigger region, and the cold area sandwiched
between the two hot areas to be a reduced region (Incropera
et al., 2007). As stated earlier, the simulation model used in this
study over-estimated sample temperatures (Resurreccion et al.,
2013). Thus, the above discussion of sample temperatures only
helped to compare relative heating intensities of different simulated
cases.
In Case 6 which simulates the process with actual operating frequencies
(Tables 2 and 3), the final heating pattern was similar to
the result between those of the simulation for Case 3 and Case 4
(Fig. 6). Although the average frequency for Case 6 is 909.34 MHz
which fell between Case 2 and Case 3 (i.e., 905 MHz and
910 MHz, respectively), generator 1 and generator 2 were operating at higher frequencies and powers (Table 3), and should therefore
have a larger contribution to heating pattern.
Comparing group (a) and group (b) in Fig. 6, the loss factor of
the circulating water inside the cavity had a significant influence
on the intensity of temperature in the sample. Although the patterns
were similar for both groups, reducing the loss factor of circulating
water by half (from e00
r ¼ 2:70 for tap water to e00
r ¼ 1:35
for deionized water) would result in a 23–37% increase in temperature.
The reason was that the amount of microwave energy dissipated
to heat is different in different circulating waters. For higher
lossy circulating water such as tap water, a relatively large amount
of the microwave energy was absorbed by the water reducing the
amount of energy that may be absorbed by the food. For relatively
lower lossy water such as deionized water, less microwave energy
was absorbed by the circulating water. This made most of the incident
microwave energy available to food material, producing
higher heating rate and final temperature of the food. In actual processing
in the MATS system, during the application of microwave
power to the heating cavities, there is an average of 2–3 C increase
in temperature of the circulating water (i.e., from 122 C to 124–
125 C) showing that water indeed absorbed microwave energy.
The microwave energy absorbed by the water is then subsequently
taken off by the chilling water through a heat exchanger attached
to the MATS system, which then reduces the temperature back to
122 C before entering the microwave heating section.
4. Conclusions
From above discussion, the following conclusions could be
derived:
The operating frequencies of four generators powering the
MATS system were influenced by the power settings. Every
0.5 kW increase in power caused an operating frequency
increase by a 0.25 MHz for generators 1 and 2, and by
0.75 MHz for generators 3 and 4. However, within a period of
one year there was no significant frequency shift for each generator
at a fixed power setting.
Both the simulation and the chemical marker method suggested
that the heating pattern was symmetrical in x–y plane and
could be summarized into three areas (i.e., Cold Area 1, Cold
Area 2, and Hot Area). The temperature distribution within a
given area was relatively uniform.
The operating frequency of the microwave generators ranged
from 900 MHz to 920 MHz did not affect the heating pattern
inside the food. But higher operating frequency resulted in an
increase of food temperature.
The overall effect of reducing the loss factor of circulating water
in the microwave heating cavities was an increase in temperature
of the food. Compared with tap water, using deionized
water as the circulating water caused a 23–37% increase in
the overall temperature of WPG.
Acknowledgements
We acknowledge the support of the Agriculture and Food
research Initiative of the USDA National Institute of Food and
Agriculture, grant number #2011-68003-20096 and Agriculture
Research center of Washington State University. The authors also
thank the Chinese Scholarship Council for providing a scholarship
to Donglei Luan for his Ph.D. studies
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