7th International Science, Social Sciences, Engineering and Energy Conference
19-21 November, 2015, Wangchan Riverview Hotel, Phitsanulok, Thailand
I-SEEC 2015
http//iseec2015.psru.ac.th
Greenhouse solar dryer for drying bael fruits (Aegle
Marmelos) in Thailand
Jagrapan Piwsaoada,e1, Chayapat Phusampaoa,e2
a
Program Physics, Department of Science, Faculty of Science, Loei Rajabhat University, Loei, Thailand, 42000
e1
Jagrapan25@gmail.com , e2joitoji@gmail.com
Abstract
This paper presents experimental performance and artificial neural network modeling of a greenhouse solar dryer for
drying bael fruits. The dryer consists of a polycarbonate sheets on a metal sheet floor. The dryer is 1.5 m in width, 1.0
m in length and 2.0 m in height. Two 15-W DC fans powered by 50-W PV module were used to ventilate the dryer.
To investigate its performance, the dryer was used to dry ten batches of bael fruits. For each batch, 5.0 kilograms of
bael fruits were dried in the dryer. Results obtained from the experiments showed that drying temperatures varied
from 32๐C to 50๐C. In addition, the drying time for drying bael fruits was 3 days, compared to 5 days required for
natural sun drying. Bael fruits dried in the dryer were completely protected from rain and high quality bael fruits were
obtained. The estimated payback period of the greenhouse solar dryer is about 1 year. A multilayer neural network
model was developed to predict the performance of this dryer. The predictive power of the model was found to be
high after it was adequately trained.
Keyword: solar drying, ANN modeling, bael fruits, greenhouse solar dryer
1. Introduction
Bael fruit tree (Aegle marmelos) occurs in dry forests on hills and plains of northern, central, eastern
and southern Thailand. Bael fruit (Ma Toom) is Thai herbal fruit growing well in tropical area. Normally,
Thai people slice the fruit into rounded pieces and dried well under sunlight. Then, use dried bael fruits
for making herbal drink by boiling in hot water and adding sugar to make it sweet and finally straining
with clean cloth sheet and keep in the fridge, can drink both hot or cool. Bael biochemical compounds of
bael leaves, fruits and seeds have been used in several diseases like diabetes, cardiovascular and antiinflammatory. The bael leaf contain seven monotorpene hydrocarbons (90.7%), three oxygenated
monoterpenes (2.9%), four sesquiterpene hydrocarbons (3.1%) and one phenolic compound (0.2%).
Limonene (82.4%) was the main constituent of bael [1]. Situated in the tropical area, Thailand receives
abundant solar radiation [2-3]. Consequently, the use of solar dryers for bael fruits drying is reasonable.
Although several types of solar dryers have been developed in the last 45 years [4-11] but, they could not
2
meet the high demand of bael fruits drying. As a result our research group has developed a solar dryer to
dry agricultural products. It was successfully used for drying fruits and vegetable. However, it has not
been tested to dry bael fruits. Therefore, the objectives of this research were to investigate the
performance of the dryer for drying bael fruits and to develop an artificial neural network (ANN) model to
predict the performance of this dryer.
Nomenclature
a*
colour green to red (decimal)
b*
colour blue to yellow (decimal)
Cannual
annual cost of the system (USD)
Cl
labor cost for the construction (USD)
Clabour,oplabour cost for operating the dryer (USD)
Cm
material cost of the dryer (USD)
Cmaint,i
maintenance cost (USD)
Cop
operating cost (USD)
Cop,i
operating cost at the year i (USD)
CT
total capital cost for the greenhouse solar dryer (USD)
C*
indicating colour saturation (decimal)
h
Hue angle (decimal)
iin
the interest rate (decimal)
if
inflation rate in percent (decimal)
L*
lightness colour (decimal)
Mdry
the dried product obtained from this dryer per year (kg)
Mdry
annual production of dry product (kg)
Mf
the amount of fresh product per year (kg)
Pd
the price of the dry product (USD/kg)
Pf
the price of the fresh product (USD/kg)
Z
dying cost (USD/kg)
3
2. Methodology
2.1. Experimental study
The greenhouse solar dryer was installed at 29 Moo 4, A. Arawan, Loei (17.48๐N, 101.72๐E), Thailand.
The dryer consists of a polycarbonate sheets on a metal sheet floor. The dryer has a width of 1.5 m, length
of 1.0 m and height of 2.0 m. Two DC fans operated by 50-W solar cell module were installed in the wall
opposite to the air inlet to ventilate the dryer. The pictorial view of the dryer is shown in Fig.1.
In this study, bael fruits were dried in the greenhouse solar dryer to investigate its potentials for drying
bael fruits. Ten experimental runs were conducted during the period of 25 June 2015-8 August 2015, and
5.0 kg of bael fruits were dried for each run. Solar radiation was measured by a pyranometer
(Kipp&Zonen model CMP 3, accuracy ±0.5%) placed on the roof of the dryer. Thermocouples (K type)
were used to measure air temperatures in the different positions of the dryer (accuracy ±2%). A hot wire
anemometer (Airflow, model TA5, accuracy ±2%) was used to monitor the air speed inside the dryer. The
relative humidity of ambient air and drying air was periodically measured by hygrometers (Moisture
content meter, model Extech SDL, accuracy±1%). The positions of all measurements are shown in Fig. 2.
Solar sell
Fan_air out let
Polycarbonate cover
rh_out let
Door
2m
rh_inside
Solar colector
T3
T1
M1
M2
T4
T2
M_ambient
0.5 m
1m
T_ambient
rh_ambient
Air_inlet
Figure 1. The pictorial view of a greenhouse solar
dryer
1.5 m
Figure 2. The structure of the greenhouse solar dryer and the
position of the thermocouples ( T), hygrometer
(
rh) and product samples (
M).
Solar radiation passing through the polycarbonate roof heats the air, the products inside the dryer, as
well as the metal sheet floor. Ambient air is drawn in through the air inlets at the bottom of the front side
of the dryer and is heated by the floor and products exposed to solar radiation. The heated air, while
passing through the products, absorbs moisture from the products. Direct exposure to solar radiation of
the products and the heated drying air enhance the drying rate of the products. Moist air is sucked from
the dryer by the DC-fan at the top of the rear side of the dryer.
4
The air speed at the inlet and outlet of the dryer was recorded during the drying experiments using the
hot wire anemometer. Bael fruits dried in each drying test were 5.0 kg. Bael fruits samples and the
pictorial view of bael fruits being dried in the dryer are shown in Fig. 3 and Fig. 4 respectively.
Figure 3. Bael fruits samples
Figure 4. The pictorial view of bael fruits being dried in the dryer
Bael fruits were place on tray inside the dryer. Each day, the experiment was conducted during 8:00
am-6:00 pm. The drying was continued on subsequent days until the desired moisture content was
reached. Product samples were placed at various positions in the dryer and were weighed periodically at
two-hour intervals using a digital balance (Kern, model 474-42, accuracy ± 0.1g). To compare the
performance of the dryer with that of natural sun drying, a control sample of bael fruits were placed near
the dryer and dried simultaneously under the same weather conditions. The moisture content during
drying was estimated from the weight of the product samples and the estimated dried solid mass of the
samples. At the end of the experimental drying, the exact dry solid mass of the product samples was
determined by the hot air oven method (103 ๐C for 24 hours, Memmert GMBH, model ULE500, accuracy
± 0.5%).
2.2. Colour measurement of dried bael fruits
The colour of dried bael fruits samples was measured by a chromometer (CR–400, Minolta Co., Ltd.,
Japan) in Commission Internationale d’Eclairage (CIE) chromaticity coordinates. L*, a* and b* represent
lightness (0 to100), green to red (−60 to +60) and from blue to yellow (−60 to +60) colours, respectively.
Out of five available colour systems, the L*a*b* [12] and L*C*h systems were selected because these are
the most-used systems for evaluation of the colour of dried food materials. The instrument was
standardized each time with a white ceramic plate. Three readings were taken at each place on the surface
of samples and then the mean values of L*, a* and b* were averaged. The different colour parameters
were calculated using the following equations [13]. Hue angle (h) indicating colour combination (i.e.
browning) is defined as:
1 *
*

(when a*  0)
 tan (b / a )
h
.
(1)
1 *
*
(when a*  0)

180  tan (b / a )
The chroma (C*) indicating colour saturation is defined as:
2
2
C* = (a* +b* )1/2 .
(2)
2.3. Economic analysis
The total capital cost for the greenhouse solar dryer (CT ) is given by the following equation:
CT = Cm +Cl ,
(3)
5
Where Cm is the material cost of the dryer and Cl is the labor cost for the construction. The annual cost
calculation method proposed by Audsley and Wheeler [17] yields:

Cannual  CT 

N

i 1
  w 1
Cmaint,i  Cop,i wi  
N
  w w  1




,


(4)
Where Cannual is the annual cost of the system. Cmaint,i and Cop,i are the maintenance cost and the
operating cost at the year i respectively. w is expressed as:
w = 100 +iin  100 +i f ,
(5)


Where iin and if are the interest rate and the inflation rate in percent, respectively.
The operating cost (Cop) is the labour cost for operating the dryer (Clabour,op). The maintenance cost of
the first year was assumed to be 1% of the capital cost. The annual cost per unit of dried product is called
the drying cost (Z, USD/kg).
It can be written as:
C
Z = annual ,
(6)
M dry
Where Mdry is the dried product obtained from this dryer per year.
Payback period 
CT
,
M dry Pd  M f Pf  M dry Z
(7)
Where Mdry is annual production of dry product (kg), Mf is the amount of fresh product per year (kg),
Pd is the price of the dry product (USD/kg) and Pf is the price of the fresh product (USD/kg).
2.4. Neural Network Modeling
The neurocomputing methods are shaped after biological neural functions and structure. As a result,
they are generally known as artificial neural network (ANN). Similar to biological neural network, the
function of ANN are developed not by programming them, but by exposing them to given sets of input
and output data on which they can learn how to perform a required task. In such modeling approach, a
formulation of analytical description of a process is not required. Instead, a black-box process model is
created by interacting the network with representative samples of measurable quantities characterizing the
process.
In this work, a multilayer ANN model of the greenhouse solar dryer for drying the bael fruits was
developed; the model has four and five-layered network. This network consists of a large number of
processing elements, called neuron (Fig. 5 and Fig. 6).
Hidden Layer-1
Hidden Layer-1
Input Layer
Air temperature (T)
Hidden Layer-2
Input Layer
Air temperature (T)
Output Layer
Relative humidity (rh)
Relative humidity (rh)

Airflow rate ( m )
Final moisture
content (Mf)
Solar radiation (I t )
Initial moisture
content (Mi)
Figure 5. The structure of the artificial neural network
(5:10:5:1)of the greenhouse solar dryer for drying bael fruits

Airflow rate ( m )
Solar radiation
Hidden Layer-2
Hidden Layer-3
Output Layer
Final moisture
content (Mf)
(I t )
Initial moisture
content (Mi)
Figure 5. The structure of the artificial neural network
(5:10:5:3:1)of the greenhouse solar dryer for drying bael fruits
6
The neural networks with various structures were investigated, including 4 and 5 layers with different
number of neurons in each hidden layer, different values of learning rate and momentum. The best ANN
structure was selected on the basis of the lowest error on the training and verification of ANN. The best
ANN model and optimum values of network parameters were obtained by trial and error. The
performances of the various ANN configurations were compared using the coefficient of determination
(R2) and the root mean square error, RMSE.
The input layer of the model comprises five neurons which correspond to solar radiation (It), airflow
rate ( m ), air relative humidity (rh), air temperature (T) and initial moisture content (Mi). The output layer
has one neuron which represents the final moisture content (Mf). A selection of the number of neurons for
hidden layers is optional. A large number of neurons can represent the system more precisely but it is
more complicated to obtain proper training of the network. In this work, (model 1) the selected number of
neuron in hidden layer 1, 2 and 3 of the model are 5, 10 and 3 respectively, (model 2) the selected number
of neuron in hidden layer 1, 2, 3 and 4 of the model are 5, 10, 5 and 3 respectively .
ANN is able to modify its behavior in response to its environment. Unlike analytical model, the
structure of ANN cannot represent the system behavior, unless it is properly trained. The aim of training
the network is to adjust the weights of the interconnecting neurons of the network so that an application
of a set of inputs produces a desired set of output. Initially, random values are given as weights. One
input-output set can be referred to as a vector. Training assumes that each input vector is paired with
target vector representing the desired output and these are called a training pair. In general, a network is
trained over number of training pairs.
In this work, the ANN model of the greenhouse solar dryer was trained by the back propagation
algorithm [14-16]. The procedures of the training are as follows: (1) An input vector was applied. (2) The
output of the network was calculated and compared to the corresponding target vector. (3) The difference
(error) between the calculated and the target outputs was fed back though the network. (5) Weights were
changed.
This procedure was repeated over the entire training set until the error was within an acceptable value
or until the outputs did not significantly change any more. The ANN model was programmed in C++.
3. Results and Discussions
3.1. Drying characteristic of bael fruits
Drying experiments of bael fruits in the greenhouse solar dryer were carried out in 25 June 2015-8
August 2015. Ten batches of experimental run were carried out. The typical results are shown in Fig. 6Fig. 9.
Solar radiation (W/m2)aa
1000
6/08/2015
800
7/08/2015
600
400
200
0
Time (hr)
Figure 6. Variation of solar radiation with time of the day during drying of bael fruits
8/08/2015
7
Temperature (๐C)
Solar radiation from Fig. 6, it was observed that solar radiation was strongly fluctuated on the second
day of the experiment due to cloud. On the first, third days, solar radiation was low because of rain (Fig.
6.). The comparison of air temperature at three different locations inside the dryer and the ambient air
temperature for the experimental runs of solar drying of bael fruits are shown in Fig. 7.
70
60
50
40
30
20
10
0
6/08/2015
T1
T2
7/08/2015
8/08/2015
T3
T_ambient
T_outlet
Time (hr)
Figure 7. Variation of ambient temperature and the temperature at different positions inside the greenhouse solar dryer during of
bael fruits.
Relative humidity (%)aa
The pattern of temperature change in different positions was comparable for all locations.
Temperatures in different positions at these five locations varied within a narrow band. In addition,
temperatures at each of the locations differed significantly from the ambient air temperature. The relative
humidity two different locations inside the dryer and ambient air relative humidity during solar drying of
bael fruits are shown in Fig. 8.
100
80
7/08/2015
6/08/2015
8/08/2015
60
40
20
Inlet
Ambeint
Outlet
0
Time (hr)
Figure 8. Variation of relative humidity with time of the day during drying of bael fruits
Relative humidity decreased over time at different locations inside the dryer during the first half of the
day while the opposite is true for the other half of the day. No significant difference was found between
relative humidity of different positions inside the dryer. However, there was a significant difference in
relative humidity for all locations inside the dryer compared to the ambient air. The relative humidity of
the air inside the dryer was lower than that of the ambient air. Hence, the air leaving the dryer had lower
relative humidity than that of the ambient air and this indicated that the exhaust air from the dryer still had
drying potential for recirculation to dry the product.
8
Moisture content (%, wb.)aa
The comparison of moisture content at three different locations inside the dryer and the open sun
drying for the experimental runs of solar drying of bael fruits are shown in Fig. 9.
70
60
50
40
30
20
10
0
6/08/2015
7/08/2015
Greenhouse dryer
8/08/2015
Open sun drying
Time (hr)
Figure. 9 Comparison of the moisture contents of bael fruits at different positions inside the greenhouse dryer with those obtained by
the open air sun drying method
The moisture content of bael fruits in the greenhouse solar dryer was reduced from an initial value of
59-60% (w.b.) to a final value of 2.0-4.0% (w.b.) within 3 days whereas the moisture content of the
natural sun-dried samples was reduced to 10.8% (w.b.) in the same period.
3.2. Colour measurement of dried bael fruits
The colour of bael fruits dried using the greenhouse solar dryer was compared with that of bael fruits
obtained by the open air sun drying.
The bael fruits dried using the greenhouse dryer has the colour changed from dense/white colour to
bright gel /yellow after having dried (L* = 44.57). The colour of bael fruits dried using open air insolation
is dark – yellow (L* = 30.47). This result indicated that drying bael fruits using the greenhouse dryer has
better than the open air insolation.
3.3. Economic evaluation
As there are now several units of this type of dryer are being used for production of dried bael fruits,
information used for economic evaluation is based on the field level data and recent prices of the
materials used for construction of the dryers. Data on costs involved and economic parameters are shown
in Table 1.
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Table 1. Data on costs and economic parameters
Items
Costs and Economic Parameters
Polycarbonate plates
160 USD
Solar modules and fans
266 USD
Materials of constructions
100 USD
Labour costs for constructions
88.9 USD
Repair and maintenance cost
1% of capital cost per year
Operating cost
171 USD per year
Price of fresh bael fruits
0.889 USD kg-1
Price of dried bael fruits
5.334 USD kg-1
Expected life of the dryer
15 years
Interest rate
1.5% (Bank of Thailand)
Inflation rate
1.0% (Bank of Thailand)
In term of economic evaluation, the capital cost for construction and installation of the solar
greenhouse dryer is estimated to be USD 1,000 (1USD=33.74 baht). It is estimated that 1,800 kg of dry
bael fruits are produced annually. Based on these production scales, capital and operating costs, the
payback period of the greenhouse solar dryer for drying bael fruits is estimated to be about 1 year.
Moisture content (%, wb.)aa
3.4. Performance prediction by ANN model
The ANN model of the solar dryer developed for bael fruits drying were trained with the experimental
data from nine experiments. The data from the tenth experiment were reserved for testing the model.
After 100,000 times of iteration step of training, the square sum of difference (error) between the
observed and the predicted output reached a significant low level. The comparison between the modelpredicted and measured moisture contents of the dryer is shown in Fig. 10.
70
60
50
40
30
20
10
0
6/08/2015
7/08/2015
ANN model 1 ANN model 2
Minside1
M_ANN2
M_ANN1
M_ambeint
8/08/2015
Open sun drying
Time (hr)
Figure 10. Predicted and measured moisture contents of bael fruits
From Fig. 10, it is found that the agreement between the predicted (model 1, model 2) and measured
moisture contents is good and the root mean square difference (RMSD) is 0.996% and 0.998% with
respect to the mean measured value. Thus, if the model is adequately trained, it can appropriately predict
the performance of the solar dryer for drying bael fruits.
10
4. Conclusion
In order to investigated the performance of a greenhouse solar dryer, ten batches of bael fruits were
dried in the greenhouse solar dryer at Loei Province, Thailand. Solar drying in the greenhouse solar dryer
resulted in considerable reductions in drying time as compared with the natural sun drying and the colour
of products dried in the greenhouse solar dryer are better than natural sun dried samples. The estimated
payback period of the greenhouse solar dryer is about 1 year. The ANN model was able to predict
variations of MC quite well with determination coefficients (R2) of 0.996 and 0.998 for training,
validation and testing, respectively. The prediction mean square error was obtained as 0.003 and 0.0028
for training, validation and testing, respectively. Results show good agreement between the experimental
data on the one hand and mathematical models as well as the ANN model on the other.
Acknowledgements
This research was supported by Faculty of Science and Technology, Loei Rajabhat University, Loei
Province, Thailand.
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