- Email: [email protected]
Drying of green bell pepper slices using an IR-assisted Spouted Bed Dryer: An assessment of drying kinetics and energy consumption
Journal Pre-proof Drying of green bell pepper slices using an IR-assisted Spouted Bed Dryer: An assessment of drying kinetics and energy consumption
Mehdi Moradi, Sadi Azizi, Mehrdad Niakosari, Saadat Kamgar, Amin Mousavi Khaneghah PII:
S1466-8564(19)31392-X
DOI:
https://doi.org/10.1016/j.ifset.2019.102280
Reference:
INNFOO 102280
To appear in:
Innovative Food Science and Emerging Technologies
Received date:
6 November 2019
Revised date:
12 December 2019
Accepted date:
22 December 2019
Please cite this article as: M. Moradi, S. Azizi, M. Niakosari, et al., Drying of green bell pepper slices using an IR-assisted Spouted Bed Dryer: An assessment of drying kinetics and energy consumption, Innovative Food Science and Emerging Technologies(2018), https://doi.org/10.1016/j.ifset.2019.102280
This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
© 2018 Published by Elsevier.
Journal Pre-proof Drying of green bell pepper slices using an IR- assisted Spouted Bed Dryer: An assessment of drying kinetics and energy consumption Mehdi Moradi1,*, Sadi Azizi1, Mehrdad Niakosari2, Saadat Kamgar1, Amin Mousavi Khaneghah3,** Department of Biosystems Engineering, College of Agriculture, Shiraz University,
Department of Food Science and Technology, College of Agriculture, Shiraz
-p
2
ro
Shiraz, Iran
of
1
Department of Food Science, Faculty of Food Engineering, University of Campinas
lP
3
re
University, Shiraz, Iran
na
(UNICAMP), Rua Monteiro Lobato, 80. Caixa Postal: 6121.CEP: 13083-862, Campinas, São Paulo, Brazil.
ur
*,**Corresponding authors:
Jo
Mehdi Moradi, Email: [email protected]; [email protected] Amin Mousavi Khaneghah, Email: [email protected]
1
Journal Pre-proof Abstract In the current investigation, an IR-assisted spouted bed dryer containing inert aluminum particles was employed for drying of Green Bell Pepper Slices (GBPS). In order to evaluate the dryer’s performance, a completely randomized factorial design with three independent factors including drying temperature (50, 60, and 70 ˚C), mass
of
percentage of inert particles to GBPS (0, 25, and 50%), and surface area of GBPS (1.44,
ro
3.24, and 7.84 cm2) were used. According to findings, by using the inert particles with
-p
mass percentage of 25%, a reduction of around 25% in drying time was realized.
re
However, increasing in the ratio to 50%, an increasing the drying time (56%) was noted.
lP
An optimum drying condition in terms of specific energy consumption (SEC) was
na
achieved at an air temperature of 70 °C, 25% inert material and GBPS with a surface area
ur
of 1.44 cm2. Moreover, a dimensionless model for describing the drying kinetics of
Jo
GBPS was established and evaluated. Keywords: Dimensional analysis; inert heat carriers; drying; Specific energy consumption; green bell pepper
2
Journal Pre-proof 1- Introduction Green bell pepper (Capsicum annuum) is a one-year-old plant with numerous applications in food industries both in fresh and dried forms. Due to high initial moisture content (95%, wet basis) of the fresh product, obtaining longtime storage seems to be difficult. However, some techniques such as drying could be approached to
of
reduce the moisture content of this product, a high energy consumption can be
ro
considered as one of the most noted challenges for the traditional drying techniques.
-p
The high initial moisture of green bell pepper may result in elongating drying time
re
besides intensifying energy consumption (Doymaz and Ismail, 2010). In this regard, in
lP
order to reduce the consumption of energy for dehydration, employing an appropriate
na
economic drying technique is crucial (Prosapio & Norton, 2017).
ur
Numerous drying techniques were used to improve product shelf life as well as
Jo
reduce energy consumption, including infrared assisted freeze-drying (Chakraborty et al., 2011), and microwave-assisted dehydration (Das et al., 2016). Also, different hybrid methods such as infrared-convective drying (El-Mesery and Mwithiga, 2015), infraredheat pump drying (Aktas et al., 2017), and microwave spouted bed dryer (Serowik et al., 2018) were introduced. In this regard, the spouted bed dryer equipped with the infrared source (SBDIR) can be used to increase the drying rate (Dehghan-Manshadi et al., 2019). In this method, infrared radiation is directed to the samples to eliminate the exceeded amounts of moisture (Hammouda and Mihoubi, 2014). Also, the heat carrier 3
Journal Pre-proof inert materials can be employed in the design of spouted or fluidized bed dryers to increase the performance, mixing up, heat transfer coefficient and to improve the fluidization behavior of the materials (Honarvar et al., 2013; Tasirin et al., 2014). Their application can result in further increases in the drying rate due to storing and transfer of heat into the products besides the improvements in fluidization properties by
of
reducing in particle adhesion, particularly while products are still wet (Tasirin et al.,
accomplish optimum
drying
conditions,
-p
To
ro
2014).
knowledge
regarding
the
re
instantaneous moisture content of the product among different drying stages is crucial.
lP
In this regard, mathematical modeling is usually utilized to predict the moisture content
na
of the samples instantaneously. Hence, the employing of mathematical models for the
Jo
energy.
ur
dehydration offers valuable information combined with further saving in time and
In order to simulate the drying, three types of model namely theoretical, empirical, and semi-empirical can be used. Among them, the theoretical models have some shortcomings, such as the inaccuracy of results due to the conducted assumptions while solving the governing equations (Badaoui et al., 2019). However, semi-empirical models such as Henderson, Page, Midilli although are simple, only can be applied to specific drying conditions to predict the moisture content or ratio (Sabarez, 2015). In this regard, the development of the empirical models based on experimental results 4
Journal Pre-proof attracted notable attention, while the coefficients should be determined for each drying products before usage in the models (Zomorodian & Moradi, 2010). Various techniques, such as dimensional analysis, regression, and superposition technique, are already used to establish an empirical model (Antonopoulos et al., 2019; Moradi et al., 2019 a). Among them, several dimensionless models have been proposed
of
to simulate the drying process (Moradi et al., 2016). In this regard, Moradi et al. (2016)
ro
developed a dimensionless model of Aloe vera drying using experimental data and
-p
Buckingham theorem. Also, another research superposition technique was used to
re
simulate the drying of Aloe vera gel slices which good agreement between predicted
lP
and experimental data was observed (Moradi et al., 2019 b).
na
Based on our knowledge, the effects of heat carrier inert particles on drying time
ur
as well as energy consumption of GBPS drying is not investigated. Therefore, this study
Jo
was performed to assess the performance of an IR-assisted spouted bed dryer containing inert medium as a mean for drying GBPS in terms of both energy and shrinkage. 2- Materials and Methods 2.1. Sample preparation The green bell pepper samples were purchased from local stores at Shiraz city (GPS coordinate: 29˚38'23.07"N 52˚31'36.3"E) on a daily basis to ensure its freshness (Summer 5
Journal Pre-proof 2018). They were cut into slices at the defined sizes (1.44, 3.24, and 7.84 cm2). Furthermore, to measure initial moisture content, a sample was placed in an electrical oven at 103 °C until reaching a constant weight. The average of initial moisture content was measured as 95±1 (% of wet basis). Afterward, the drying process of GBPS was performed in the IR-assisted spouted bed dryer (Figure 1).
ro
of
2.2 Drying chamber
-p
The proposed dryer consists of a centrifugal blower (3 kW with an airflow rate of 200
re
m3/h), dryer chamber, heating channel (6 kW), and infrared bulb (OSRAM, Germany,
lP
250 W). The centrifugal fan blows the ambient air through the heating channel into the drying chamber (Figure 1). As shown in the figure, the drying chamber consists of two
na
conics at the top and bottom and a cylinder in the medium. The upper conic which was
ur
used to hold the infrared bulb has a height and diameter of 350 mm and 190 mm,
Jo
respectively. Lower conic with a height of 100 mm and a diameter of 190 mm, was used to supply uniform airflow from electrical fan to the drying chamber. Also, the height and diameter of the cylinder are 400 mm and 190 mm, respectively. In order to investigate the effects of using an inert particle on the drying kinetics of GBPS, aluminum beads (with a diameter of 4 mm) which have high heat conductivity, low heat capacity and high availability, were employed. According to primary experiments, the minimum value of air velocity which can spout both the
6
Journal Pre-proof GBPS and inert particles was determined as 18 m/s which was measured in the air outlet with a diameter of 5 cm which since the drying bed has diameter of 19 cm, the air velocity in drying bed was calculated as 1.25 m/s. The infrared bulb was installed in the upper section of the drying bin for radiative heat transfer into the drying chamber.
of
2.3. Design of study
ro
To investigate the drying kinetics of GBPS, several factors include cube size (7.84,
-p
3.24, and 1.44 cm2), drying temperature (50, 60, and 70 ˚C), and the ratio of inert
re
particles (with diameter of 4 mm) to the samples (0, 25, and 50 (Weight percentage) was
lP
considered. The applied variable ranges were selected on the basis of different researches which were investigated the drying of GBPS or application of inert particles
na
on drying mediums (Doymaz & Ismail, 2010; Honarvar et al., 2013; Hatamipour &
ur
Mowla, 2006; Faustino et al., 2007). In this regard, the drying air temperature was
Jo
monitored with the aid of a digital thermostat (Samwon, the accuracy of ±0.5 °C) installed in the air channel just before the entrance of the chamber. According to the literature (Darvish et al., 2013; Taseri et al., 2018), the slices were dried to a moisture content of about 10 % (wet basis). In order to investigate the effect of independent factors (air temperature, slice size, and percentage of inert particles to GBPS) on the drying time and shrinkage value of dried GBPS, a completely randomized factorial design was chosen, and analysis of variance (based on Duncan test in the level of 1%)
7
Journal Pre-proof was performed using SPSS 16.0 software. All experiments were carried out at three replications. 2.4. Drying process and related factors In order to conduct the experiment, 100 g of fresh GBPS was mixed with the
of
pertinent mass of inert particles (0, 25, and 50 g) and then were placed into the spouted
ro
bed dryer. Therefore, for the experiments with 0, 25, and 50 % of inert particles, the total
-p
mass of materials inside the dryer chamber (GBPS and inert particles) was adjusted as
re
100, 125, and 150 g, respectively.
lP
The shrinkage, as one of the most important and obvious changes in the drying of food products, could cause an increase in drying time (Mayor et al., 2011). In this study, it
ur
(Equation 1)
Jo
S= Δv/v0
na
was defined by Equation (1):
Where; “Δv” is the difference between the initial and final volume (cm3) and “v0” is the initial volume of the drying slice (cm3). 2.5. Energy performance Specific energy consumption (SEC) of the drying process of GBPS was calculated by Equation (2): (Equation 2) 8
Journal Pre-proof Where, SEC: Specific energy consumption (kJ/kg of removed water), E: Measured energy consumption of the dryer (kJ), and M: Mass of removed water from the drying material (kg). The energy consumption of the dryer (heater, Infrared bulb, and blower) was measured using a digital counter, with an accuracy of 0.01kWh. Furthermore, the energy balance
of
with an assumption of “No heat loss to the environment from the dryer” was
ro
performed. For this purpose, a useful energy rate (Eout) (kW) of the drying sample was
re
-p
calculated by Equation (3): (Moradi et al., 2019 a; Yogendrasasidhar and Setty, 2018) (Equation 3)
lP
Eout= ms.Lg
Where, ms is the evaporation rate of water in the drying product (kg/s), and Lg (kJ/kg of
na
water) is the latent heat of the vaporization of moisture. ms was determined from the
EUR= Eout/Ein
Jo
from Equation (4):
ur
drying rate of the product. Therefore, Energy Utilization Ratio (EUR) was calculated
(Equation 4)
Here, Eout, obtained from Equation (3) and Ein is the energy input rate (kW) which calculated from data provided by the digital counter (Yogendrasasidhar and Setty, 2018). 2.6. Mathematical modeling
9
Journal Pre-proof Dimensional analysis, as a simple method, can be employed to solve some of the complex problems. In order to establish a new model by this technique, the effective variables on the drying process must be recognized and merged to make dimensionless groups (pi terms). Based on the Buckingham theorem, several dimensionless groups are equal to the number of total effective parameters minus the number of primary
of
dimensions (Gibbings, 2011). Hence, the effective parameters in the research were
ro
recognized to be:
re
-p
M.C, MI, Mp, t, t0, T, Ta, v, v0, and V.
lP
Where; M.C: instantaneous moisture content of drying sample, MI: mass of inert particle (kg), Mp: mass of GBPS (kg), t: elapsed drying time (min), t 0: total drying time (min), T:
na
drying temperature (˚C), Ta: ambient temperature (˚C), v, v0: Final and initial volume of
ur
the drying product (m3), respectively, and V: drying air velocity (m/s).
Jo
Among these 10 parameters, 4 primary dimensions (meter, second, kg, and Celsius) were found. Hence, based on Buckingham theorem, 6 dimensionless groups were found as illustrated in Equation (5): Π1= M.C, Π2=t/t0, Π3=Vt/v01/3, Π4= (MI+Mp)/Mp, Π5=T/Ta, Π6=(v-v0)/v0
(Equation 5)
Where, Π1 is dependent pi-term, and the others are independent ones while Equation (6) describing the correlation between dependent and independent groups obtained by using dimensional analysis technique (Moradi et al., 2016):
10
Journal Pre-proof Π1=F (Π2, Π3, Π4, Π5, Π6)
(Equation 6)
Usually, the following form is proposed in the literature for a dimensionless model (Equation 7): (Gibbings, 2011) Π1=a Π2b Π3c Π4d Π5e Π6f+g
(Equation 7)
Where "a" to "g," are constant coefficients that should be determined.
of
In order to determine constants involved in the equation (7), an optimization algorithm
|
|
-p
∑
ro
was used in such a way to minimize Mean Bias Error (MBE): (Equation 8) (Equation 8)
re
Where, N is a total number of the experiments, MCiex, and MCipre is experimental and
lP
predicted the moisture content of GBPS, respectively.
na
2.7. Calculation of moisture diffusivity and activation energy
ur
To calculate moisture diffusivity, the drying equation for a slab body was proposed
Jo
analytically. While a lower thickness as compared with the length and width were noted for GBPS taking a slice as an infinite slab can be considered as an accurate approximation. Analytical solution of the drying for an infinite slab was defined as Equation (9): (Agnihotri et al., 2017)
ln MR=ln( ) -(
)
(Equation 9)
Where: MR is moisture ratio, “t” and “L” are drying time (s) and thickness of the drying sample (m), respectively and Deff is effective moisture diffusivity (m2/s). If the variation 11
Journal Pre-proof of Ln MR versus time to be considered, the slope can be evaluated using equation (10) (Agnihotri et al., 2017):
k=
(Equation 10)
In equation (10), the value of (k) was obtained from the experimental results. Thus, the
of
quantity of Deff can be easily calculated.
ro
The effect of drying air temperature on the moisture diffusivity is often
-p
expressed using the Arrhenius type equation (Adabi et al., 2013):
re
(Equation 11)
lP
Where, D0: Constant equivalent to the diffusivity at an infinitely high temperature (m2/s), Deff: effective moisture diffusivity (m2/s), Ea: activation energy (kJ/mol), and R:
ur
3- Results and discussion
na
Gas constant (8.314 J/mol. K).
Jo
3.1. Effects of each factor
As can be seen in Table 1, a significant effect on the drying time of GBPS was noted as results of the main and dual effects of applied parameters (temperature, slice size, and Mass percentage of the inert particle. Average drying time for different drying temperatures, slice sizes, and mass percentage of inert to the grain were shown in Table 2. Also, according to Figure 1s, where the interaction effects of temperature and inert particle percentage were demonstrated, the following outcomes can be achieved: 12
Journal Pre-proof a) Among all three levels of inert particle percentages, the lower drying time was correlated with the higher temperature which is consistent with the reported findings by previous investigations (Hasan et al., 2014; Jiang et al., 2017; Moradi et al., 2019 a). In another research, thin layer drying characteristics of hybrid rice seed was investigated, which their findings showed drying rate increased as a consequence of an increase in
of
drying temperature (Hasan et al., 2014).
ro
b) Among used drying temperatures, the minimum drying time was obtained with the
-p
inert particle percentage of 25 %. In this regard, the average of drying time reduced
re
about 24.8 % while compared with employing of inert percentage of 0%. While this
lP
obervation can be correlated with variation in heat transfer mechanisms of conductive
na
heat transfer (from the inert particles) and convective heat transfer (from the hot drying
ur
air) which consequently resulted in the higher heating and drying rate. In other words,
Jo
higher energy efficiency can be obtained with employing of inert particles of 25%. In a similar investigation, different mass ratios of kaffir lime leaves to sand was used (without sand, 0.04, 0.02, and 0.01). Their results showed a drying rate of kaffir lime leaves increased by the incorporation of inert particles. However, in our research, for the experiments with 50 % of inert particles, the reverse result was noted, the covering of slices by inert particles of 50 %, could prevent the effective convective heat transfer into the product.
13
Journal Pre-proof c) As drying temperature increased, the effects of inert particles on the drying time (slope of each curve) were decreased. In other words, the effect of inert particles incorporation on the drying time for the higher temperatures had a decreasing trend. In terms of higher temperature, the proposed heat could overcome the internal resistance, and consequently, the effect of inert particles on the reduction of drying time was
of
reduced. Similar results were obtained by another research performed regarding the
ro
drying of green peas in a fluidized dryer of inert particles (Honarvar et al., 2013). They
-p
investigated the drying of green peas in a fluidized bed dryer at three temperatures of
re
40, 50, and 60 ˚C which their results showed higher drying rate for the higher inert
lP
particle percentages (Honarvar et al., 2013).
na
Moreover, based on Figure 2s, which shows the interaction effects between
ur
temperature and slice size, the following results can be concluded:
Jo
a) Drying time was reduced while the slice size decreased, which can be correlated with the higher ratio of area to volume in a smaller size. For this reason, the lower drying time was required. In another research, Kothimbda was dried using a tray drier in three slice sizes of 3, 5, and 7 mm which results showed lower drying time obtained in the smaller slice sizes (Gojiya and Vyas, 2015). Also, a similar study; the drying behavior of maize and green peas with different diameters in a fluidized bed dryer while combined with inert particles was investigated. They found that the drying rate increased with a decrease in the sample diameter (Hatamipour & Mowla, 2006). Moreover, by evaluation 14
Journal Pre-proof of drying behavior of carrot slices, lower drying time was achieved by using of the smaller slices (Sonmete et al., 2017). b) As a result of the interactive effects, by applying of the higher temperatures, the effect of slice size on the drying time was decreased while sufficient heating energy was provided to penetrate to the entire of the pepper slice and therefore, for the larger slices,
of
drying time is closer to the smaller slices.
ro
Considering the demonstrated findings in Figure 3s regarding the interaction of inert
-p
material percentage and slice size, it can be concluded that:
re
a) For all percentages inert particles, with decreasing in slice size, the drying time was
lP
reduced.
na
b) In all three slice sizes, the lowest drying time can be achieved via the application of inert particles with a percentage of 25 % which demonstrates the notable impact of inert
ur
particles on the reduction of drying time. The results are consistent with the previously
Jo
reported findings (Honarvar et al., 2013; Tasirin et al., 2014) while with the application of inert particles in the drying bed, more moderate drying time for the sample was obtained. c) As the slice size becomes bigger, the slope of variations of drying time versus inert particles ratio was increased. For the larger slices, the internal resistance of the product was higher than the smaller ones. Hence, the application of inert particles (25 %) can be
15
Journal Pre-proof proposed as a suitable strategy to enhance heat penetration into the sample and further reductions in the drying time.
3.2. Drying kinetics The drying kinetics of GBPS under different drying conditions was shown in Figures 4s
of
to 6s.
ro
In Figure 4s, the drying kinetics of the large size of green pepper slices for
-p
different temperatures and inert particle ratios has been demonstrated. The time
re
required for GBPS to lose their moisture content until 10 % (wet basis), in control
lP
experiments (without inert particles) and the drying temperatures of 50, 60, and 70 ˚C
na
was 1162, 855, and 380 min, respectively, In this context, in a similar research, drying kinetics of green bell pepper in temperature range of 40 to 80 ˚C was investigated.their
ur
findings showed that a lower drying time was achivied while higher drying
Jo
temperature was applied(Taheri-Gravand et al., 2011). According to our findings, the drying time reduced as temperature increased. In this reagrd,for the inert particle 25% and temperatures of 50, 60, and 70 ˚C , the drying time in was noted as 779, 598, and 284 min, respectively. Hence, application of inert particle of 25 % at the temperatures of 50, 60, and 70 ˚C reduced the drying time by about 33 %, 30 %, and 25 %, respectively. In other words, as discussed previously, the application of inert particles at a level of 25 % showed higher efficiency in the lower drying temperatures. Also, the drying time for
16
Journal Pre-proof these pepper slices at the presence of inert particle percentage of 50% and temperatures of 50, 60, 70 ˚C was 1915 min (31.9 hour), 1219 min (20.3 hour), and 480 min (8 hour), respectively, which were higher while compared with the corresponded values for other percentages of inert particles. Therefore, in the presence of 50 % of inert particle, a longer drying time can be correlated with the lower air convection in the drying bed .
of
Figure 5s shows the drying kinetics of medium size of green bell pepper slice for
ro
different temperature and percentage of inert particles. Accordingly, the drying time at
-p
temperatures of 50, 60, and 70 ˚C and the mass percentage of inert particles of 0 % was
re
905, 786, and 292 min while the corresponded values for 25 and 50% of the inert particle
lP
were obtained as 696, 572, 250, and 1856, 1066, 477 min, respectively. Therefore, usage
na
of 25% of inert particles showed a positive effect on the reduction of drying time and reduced the drying time for 50, 60, and 70 ˚C about 23.1 %, 27 %, 13.7 %, respectively.
ur
However, with the application of the inert particles of 50 %, the drying time was
Jo
increased. However, prior to the recommendation of the inert particle perecentage, the chemical aspects of the product in the drying bed, as well as the quality of dried products, must be studied in future studies. Finally, the drying kinetics of GBPS in small size was shown in Figure 6s, while the drying time without inert particles in the drying temperatures of 50, 60, and 70 ˚C was recorded as 633, 625, and 281 min, respectively. Also, the drying time with an inert particle of 25 % and 50 % in the temperatures of 50, 60, and 70 ˚C were noted as 541, 504,
17
Journal Pre-proof 237, and 1256, 959, and 390 min, respectively. Therefore, the application of 25 % of inert particle decreased the drying time for the temperature of 50, 60, and 70 ˚C about 14.5 %, 19.4 %, and 15.6 %, respectively. 3.3. Shrinkage
of
The conducted ANOVA analysis to investigate the effect of temperature, slice size and
ro
inert particle percentage on shrinkage of the dried samples was demonstrated in Table
-p
(1). Accordingly, a significant effect of different drying temperatures, slice sizes, and
re
mass percentage of inert material on the shrinkage was noted. A higher effect of slice
lP
size on shrinkage was demonstrated by the higher F value of slice size (84.39) while compared with the F value associated with temperature and inert particle percentages.
na
The average value of shrinkage for all experiments was shown in Table 3. In this
ur
regard, the mean values of shrinkage for drying temperatures of 50, 60, and 70 ˚C were
Jo
recorded as 92.4%, 91.8%, and 91%, respectively, which demonstrated that with an increase in temperature, a decreasing trend can be correlated with shrinkage. Our findings are in good agreement with a previously conducted investigation (Coradi et al., 2016) that investigated the effects of air temperature (75, 90, and 105 ˚C) on the shrinkage of soybean during the drying. According to their findings, for the temperature of 75, 90, and 105 ˚C, the shrinkage values were obtained as 80, 79, and 77%, respectively.
18
Journal Pre-proof Also, the mean values of shrinkage for the large, medium and small size of pepper slices were obtained as 93.9%, 92.0 %, and 89.4%, respectively. Therefore, due to the higher ratio of surface area to the volume, a lower shrinkage can be associated with smaller samples. The mean values of shrinkage for the inert particle percentage of 0, 25, and 50 % were reported as 92.3%, 91.8%, and 91.1%, respectively, demonstrating the
of
less levels of shrinkage correlated with higher percentages of inert particles. This
ro
finding can be associated with uniform heat distribution in the drying bed while the
-p
higher percentage of inert particles were used. Therefore, the shrinkage of the dried
re
product was highly affected by slice size.
lP
3.4. Moisture diffusion coefficient and activation energy
na
The corresponded values for the moisture diffusion coefficient of GBPS, calculated with
fall
within
the
proposed
general
range
of
Jo
they
ur
the aid of equations (8) and (9) were ranged from 2.16×10 -10m2/s to 8.12×10-10m2/s while
10-8 to 10-12 m2/s for the diffusivity of food materials (Chen, 2007). The effective diffusivity can be considered as a strong function of the drying air temperature, inert particle percentage, and slice size. In this context, based on equations (12) to (14), the effective diffusion coefficient (D eff, m2/s) can be defined as a function of air temperature and slice size for the inert percentage of 0, 25, and 50%, respectively. Deff=1.642×10-11 T-2.19×10-11S-4.859×10-10
(Equation 12)
Deff=2.093×10-11 T-2.53×10-11S-6.56×10-10
(Equation 13) 19
Journal Pre-proof Deff=1.357×10-11 T-2.013×10-11S-3.465×10-10
(Equation 14)
Where, T had values of 50, 60, and 70 ˚C and S had the values of 7.84 mm 2 (for large slices), 3.24 mm2 (for medium slices), and 1.44 mm2 (for small slices). It is evident from the equations (12), (13), and (14) that the coefficients of the slice size(S) are negative, whereas coefficients of temperature (T) are positive exploring the
of
increasing and decreasing trends in moisture diffusivity while the temperature
ro
increased and slice size decreased. Also, for each equation, the absolute value of the
-p
coefficient of "S" is higher than the coefficient of "T." It means the slice size reduction
re
compared with the air temperature increment has a higher effect on the reduction of the
lP
moisture content of the drying sample although the effect of slice size and temperature
na
increased by 25% of inert particle compared with 0%. According to equation (8), effective diffusivity is a function of the drying air
ur
temperature while this correlation for different inert particle percentages as shown in
Jo
Figure 7s. Therefore, based on equation (8), activation energy can be calculated from the slope of the curves in Figures 7s. It should be pointed out that the value of the activation energy could be considered as a good indicator of temperature influence on the drying. Average value of activation energy for the control experiments, inert percentage of 25 %, and 50 % were calculated to be 36.3, 38.5, and 32.4 kJ/mole hence, higher values of activation energy for the inert percentage of 25 % was observed which means the higher effects of temperature in the inert percentage of 25 % can be expected.
20
Journal Pre-proof
3.5. Specific energy consumption The calculated Specific Energy Consumption (SEC) of drying of green bell pepper for large, medium, and small slice size was demonstrated in Figure 2demonstrating that a less SEC was obtained for the higher drying temperatures, which can be correlated with
of
higher internal resistance (defined as resistance against water movement from into the
ro
drying sample to its surface) while compared with the external resistance (defined as
-p
resistance against water movement from the surface of drying sample into the drying
re
air). However, curves assigned for the smaller slices were closer together, mainly due to
lP
lower internal resistance. In other words, in smaller slices while compared with the
na
larger ones.
The minimum value of SEC was obtained for all the experiments in the inert
ur
percentage of 25 % showing the positive influence of the inert particles of 25 % for a
Jo
significant reduction in energy consumption. Hence, the lowest SEC was calculated as 3.31 MJ/kg while the small slice size of samples, inert particles with a percentage of 25 % and temperature of 70 ˚C was used. In another research, the lowest SEC of green pepper samples which were dried in a microwave-convective drying was 7.14 MJ/kg (Darvishi et al., 2013). Therefore, in the present research due to the strategy of using of inert particles in the drying bed, lower SEC for drying of GBPS was observed..
21
Journal Pre-proof Based on Ferhat's equation, 800g of carbon dioxide emitted due to each kWh of energy produced from fossil fuel (Seidi and Niakousari, 2017). Hence, with 25 % of inert particles in drying temperature of 50, 60, and 70 ˚C about 25.26 kg, 18.65 kg, and 5.26 kg of less carbon dioxide emitted to the environment which shows the positive influence of heat carrier particles in the preservation of fossil fuels as well as the environment.
of
3.6- Energy Utilization Ratio (EUR)
ro
According to Figure 3, which exhibits the average EUR of GBPS for drying experiments
-p
in three different slice sizes, the following outcomes can be obtained:
re
a) In each temperature, the highest EUR was obtained for 25 % of inert particle which
lP
may be related to the lower corresponded drying time while compared with the other
na
inert particles. However, as temperature decreased, the slope of variations of EUR versus the percentage of inert particles was reduced, representing the lower effect of
Jo
ur
inert particles on EUR in the higher temperatures.
22
Journal Pre-proof b) In each slice size, the highest EUR was assigned for the drying temperature of 70 ˚C. Increasing drying temperature enhances heat transfer from hot air to the drying sample resulting in increasing moisture removal rate, and consequently increase in EUR. An increase in EUR with an increase in the drying temperature was noted in another research where the EUR profiles of Fenugreek seeds in fluidized bed dryers at different
of
temperatures of 40, 50, and 60 ˚C were calculated (Yogendrasasidhar and Setty, 2018).
ro
c) The highest EUR value (0.68) is correlated with the smallest samples in the mass
-p
percentage of inert particles of 25 % and temperature of 70 °C. Therefore, the lowest loss
lP
material percentage of 25 %.
re
of energy was noted in the highest drying temperature, smallest slice size, and inert
na
3.7. Mathematical modeling of the drying process
ur
Dimensional analysis was used to establish a new drying model for the drying of GBPS
Jo
in an IR-assisted spouted bed dryer containing inert particles as a heat carrier. The obtained dimensionless groups in the equation (5) were used to establish a new dimensionless model with the proposed form of the equation (7). Finally, a dimensionless equation was established as equation (15): M.C=6.933×( t/t0)-0.27715×( Vt/v01/3)-0.21959×[(MI+Mp)/M p] -0.3765×( T/Ta )-0.40252×[ v-v0)/v0]-1.99876 -3.47944 (Equation 15)
23
Journal Pre-proof In this equation, π1 (moisture content of drying sample) is dependent pi- term and the others are independent. As already explained, about 80 % of experimental data was used to establish the dimensionless model; the remaining 20 % was utilized to evaluate the established model. Figure 8s shows the experimental moisture content versus the predicted moisture content of the GBPS. The simulation results showed good
of
agreements between predicted and measured moisture content such that R2, MBE, and
ro
RMSE were calculated to be 0.90, 0.052, and 0.015, respectively. In another research, two
-p
dimensionless models were established for the description of the drying kinetics of aloe
re
vera gel dried in a convective and osmo-convective manner (Moradi et al., 2016). The
lP
obtained dimensionless models were developed in such a way to incorporate three
na
independent pi terms for the description of instantaneously moisture content of aloe vera gel. The validation results of the models showed a coefficient of determination for
ur
convective and osmo-convective models were, 0.98 and 0.99, respectively. In another
Jo
study, a dimensionless model was established and evaluated for drying of corn grains in a continuous dryer with inert energy carrier particles. To do the simulation, five independent π terms which were responsible for the drying rate of the grains were identified, and a dimensionless model includes the effect of all independent π terms on the dependent π term, which was derived and evaluated. The evaluation results showed that R2, MBE, and RMSE were calculated as 0.85, 0.0648, and 0.018, respectively (Moradi and Karparvar fard, 2016). Therefore, the obtained dimensionless model can be
24
Journal Pre-proof confidently used to predict the moisture content of drying GBPS in defined condition ranges. 4- Conclusion In order to investigate the effects of heat carrier inert particles on the drying kinetics
of
and energy consumption of GBPS, an IR-assisted spouted bed dryer is containing
ro
aluminum beads as heat carriers were used. Analysis of the data obtained from the
-p
dryer indicated that the mass percentage of inert particles to GBPS influences the drying
re
time and drying rate. When the inert particle percentage 25%, was used 25% reduction
lP
in drying time was realized. However, increasing the percentage to 50%, surprisingly, reversed the positive influence of this presence. Analyze of shrinkage of dried samples
na
showed less amount of shrinkage observed at more inert particle percentage.
ur
Furthermore, using 25% of inert particles to GBPS improves the energy efficiency of the
Jo
drying operation by reducing specific energy consumption. However, the minimum SEC observed in 25 % of heat carriers, the temperature of 70 ˚C, and the lowest drying samples. The diffusion coefficient and activation energy calculations showed that the maximum influence of drying temperature occurred in 25 % of inert particles. To better explain the drying behavior of green bell pepper, a dimensionless equation, incorporating all effective parameters, was established and evaluated which validation results revealed a good agreement between experimental and predicted moisture content. 25
Journal Pre-proof Conflict of interest The authors declare that they have no conflict of interest.
References
of
Agnihotri, V., Jantwal, A., &Joshi, R. (2017). Determination of effective moisture
ro
diffusivity, energy consumption, and active ingredient concentration variation in
-p
Inularacemosa rhizomes during drying. Industrial Crops and Products, 106, 40-47.
re
Aktas, M., Khanlari, A., Amini, A., & Şevik, S. (2017). Performance analysis of heat
lP
pump and infrared–heat pump drying of grated carrot using energy-exergy
na
methodology. Energy Conversion and Management, 132, 327-338.
ur
Antonopoulos, V. Z., Papamichail, D. M., Aschonitis, V. G., & Antonopoulos, A. V.
Jo
(2019). Solar radiation estimation methods using ANN and empirical models. Computers and Electronics in Agriculture, 160, 160-167. Badaoui, O., Hanini, S., Djebli, A., Haddad, B., & Benhamou, A. (2019). Experimental and modelling study of tomato pomace waste drying in a new solar greenhouse: Evaluation of new drying models. Renewable Energy, 133, 144-155. Chakraborty, R., Bera, M., Mukhopadhyay, P., &Bhattacharya, P. (2011). Prediction of optimal conditions of infrared assisted freeze-drying of aloe vera (Aloe barbadensis)
26
Journal Pre-proof using response surface methodology. Separation and Purification Technology, 80(2), 375384. Chen, X. D. (2007). Moisture diffusivity in food and biological materials. Drying Technology, 25(7-8), 1203-1213.
of
Coradi, P. C., Fernandes, C. H. P., Helmich, J. C., & Goneli, A. L. D. (2016). Effects of
ro
drying air temperature and grain initial moisture content on soybean quality (Glycine
-p
max (L.) Merrill). Engenharia Agrícola, 36(5), 866–876.
re
Darvishi, H., KHOSH, T. M., Najafi, G., & Nargesi, F. (2013). Mathematical modeling of
na
and Technology, 15, 457-465.
lP
green pepper drying in microwave-convective dryer. Journal of Agricultural Science
Das, C., Das, A., &Golder, K. (2016). Optimality in microwave-assisted drying of Aloe
ur
vera (Aloe barbadensis Miller) gel using response surface methodology and artificial
143-149.
Jo
neural network modeling. Journal of The Institution of Engineers (India): Series E, 97(2),
Dehghan‐Manshadi,
A.,
Peighambardoust,
S.
H.,
Azadmard‐Damirchi,
S.,
&
Niakousari, M. (2019). Effect of infrared‐assisted spouted bed drying of flaxseed on the quality characteristics of its oil extracted by different methods. Journal of the Science of Food and Agriculture.
27
Journal Pre-proof Doymaz, I., & Ismail, O. (2010). Drying and rehydration behaviors of green bell peppers. Food Science and Biotechnology, 19(6), 1449-1455. El-Mesery, H. S., & Mwithiga, G. (2015). Performance of a convective, infrared and combined infrared-convective heated conveyor-belt dryer. Journal of food science and technology, 52(5), 2721-2730.
of
Faustino, J.M.F., Barroca, M.J., and Guine, R.PF. (2007). Study of the drying kinetics of
ro
green bell pepper and chemical characterization. Food and Bioproducts Processing, 85 (C3),
-p
163–170.
lP
re
Gibbings, J. C. (2011). Dimensional analysis. Springer Science & Business Media. Gojiya, D. K., & Vyas, D. M. (2015). Studies on effect of slice thickness and temperature
na
on drying kinetics of Kothimbda (Cucumis callosus) and its storage. Journal of Food
ur
Processing and Technology, 6(1). doi: 10.4172/2157-7110.1000406
Jo
Hammouda, I., & Mihoubi, D. (2014). Comparative numerical study of kaolin clay with three drying methods: Convective, convective–microwave and convective infrared modes. Energy conversion and management, 87, 832-839. Hasan, A. A. M., Bala, B. K., & Rowshon, M. K. (2014). Thin layer drying of hybrid rice seed. Engineering in agriculture, environment and food, 7(4), 169-175.
28
Journal Pre-proof Hatamipour, M. S., & Mowla, D. (2006). Drying behaviour of maize and green peas immersed in fluidized bed of inert energy carrier particles. food and bioproducts processing, 84(3), 220-226. Honarvar, B., Mowla, D., & Safekordi, A. A. (2013). Experimental and theoretical investigation of drying of green peas in a fluidized bed dryer of inert particles assisted
of
by infrared heat source. Iranian Journal of Chemistry and Chemical Engineering
-p
ro
(IJCCE), 32(1), 83-94.
re
Jiang, J., Dang, L., Tan, H., Pan, B., & Wei, H. (2017). Thin layer drying kinetics of pre-
lP
gelatinized starch under microwave. Journal of the Taiwan Institute of Chemical Engineers, 72, 10-18.
na
Mayor, L., Moreira, R., & Sereno, A. M. (2011). Shrinkage, density, porosity and shape
ur
changes during dehydration of pumpkin (Cucurbita pepo L.) fruits. Journal of Food
Jo
Engineering, 103(1), 29–37.
Moradi, M., & Karparvar fard S. H. (2016). Mathematical modeling of a corn grains drying process in a continuous dryer including inert particles. Iranian journal of chemical engineering, 14(83): 82-90. Moradi, M., Niakousari, M., & Etemadi, A. (2016). Dimensionless modeling of thin layer drying process of Aloe vera gel. Iranian Food Science and Technology Research Journal 12, 362-370. doi: 10.22067/ifstrj.v12i3.55168 29
Journal Pre-proof Moradi, M, Fallahi, M, and Mousavi Khaneghah, A. (2019 a). Kinetics and mathematical modeling of thin layer drying of mint leaves by a hot water recirculating solar dryer. Journal of Food Process Engineering, In press. https://doi.org/10.1111/jfpe.13181 Moradi, M., Niakousari, M., & Mousavi Khaneghah, A. (2019 b). Kinetics and mathematical modeling of thin layer drying of osmo‐treated Aloe vera (Aloe
of
barbadensis) gel slices. Journal of Food Process Engineering, 42(6), e13180.
ro
Prosapio, V. & Norton, I. (2017). Influence of osmotic dehydration pre-treatment on
-p
oven drying and freeze drying performance, LWT, 80, 401-408.
re
Seidi damyeh M, Niakousari M (2017) Ohmic hydrodistillation, an accelerated
lP
energysaver green process in the extraction of Pulicaria undulata essential oil Industrial
na
Crops and Products 98:100-107. https://doi.org/10.1016/j.indcrop.2017.01.029
ur
Serowik, M., Figiel, A., Nejman, M., Pudlo, A., Chorazyk, D., Kopec, W., ... & Rychlicka,
Jo
J. (2018). Drying characteristics and properties of microwave− assisted spouted bed dried semi− refined carrageenan. Journal of food engineering, 221, 20-28. Sonmete, M. H., Mengeş, H. O., Ertekin, C., & Özcan, M. M. (2017). Mathematical modeling of thin layer drying of carrot slices by forced convection. Journal of Food Measurement and Characterization, 11(2), 629-638.
30
Journal Pre-proof Taheri-Garavand, A., Rafiee, S., & Keyhani, A. (2011). Study on effective moisture diffusivity, activation energy and mathematical modeling of thin layer drying kinetics of bell pepper. Australian Journal of Crop Science, 5(2), 128. Taseri, L., Aktaş, M., Şevik, S., Gülcü, M., Seçkin, G. U., & Aktekeli, B. (2018). Determination of drying kinetics and quality parameters of grape pomace dried with a
of
heat pump dryer. Food chemistry, 260, 152-159.
ro
Tasirin, S. M., Puspasari, I., Lun, A. W., Chai, P. V., & Lee, W. T. (2014). Drying of kaffir
-p
lime leaves in a fluidized bed dryer with inert particles: Kinetics and quality
re
determination. Industrial Crops and Products, 61, 193-201.
lP
Yogendrasasidhar, D., & Setty, Y. P. (2018). Drying kinetics, exergy and energy analyses
na
of Kodo millet grains and Fenugreek seeds using wall heated fluidized bed
ur
dryer. Energy, 151, 799-811.
Jo
Zomorodian, A, &Moradi, M. (2010). Mathematical modeling of forced convection thin layer solar drying for CuminumCyminum. Journal of Agricultural Science and Technology, 12, 401-408.
31
Journal Pre-proof
Variation sources
Degree of freedom
Sum squares
Mean squares
F value
Drying time
Temperature
2
6403599.43
3201799.7
456617.2**
Slice size
2
507281.28
253640.64
36172.4**
Percentage of inert particle
2
4181646.84
2090823.42
298177.9**
Temperature×slice size
4
1630974.49
407743.62
58149.4**
Temperature×percentage of inert particle
4
1806472.27
451618.07
64406.5**
Slice size×percentage of inert particle
4
193844.42
48461.10
6911.2**
Error
54
-p
re
lP
378.67
7.012
72
14724197.5
2
16.092
8.046
7.23**
2
187.872
93.936
84.39**
Inert particle percentage
2
16.753
8.376
7.53**
Temperature*slice size
4
6.782
1.695
1.52ns
Temperature*inert particle 4 percentage
0.188
0.047
0.04ns
Slice size* inert particle percentage
4
1.440
0.360
0.32ns
Temperature*slice size* inert particle percentage
8
1.486
0.1862
0.17ns
Error
54
60.102
1.113
Total
80
290.715
Temperature
Jo
ur
Slice size
na
Total Shrinkage value
of
Dependent variable
ro
Table1- ANOVA for main and interaction effects of independent factors on drying time and shrinkage value
**: significant in level of 1% Ns: non-significant 32
Journal Pre-proof
Table 2- Average drying time for different drying conditions
50
Medium
Small
Medium
na
60
lP
re
Large
Jo
ur
Small
70
Large
Medium
Small
Average drying time±SD 1148±20 785±15 1900±25 910±10 690±9 1850±22 626±10 510±7 1220±15 835±10 590±10 1150±15 785±15 570±10 1050±16 611±8 495±10 945±12 370±10 301±5 520±7 300±6 240±5 475±6 280±5 230±6 378±5
of
Large
Inert particle percentage 0% 25% 50% 0% 25% 50% 0% 25% 50% 0% 25% 50% 0% 25% 50% 0% 25% 50% 0% 25% 50% 0% 25% 50% 0% 25% 50%
ro
Slice size
-p
Drying temperature (˚C)
33
Journal Pre-proof
Table 3- Average shrinkage for different drying conditions
Medium
re
50
lP
Small
Medium
Jo
ur
60
na
Large
Small
Large
70
Medium
Small
Average shrinkage±SD
of
Large
Inert particle percentage 0% 25% 50% 0% 25% 50% 0% 25% 50% 0% 25% 50% 0% 25% 50% 0% 25% 50% 0% 25% 50% 0% 25% 50% 0% 25% 50%
94.3±0.6 93.81±0.5 93.06±0.4 94.66±0.6 94.19±0.5 93.74±0.8 90.79±0.7 89.99±0.4 88.31±0.6 94.48±0.8 94.22±0.6 93.84±0.8 91.26±0.7 91.62±0.5 91.32±0.4 91.05±0.6 90.11±0.5 88.62±0.4 94.50±0.6 93.88±0.3 93.27±0.5 90.83±0.4 90.73±0.7 90.24±0.2 89.28±0.4 88.14±0.5 88.06±0.3
ro
Slice size
-p
Drying temperature (˚C)
34
Journal Pre-proof
Figure 1. Pilot scale IR-assisted spouted bed dryer Figure 2. The specific energy consumption for slice size of: a) large b) medium small b) medium
Jo
ur
na
lP
re
-p
ro
of
Figure 3. Energy utilization ratio for slice size of: a) large
35
c)
c) small
Journal Pre-proof Author statement Conceptualization; Mehdi Moradi Data curation; Mehdi Moradi Formal analysis; Sadi Azizi Funding acquisition; Mehdi Moradi Investigation; Mehdi Moradi; Saadat Kamga
Project administration; Amin Mousavi Khaneghah
ro
Resources; Sadi Azizi
of
Methodology; Mehdi Moradi
-p
Software; Sadi Azizi; Saadat Kamgar
re
Supervision; Mehrdad Niakosari; Amin Mousavi Khaneghah Validation; Mehdi Moradi; Saadat Kamgar
lP
Visualization; Mehrdad Niakosari
na
Roles/Writing - original draft; Amin Mousavi Khaneghah
Jo
ur
Writing - review & editing; Mehdi Moradi ;Amin Mousavi Khaneghah
36
Journal Pre-proof Conflict of interest
Jo
ur
na
lP
re
-p
ro
of
The authors declare that they have no conflict of interest.
37
Journal Pre-proof Highlights
Investigating the drying kinetics of GBPS in an IR-assisted spouted bed dryer containing inert particles.
Calculation specific energy consumption in different operating conditions in an IR-assisted spouted bed dryer.
of
Establishment of a new empirical model, describing drying kinetics of GBPS in
ur
na
lP
re
-p
ro
the spouted bed dryer.
Jo
38
Figure 1
Figure 2
Figure 3
Mehdi Moradi, Sadi Azizi, Mehrdad Niakosari, Saadat Kamgar, Amin Mousavi Khaneghah PII:
S1466-8564(19)31392-X
DOI:
https://doi.org/10.1016/j.ifset.2019.102280
Reference:
INNFOO 102280
To appear in:
Innovative Food Science and Emerging Technologies
Received date:
6 November 2019
Revised date:
12 December 2019
Accepted date:
22 December 2019
Please cite this article as: M. Moradi, S. Azizi, M. Niakosari, et al., Drying of green bell pepper slices using an IR-assisted Spouted Bed Dryer: An assessment of drying kinetics and energy consumption, Innovative Food Science and Emerging Technologies(2018), https://doi.org/10.1016/j.ifset.2019.102280
This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
© 2018 Published by Elsevier.
Journal Pre-proof Drying of green bell pepper slices using an IR- assisted Spouted Bed Dryer: An assessment of drying kinetics and energy consumption Mehdi Moradi1,*, Sadi Azizi1, Mehrdad Niakosari2, Saadat Kamgar1, Amin Mousavi Khaneghah3,** Department of Biosystems Engineering, College of Agriculture, Shiraz University,
Department of Food Science and Technology, College of Agriculture, Shiraz
-p
2
ro
Shiraz, Iran
of
1
Department of Food Science, Faculty of Food Engineering, University of Campinas
lP
3
re
University, Shiraz, Iran
na
(UNICAMP), Rua Monteiro Lobato, 80. Caixa Postal: 6121.CEP: 13083-862, Campinas, São Paulo, Brazil.
ur
*,**Corresponding authors:
Jo
Mehdi Moradi, Email: [email protected]; [email protected] Amin Mousavi Khaneghah, Email: [email protected]
1
Journal Pre-proof Abstract In the current investigation, an IR-assisted spouted bed dryer containing inert aluminum particles was employed for drying of Green Bell Pepper Slices (GBPS). In order to evaluate the dryer’s performance, a completely randomized factorial design with three independent factors including drying temperature (50, 60, and 70 ˚C), mass
of
percentage of inert particles to GBPS (0, 25, and 50%), and surface area of GBPS (1.44,
ro
3.24, and 7.84 cm2) were used. According to findings, by using the inert particles with
-p
mass percentage of 25%, a reduction of around 25% in drying time was realized.
re
However, increasing in the ratio to 50%, an increasing the drying time (56%) was noted.
lP
An optimum drying condition in terms of specific energy consumption (SEC) was
na
achieved at an air temperature of 70 °C, 25% inert material and GBPS with a surface area
ur
of 1.44 cm2. Moreover, a dimensionless model for describing the drying kinetics of
Jo
GBPS was established and evaluated. Keywords: Dimensional analysis; inert heat carriers; drying; Specific energy consumption; green bell pepper
2
Journal Pre-proof 1- Introduction Green bell pepper (Capsicum annuum) is a one-year-old plant with numerous applications in food industries both in fresh and dried forms. Due to high initial moisture content (95%, wet basis) of the fresh product, obtaining longtime storage seems to be difficult. However, some techniques such as drying could be approached to
of
reduce the moisture content of this product, a high energy consumption can be
ro
considered as one of the most noted challenges for the traditional drying techniques.
-p
The high initial moisture of green bell pepper may result in elongating drying time
re
besides intensifying energy consumption (Doymaz and Ismail, 2010). In this regard, in
lP
order to reduce the consumption of energy for dehydration, employing an appropriate
na
economic drying technique is crucial (Prosapio & Norton, 2017).
ur
Numerous drying techniques were used to improve product shelf life as well as
Jo
reduce energy consumption, including infrared assisted freeze-drying (Chakraborty et al., 2011), and microwave-assisted dehydration (Das et al., 2016). Also, different hybrid methods such as infrared-convective drying (El-Mesery and Mwithiga, 2015), infraredheat pump drying (Aktas et al., 2017), and microwave spouted bed dryer (Serowik et al., 2018) were introduced. In this regard, the spouted bed dryer equipped with the infrared source (SBDIR) can be used to increase the drying rate (Dehghan-Manshadi et al., 2019). In this method, infrared radiation is directed to the samples to eliminate the exceeded amounts of moisture (Hammouda and Mihoubi, 2014). Also, the heat carrier 3
Journal Pre-proof inert materials can be employed in the design of spouted or fluidized bed dryers to increase the performance, mixing up, heat transfer coefficient and to improve the fluidization behavior of the materials (Honarvar et al., 2013; Tasirin et al., 2014). Their application can result in further increases in the drying rate due to storing and transfer of heat into the products besides the improvements in fluidization properties by
of
reducing in particle adhesion, particularly while products are still wet (Tasirin et al.,
accomplish optimum
drying
conditions,
-p
To
ro
2014).
knowledge
regarding
the
re
instantaneous moisture content of the product among different drying stages is crucial.
lP
In this regard, mathematical modeling is usually utilized to predict the moisture content
na
of the samples instantaneously. Hence, the employing of mathematical models for the
Jo
energy.
ur
dehydration offers valuable information combined with further saving in time and
In order to simulate the drying, three types of model namely theoretical, empirical, and semi-empirical can be used. Among them, the theoretical models have some shortcomings, such as the inaccuracy of results due to the conducted assumptions while solving the governing equations (Badaoui et al., 2019). However, semi-empirical models such as Henderson, Page, Midilli although are simple, only can be applied to specific drying conditions to predict the moisture content or ratio (Sabarez, 2015). In this regard, the development of the empirical models based on experimental results 4
Journal Pre-proof attracted notable attention, while the coefficients should be determined for each drying products before usage in the models (Zomorodian & Moradi, 2010). Various techniques, such as dimensional analysis, regression, and superposition technique, are already used to establish an empirical model (Antonopoulos et al., 2019; Moradi et al., 2019 a). Among them, several dimensionless models have been proposed
of
to simulate the drying process (Moradi et al., 2016). In this regard, Moradi et al. (2016)
ro
developed a dimensionless model of Aloe vera drying using experimental data and
-p
Buckingham theorem. Also, another research superposition technique was used to
re
simulate the drying of Aloe vera gel slices which good agreement between predicted
lP
and experimental data was observed (Moradi et al., 2019 b).
na
Based on our knowledge, the effects of heat carrier inert particles on drying time
ur
as well as energy consumption of GBPS drying is not investigated. Therefore, this study
Jo
was performed to assess the performance of an IR-assisted spouted bed dryer containing inert medium as a mean for drying GBPS in terms of both energy and shrinkage. 2- Materials and Methods 2.1. Sample preparation The green bell pepper samples were purchased from local stores at Shiraz city (GPS coordinate: 29˚38'23.07"N 52˚31'36.3"E) on a daily basis to ensure its freshness (Summer 5
Journal Pre-proof 2018). They were cut into slices at the defined sizes (1.44, 3.24, and 7.84 cm2). Furthermore, to measure initial moisture content, a sample was placed in an electrical oven at 103 °C until reaching a constant weight. The average of initial moisture content was measured as 95±1 (% of wet basis). Afterward, the drying process of GBPS was performed in the IR-assisted spouted bed dryer (Figure 1).
ro
of
2.2 Drying chamber
-p
The proposed dryer consists of a centrifugal blower (3 kW with an airflow rate of 200
re
m3/h), dryer chamber, heating channel (6 kW), and infrared bulb (OSRAM, Germany,
lP
250 W). The centrifugal fan blows the ambient air through the heating channel into the drying chamber (Figure 1). As shown in the figure, the drying chamber consists of two
na
conics at the top and bottom and a cylinder in the medium. The upper conic which was
ur
used to hold the infrared bulb has a height and diameter of 350 mm and 190 mm,
Jo
respectively. Lower conic with a height of 100 mm and a diameter of 190 mm, was used to supply uniform airflow from electrical fan to the drying chamber. Also, the height and diameter of the cylinder are 400 mm and 190 mm, respectively. In order to investigate the effects of using an inert particle on the drying kinetics of GBPS, aluminum beads (with a diameter of 4 mm) which have high heat conductivity, low heat capacity and high availability, were employed. According to primary experiments, the minimum value of air velocity which can spout both the
6
Journal Pre-proof GBPS and inert particles was determined as 18 m/s which was measured in the air outlet with a diameter of 5 cm which since the drying bed has diameter of 19 cm, the air velocity in drying bed was calculated as 1.25 m/s. The infrared bulb was installed in the upper section of the drying bin for radiative heat transfer into the drying chamber.
of
2.3. Design of study
ro
To investigate the drying kinetics of GBPS, several factors include cube size (7.84,
-p
3.24, and 1.44 cm2), drying temperature (50, 60, and 70 ˚C), and the ratio of inert
re
particles (with diameter of 4 mm) to the samples (0, 25, and 50 (Weight percentage) was
lP
considered. The applied variable ranges were selected on the basis of different researches which were investigated the drying of GBPS or application of inert particles
na
on drying mediums (Doymaz & Ismail, 2010; Honarvar et al., 2013; Hatamipour &
ur
Mowla, 2006; Faustino et al., 2007). In this regard, the drying air temperature was
Jo
monitored with the aid of a digital thermostat (Samwon, the accuracy of ±0.5 °C) installed in the air channel just before the entrance of the chamber. According to the literature (Darvish et al., 2013; Taseri et al., 2018), the slices were dried to a moisture content of about 10 % (wet basis). In order to investigate the effect of independent factors (air temperature, slice size, and percentage of inert particles to GBPS) on the drying time and shrinkage value of dried GBPS, a completely randomized factorial design was chosen, and analysis of variance (based on Duncan test in the level of 1%)
7
Journal Pre-proof was performed using SPSS 16.0 software. All experiments were carried out at three replications. 2.4. Drying process and related factors In order to conduct the experiment, 100 g of fresh GBPS was mixed with the
of
pertinent mass of inert particles (0, 25, and 50 g) and then were placed into the spouted
ro
bed dryer. Therefore, for the experiments with 0, 25, and 50 % of inert particles, the total
-p
mass of materials inside the dryer chamber (GBPS and inert particles) was adjusted as
re
100, 125, and 150 g, respectively.
lP
The shrinkage, as one of the most important and obvious changes in the drying of food products, could cause an increase in drying time (Mayor et al., 2011). In this study, it
ur
(Equation 1)
Jo
S= Δv/v0
na
was defined by Equation (1):
Where; “Δv” is the difference between the initial and final volume (cm3) and “v0” is the initial volume of the drying slice (cm3). 2.5. Energy performance Specific energy consumption (SEC) of the drying process of GBPS was calculated by Equation (2): (Equation 2) 8
Journal Pre-proof Where, SEC: Specific energy consumption (kJ/kg of removed water), E: Measured energy consumption of the dryer (kJ), and M: Mass of removed water from the drying material (kg). The energy consumption of the dryer (heater, Infrared bulb, and blower) was measured using a digital counter, with an accuracy of 0.01kWh. Furthermore, the energy balance
of
with an assumption of “No heat loss to the environment from the dryer” was
ro
performed. For this purpose, a useful energy rate (Eout) (kW) of the drying sample was
re
-p
calculated by Equation (3): (Moradi et al., 2019 a; Yogendrasasidhar and Setty, 2018) (Equation 3)
lP
Eout= ms.Lg
Where, ms is the evaporation rate of water in the drying product (kg/s), and Lg (kJ/kg of
na
water) is the latent heat of the vaporization of moisture. ms was determined from the
EUR= Eout/Ein
Jo
from Equation (4):
ur
drying rate of the product. Therefore, Energy Utilization Ratio (EUR) was calculated
(Equation 4)
Here, Eout, obtained from Equation (3) and Ein is the energy input rate (kW) which calculated from data provided by the digital counter (Yogendrasasidhar and Setty, 2018). 2.6. Mathematical modeling
9
Journal Pre-proof Dimensional analysis, as a simple method, can be employed to solve some of the complex problems. In order to establish a new model by this technique, the effective variables on the drying process must be recognized and merged to make dimensionless groups (pi terms). Based on the Buckingham theorem, several dimensionless groups are equal to the number of total effective parameters minus the number of primary
of
dimensions (Gibbings, 2011). Hence, the effective parameters in the research were
ro
recognized to be:
re
-p
M.C, MI, Mp, t, t0, T, Ta, v, v0, and V.
lP
Where; M.C: instantaneous moisture content of drying sample, MI: mass of inert particle (kg), Mp: mass of GBPS (kg), t: elapsed drying time (min), t 0: total drying time (min), T:
na
drying temperature (˚C), Ta: ambient temperature (˚C), v, v0: Final and initial volume of
ur
the drying product (m3), respectively, and V: drying air velocity (m/s).
Jo
Among these 10 parameters, 4 primary dimensions (meter, second, kg, and Celsius) were found. Hence, based on Buckingham theorem, 6 dimensionless groups were found as illustrated in Equation (5): Π1= M.C, Π2=t/t0, Π3=Vt/v01/3, Π4= (MI+Mp)/Mp, Π5=T/Ta, Π6=(v-v0)/v0
(Equation 5)
Where, Π1 is dependent pi-term, and the others are independent ones while Equation (6) describing the correlation between dependent and independent groups obtained by using dimensional analysis technique (Moradi et al., 2016):
10
Journal Pre-proof Π1=F (Π2, Π3, Π4, Π5, Π6)
(Equation 6)
Usually, the following form is proposed in the literature for a dimensionless model (Equation 7): (Gibbings, 2011) Π1=a Π2b Π3c Π4d Π5e Π6f+g
(Equation 7)
Where "a" to "g," are constant coefficients that should be determined.
of
In order to determine constants involved in the equation (7), an optimization algorithm
|
|
-p
∑
ro
was used in such a way to minimize Mean Bias Error (MBE): (Equation 8) (Equation 8)
re
Where, N is a total number of the experiments, MCiex, and MCipre is experimental and
lP
predicted the moisture content of GBPS, respectively.
na
2.7. Calculation of moisture diffusivity and activation energy
ur
To calculate moisture diffusivity, the drying equation for a slab body was proposed
Jo
analytically. While a lower thickness as compared with the length and width were noted for GBPS taking a slice as an infinite slab can be considered as an accurate approximation. Analytical solution of the drying for an infinite slab was defined as Equation (9): (Agnihotri et al., 2017)
ln MR=ln( ) -(
)
(Equation 9)
Where: MR is moisture ratio, “t” and “L” are drying time (s) and thickness of the drying sample (m), respectively and Deff is effective moisture diffusivity (m2/s). If the variation 11
Journal Pre-proof of Ln MR versus time to be considered, the slope can be evaluated using equation (10) (Agnihotri et al., 2017):
k=
(Equation 10)
In equation (10), the value of (k) was obtained from the experimental results. Thus, the
of
quantity of Deff can be easily calculated.
ro
The effect of drying air temperature on the moisture diffusivity is often
-p
expressed using the Arrhenius type equation (Adabi et al., 2013):
re
(Equation 11)
lP
Where, D0: Constant equivalent to the diffusivity at an infinitely high temperature (m2/s), Deff: effective moisture diffusivity (m2/s), Ea: activation energy (kJ/mol), and R:
ur
3- Results and discussion
na
Gas constant (8.314 J/mol. K).
Jo
3.1. Effects of each factor
As can be seen in Table 1, a significant effect on the drying time of GBPS was noted as results of the main and dual effects of applied parameters (temperature, slice size, and Mass percentage of the inert particle. Average drying time for different drying temperatures, slice sizes, and mass percentage of inert to the grain were shown in Table 2. Also, according to Figure 1s, where the interaction effects of temperature and inert particle percentage were demonstrated, the following outcomes can be achieved: 12
Journal Pre-proof a) Among all three levels of inert particle percentages, the lower drying time was correlated with the higher temperature which is consistent with the reported findings by previous investigations (Hasan et al., 2014; Jiang et al., 2017; Moradi et al., 2019 a). In another research, thin layer drying characteristics of hybrid rice seed was investigated, which their findings showed drying rate increased as a consequence of an increase in
of
drying temperature (Hasan et al., 2014).
ro
b) Among used drying temperatures, the minimum drying time was obtained with the
-p
inert particle percentage of 25 %. In this regard, the average of drying time reduced
re
about 24.8 % while compared with employing of inert percentage of 0%. While this
lP
obervation can be correlated with variation in heat transfer mechanisms of conductive
na
heat transfer (from the inert particles) and convective heat transfer (from the hot drying
ur
air) which consequently resulted in the higher heating and drying rate. In other words,
Jo
higher energy efficiency can be obtained with employing of inert particles of 25%. In a similar investigation, different mass ratios of kaffir lime leaves to sand was used (without sand, 0.04, 0.02, and 0.01). Their results showed a drying rate of kaffir lime leaves increased by the incorporation of inert particles. However, in our research, for the experiments with 50 % of inert particles, the reverse result was noted, the covering of slices by inert particles of 50 %, could prevent the effective convective heat transfer into the product.
13
Journal Pre-proof c) As drying temperature increased, the effects of inert particles on the drying time (slope of each curve) were decreased. In other words, the effect of inert particles incorporation on the drying time for the higher temperatures had a decreasing trend. In terms of higher temperature, the proposed heat could overcome the internal resistance, and consequently, the effect of inert particles on the reduction of drying time was
of
reduced. Similar results were obtained by another research performed regarding the
ro
drying of green peas in a fluidized dryer of inert particles (Honarvar et al., 2013). They
-p
investigated the drying of green peas in a fluidized bed dryer at three temperatures of
re
40, 50, and 60 ˚C which their results showed higher drying rate for the higher inert
lP
particle percentages (Honarvar et al., 2013).
na
Moreover, based on Figure 2s, which shows the interaction effects between
ur
temperature and slice size, the following results can be concluded:
Jo
a) Drying time was reduced while the slice size decreased, which can be correlated with the higher ratio of area to volume in a smaller size. For this reason, the lower drying time was required. In another research, Kothimbda was dried using a tray drier in three slice sizes of 3, 5, and 7 mm which results showed lower drying time obtained in the smaller slice sizes (Gojiya and Vyas, 2015). Also, a similar study; the drying behavior of maize and green peas with different diameters in a fluidized bed dryer while combined with inert particles was investigated. They found that the drying rate increased with a decrease in the sample diameter (Hatamipour & Mowla, 2006). Moreover, by evaluation 14
Journal Pre-proof of drying behavior of carrot slices, lower drying time was achieved by using of the smaller slices (Sonmete et al., 2017). b) As a result of the interactive effects, by applying of the higher temperatures, the effect of slice size on the drying time was decreased while sufficient heating energy was provided to penetrate to the entire of the pepper slice and therefore, for the larger slices,
of
drying time is closer to the smaller slices.
ro
Considering the demonstrated findings in Figure 3s regarding the interaction of inert
-p
material percentage and slice size, it can be concluded that:
re
a) For all percentages inert particles, with decreasing in slice size, the drying time was
lP
reduced.
na
b) In all three slice sizes, the lowest drying time can be achieved via the application of inert particles with a percentage of 25 % which demonstrates the notable impact of inert
ur
particles on the reduction of drying time. The results are consistent with the previously
Jo
reported findings (Honarvar et al., 2013; Tasirin et al., 2014) while with the application of inert particles in the drying bed, more moderate drying time for the sample was obtained. c) As the slice size becomes bigger, the slope of variations of drying time versus inert particles ratio was increased. For the larger slices, the internal resistance of the product was higher than the smaller ones. Hence, the application of inert particles (25 %) can be
15
Journal Pre-proof proposed as a suitable strategy to enhance heat penetration into the sample and further reductions in the drying time.
3.2. Drying kinetics The drying kinetics of GBPS under different drying conditions was shown in Figures 4s
of
to 6s.
ro
In Figure 4s, the drying kinetics of the large size of green pepper slices for
-p
different temperatures and inert particle ratios has been demonstrated. The time
re
required for GBPS to lose their moisture content until 10 % (wet basis), in control
lP
experiments (without inert particles) and the drying temperatures of 50, 60, and 70 ˚C
na
was 1162, 855, and 380 min, respectively, In this context, in a similar research, drying kinetics of green bell pepper in temperature range of 40 to 80 ˚C was investigated.their
ur
findings showed that a lower drying time was achivied while higher drying
Jo
temperature was applied(Taheri-Gravand et al., 2011). According to our findings, the drying time reduced as temperature increased. In this reagrd,for the inert particle 25% and temperatures of 50, 60, and 70 ˚C , the drying time in was noted as 779, 598, and 284 min, respectively. Hence, application of inert particle of 25 % at the temperatures of 50, 60, and 70 ˚C reduced the drying time by about 33 %, 30 %, and 25 %, respectively. In other words, as discussed previously, the application of inert particles at a level of 25 % showed higher efficiency in the lower drying temperatures. Also, the drying time for
16
Journal Pre-proof these pepper slices at the presence of inert particle percentage of 50% and temperatures of 50, 60, 70 ˚C was 1915 min (31.9 hour), 1219 min (20.3 hour), and 480 min (8 hour), respectively, which were higher while compared with the corresponded values for other percentages of inert particles. Therefore, in the presence of 50 % of inert particle, a longer drying time can be correlated with the lower air convection in the drying bed .
of
Figure 5s shows the drying kinetics of medium size of green bell pepper slice for
ro
different temperature and percentage of inert particles. Accordingly, the drying time at
-p
temperatures of 50, 60, and 70 ˚C and the mass percentage of inert particles of 0 % was
re
905, 786, and 292 min while the corresponded values for 25 and 50% of the inert particle
lP
were obtained as 696, 572, 250, and 1856, 1066, 477 min, respectively. Therefore, usage
na
of 25% of inert particles showed a positive effect on the reduction of drying time and reduced the drying time for 50, 60, and 70 ˚C about 23.1 %, 27 %, 13.7 %, respectively.
ur
However, with the application of the inert particles of 50 %, the drying time was
Jo
increased. However, prior to the recommendation of the inert particle perecentage, the chemical aspects of the product in the drying bed, as well as the quality of dried products, must be studied in future studies. Finally, the drying kinetics of GBPS in small size was shown in Figure 6s, while the drying time without inert particles in the drying temperatures of 50, 60, and 70 ˚C was recorded as 633, 625, and 281 min, respectively. Also, the drying time with an inert particle of 25 % and 50 % in the temperatures of 50, 60, and 70 ˚C were noted as 541, 504,
17
Journal Pre-proof 237, and 1256, 959, and 390 min, respectively. Therefore, the application of 25 % of inert particle decreased the drying time for the temperature of 50, 60, and 70 ˚C about 14.5 %, 19.4 %, and 15.6 %, respectively. 3.3. Shrinkage
of
The conducted ANOVA analysis to investigate the effect of temperature, slice size and
ro
inert particle percentage on shrinkage of the dried samples was demonstrated in Table
-p
(1). Accordingly, a significant effect of different drying temperatures, slice sizes, and
re
mass percentage of inert material on the shrinkage was noted. A higher effect of slice
lP
size on shrinkage was demonstrated by the higher F value of slice size (84.39) while compared with the F value associated with temperature and inert particle percentages.
na
The average value of shrinkage for all experiments was shown in Table 3. In this
ur
regard, the mean values of shrinkage for drying temperatures of 50, 60, and 70 ˚C were
Jo
recorded as 92.4%, 91.8%, and 91%, respectively, which demonstrated that with an increase in temperature, a decreasing trend can be correlated with shrinkage. Our findings are in good agreement with a previously conducted investigation (Coradi et al., 2016) that investigated the effects of air temperature (75, 90, and 105 ˚C) on the shrinkage of soybean during the drying. According to their findings, for the temperature of 75, 90, and 105 ˚C, the shrinkage values were obtained as 80, 79, and 77%, respectively.
18
Journal Pre-proof Also, the mean values of shrinkage for the large, medium and small size of pepper slices were obtained as 93.9%, 92.0 %, and 89.4%, respectively. Therefore, due to the higher ratio of surface area to the volume, a lower shrinkage can be associated with smaller samples. The mean values of shrinkage for the inert particle percentage of 0, 25, and 50 % were reported as 92.3%, 91.8%, and 91.1%, respectively, demonstrating the
of
less levels of shrinkage correlated with higher percentages of inert particles. This
ro
finding can be associated with uniform heat distribution in the drying bed while the
-p
higher percentage of inert particles were used. Therefore, the shrinkage of the dried
re
product was highly affected by slice size.
lP
3.4. Moisture diffusion coefficient and activation energy
na
The corresponded values for the moisture diffusion coefficient of GBPS, calculated with
fall
within
the
proposed
general
range
of
Jo
they
ur
the aid of equations (8) and (9) were ranged from 2.16×10 -10m2/s to 8.12×10-10m2/s while
10-8 to 10-12 m2/s for the diffusivity of food materials (Chen, 2007). The effective diffusivity can be considered as a strong function of the drying air temperature, inert particle percentage, and slice size. In this context, based on equations (12) to (14), the effective diffusion coefficient (D eff, m2/s) can be defined as a function of air temperature and slice size for the inert percentage of 0, 25, and 50%, respectively. Deff=1.642×10-11 T-2.19×10-11S-4.859×10-10
(Equation 12)
Deff=2.093×10-11 T-2.53×10-11S-6.56×10-10
(Equation 13) 19
Journal Pre-proof Deff=1.357×10-11 T-2.013×10-11S-3.465×10-10
(Equation 14)
Where, T had values of 50, 60, and 70 ˚C and S had the values of 7.84 mm 2 (for large slices), 3.24 mm2 (for medium slices), and 1.44 mm2 (for small slices). It is evident from the equations (12), (13), and (14) that the coefficients of the slice size(S) are negative, whereas coefficients of temperature (T) are positive exploring the
of
increasing and decreasing trends in moisture diffusivity while the temperature
ro
increased and slice size decreased. Also, for each equation, the absolute value of the
-p
coefficient of "S" is higher than the coefficient of "T." It means the slice size reduction
re
compared with the air temperature increment has a higher effect on the reduction of the
lP
moisture content of the drying sample although the effect of slice size and temperature
na
increased by 25% of inert particle compared with 0%. According to equation (8), effective diffusivity is a function of the drying air
ur
temperature while this correlation for different inert particle percentages as shown in
Jo
Figure 7s. Therefore, based on equation (8), activation energy can be calculated from the slope of the curves in Figures 7s. It should be pointed out that the value of the activation energy could be considered as a good indicator of temperature influence on the drying. Average value of activation energy for the control experiments, inert percentage of 25 %, and 50 % were calculated to be 36.3, 38.5, and 32.4 kJ/mole hence, higher values of activation energy for the inert percentage of 25 % was observed which means the higher effects of temperature in the inert percentage of 25 % can be expected.
20
Journal Pre-proof
3.5. Specific energy consumption The calculated Specific Energy Consumption (SEC) of drying of green bell pepper for large, medium, and small slice size was demonstrated in Figure 2demonstrating that a less SEC was obtained for the higher drying temperatures, which can be correlated with
of
higher internal resistance (defined as resistance against water movement from into the
ro
drying sample to its surface) while compared with the external resistance (defined as
-p
resistance against water movement from the surface of drying sample into the drying
re
air). However, curves assigned for the smaller slices were closer together, mainly due to
lP
lower internal resistance. In other words, in smaller slices while compared with the
na
larger ones.
The minimum value of SEC was obtained for all the experiments in the inert
ur
percentage of 25 % showing the positive influence of the inert particles of 25 % for a
Jo
significant reduction in energy consumption. Hence, the lowest SEC was calculated as 3.31 MJ/kg while the small slice size of samples, inert particles with a percentage of 25 % and temperature of 70 ˚C was used. In another research, the lowest SEC of green pepper samples which were dried in a microwave-convective drying was 7.14 MJ/kg (Darvishi et al., 2013). Therefore, in the present research due to the strategy of using of inert particles in the drying bed, lower SEC for drying of GBPS was observed..
21
Journal Pre-proof Based on Ferhat's equation, 800g of carbon dioxide emitted due to each kWh of energy produced from fossil fuel (Seidi and Niakousari, 2017). Hence, with 25 % of inert particles in drying temperature of 50, 60, and 70 ˚C about 25.26 kg, 18.65 kg, and 5.26 kg of less carbon dioxide emitted to the environment which shows the positive influence of heat carrier particles in the preservation of fossil fuels as well as the environment.
of
3.6- Energy Utilization Ratio (EUR)
ro
According to Figure 3, which exhibits the average EUR of GBPS for drying experiments
-p
in three different slice sizes, the following outcomes can be obtained:
re
a) In each temperature, the highest EUR was obtained for 25 % of inert particle which
lP
may be related to the lower corresponded drying time while compared with the other
na
inert particles. However, as temperature decreased, the slope of variations of EUR versus the percentage of inert particles was reduced, representing the lower effect of
Jo
ur
inert particles on EUR in the higher temperatures.
22
Journal Pre-proof b) In each slice size, the highest EUR was assigned for the drying temperature of 70 ˚C. Increasing drying temperature enhances heat transfer from hot air to the drying sample resulting in increasing moisture removal rate, and consequently increase in EUR. An increase in EUR with an increase in the drying temperature was noted in another research where the EUR profiles of Fenugreek seeds in fluidized bed dryers at different
of
temperatures of 40, 50, and 60 ˚C were calculated (Yogendrasasidhar and Setty, 2018).
ro
c) The highest EUR value (0.68) is correlated with the smallest samples in the mass
-p
percentage of inert particles of 25 % and temperature of 70 °C. Therefore, the lowest loss
lP
material percentage of 25 %.
re
of energy was noted in the highest drying temperature, smallest slice size, and inert
na
3.7. Mathematical modeling of the drying process
ur
Dimensional analysis was used to establish a new drying model for the drying of GBPS
Jo
in an IR-assisted spouted bed dryer containing inert particles as a heat carrier. The obtained dimensionless groups in the equation (5) were used to establish a new dimensionless model with the proposed form of the equation (7). Finally, a dimensionless equation was established as equation (15): M.C=6.933×( t/t0)-0.27715×( Vt/v01/3)-0.21959×[(MI+Mp)/M p] -0.3765×( T/Ta )-0.40252×[ v-v0)/v0]-1.99876 -3.47944 (Equation 15)
23
Journal Pre-proof In this equation, π1 (moisture content of drying sample) is dependent pi- term and the others are independent. As already explained, about 80 % of experimental data was used to establish the dimensionless model; the remaining 20 % was utilized to evaluate the established model. Figure 8s shows the experimental moisture content versus the predicted moisture content of the GBPS. The simulation results showed good
of
agreements between predicted and measured moisture content such that R2, MBE, and
ro
RMSE were calculated to be 0.90, 0.052, and 0.015, respectively. In another research, two
-p
dimensionless models were established for the description of the drying kinetics of aloe
re
vera gel dried in a convective and osmo-convective manner (Moradi et al., 2016). The
lP
obtained dimensionless models were developed in such a way to incorporate three
na
independent pi terms for the description of instantaneously moisture content of aloe vera gel. The validation results of the models showed a coefficient of determination for
ur
convective and osmo-convective models were, 0.98 and 0.99, respectively. In another
Jo
study, a dimensionless model was established and evaluated for drying of corn grains in a continuous dryer with inert energy carrier particles. To do the simulation, five independent π terms which were responsible for the drying rate of the grains were identified, and a dimensionless model includes the effect of all independent π terms on the dependent π term, which was derived and evaluated. The evaluation results showed that R2, MBE, and RMSE were calculated as 0.85, 0.0648, and 0.018, respectively (Moradi and Karparvar fard, 2016). Therefore, the obtained dimensionless model can be
24
Journal Pre-proof confidently used to predict the moisture content of drying GBPS in defined condition ranges. 4- Conclusion In order to investigate the effects of heat carrier inert particles on the drying kinetics
of
and energy consumption of GBPS, an IR-assisted spouted bed dryer is containing
ro
aluminum beads as heat carriers were used. Analysis of the data obtained from the
-p
dryer indicated that the mass percentage of inert particles to GBPS influences the drying
re
time and drying rate. When the inert particle percentage 25%, was used 25% reduction
lP
in drying time was realized. However, increasing the percentage to 50%, surprisingly, reversed the positive influence of this presence. Analyze of shrinkage of dried samples
na
showed less amount of shrinkage observed at more inert particle percentage.
ur
Furthermore, using 25% of inert particles to GBPS improves the energy efficiency of the
Jo
drying operation by reducing specific energy consumption. However, the minimum SEC observed in 25 % of heat carriers, the temperature of 70 ˚C, and the lowest drying samples. The diffusion coefficient and activation energy calculations showed that the maximum influence of drying temperature occurred in 25 % of inert particles. To better explain the drying behavior of green bell pepper, a dimensionless equation, incorporating all effective parameters, was established and evaluated which validation results revealed a good agreement between experimental and predicted moisture content. 25
Journal Pre-proof Conflict of interest The authors declare that they have no conflict of interest.
References
of
Agnihotri, V., Jantwal, A., &Joshi, R. (2017). Determination of effective moisture
ro
diffusivity, energy consumption, and active ingredient concentration variation in
-p
Inularacemosa rhizomes during drying. Industrial Crops and Products, 106, 40-47.
re
Aktas, M., Khanlari, A., Amini, A., & Şevik, S. (2017). Performance analysis of heat
lP
pump and infrared–heat pump drying of grated carrot using energy-exergy
na
methodology. Energy Conversion and Management, 132, 327-338.
ur
Antonopoulos, V. Z., Papamichail, D. M., Aschonitis, V. G., & Antonopoulos, A. V.
Jo
(2019). Solar radiation estimation methods using ANN and empirical models. Computers and Electronics in Agriculture, 160, 160-167. Badaoui, O., Hanini, S., Djebli, A., Haddad, B., & Benhamou, A. (2019). Experimental and modelling study of tomato pomace waste drying in a new solar greenhouse: Evaluation of new drying models. Renewable Energy, 133, 144-155. Chakraborty, R., Bera, M., Mukhopadhyay, P., &Bhattacharya, P. (2011). Prediction of optimal conditions of infrared assisted freeze-drying of aloe vera (Aloe barbadensis)
26
Journal Pre-proof using response surface methodology. Separation and Purification Technology, 80(2), 375384. Chen, X. D. (2007). Moisture diffusivity in food and biological materials. Drying Technology, 25(7-8), 1203-1213.
of
Coradi, P. C., Fernandes, C. H. P., Helmich, J. C., & Goneli, A. L. D. (2016). Effects of
ro
drying air temperature and grain initial moisture content on soybean quality (Glycine
-p
max (L.) Merrill). Engenharia Agrícola, 36(5), 866–876.
re
Darvishi, H., KHOSH, T. M., Najafi, G., & Nargesi, F. (2013). Mathematical modeling of
na
and Technology, 15, 457-465.
lP
green pepper drying in microwave-convective dryer. Journal of Agricultural Science
Das, C., Das, A., &Golder, K. (2016). Optimality in microwave-assisted drying of Aloe
ur
vera (Aloe barbadensis Miller) gel using response surface methodology and artificial
143-149.
Jo
neural network modeling. Journal of The Institution of Engineers (India): Series E, 97(2),
Dehghan‐Manshadi,
A.,
Peighambardoust,
S.
H.,
Azadmard‐Damirchi,
S.,
&
Niakousari, M. (2019). Effect of infrared‐assisted spouted bed drying of flaxseed on the quality characteristics of its oil extracted by different methods. Journal of the Science of Food and Agriculture.
27
Journal Pre-proof Doymaz, I., & Ismail, O. (2010). Drying and rehydration behaviors of green bell peppers. Food Science and Biotechnology, 19(6), 1449-1455. El-Mesery, H. S., & Mwithiga, G. (2015). Performance of a convective, infrared and combined infrared-convective heated conveyor-belt dryer. Journal of food science and technology, 52(5), 2721-2730.
of
Faustino, J.M.F., Barroca, M.J., and Guine, R.PF. (2007). Study of the drying kinetics of
ro
green bell pepper and chemical characterization. Food and Bioproducts Processing, 85 (C3),
-p
163–170.
lP
re
Gibbings, J. C. (2011). Dimensional analysis. Springer Science & Business Media. Gojiya, D. K., & Vyas, D. M. (2015). Studies on effect of slice thickness and temperature
na
on drying kinetics of Kothimbda (Cucumis callosus) and its storage. Journal of Food
ur
Processing and Technology, 6(1). doi: 10.4172/2157-7110.1000406
Jo
Hammouda, I., & Mihoubi, D. (2014). Comparative numerical study of kaolin clay with three drying methods: Convective, convective–microwave and convective infrared modes. Energy conversion and management, 87, 832-839. Hasan, A. A. M., Bala, B. K., & Rowshon, M. K. (2014). Thin layer drying of hybrid rice seed. Engineering in agriculture, environment and food, 7(4), 169-175.
28
Journal Pre-proof Hatamipour, M. S., & Mowla, D. (2006). Drying behaviour of maize and green peas immersed in fluidized bed of inert energy carrier particles. food and bioproducts processing, 84(3), 220-226. Honarvar, B., Mowla, D., & Safekordi, A. A. (2013). Experimental and theoretical investigation of drying of green peas in a fluidized bed dryer of inert particles assisted
of
by infrared heat source. Iranian Journal of Chemistry and Chemical Engineering
-p
ro
(IJCCE), 32(1), 83-94.
re
Jiang, J., Dang, L., Tan, H., Pan, B., & Wei, H. (2017). Thin layer drying kinetics of pre-
lP
gelatinized starch under microwave. Journal of the Taiwan Institute of Chemical Engineers, 72, 10-18.
na
Mayor, L., Moreira, R., & Sereno, A. M. (2011). Shrinkage, density, porosity and shape
ur
changes during dehydration of pumpkin (Cucurbita pepo L.) fruits. Journal of Food
Jo
Engineering, 103(1), 29–37.
Moradi, M., & Karparvar fard S. H. (2016). Mathematical modeling of a corn grains drying process in a continuous dryer including inert particles. Iranian journal of chemical engineering, 14(83): 82-90. Moradi, M., Niakousari, M., & Etemadi, A. (2016). Dimensionless modeling of thin layer drying process of Aloe vera gel. Iranian Food Science and Technology Research Journal 12, 362-370. doi: 10.22067/ifstrj.v12i3.55168 29
Journal Pre-proof Moradi, M, Fallahi, M, and Mousavi Khaneghah, A. (2019 a). Kinetics and mathematical modeling of thin layer drying of mint leaves by a hot water recirculating solar dryer. Journal of Food Process Engineering, In press. https://doi.org/10.1111/jfpe.13181 Moradi, M., Niakousari, M., & Mousavi Khaneghah, A. (2019 b). Kinetics and mathematical modeling of thin layer drying of osmo‐treated Aloe vera (Aloe
of
barbadensis) gel slices. Journal of Food Process Engineering, 42(6), e13180.
ro
Prosapio, V. & Norton, I. (2017). Influence of osmotic dehydration pre-treatment on
-p
oven drying and freeze drying performance, LWT, 80, 401-408.
re
Seidi damyeh M, Niakousari M (2017) Ohmic hydrodistillation, an accelerated
lP
energysaver green process in the extraction of Pulicaria undulata essential oil Industrial
na
Crops and Products 98:100-107. https://doi.org/10.1016/j.indcrop.2017.01.029
ur
Serowik, M., Figiel, A., Nejman, M., Pudlo, A., Chorazyk, D., Kopec, W., ... & Rychlicka,
Jo
J. (2018). Drying characteristics and properties of microwave− assisted spouted bed dried semi− refined carrageenan. Journal of food engineering, 221, 20-28. Sonmete, M. H., Mengeş, H. O., Ertekin, C., & Özcan, M. M. (2017). Mathematical modeling of thin layer drying of carrot slices by forced convection. Journal of Food Measurement and Characterization, 11(2), 629-638.
30
Journal Pre-proof Taheri-Garavand, A., Rafiee, S., & Keyhani, A. (2011). Study on effective moisture diffusivity, activation energy and mathematical modeling of thin layer drying kinetics of bell pepper. Australian Journal of Crop Science, 5(2), 128. Taseri, L., Aktaş, M., Şevik, S., Gülcü, M., Seçkin, G. U., & Aktekeli, B. (2018). Determination of drying kinetics and quality parameters of grape pomace dried with a
of
heat pump dryer. Food chemistry, 260, 152-159.
ro
Tasirin, S. M., Puspasari, I., Lun, A. W., Chai, P. V., & Lee, W. T. (2014). Drying of kaffir
-p
lime leaves in a fluidized bed dryer with inert particles: Kinetics and quality
re
determination. Industrial Crops and Products, 61, 193-201.
lP
Yogendrasasidhar, D., & Setty, Y. P. (2018). Drying kinetics, exergy and energy analyses
na
of Kodo millet grains and Fenugreek seeds using wall heated fluidized bed
ur
dryer. Energy, 151, 799-811.
Jo
Zomorodian, A, &Moradi, M. (2010). Mathematical modeling of forced convection thin layer solar drying for CuminumCyminum. Journal of Agricultural Science and Technology, 12, 401-408.
31
Journal Pre-proof
Variation sources
Degree of freedom
Sum squares
Mean squares
F value
Drying time
Temperature
2
6403599.43
3201799.7
456617.2**
Slice size
2
507281.28
253640.64
36172.4**
Percentage of inert particle
2
4181646.84
2090823.42
298177.9**
Temperature×slice size
4
1630974.49
407743.62
58149.4**
Temperature×percentage of inert particle
4
1806472.27
451618.07
64406.5**
Slice size×percentage of inert particle
4
193844.42
48461.10
6911.2**
Error
54
-p
re
lP
378.67
7.012
72
14724197.5
2
16.092
8.046
7.23**
2
187.872
93.936
84.39**
Inert particle percentage
2
16.753
8.376
7.53**
Temperature*slice size
4
6.782
1.695
1.52ns
Temperature*inert particle 4 percentage
0.188
0.047
0.04ns
Slice size* inert particle percentage
4
1.440
0.360
0.32ns
Temperature*slice size* inert particle percentage
8
1.486
0.1862
0.17ns
Error
54
60.102
1.113
Total
80
290.715
Temperature
Jo
ur
Slice size
na
Total Shrinkage value
of
Dependent variable
ro
Table1- ANOVA for main and interaction effects of independent factors on drying time and shrinkage value
**: significant in level of 1% Ns: non-significant 32
Journal Pre-proof
Table 2- Average drying time for different drying conditions
50
Medium
Small
Medium
na
60
lP
re
Large
Jo
ur
Small
70
Large
Medium
Small
Average drying time±SD 1148±20 785±15 1900±25 910±10 690±9 1850±22 626±10 510±7 1220±15 835±10 590±10 1150±15 785±15 570±10 1050±16 611±8 495±10 945±12 370±10 301±5 520±7 300±6 240±5 475±6 280±5 230±6 378±5
of
Large
Inert particle percentage 0% 25% 50% 0% 25% 50% 0% 25% 50% 0% 25% 50% 0% 25% 50% 0% 25% 50% 0% 25% 50% 0% 25% 50% 0% 25% 50%
ro
Slice size
-p
Drying temperature (˚C)
33
Journal Pre-proof
Table 3- Average shrinkage for different drying conditions
Medium
re
50
lP
Small
Medium
Jo
ur
60
na
Large
Small
Large
70
Medium
Small
Average shrinkage±SD
of
Large
Inert particle percentage 0% 25% 50% 0% 25% 50% 0% 25% 50% 0% 25% 50% 0% 25% 50% 0% 25% 50% 0% 25% 50% 0% 25% 50% 0% 25% 50%
94.3±0.6 93.81±0.5 93.06±0.4 94.66±0.6 94.19±0.5 93.74±0.8 90.79±0.7 89.99±0.4 88.31±0.6 94.48±0.8 94.22±0.6 93.84±0.8 91.26±0.7 91.62±0.5 91.32±0.4 91.05±0.6 90.11±0.5 88.62±0.4 94.50±0.6 93.88±0.3 93.27±0.5 90.83±0.4 90.73±0.7 90.24±0.2 89.28±0.4 88.14±0.5 88.06±0.3
ro
Slice size
-p
Drying temperature (˚C)
34
Journal Pre-proof
Figure 1. Pilot scale IR-assisted spouted bed dryer Figure 2. The specific energy consumption for slice size of: a) large b) medium small b) medium
Jo
ur
na
lP
re
-p
ro
of
Figure 3. Energy utilization ratio for slice size of: a) large
35
c)
c) small
Journal Pre-proof Author statement Conceptualization; Mehdi Moradi Data curation; Mehdi Moradi Formal analysis; Sadi Azizi Funding acquisition; Mehdi Moradi Investigation; Mehdi Moradi; Saadat Kamga
Project administration; Amin Mousavi Khaneghah
ro
Resources; Sadi Azizi
of
Methodology; Mehdi Moradi
-p
Software; Sadi Azizi; Saadat Kamgar
re
Supervision; Mehrdad Niakosari; Amin Mousavi Khaneghah Validation; Mehdi Moradi; Saadat Kamgar
lP
Visualization; Mehrdad Niakosari
na
Roles/Writing - original draft; Amin Mousavi Khaneghah
Jo
ur
Writing - review & editing; Mehdi Moradi ;Amin Mousavi Khaneghah
36
Journal Pre-proof Conflict of interest
Jo
ur
na
lP
re
-p
ro
of
The authors declare that they have no conflict of interest.
37
Journal Pre-proof Highlights
Investigating the drying kinetics of GBPS in an IR-assisted spouted bed dryer containing inert particles.
Calculation specific energy consumption in different operating conditions in an IR-assisted spouted bed dryer.
of
Establishment of a new empirical model, describing drying kinetics of GBPS in
ur
na
lP
re
-p
ro
the spouted bed dryer.
Jo
38
Figure 1
Figure 2
Figure 3