Comparison of drying kinetics, energy efficiency

Full text

Full Terms & Conditions of access and use can be found at
https://www.tandfonline.com/action/journalInformation?journalCode=ueso20
Energy Sources, Part A: Recovery, Utilization, and
Environmental Effects
ISSN: (Print) (Online) Journal homepage: https://www.tandfonline.com/loi/ueso20
Comparison of drying kinetics, energy efficiency
and color of dried eggplant slices with two
different configurations of a heat pump dryer
İbrahim Doymaz, Cüneyt Tunçkal & Zekiye Göksel
To cite this article: İbrahim Doymaz, Cüneyt Tunçkal & Zekiye Göksel (2023) Comparison of
drying kinetics, energy efficiency and color of dried eggplant slices with two different configurations
of a heat pump dryer, Energy Sources, Part A: Recovery, Utilization, and Environmental Effects,
45:1, 690-707, DOI: 10.1080/15567036.2023.2169415
To link to this article: https://doi.org/10.1080/15567036.2023.2169415
Published online: 31 Jan 2023.
Submit your article to this journal
Article views: 15
View related articles
View Crossmark data

Comparison of drying kinetics, energy eciency and color of dried
eggplant slices with two dierent congurations of a heat pump
dryer
İ

brahim Doymaz
a
, Cüneyt Tunçkal
b
, and Zekiye Göksel
c
a
Department of Chemical Engineering, Yıldız Technical University, Istanbul, Turkey;
b
Electric and Energy Department,
Air Conditioning and Refrigeration Technology Program, Yalova Community College, Yalova University, Yalova,
Turkey;
c
Food Technology Department, Atatürk Horticultural Research Institute, Yalova, Turkey
ABSTRACT
In this study, Kemer eggplants (Solanum melongena L.) were examined in
terms of system performance and energy eciency after drying at constant
air temperature and dierent operating systems developed on a heat pump
dryer. The air velocity and the drying time have a negative correlation was
observed. In addition, the Midilli & Kucuk model was the most appropriate
model to explain the experimental data of dried eggplant samples obtained
from this study. Eective moisture diusivity was calculated between 6.08 ×
10
−10
and 7.30 × 10
−10
m
2
/s depending on air velocity values. The reduction
of the eggplant slices from 12.158 (g water/g dry matter (dm)) moisture value
to 0.645 (g water/g dm) moisture value took 236 min at 1 m/s air velocity
with the application of Serial Condenser and Parallel Evaporator Drying
System (SCPEDS). The Serial Condenser Drying System (SCDS) was found to
yield both the highest average condenser capacity with 2.256 kW and the
highest system performance coecient with 3.181. The brightness values (L)
of dried eggplant samples increased compared to the fresh eggplant sam-
ples. The closest brightness value to the fresh samples was determined as
84.7 in the drying experiments with the air velocity of 0.5 m/s and the
SCPEDS system. The other color parameters (b, C and h°) decreased during
drying. The highest total color change value (8.05) was obtained with
SCPEDS system at 1.0 m/s. The highest browning index value (63.48) was
reached in the drying experiments performed with the SCDS system at 0.75
m/s.
ARTICLE HISTORY
Received 2 August 2022
Revised 5 January 2023
Accepted 11 January 2023
KEYWORDS
Drying system; eggplant;
effective moisture diffusivity;
energy efficiency;
mathematical modeling
Introduction
People have been using the fruit and vegetable drying techniques for centuries. Fruit and vegetable
drying activity is one of the most important industries. In Turkey, the dried product exports started
with raisin, fig, and apricot and have expanded to involve various other fruits and vegetables over the
years.
The eggplant (Solanum melongena L.), belonging to the Solanaceae family, can be of different
shapes, sizes and colors (Ferrão et al. 2019). In addition, eggplant is grown in Mediterranean, Asian
and North American regions and is widely consumed in the world. Some restrictions may be
encountered in the trade of fresh eggplant since its shelf-life is 10 days. The suitable temperature
value is between 10°C and 15°C for the conservation of fresh eggplant (Kaveh, Chayjan, and Nikbakht
2017). The amount of production in Turkey in 2020 was 835,422 tons (2022).
CONTACT İbrahim Doymaz doymaz@yildiz.edu.tr Department of Chemical Engineering, Yıldız Technical University, Esenler,
Istanbul 34220, Turkey
ENERGY SOURCES, PART A: RECOVERY, UTILIZATION, AND ENVIRONMENTAL EFFECTS
2023, VOL. 45, NO. 1, 690–707
https://doi.org/10.1080/15567036.2023.2169415
© 2023 Taylor & Francis Group, LLC

Drying the eggplant is an alternative method for long-term storage (Lili et al. 2012). Food drying is
a storage method that has a long shelf life and takes up less space for shipping. People have been using
the fruit and vegetable drying techniques for centuries. Fruit and vegetable drying activity is one of the
most important industries. Eggplant is usually dried using the natural sun drying method. The sun
drying technique has some benefit such as simplicity, lower expenses and low operating expenses;
however, on the other hand, it can have some negative consequences on the product quality. In
addition, it is widely influenced by various restrictions such as long processing times, meteorological
conditions, and mold or pest contamination. For this reason, the drying operations should be carried
out in a closed environment and with equipment in order to improve product quality.
In addition to numerous varied methods already being used (hot air, spray, fluid bed, freezing, etc.),
some new drying methods (microwave, radio frequency, Heat Pump Drying (HPD), etc.) have also
been studied. The primary significant advantage of the heat pump (HP) is that it can provide large
amounts of mid-high temperature heat energy with the few amount of electrical energy it consumes
(Liu et al. 2018). It has been understood that many problems that arise with the use of fossil fuels can
be avoided by using the HP as a heat source in drying of food process, and this situation has affected
the interest of investigators. HPD has low energy consumption, low drying temperature and simple
control features, thus making it has become popular in the drying operations of many products (Liu et
al. 2018; Taşeri et al. 2018; Yang, Zhu, and Zhao 2016). The method used is very important for food
quality and energy yield (Ertekin and Yaldiz 2004). The most prevalently used industrial procedure is
conventional drying in which the products are dried on tray dryers. In scientific studies on eggplant
drying, convective dryers are generally used, but different methods have been used and the results have
been compared with the study.
Kamer et al. (2016) experimentally examined the drying behavior of zucchini and eggplant samples.
They applied the trials at three velocities (0.5, 1.0 and 1.5 m/s) and at three various temperatures (40°C,
50°C and 60°C) using by a convective dryer. For the experiments, eggplant slices weighing 300 g and 1
cm thick were placed on the precision balance in each experiment. They examined the influence of air
velocity on the drying time of eggplant and determined that as the drying air velocity was increment
from 0.5 to 1.5 m/s, the drying time was reduced by 18%. The study investigated the optimum drying
temperature for eggplant samples at low and high temperatures. Taşova (2018) determined that the
longest drying process was 1085 min at 40°C drying temperature while the shortest one was 90 min at
90°C. In their study examining the drying properties and quality features of eggplants under various
drying terms, and 2015) dried 6 mm thick eggplant slices using convective and infrared-assisted
convective driers at three temperatures (40°C, 50°C, and 60°C). They found that at 40°C, the drying
process was completed in 10 h when using the convective dryer and in 9 h in the infrared assisted
convective dryer. The most important advantages of HPD systems are that it can dry at low
temperatures (35–45°C) under hygienic conditions due to its closed circuit, and lower energy
consumption during drying compared to other drying methods. In this study, a different design was
developed from the classical configurations of HPD. The location, drying air velocity and drying
temperature of the condensers and evaporators used in the design of the researchers in the HPD
system are dedicated in Table 1.
Table 1. Locations of condensers and evaporators in HPD systems.
Investigators
Velocity
(m/s) Temperature (°C)
Location of condenser
in the System
Location of evaporator
in the system
Aktaş et al. (2017) 0.5 45 -50 Internal Internal
2016) 0.4 50 Int. and External (parallel) Internal
2018) 1 30-35-40 Int. and Ext.(parallel) Internal
2010) 0.5-1-1.5 45-50-55 Int. and Ext.(parallel) Internal
2008) 0.5–2.5 45-50-55 Int. and Ext.(serial) Int. and Ext.(parallel)
ENERGY SOURCES, PART A: RECOVERY, UTILIZATION, AND ENVIRONMENTAL EFFECTS 691

According to Tunçkal, Coşkun, and Erdoğan (2016), four varied samples were dried using a closed-
loop HPD system with a slice thickness of 5 mm, a drying air temperature of 40°C, and a drying air
velocity of 1.0 m/s. According to the results obtained, the Specific Moisture Extraction Rates (SMER)
for melon, apple, kiwi and banana were measured to be 0.332, 0.306, 0.303 and 0.232 kg/kWh. In a
study, tomato slices were dried using by HPD system at 35°C, 40°C and 45°C. The drying temperature
and the drying rate presented positive correlation. The SMER value was obtained as 0.324 kg/kWh for
45°C. Specific Energy Consumption (SEC) in drying trials is important for the performance evaluation
of the device. According to (Karacabey et al. 2020), 200 g of seedless grapes at a drying temperature of
50, 60 and 70°C made with samples weighing in. They calculated the SEC value as 82.51 kWh/kg at 50°
C and 19.15 kWh/kg at 70°C drying temperature. (Chayjan and Kaveh 2016) dried the sliced eggplant
vegetable in a microwave dryer with a microwave power of 270–630 W, an air temperature of 40–70°C
and an air velocity of 0.5–1.7 m/s. They calculated the total SEC value for drying eggplant slices
between 86.47 and 194.37 MJ/kg. (Kaveh et al. 2018) dried sliced eggplants in a belt dryer at
temperatures of 45°C, 60°C and 75°Cand air velocity of 1.0, 1.5 and 2.0 m/s. They calculated the
lowest SEC value as 130.62 MJ/kg. Ye et al. (2021) tried to obtain the energy requirement values for
drying mint leaves using a hot air dryer at varied drying temperatures and air speeds. They calculated
the SEC value between 2.6 and 19.7 MJ/kg.
The biggest innovation of this research is the thermostat-controlled operation of the condensers
mounted in series and the evaporators mounted in parallel in the system and investigating the effects
of the system on the drying efficiency. The purpose of this research was to measure the drying
performance and energy efficiency of the two different HPD systems with closed cycle at 40°C air
temperature and 0.5, 0.75, 1.0 m/s air velocities for eggplant drying. Also, the suitable drying model
was chosen in terms of the experimental data of eggplant slices.
Materials
Fresh Kemer eggplants obtained from a regional supermarket in Yalova were used in the study.
To determine the moisture content in fresh eggplant, a sample weighing about 5 g was placed
in a vacuum oven at 70°C and dried until the sample mass remained at a constant value
(Ceylan, Aktaş, and Doğan 2007). The average dry substance content of eggplants was
determined to be 7.6% (wet basis). Eggplants were sliced 6 mm thick without peeling with a
food slicer and they were mean 3.5 ± 0.5 cm in diameter. No pre-treatment was applied to the
eggplant slices.
Experiment system and operation
In the drying operation steps given in Figure 1, the samples were first sliced in the desired thickness in
the slicer without any pre-treatment, and then placed in the drying cabinet of the system, which
became stable at the specified drying temperature, and the drying process was started. All data
measured during drying were evaluated in excel program. In addition, moisture from the product
was collected in the condensation tank as seen in Figure 1 (Tunçkal 2020).
The HPD test mechanism was designed in two different configurations. The diagram of the
Serial Condenser Drying System (SCDS) is given in Figure 2. Two serially connected internal
and external condensers were used. The refrigerant circulating within the system, on the other
hand, is absorbed by the compressor, signaled to the internal condenser, then passed through
the capillary tube and sent to the evaporator. The external condenser is serially connected to
the internal condenser with a digital thermostat that takes the drying division exit temperature
(the desired drying temperature) as reference and the drying division temperature is brought
to the desired temperature. The performance was enhanced by taking a part of the internal
condenser load after the external condenser was actuated with the help of the thermostat while
the drying system was working. The evaporator capacity increased in proportion to the
692 İ. DOYMAZ ET AL.

Figure 2. Serial Condenser Drying System (SCDS) thermostatic control. (a) compressor, (b) int. condenser, (c) ext. condenser, (d)
capillary tube, (e) evaporator, (f) solenoid valve, (g) SCDS thermostat, (h) axial fan, (i) speed switch, (j) datalogger, (k) load-cell, (l)
drying cabined, (m) check valve, (n) anemometer (Tunçkal, Coşkun, and Erdoğan 2016; Tunçkal, Yüksel, and Coşkun 2022).
Figure 1. HPD system operation cycle.
ENERGY SOURCES, PART A: RECOVERY, UTILIZATION, AND ENVIRONMENTAL EFFECTS 693

condenser capacity and so further moisture was dehumidified than the air leaving the drying
division. The diagram of the Serial Condenser and Parallel Evaporator drying system
(SCPEDS) is given in Figure 3.
SCPEDS, a digital thermostat, two solenoid valves, and an external evaporator of the same
capacity as the internal evaporator were added to the HPD system in the parallel evaporator
application. After setting the desired drying temperature at the digital thermostats, the internal
condenser and external evaporator become active until reaching the drying temperature, thus the
release of humidity from the product is ensured, but no dehumidification takes place. When the
process reaches the desired drying air temperature, the external condenser is serially activated with
the internal condenser and the external evaporator is deactivated whereas the internal evaporator is
activated. These two operations take place simultaneously, thus leaving a differential range of 1°C
to allow the system to operate at 40°C with an accuracy of ±0.5. With the parallel evaporator
application, it was aimed to reach the desired drying temperature in a much shorter time and to
get the system to dehumidify better. The drying time for reaching the drying air temperature of
40°C at the ambient temperature of 21.5°C from the first start-up of the two different systems is
given in Table 2.
Figure 3. Serial Condenser Parallel Evaporator Drying System (SCPEDS) thermostatic control. (a) compressor, (b) Int. Condenser,
(c) ext. Condenser, (d) capillary tube, (e) evaporator, (f) solenoid valve, (g) SCDS thermostat, (h) axial fan, (i) speed switch,
(j) datalogger, (k) load-cell, (l) drying cabined, (m) check valve, (n) anemometer, (o) ext. evaporator, (p) SCPEDS thermostat.
Table 2. The time to get 40°C after starting the system.
Time to get 40°C (s)
Drying air velocity (m/s) 0.5 0.75 1.0
SCDS 1130 1025 865
SCPEDS 785 665 535
694 İ. DOYMAZ ET AL.

Experiment process
The system was activated to be operated 30 min ago before each experiment and it was aimed to
provide drying conditions in this way. Eggplant samples were dried at various drying air velocities (0.5,
0.75, and 1.0 m/s) and at constant drying air temperature (40°C). Air velocities were altered with the
fan velocity switch placed on the system. In trials, the air flux rates were measured using an
anemometer (Testo 410–2). The drying treatment was started by placing a total of 200 g of eggplant
slices on the drying tray. For measuring weight loss was used to a load-cell. Experimental data were
saved on the computer at two-minute ranges during the drying treatment. The drying process was
pursued till the moisture substance in the eggplant slices from 92.4% to about 5% (wet basis). All
experiments were performed in three repetitions. In addition, the mean moisture values were used to
plot the drying curves (p<%5).
The refrigerant temperatures were measured with a 4-channel digital thermometer over the copper
tubes. The low and high pressure values of the refrigerant were read with the digital manifold
connected to the system. During the drying operation, the total energy consumption of the compres-
sor and all system was measured utilizing a digital wattmeter separately connected to the system and
recorded at 60-min intervals from the start of drying. Experimental data were obtained and evaluated
using by Excel program and graphs were drawn.
Mathematical modeling
Moisture content values are calculated using Equation 1:
M¼Mw
Md
(1)
Here M, M
w
and M
d
show the moisture (kg water/kg dm), water and M
d
dry matter amounts (kg),
respectively. Moisture contents are converted to moisture ratio (MR) using by Eq. 2:
MR ¼MtMe
M0Me
(2)
Drying time was taken as minutes. When M
e
compared to M
t
or M
0
, M
e
is very minor and
insignificant. So, MR can be specified to:
MR ¼Mt
M0
(3)
Table 3 shows ten thin-layer drying models used to detect the best fit model describing the drying
curves of eggplant slices.
Using the Statistica 8.0.550 (StatSoft Inc., Tulsa, OK, USA), the models were applied to experi-
mental data by non-linear regression procedures. The coefficient of determination (R
2
), reduced chi-
square (χ
2
) and root mean square error (RMSE) were used to evaluate the quality of fit of the
experimental result to all models. The R
2
, χ
2
and RMSE values were calculated using the following
equations:
R2¼1PN
i¼1ðMRpre;iMRexp;iÞ2
PN
i¼1MRpre MRexp;i
 2(4)
χ2¼PN
i¼1MRexp;iMRpre;i
 2
Nz(5)
ENERGY SOURCES, PART A: RECOVERY, UTILIZATION, AND ENVIRONMENTAL EFFECTS 695

RMSE ¼1
2XN
i¼1ðMRpre;iMRexp;iÞ2
 1
2(6)
where MR
exp,i
and MR
pre,i
are experimental and predicted moisture ratios, respectively, N and z are
number of observations and constants, respectively.
Determination of eective moisture diusivity
The second law of Fick’s diffusion equation is qualified a mass diffusion equation for agricultural
products drying in the decreasing rate period. For measuring the moisture ration in Eq. 7 can be used
to the Fick’s second law of unsteady state diffusion. The answer of diffusion formula for infinite slab
dedicated by Crank (1975) and supposed uniform initial moisture distribution, insignificant external
resistance, constant diffusivity and insignificant shrinkage, is:
MR ¼8
π2X1
n¼0
1
2nþ1ð Þ2exp 2nþ1ð Þ2π2Deff t
4L2
  (7)
where D
eff
indicates the effective moisture diffusivity (m
2
/s). In addition, t indicate the time (s), L, the
half-thickness of samples (m), and n positive integer, respectively. In longer drying periods, Eq. 7 can
be the solution to only first term of series, without much impressive the precision of the prediction.
MR ¼8
π2exp π2Deff t
4L2
  (8)
Equation 7 can be linearized by applying natural logarithmic at both sides as given that the equation,
where the slope (K) of the graph (ln MR vs time) was used to estimate the D
eff
:
slope ¼π2Deff
4L2(9)
HPD system analyses
The performance analysis of the drying system was performed in two ways. The first way is the SMER,
familiar as the water taken from the product per energy expended; other way is the COP value
(Coefficient of Performance), familiar as the heat transferred from the condenser to the drying air per
electrical energy consumed in the compressor (Jia, Jolly, and Clements 1990). COP result was
calculated using by Eq. 10.
Table 3. Mathematical models used to explain drying kinetics.
Model name Mathematical Model
1)
Reference
Lewis MR ¼exp ktð Þ Roberts et al. (2008)
Henderson & Pabis MR ¼aexp ktð Þ Panchariya et al. (2002)
Logarithmic MR ¼aexp ktð Þ þ cLee and Kim (2009)
Page MR ¼exp ktn
ð Þ Simal et al. (2005)
Midilli & Kucuk MR ¼aexp ktn
ð Þ þ bt Midilli et al. (2002)
Mod. Midilli et al. MR ¼exp ktð Þ þ bt Erbay and Icier (2010)
Jena & Das MR ¼aexp kt þbt0:5
ð Þ+c Jena and Das (2007)
Vega-Galvez et al. -I MR ¼aþbtð Þ2Lemus-Mondaca et al. (2009)
Vega & Lemus MR ¼aþktð Þ Vega-Gálvez et al. (2009)
Aghashlo et al. MR ¼expð k1t
1þk2tÞAghbashlo et al. (2009)
Note:
1)
a, b, c, k, k
1
, k
2
, n: empirical constants and coefficients in models.
696 İ. DOYMAZ ET AL.

COPhp ¼_
Qcd
_
Wcomp
(10)
where; _
Qcd, quantity of heat dissipated by internal condenser, _
Wcomp, compressor power consumption.
COP of the entire system was measured using Eq. 11.
COPsys ¼_
Qcd
_
Wcomp þ_
Wfan;iþ_
Wfan;e
(11)
where; _
Wfan, internal and external fan power consumption. Using the Eq. 12, the quantity of heat
transferred by the internal condenser to the drying air was measured.
_
Qcd ¼_
mahcd;out hcd;in
  (12)
where; hcd;out, specific enthalpy of dry air at the internal condenser outlet, hcd;in, specific enthalpy of
dry air at internal condenser inlet. In the equation _
ma, the mass flow rate of the air, was determined as
follows.
_
ma¼A:ρ:# (13)
where; A, surface area, ρ, density of air, #, air velocity. The value of heat absorbed by the internal
evaporator from the drying air was estimated using Eq. 14.
_
Qev ¼_
mahev;in hev;out
  (14)
where; hev;in, significant enthalpy of dry air at internal evaporator input, hev;out, specific enthalpy of dry
air at internal evaporator outlet. SMER was explained as the total amount of energy consumed per
moisture removed from the sample and was measured utilizing Eq. 15 (Jia, Jolly, and Clements 1990).
SMER ¼_
mw
_
Wcomp þ_
Wfan;iþ_
Wfan;e
(15)
where; _
mw, mass flow rate of water extracted from product.
Determination of specic energy consumption
The specific energy consumption was evaluated by the total energy consumption supplied to the
eggplant slices. SEC of eggplant samples during drying is stated as MJ/kg of evaporated water as shown
in Eq. (16) (Ye et al. 2021):
SEC ¼_
Wcomp þ_
Wfan;iþ_
Wfan;e
Weight loss kgð Þ (16)
Color measurements
Color values were measured with a Minolta brand CR400 colorimeter and these values (L, a and b)
were obtained using the Hunter Lab color scale (McGuire 1992). Here, “L” value specify the brightness
value, “a” value redness-greenness, and “b” value yellowness-blueness. Chroma value refers to the
color tone of the eggplant slices. According to a study, for obtaining chroma values of products were
used Eq. 16 (Lopez et al. 2013).
C¼a2þb2
 1=2(17)
ENERGY SOURCES, PART A: RECOVERY, UTILIZATION, AND ENVIRONMENTAL EFFECTS 697

The equation was utilized to measure the hue color angle value (Agudo et al. 2014; Vega-Gálvez et al.
2012).
ho¼tan1b
a
  (18)
Total discoloration (ΔE) of dried eggplant slices samples was calculated using the Eq. 18.
ΔE¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
L0Lð Þ2þa0að Þ2þb0bð Þ2
q(19)
Here, L
0
, a
0
and b
0
indicated the color values of fresh samples before drying. According to equations 19
and 20, the color parameters were used to measure the Browning Index (BI).
BI ¼100:x0:31ð Þ
0:17 (20)
x¼aþ1:75L
5:65Lþa03:012b(21)
where a
0
, is the beginning color result of raw eggplant slices and L, a and b are the color results at dried
samples.
Results and discussion
Drying time
The dry matter content of the eggplants used in the experiments was found to be a mean of 7.6% (on
wet basis). The air velocity had a crucial effect on the moisture content of the eggplant samples.
Outcomes of this research indicated that the air velocity and the drying time had negative correlation.
For the samples dried using by SCDS, the drying times required to arrive at the last moisture value
were found to be 308, 278, and 248 min at 0.5, 0.75, and 1.0 m/s, respectively. On the other hand, for
those dried in SCPEDS, the drying times at the same air velocities were found to be 282, 250 and 236
min., respectively. The total drying time of slices dried in SCPEDS was between 5.08% and 11.2%
shorter than slices dried in SCDS at the same drying temperature. As can be given in Table 2, the time
to get a cabinet drying temperature of 40°C in SCPEDS was occurred 44%, 54% and 61% in less time at
0.5, 0.75 and 1.0 m/s air velocities, respectively, compared with SCDS. In the studies carried out by.
Ürün, Yaman, and Köse (2015) and Taşova (2018), the drying continued for 600 and 1085 min,
respectively, at 40°C. When compared with the studies of Ürün, Yaman, and Köse (2015) and Taşova
(2018), it was seen that the drying time determined in this study was 60% and 78% shorter,
respectively.
Assessment of the models
The moisture value data acquired in the 6 mm thick eggplant slices were converted into the moisture
ratio (MR) and appointed to the ten thin-layer drying models saved in Table 3. The model including
the highest R
2
and the lowest χ
2
and RMSE results was determined as the best. The conclusions of the
statistical computing were given in the Tables 4 and 5. The R
2
value for all models was above 0.9465.
According to the values in Tables 4 and 5, the Midilli & Kucuk model can be accepted as the best
drying model with the highest R
2
and minimum χ
2
and RMSE results for all drying models. The R
2
, χ
2
,
and RMSE results calculated of this model were ranged between 0.9994–0.9997, 0.000021–0.000055,
and 0.038868–0.067802. Similar to these outcomes, Tunckal and Doymaz (2020) for dried banana
samples and Coşkun et al. (2017) reported for the tomato samples dried by using HPD.
698 İ. DOYMAZ ET AL.

Eective moisture diusivity
The effective moisture diffusivity values at the air velocities of 0.5, 0.75, and 1.0 m/s were calculated
using Eq. (8). For the eggplant slices dried in SCDS, the D
eff
values at 0.5, 0.75, and 1.0 m/s were 5.47 ×
10
−10
m
2
/s, 6.08 × 10
−10
m
2
/s, and 6.69 × 10
−10
m
2
/s, respectively. On the other hand, for those dried in
SCPEDS, the D
eff
values under the same conditions were 6.08 × 10
−10
m
2
/s, 6.69 × 10
−10
m
2
/s, and
7.30 × 10
−10
m
2
/s, respectively. It can be said that the D
eff
results with the air velocity have a positive
correlation. In addition to, the D
eff
results of the eggplant slices dried using by SCPEDS were higher
than those dried in the other system. The D
eff
values of the eggplant slices ranged from 10
−12
m
2
/s to
10
−8
m
2
/s, the widely accepted range for the biological materials (Zogzas, Maroulis, and Marinos-
Kouris 1996). The results of D
eff
are comparable with the determined values of 0.13–5.65 × 10
−10
m
2
/s
mentioned for eggplant drying in the temperature between 70°C and 90°C (Aversa et al. 2011), 0.93 to
8.84 × 10
−10
m
2
/s for eggplant slices at 50–80°C (Doymaz and Göl 2011), and 1.65 to 3.41 × 10
−9
m
2
/s
eggplant at 30–50°C (Wu et al. 2007). These results are coherent with the present calculated D
eff
results
for eggplant slices.
Table 4. Statistical analysis of various models used for SCDS drying of eggplant slices.
Model v (m/s) R
2
χ
2
RMSE
Lewis 0.5 0.9465 0.047540 0.755969
0.75 0.9534 0.004140 0.690728
1.0 0.9602 0.034603 0.575815
Henderson & Pabis 0.5 0.9689 0.002779 0.558704
0.75 0.9739 0.002332 0.502866
1.0 0.9758 0.002051 0.433066
Logarithmic 0.5 0.9979 0.000189 0.137408
0.75 0.9974 0.000232 0.149030
1.0 0.9993 0.000056 0.060973
Page 0.5 0.9973 0.002382 0.164802
0.75 0.9983 0.000146 0.129100
1.0 0.9960 0.000331 0.173143
Midilli & Kucuk 0.5 0.9997 0.000026 0.050332
0.75 0.9997 0.000026 0.049788
1.0 0.9997 0.000021 0.038868
Mod. Midilli 0.5 0.9953 0.000417 0.210903
0.75 0.9942 0.000514 0.231665
1.0 0.9838 0.001368 0.351494
Jena & Das 0.5 0.9901 0.000889 0.299450
0.75 0.9922 0.000701 0.263321
1.0 0.9903 0.000824 0.260823
Vega-Galvez et al.- I 0.5 0.9689 0.002779 0.558706
1.0 0.9758 0.002051 0.433066
1.0 0.9758 0.002051 0.433066
Vega & Lemus 0.5 0.9966 0.000304 0.168635
0.75 0.9981 0.000169 0.121322
1.0 0.9987 0.000107 0.092632
Aghbashlo et al. 0.5 0.9988 0.000533 0.211557
0.75 0.9987 0.000114 0.104638
1.0 0.9995 0.000034 0.050881
ENERGY SOURCES, PART A: RECOVERY, UTILIZATION, AND ENVIRONMENTAL EFFECTS 699

System performance
The average ambient temperature was 21.5°C and the average humidity was 36% during the experi-
ments. Total amount of energy consumed during the drying of eggplants and SMER values are given in
Table 6.
According to Table 6, total energy consumption and the drying air velocity had a negative
correlation. Less energy was consumed in total, although there was an external evaporator fan
which was activated only in the SCPEDS applications in certain periods. Shengchun et al. (2018), 3
mm slice thickness, 30°C, 35°C and 40°C drying temperature and 1.0 m/s air velocity 500 g of carrot
slices were dried in the HPD system. Compressor energy consumption during drying was measured
between 3.63 and 4.27 kWh. The only reason for the SMER values being low were that the amount of
product put into the drying system was very low (200 g). In all experiments, SMER values were
calculated as the highest in the first 2 hours and then decreased. One thousand-gram ginger fruit sliced
3 mm dried by using HPD system at a drying temperature of 50°C and an air velocity of 0.4 m/s. They
determined the SMER value as 0.06 kg-water/MJ. So that, evaluate the performance of the system
separately in two different applications, the temperature and relative humidity values evaluated
simultaneously from five points on the drying system were recorded and placed in Eq. 12 and 14.
The _
Qcd and _
Qev capacities were calculated at various air velocities (0.5 m/s, 0.75 m/s and 1.0 m/s) and
graphically given in Figure 4.
As can be understood from the graphs in Figure 4, it is seen that the change in air velocity directly
affects the _
Qev and _
Qcd capacities. The highest average was obtained at _
Qev and _
Qcd at 1.0 m/s air
velocity. In SCPEDS, the average _
Qev and _
Qcd values were lower at all air velocities compared to SCDS.
Table 5. Statistical analysis of various models used for SCPEDS drying of eggplant slices.
Model v (m/s) R
2
χ
2
RMSE
Lewis 0.5
0.75
1.0
0.9532
0.9592
0.9638
0.004175
0.003051
0.003033
0.699931
0.594019
0.577560
Henderson & Pabis 0.5
0.75
1.0
0.9727
0.9758
0.9766
0.002436
0.002089
0.001975
0.522476
0.436264
0.431753
Logarithmic 0.5
0.75
1.0
0.9972
0.9988
0.9982
0.000245
0.000102
0.000146
0.148709
0.085753
0.107755
Page 0.5
0.75
1.0
0.9975
0.9968
0.9964
0.000222
0.000271
0.000303
0.161984
0.156192
0.166501
Midilli & Kucuk 0.5
0.75
1.0
0.9994
0.9996
0.9996
0.000055
0.000030
0.000031
0.067802
0.045256
0.048622
Mod. Midilli 0.5
0.75
1.0
0.9945
0.9912
0.9972
0.000488
0.000250
0.000235
0.219973
0.138243
0.137952
Jena & Das 0.5
0.75
1.0
0.9910
0.9971
0.9910
0.000815
0.000761
0.000763
0.283885
0.246292
0.250019
Vega-Galvez et al.- I 0.5
0.75
1.0
0.9727
0.9758
0.9766
0.002456
0.002089
0.001975
0.522476
0.436264
0.431753
Vega & Lemus 0.5
0.75
1.0
0.9978
0.9987
0.9987
0.000192
0.000110
0.000103
0.136300
0.085145
0.094960
Aghbashlo et al. 0.5
0.75
1.0
0.9990
0.9993
0.9993
0.000089
0.000054
0.000056
0.087560
0.062434
0.068340
700 İ. DOYMAZ ET AL.

The COP
hp
and COP
sys
values calculated according to the drying air velocity in Eq. 10 and 11 are
shown in Figure 5.
The COP
hp
and COP
sys
values changed in directly proportional to the change in the drying air
velocity. Likewise in the capacity values, the COP
hp
and COP
sys
values in SCPEDS were lower than
those in SCDS. Aktaş et al. (2017) dried grated carrots at an air velocity of 0.5 m/s at 45 and 50°C and
they obtained the lowest and highest COP
sys
values as 2.11 and 2.96, respectively. As seen in Figure 5,
COP
sys
values were found to be lower at 0.5 m/s air velocity.
Specic energy consumption
It was observed that the SEC values calculated as a result of drying the eggplant slices decreased with
increasing drying air speed. In the SCDS study, the SEC value was calculated at an air velocity of 0.5 m/
s with the lowest value of 18.81 MJ/kg and 1.0 m/s, and the highest value of 22.83 MJ/kg. In the
SCPEDS system study, the SEC value was found with the lowest value of 18.34 MJ/kg at 1.0 m/s air
velocity. It was observed that the SEC values calculated in both system studies did not differ much.
Although the drying operation is completed in a shorter time with the SCPEDS system operation, the
total energy consumption has increased due to the activation of the external evaporator fan. Chayjan
and Kaveh (2016) found the SEC value between 86.47 and 194.37 MJ/kg in their research. Kaveh et al.
(2018) calculated the lowest SEC value of 130.62 MJ/kg.
Color parameters
The aim of this study is to make the product resistant to microbiological, chemical and enzymatic
spoilage by reducing the moisture value below a certain value as a result of drying process of eggplant
slices containing high water content. Many physical, chemical, mechanical, biochemical and microbial
events occur simultaneously during drying. These changes affect the quality characteristics of the final
product. The color measurement results obtained from fresh and dried eggplant samples are given in
Table 7.
The color results of the dried eggplant samples showed more variation than those of the fresh ones.
The brightness increased with the drying. The maximum increase in the fresh eggplants was observed
in SCDS (0.5 m/s) where the L value increased from 81.75 to 87.94 after the drying. The closest result
was found to be 84.70 through the SCPEDS (0.5 m/s) drying. Although there were decreases in the
value of the color values after the drying compared to the fresh, it was observed that there was not
much difference (Adiletta et al. 2014; Taşova 2018). As for the b color value, it decreased compared to
the fresh, SCDS (0.75 m/s) and SCPEDS (0.75 m/s) were the closest to the fresh. The maximum
decrease in the b color value was found to be 14.64 in the SCPEDS (1.0 m/s) drying. Martins, Jongen,
and Van Boekel (2000) stated in their study that the Maillard reactions in the product increased with
the effect of drying, and thus it significantly affected the color values. The chroma values are calculated
using the measured color values and provide important information about the quality of the dried
samples. It was found that while the C value of fresh eggplant slices was 20.44, it decreased with the
drying. The highest decrease was found to be 14.86 in the SCPEDS (1.0 m/s) drying and the least
change was found to be 18.81 in the SCPEDS (0.75 m/s) drying. For this reason, SCDS (0.5 m/s) and
Table 6. Total energy consumption and average SMER values according to system air velocity.
Total energy consumption (kWh)
Average SMER
(kg/kWh)
Drying air velocity (m/s) 0.5 0.75 1.0 0.5 0.75 1.0
SCDS 3.99 3.51 3.29 0.051 0.057 0.062
SCPEDS 3.84 3.41 3.21 0.057 0.060 0.068
ENERGY SOURCES, PART A: RECOVERY, UTILIZATION, AND ENVIRONMENTAL EFFECTS 701

SCPEDS (0.5 m/s) were identified as the drying methods that changed the color the least, that is, as the
methods yielding the closest chroma values to the fresh. Chroma and browning index values calculated
by using color values that give important information about the quality of dried products are an
important color criterion for dried eggplant slices. In SCPEDS, the highest overall color change (ΔE)
(a)
(b)
(c)
0.4
0.6
0.8
1.0
1.2
1.4
0 3600 7200 10800 14400 18000
Capacities of Qev and Qcd (kW)
Time (s)
Qcd - s Qev -s Qcd-s+p Qev-s+p
Log. (Qcd - s) Log. (Qev -s ) Log. (Qcd-s+p ) Log. (Qev-s+p )
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
0 3600 7200 10800 14400
Capacities of Qev and Qcd (kW)
Time (s)
Qcd - s Qev -s Qcd-s+p Qev-s+p
Log. (Qcd - s) Log. (Qev -s ) Log. (Qcd-s+p ) Log. (Qev-s+p )
1.0
1.2
1.4
1.6
1.8
2.0
2.2
2.4
2.6
0 3600 7200 10800 14400
Capacities of Qev and Qcd (kW)
Time (s)
Qcd - s Qev -s Qcd-s+p Qev-s+p
Log. (Qcd - s) Log. (Qev -s ) Log. (Qcd-s+p ) Log. (Qev-s+p )
Figure 4. _
Qevand _
Qcd capacities at different air velocities (a) 0.5, b) 0.75 and c) 1.0 m/s).
702 İ. DOYMAZ ET AL.

was found 8.05 at 1.0 m/s. In SCDS, the highest browning index (BI) was found as 63.48 at 0.75 m/s. As
for the hue color value, which was 100.01 in fresh eggplants; the highest change was found to occur in
the SCDS (0.75 m/s) drying whereas the least change occurred in the SCPEDS (1.0 m/s) in the dried
products.
All of the color values calculated according to Table 7 for dried products differ according to the
condition before drying. Color difference and browning index values calculated using the measured
color values and providing important information about the quality of dried products are an
important color criterion for dried eggplant slices. When the calculated values were examined, it
was seen that the most color difference compared to fresh eggplant was detected in SCPEDS (1.0 m/s).
The browning index was found to be very close to each other and the lowest value was determined in
the SCPEDS (1.0 m/s). In addition, since the color darkening is an undesirable effect in drying the
eggplant, the application in which the brightness is increased and the darkening is determined the least
is considered as the most ideal application in terms of color. Therefore, it has been observed that SCD
SCDS
)b)a
SCPEDS
)d)c
Figure 5. Cop
hp
and COP
sys
values based on hourly drying times three-dimensional mesh graphics. a) SCDS-COP
hp
, b) SCDS- COP
sys,
c)
SCPEDS-COP
hp
, d) SCPEDS-COP
sys
.
ENERGY SOURCES, PART A: RECOVERY, UTILIZATION, AND ENVIRONMENTAL EFFECTS 703

Table 7. Statistical analysis of average color measurement values of fresh and dried eggplant samples.
Drying air velocity (m/s)
Manner of application
Color values
L a b C h
0
BI ΔE
Fresh 81.75 −2.85 19.78 20.44 100.01 - -
0.5 SCDS 87.88
a±0.27
−4.05
e±0.07
17.17
d±0.11
17.73
b±0.08
98.76
c±0.03
62.05
e±0.01
6.78
b±0.02
SCPEDS 84.70
d±0.2
−2.69
d±0.04
17.48
c±0.05
17.80
b±0.14
95.66
e±0.04
62.78
c±0.00
3.74
e±0.00
0.75 SCDS 85.51
b±0.02
−1.84
a±0.02
18.68
a±0.03
18.77
a±0.02
98.70
c±0.02
63.48
a±0.00
4.05
d±0.01
SCPEDS 85.15
c±0.02
−2.76
d±0.03
18.59
b±0.04
18.81
a±0.02
96.45
d±0.07
63.19
b±0.00
3.60
f±0.01
1.0 SCDS 87.78
a±0.13
−2.27
b±0.27
17.40
c±0.01
14.55
d±0.06
99.86
b±0.02
62.60
d±0.00
6.52
c±0.00
SCPEDS 87.93
a±0.04
−2.53
c±0.04
14.64
e±0.03
14.86
c±0.03
103.36
a±0.18
61.37
f±0.01
8.05
a±0.00
Note: *a, b, c, d and e represent interaction of the factors (p < .05).
704 İ. DOYMAZ ET AL.

(0.5 m/s) with the best L* luminance color value and SCPEDS (1.0 m/s) with the least color change
have a positive effect.
In studies conducted on dried fruits and vegetables, it was stated that Maillard reactions in the
product accelerated with the effect of temperature and thus, color values were significantly affected.
Aydogdu, Sumnu, and Sahin (2015), in their study on eggplant drying with convective and microwave
assisted infrared dryers, stated that the change in brightness and redness values increased with the
increase in drying temperature and microwave power values.
Conclusions
In this study, the performances of the systems were compared as a result of drying the eggplant slices at
different drying air velocities in two different drying systems developed on the laboratory test system.
The change in air velocity directly affected the drying time in both systems. The COP
hp
and COP
sys
values are higher in all experiments in SCDS application. Midilli & Kucuk model was obtained to be
the ideal model to explain the experimental data of eggplant samples drying. The results of effective
moisture diffusivity ranged between 6.08 × 10
−10
and 7.30 × 10
−10
m
2
/s. The longest drying time was
found to be 308 min in the SCDS application at an air velocity of 0.5 m/s. The SEC result was
calculated between 18.81 and 22.83 MJ/kg in the SCDS study, and between 18.34 and 21.9 MJ/kg in
the SCPEDS study. With the SCPEDS design in L color, the closest value to fresh was obtained as 84.70
at 0.5 m/s. It was determined that the color values of b, C and h° decreased compared to the fresh
samples. The highest total color change value in SCPEDS (for 1.0 m/s): 8.05, and the highest browning
index in SCDS (for 0.75 m/s): 63.48. In future studies, the comparison of the performance parameters
to be obtained from two different combinations created by connecting the external condenser to the
system in parallel and in series can be examined. In addition, by adding a parallel evaporator system to
these two different systems, their efficiency can be investigated by working in different combinations.
However, it is planned to add solar energy support to the system in future studies.
Disclosure statement
No potential conflict of interest was reported by the author(s).
ORCID
İbrahim Doymaz http://orcid.org/0000-0002-4429-6443
Cüneyt Tunçkal http://orcid.org/0000-0002-9395-3534
Zekiye Göksel http://orcid.org/0000-0002-2903-6459
Authors contribution
I. Doymaz; moisture effect diffusion and kinetic modeling analysis, creation of tables. Manuscript writing and inter-
pretation. C. Tunçkal; preparing system diagrams, conducting experiments, analyzing energy performance, drawing
graphs and figures, writing and interpreting the article. Z. Goksel; Color and statistical analysis of fresh and dried
products, creation of tables, contribution to writing.
References
Adiletta, G., G. Iannone, P. Russo, G. Patimo, S. De Pasquale, and M. Di Matteo. 2014. Moisture migration by magnetic
resonance imaging during eggplant drying: A preliminary study. International Journal of Food Science & Technology
49 (12):2602–09. doi:10.1111/ijfs.12591.
Aghbashlo, M., M. H. Kianmehr, S. Khani, and M. Ghasemi. 2009. Mathematical modelling of thin-layer drying of
carrot. International Agrophysics 23 (4):313–17.
ENERGY SOURCES, PART A: RECOVERY, UTILIZATION, AND ENVIRONMENTAL EFFECTS 705

Agudo, J., P. Pardo, H. Sánchez, Á. Pérez, and M. Suero. 2014. A low-cost real color picker based on arduino. Sensors 14
(7):11943–56. doi:10.3390/s140711943.
Aktaş, M., A. Khanlari, A. Amini, and S. Şevik. 2017. Performance analysis of heat pump and infrared–heat pump drying
of grated carrot using energy-exergy methodology. Energy Conversion and Management 132:327–38. doi:10.1016/j.
enconman.2016.11.027.
Aversa, M., S. Curcio, V. Calabrò, and G. Iorio. 2011. Measurement of the water-diffusion coefficient, apparent density
changes and shrinkage during the drying of eggplant (Solanum melongena). International Journal of Food Properties
14 (3):523–37. Taylor & Francis. doi:10.1080/10942910903261410.
Aydogdu, A., G. Sumnu, and S. Sahin. 2015. Effects of microwave-infrared combination drying on quality of eggplants.
Food and Bioprocess Technology 8 (6):1198–210. doi:10.1007/s11947-015-1484-1.
Ceylan, İ., M. Aktaş, and H. Doğan. 2007. Mathematical modeling of drying characteristics of tropical fruits. Applied
Thermal Engineering 27 (11–12):1931–36. doi:10.1016/j.applthermaleng.2006.12.020.
Chapchaimoh, K., N. Poomsa-Ad, L. Wiset, and J. Morris. 2016. Thermal characteristics of heat pump dryer for ginger
drying. Applied Thermal Engineering 95:491–98. doi:10.1016/j.applthermaleng.2015.09.025.
Chayjan, R. A., and M. Kaveh. 2016. Drying characteristics of eggplant (Solanum Melongena L.) slices under microwave-
convective drying. Research in Agricultural Engineering 62 (No. 4):170–78. doi:10.17221/13/2015-RAE.
Coşkun, S., İ. Doymaz, C. Tunçkal, and S. Erdoğan. 2017. Investigation of drying kinetics of tomato slices dried by using
a closed loop heat pump dryer. Heat and Mass Transfer 53 (6):1863–71. doi:10.1007/s00231-016-1946-7.
Crank, J. 1975. The mathematics of diffusion. 2d ed. Oxford, [Eng]: Clarendon Press.
Doymaz, İ., and E. Göl. 2011. Convective drying characteristics of eggplant slices. Journal of Food Process Engineering 34
(4):1234–52. doi:10.1111/j.1745-4530.2009.00426.x.
Erbay, Z., and F. Icier. 2010. A review of thin layer drying of foods: theory, modeling, and experimental results. Critical
Reviews in Food Science and Nutrition 50 (5):441–64. doi:10.1080/10408390802437063.
Ertekin, C., and O. Yaldiz. 2004. Drying of eggplant and selection of a suitable thin layer drying model. Journal of Food
Engineering 63 (3):349–59. doi:10.1016/j.jfoodeng.2003.08.007.
FAO, 2022. FAO Statistical Yearbook, Available On. http://faostat3.fao.org/browse/Q/QC/E .
Ferrão, A. C., R. P. F. Guiné, T. Correia, and R. Rodrigues. 2019. Analysis of drying kinetics of eggplant through thin
layer models and evaluation of texture and colour properties. Chemistry Research Journal 4 (1):24–32.
Hawlader, M. N. A., S. M. A. Rahman, and K. A. Jahangeer. 2008. Performance of evaporator-collector and air collector
in solar assisted heat pump dryer. Energy Conversion and Management 49 (6):1612–19. doi:10.1016/j.enconman.2007.
12.001.
Icier, F., N. Colak, Z. Erbay, E. H. Kuzgunkaya, and A. Hepbasli. 2010. A comparative study on exergetic performance
assessment for drying of broccoli florets in three diff. Drying Technology 28 (2):193–204. doi:10.1080/
07373930903524017.
Jena, S., and H. Das. 2007. Modelling for vacuum drying characteristics of coconut presscake. Journal of Food
Engineering 79 (1):92–99. doi:10.1016/j.jfoodeng.2006.01.032.
Jia, X., P. Jolly, and S. Clements. 1990. Heat pump assisted continuous drying part 2: Simulation results. International
Journal of Energy Research 14 (7):771–82. doi:10.1002/er.4440140708.
Kamer, M. S., H. E. Şahin, K. Sönmez, M. İmal, and A. Kaya. 2016. Kabak ve Patlıcan Dilimlerinin Kuruma Davranışının
Deneysel İncelenmesi. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi 19 (2):1. doi:10.17780/
ksujes.12286.
Karacabey, E., T. Aktaş, L. Taşeri, and G. U. Uysal Seçkin. 2020. Sultani Çekirdeksiz Üzüm Çeşidinde Farklı Kurutma
Yöntemlerinin Kurutma Kinetiği, Enerji Tüketimi ve Ürün Kalitesi Açısından İncelenmesi. Tekirdağ Ziraat Fakültesi
Dergisi 17 (January):53–65. doi:10.33462/jotaf.578962.
Kaveh, M., R. A. Chayjan, Y. Abbaspour-Gilandeh, and T. M. Gundoshmian. 2018. Prediction of effective moisture
diffusity and specific energy consumption of eggplant in continuous band drying using some novel methods. Ferdowsi
University Of Mashhad. https://ifstrj.um.ac.ir/article_36880_fea8ef1660c6f9d72afdf9c43d1c1e1f.pdf .
Kaveh, M., R. A. Chayjan, and A. M. Nikbakht. 2017. Mass transfer characteristics of eggplant slices during length of
continuous band dryer. Heat and Mass Transfer 53 (6):2045–59. doi:10.1007/s00231-016-1961-8.
Lee, J. H., and H. J. Kim. 2009. Vacuum drying kinetics of asian white radish (Raphanus Sativus L.) slices. LWT - Food
Science and Technology 42 (1):180–86. doi:10.1016/j.lwt.2008.05.017.
Lemus-Mondaca, R., N. Betoret, A. Vega-Galvéz, and E. Lara-Aravena. 2009. Dehydration characteristics of papaya
(carica pubenscens): Determination of equilibrium moisture content and diffusion coefficient. Journal of Food Process
Engineering 32 (5):645–63. doi:10.1111/j.1745-4530.2007.00236.x.
Lili, Z., W. Xiangyou, Y. Long, and Z. Haipeng. 2012. Drying characteristics and color changes of infrared drying
eggplant. Transactions of the Chinese Society of Agricultural Engineering 28 (2):291–96. doi:10.3969/j.issn.1002-6819.
2012.z2.051.
Liu, H., K. Yousaf, K. Chen, R. Fan, J. Liu, and S. Soomro. 2018. Design and thermal analysis of an air source heat pump
dryer for food drying. Sustainability 10 (9):3216. doi:10.3390/su10093216.
706 İ. DOYMAZ ET AL.

Lopez, J., A. Vega-Gálvez, M. J. Torres, R. Lemus-Mondaca, I. Quispe-Fuentes, and K. Di Scala. 2013. Effect of
dehydration temperature on physico-chemical properties and antioxidant capacity of goldenberry (Physalis
Peruviana L.). Chilean Journal of Agricultural Research 73 (3):293–300. doi:10.4067/S0718-58392013000300013.
Martins, S. I. F. S., W. M. F. Jongen, and M. A. J. S. Van Boekel. 2000. A review of maillard reaction in food and
implications to kinetic modelling. Trends in Food Science & Technology 11 (9):364–73. doi:10.1016/S0924-2244(01)
00022-X.
McGuire, R. G. 1992. Reporting of objective color measurements. HortScience 27 (12):1254–55. doi:10.21273/HORTSCI.
27.12.1254.
Midilli, A., H. Kucuk, and Z. Yapar. 2002. A new model for single-layer drying. Drying Technology 20 (7):1503–13.
doi:10.1081/DRT-120005864.
Panchariya, P. C., D. Popovic, and A. L. Sharma. 2002. Thin-Layer modelling of black tea drying process. Journal of Food
Engineering 52 (4):349–57. doi:10.1016/S0260-8774(01)00126-1.
Roberts, J. S., D. R. Kidd, and O. Padilla-Zakour. 2008. Drying kinetics of grape seeds. Journal of Food Engineering 89
(4):460–65. doi:10.1016/j.jfoodeng.2008.05.030.
Shengchun, L., L. Xueqiang, S. Mengjie, L. Hailong, and S. Zhili. 2018. Experimental investigation on drying perfor-
mance of an existed enclosed fixed frequency air source heat pump drying system. Applied Thermal Engineering 130
(February):735–44. doi:10.1016/j.applthermaleng.2017.11.068.
Simal, S., A. Femenia, M. C. Garau, and C. Rosselló. 2005. Use of exponential, page’s and diffusional models to simulate
the drying kinetics of kiwi fruit. Journal of Food Engineering 66 (3):323–28. doi:10.1016/j.jfoodeng.2004.03.025.
Taşeri, L., M. Aktaş, S. Şevik, M. Gülcü, G. U. Seçkin, and B. Aktekeli. 2018. Determination of drying kinetics and quality
parameters of grape pomace dried with a heat pump dryer. Food Chemistry 260 (September):152–59. doi:10.1016/j.
foodchem.2018.03.122.
Taşova, M. 2018. Düşük ve Yüksek Sıcaklıklarda Kurutulan Patlıcan (Solanum melongena L.) Dilimlerinin Kurutma
Kinetiği ve Renk Değerleri Açısından En Uygun Kurutma Sıcaklığının Belirlenmesi. Kafkas Üniversitesi Fen Bilimleri
Enstitüsü Dergisi 11 (2):84–93.
Tunçkal, C. 2020. Investigation of performance and drying kinetics of the closed, partially open, and open heat pump
drying systems. Journal of Food Process Engineering 43 (12):e13566. doi:10.1111/jfpe.13566.
Tunçkal, C., S. Coşkun, and S. Erdoğan. 2016. Kapalı Döngü Bir Isı Pompalı Kurutma Sisteminin Performans Analizi. Isı
Bilimi ve Tekniği Dergisi 36 (1):161–72.
Tunckal, C., and İ. Doymaz. 2020. Performance analysis and mathematical modelling of banana slices in a heat pump
drying system. Renewable Energy 150 (May):918–23. doi:10.1016/j.renene.2020.01.040.
Tunçkal, C., A. Yüksel, and S. Coşkun. 2022. Exergy analysis of banana drying process via a closed-loop air source heat
pump system. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects 44 (3):6777–92. Taylor &
Francis. doi:10.1080/15567036.2022.2101716.
Ürün, G. B., Ü. R. Yaman, and E. Köse. 2015. Determination of drying characteristics and quality properties of eggplant
in different drying conditions. Italian Journal of Food Safety 27 (4):459–67. doi:10.14674/1120-1770/ijfs.v378.
Vega-Gálvez, A., K. Ah-Hen, M. Chacana, J. Vergara, J. Martínez-Monzó, P. García-Segovia, R. Lemus-Mondaca, and K.
Di Scala. 2012. Effect of temperature and air velocity on drying kinetics, antioxidant capacity, total phenolic content,
colour, texture and microstructure of apple (var. granny smith) slices. Food Chemistry 132 (1):51–59. doi:10.1016/j.
foodchem.2011.10.029.
Vega-Gálvez, A., A. Andrés, E. Gonzalez, E. Notte-Cuello, M. Chacana, and R. Lemus-Mondaca. 2009. Mathematical
modelling on the drying process of yellow squat lobster (cervimunida jhoni) fishery waste for animal feed. Animal
Feed Science and Technology 151 (3–4):268–79. doi:10.1016/j.anifeedsci.2009.01.003.
Wu, L., T. Orikasa, Y. Ogawa, and A. Tagawa. 2007. Vacuum drying characteristics of eggplants. Journal of Food
Engineering 83 (3):422–29. doi:10.1016/j.jfoodeng.2007.03.030.
Yang, Z., Z. Zhu, and F. Zhao. 2016. Simultaneous control of drying temperature and superheat for a closed-loop heat
pump dryer. Applied Thermal Engineering 93:571–79. doi:10.1016/j.applthermaleng.2015.09.117.
Ye, L., H. S. EL-Mesery, M. M. Ashfaq, Y. Shi, H. Zicheng, and W. G. Alshaer. 2021. Analysis of energy and specific
energy requirements in various drying process of mint leaves. Case Studies in Thermal Engineering 26
(August):101113. doi:10.1016/j.csite.2021.101113.
Zogzas, N. P., Z. B. Maroulis, and D. Marinos-Kouris. 1996. Moisture diffusivity data compilation in foodstuffs. Drying
Technology 14 (10):2225–53. doi:10.1080/07373939608917205.
ENERGY SOURCES, PART A: RECOVERY, UTILIZATION, AND ENVIRONMENTAL EFFECTS 707