Advances in Environmental Biology Mohammad Zarein and

Advances in Environmental Biology, 8(16) Special 2014, Pages: 172-178
AENSI Journals
Advances in Environmental Biology
ISSN-1995-0756
EISSN-1998-1066
Journal home page: http://www.aensiweb.com/AEB/
ANN Modeling of White Mulberry Drying by Microwave Oven
1Mohammad
1
2
Zarein and 2Farzad Jaliliantabar
Department of Engineering, Shahre-Ray Branch, Islamic Azad University, Share Ray, Iran
Young Researchers Club. Shahr-e-Qods Branch. Islamic Azad University. Tehran, Iran
ARTICLE INFO
Article history:
Received 25 September 2014
Received in revised form
26 October 2014
Accepted 25 November 2014
Available online 1 December 2014
Keywords:
Artificial neural network, BFGS,
Microwave drying, White Mulberry
ABSTRACT
This paper is concerned with developinganartificial neural network (ANN)model for
predicting the moisture content, moisture ratio, drying rate and energy efficiency of
microwave drying of white mulberry. The drying experiments were carried out at 200,
300, 400 and 500 W of microwave power.Time and power of drying considered as the
input variables to the topology of neural network.The results revealed that a network
withthe exponential and logistic transfer function and BFGS algorithm with 8 neuron in
hidden layer, made the most accurate predictions for the white mulberry drying system.
The R2 of best model for training and testing of data were 0.98 and 0.96, respectively.
Simulation test on the prediction of network output parameters performed and the
accuracy of the predicted values are excellent.
© 2014 AENSI Publisher All rights reserved.
To Cite This Article: Mohammad Zarein and Farzad Jaliliantabar., ANN Modeling of White Mulberry Drying by Microwave Oven. Adv.
Environ. Biol., 8(16), 172-178, 2014
INTRODUCTION
Food drying is a most important process for preservingagricultural products since it has a great effect onthe
quality of the dried products. Most cereals, vegetables,and fruits can be preserved after drying. The
majorobjective in drying agricultural products is the reductionof the moisture content to a level, which allows
safestorage over an extended period.Mulberry trees are extensively grown (e.g. SouthernEurope, India) for their
leaves as food and fruits. Thereare three kinds of mulberry: white mulberry (Morus albaL.), black mulberry
(Morus nigra L.), and red mulberry(Morus rubra L.). White mulberry originated in WesternAsia, red mulberry
in North and South America, andblack mulberry is from Southern Russia. Thefruits of white mulberries are
often harvested byspreading a sheet on the ground and shaking the limbs.They have a high level of moisture
content at harvest.Because of the short season and the sensitivity to storagedrying is often used as a preservation
method. In addition,mulberry is used in mulberry pekmez, juices, paste,marmalade and wine production [1].The
drying time of the convectivetechnique can be shortened by using higher temperatureswhich increase moisture
diffusivity [2] and bycutting the material into small pieces [3]. Increaseddrying temperature entails higher costs
and may cause biochemicalchanges that degrade the dried product quality; whereassubdividing the material is
an additional process that results, especiallyunder industrial conditions, in mass losses and lowering ofthe
product quality [4]. The drying time can be greatly reduced [5] and the quality of finished product insured [6]by
applying the microwave energy to thedried material. Furthermore, commonly used hot air techniques are limited
by high energy consumption, long drying times, low energy efficiency and high costs, which is not desirable for
the food industry. Due to these difficulties, more rapid, safe and controllable drying methods are required. Also,
it is necessary to dry the product with minimum cost, energy and time. In microwave drying, drying time is
shortened due to quick absorption of energy by water molecules, causes rapid evaporation of water, resulting in
high drying rates of the food.One of the most important aspects of drying technology is the modeling of the
drying process. There are various studies at the research level about drying of vegetables. For example; Bakal et
al. [7] and Senadeera et al. [8] reported that the Page model best described the drying behavior of potato. As
little research has been performed effect of microwave power on energy consumption and drying efficiency in
microwave drying method, the present research is focused on this issue.
Several studies have been conducted on the experimental investigation and modeling of experimental
investigations on microwave heating of foodproducts. This subject has been of special interest in recentyears [912].
Corresponding Author: Mohammad Zarein, Department of Engineering, Shahre-Ray Branch, Islamic Azad University,
Share Ray, Iran
E-mail: [email protected]
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Mohammad Zarein and Farzad Jaliliantabar,2014
Advances in Environmental Biology, 8(16) Special 2014, Pages: 172-178
ANNs are good for some tasks. Specifically, they are good for tasks involving incomplete data sets, fuzzy
or incomplete information, and for highly complex and ill-defined problems, where humans usually decide on
an intuitional basis. They can learn from examples, and are able to deal with non-linear problems. Furthermore,
they exhibit robustness and fault tolerance. In such situations, where the relationship between variousvariables
describing the drying problem is complex and ill-defined, the widely used Artificial Neural Network (ANN)
canprovide a platform where these problems can be solved withreasonable accuraciesandcomputation times[13].
The predictability of an ANN is a result of training with experimental data and validation with an
independent set of data. The ANN has the ability to learn and improve its performance if new data are available
[14]. If there are enough experimental data, a well-trained ANN can be used as a predictive model for specific
applications, such the prediction of white mulberry drying rate [11].
The aim of this study was to developa BP structured ANN to predict the moisture content, moisture ratio,
drying rate and energy efficiency of microwave drying of white mulberryusing a group of dryer characteristic
operating parameters as the ANN inputs.
MATERIALS AND METHODS
White mulberry samples were purchased from a local market, in Tehran, Iran, and were stored in the
refrigerator at temperature of 4±1°C until the experiments were carried out. Samples were washed with tap
water, and then samples with mean length of 10 mm were selected. The initial moisture content of the samples
found about 78.5±1.5% (w.b.), and was determined by drying in an air convection oven at 103±1 °C till the
weight did not change any more. A domestic microwave oven (M945, Samsung Electronics Ins) with maximum
output of 1000 W at 2450MHz was used for the drying experiments. The oven has a fan for air flow in drying
chamber and cooling of magnetron. The moisture from drying chamber was removed with this fan by passing it
through the openings on the right side of the oven wall to the outer atmosphere. The microwave dryer was
operated by a control terminal which could control both microwave power level and emission time. Experiments
were performed at four microwave powers of 200, 300, 400 and 500 W. The moisture losses of samples were
recorded at 30s intervals during the drying process by a digital balance (GF-600, A & D, Japan) and an accuracy
of±0.01 g.For measuring the weight of the samples during experimentation without taking them out of the oven,
the tray with sample was suspended on the balance with a nylon wire through a ventilation hole in the center of
chamber ceiling. Drying was carried out until the final moisture content reaches to a level less than 4%
(w.b.)[8]. All measurements were carried out in triplicate.
The moisture ratio (MR) was calculated using the following equation:
Mt − Me
MR =
(1)
M0 − Me
where, MR is the moisture ratio (dimensionless); M t, Me and M0are the moisture content at any time,the
equilibrium moisture content, the initial moisture content (kg[H2O]/kg dry mater), respectively. The values of
Me are relatively small compared to Mt and M0, hence the error involved in the simplification by assuming that
Me is equal to zero is negligible.
Drying rate was defined as:
DR
Mt+∆t − Mt
=
(2)
∆t
where Mt+Δtis moisture content at time t+Δt (kg [H2O]/kg dry mater), t is the time (min) and DR is the
drying rate (kg [H2O]/kg dry mater.min).
The microwave drying efficiency was calculated as the ratio of heat energy utilised for evaporating water from
the sample to the heat supplied by the dryer.
mw × λw
η=
(3)
P×t
where η is the microwave-convective drying efficiency (%);P is the microwave power (W); mw is the mass
of evaporated water (kg), and λw is the latent heat of vaporization of water (2257 kJ/kg).
The Multi-layer perceptron (MLP) networks trained using back propagation (BP) algorithm are the most
popularchoice in neural network applications. The MLP consists of three types of layers.The first layer is the
input layer and corresponds to the problem input variables with one node foreach input variable. The second
layer is the hidden layer used to capture non-linear relationshipsamong variables. The third layer is the output
layer used to provide predicted values. The BP algorithm is one of the most powerful learning algorithms in
neural networks. This algorithm was used in many of research conducted ondrying process [12, 15, 16]. The
training of all patterns of a training data set is called an epoch[13].
BP networks are known for their ability to generalize well on a wide variety of problems. BP networks are a
supervised type of networks, i.e. trained with both inputs and outputs. BP networks are used in a large number
of working applications as they tend to generalize well. The first category of neural network architectures is the
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Mohammad Zarein and Farzad Jaliliantabar,2014
Advances in Environmental Biology, 8(16) Special 2014, Pages: 172-178
one where each layer connected to the immediately previous layer. Generally, three layers (input, hidden, and
output layer) are sufficient for the majority of problems to be handled. Three layers BP network with standard
connections is suitable for almost all problems. One, two or three hidden layers architecture can be used
however, depending on the problem characteristics. Use of more than five layers in total generally offers no
benefit and should be avoided [13].
The inputs correspond to the values of the input parameters used in a problem. The learning procedure in
this network may be implemented by using the BP algorithm. In BP networks, the number of hidden neurons
determines how well a problem can be learned. If too many are used, the network will tend to try to memorize
the problem, and thus not generalize well later. If too few are used, the network will generalize well but may not
have enough „power‟ to learn the patterns well [13]. So, the right numbers of hidden neurons were determined
by trial and error [17].
ANN consists of artificial neural cells (neurons). ANN has three main layers, namely, input, hidden and
output layers. Neurons (processing elements) at input layer transfer data from external world to hidden layer
[18]. The data in input layer do not process as the data in the other layers. In the hidden layer, outputs are
produced using data from neurons in input layer and bias, and summation and activation functions. There can be
more than one hidden layer. In this case, each hidden layer sends outputs to the following layer. In the output
layer, the output of network is produced by processing data from hidden layer and sent to external world. The
summation function calculates net input coming to a cell. For this reason, different functions are used. The most
common one is to calculate the weighted sum.
Activation function provides a curvilinear match between input and output layers. In addition, it determines
the output of the cell by processing net input to the cell. Selection of appropriate activation function
significantly affects network performance. Recently, logistic sigmoid transfer function has been commonly used
as an activation function in multilayer perception model, because it is a differentiable, continuous and non-linear
function[19].
The Statistica Neural Networks software version 10 was used to form the ANN. Different transfer
functionswereapplied in the hidden layer and output layer. Inputs of system determine the neuron number in the
input layer of the network and its outputs determine the neuron number in the output layer of the network. Thus,
input layer of network has two neurons and the output layer has four neurons (Figur 1).
Fig. 1: architecture of created ANN.
There are many learning algorithm in order to determine weights in ANN. One of the most common
learning algorithms is back propagation. The back propagation method updates the weights in accordance with
difference between available data and network output. Learning parameter used in the method has a great
importance in order to reach the optimal results. Learning parameter can be constant or dynamically updated in
the model.There are various training functions that have been applied by the previous studies such as Bayesian
regularization, gradient descent with adaptive learning rule, gradient descent with momentum and adaptive
learning rule, scaled conjugate gradient and LevenbergeMarquardt [18]. But BFGS algorithmwas not studyed
and applied in developing ANN to predict dryer parameter.In order to acquire the closest output values to
experimental results, the best learning algorithm and optimum number of neurons in hidden layer was
determined. For this reason BFGS learning algorithms and different numbers of neurons in hidden layer were
used in the built network structure for drying parameters.
In back propagation model, scaling of inputs and outputs dramatically affects performance of ANN. As
mentioned above, logistic sigmoid transfer function was used in this study. One of the characteristics of this
function is that only a value between 0 and 1 can be produced. Input and output data sets are normalized before
training and testing process. In ANN, normalization can be performed between 0 and 1[20].
X −X
𝑋n = i min
(4)
X max −X min
Where Xi is actual data, 𝑋n is normalized data, Xmin is minimum of actual data and X max maximum of
actual data. In this study, the input and output values were normalized between 0 and 1 using equation 4.
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Advances in Environmental Biology, 8(16) Special 2014, Pages: 172-178
Three types of data files prepared: a training data file, a test data file and a validation data file. During
training, the network was tested against the test file to determine accuracy and training stopped when the mean
average error remains unchanged for a number of epochs. This is done in order to avoid overtraining, in which
case, the network learns perfectly the training patterns but is unable to make predictions when an unknown
training set is presented to it [13].
Determination of percentages of training and testing data has an important role for building of ANN
architecture. When the studies in literature are analyzed, it is revealed that different ratios for training and
testing data are used 80%:20% [21], 75%:25% [22], 70%:30% [23-25], although, the percentage of training and
testing data of 70%:30% are most in common. Thus, in this context, Approximately 81 of the total experimental
data was selected at random and was used for training purpose, while the 35 values was reserved for validation
and testing, i.e. the ratio for training, validation and testing data was selected as 70%:15%:15%. The steps of
determing the best ANN model to predict the drying parameters are given as follows:
1. In order to make the architecture of the BP neural network model simple, only one hidden layer will be
used in it. The prediction accuracy of the network can be improved by choosing the proper number of hidden
neurons.
3. The proper goal error along with the initial weights and biases of the network can be chosen with the
training samples training the model. By changing the number of the hidden neurons while training the model,
the proper number of the hidden neurons can be selected by observing the training results of the model.
According to the following three principles, the number of the hidden neurons can be selected: (1) the expected
goal errors must be reached within the given training numbers. (2) The architecture of the network is not too
complicated. (3) After training the network with the training samples, the network can reflect well the sample
change law and have higher prediction accuracy. If the number of the hidden neurons is too little, the network
does not reach the expected goal error, or if the number of the hidden neurons is too many, the network is too
complicated.
BP training algorithm is a ramp descent algorithm. The BP algorithm minimizes total error by changing the
weights through its ramp and thus tries to improve the performance of the network. The training of the network
is stopped when the tested values of RMS stop decreasing and begin to increase. In order to understand whether
an ANN is making good predictions, the testing data that has never been presented to the network, is used and
the results are checked at this stage [18].
The performance of ANN model in testing sets is validated in terms of the common statistical measures; R2
(coefficient of determination which presents the degree of association between predicted and true values).
𝑅2 = 1 −
𝑁
𝑖=1 𝑌𝑖 𝑜𝑏𝑠𝑒𝑟𝑒𝑣𝑒𝑑 −𝑌𝑖 𝑝𝑟𝑒𝑑𝑖𝑐𝑡𝑒𝑑
2
𝑁
𝑖=1 𝑌𝑖 𝑝𝑟𝑒𝑑𝑖𝑐𝑡𝑒𝑑
2
(5)
Where Ypredicted is the estimated data, Yobserved is the observed data. These statistical measures provide
information on the strength of linear relationship between the observed and the estimated values. If the model is
“perfect”, R2 is 1. If the model is a total failure, R2 is zero.
RESULTS AND DISCUSSION
Determination of the optimal number of neurons for drying parameters is demonstrated in Table 1.In
consequence of trials, the best network architecture for prediction of effective power became 2-8-4 after 132
epochs.Considering the architecture and prediction accuracy of the network, 8 hidden neuronshad been selected.
The aim of using the ANN model, considered as a practical approach, is to test the ability to predict drying
parameter. The network has two input parameters: power of microwave and time of drying.Statistical values
obtained for the network is given in Table 1. As shown in Table 1, it was shown that R2 are close to 1.
Predictive ability of the network for drying parameter was found to be satisfactory.
Table 1: Statistical data for the effective power using two different algorithms.
R2
Topology
Training
Validation
Test
2-8-4
0.98
0.98
0.96
2-41-4
0.97
0.98
0.91
2-30-4
0.98
0.97
0.94
2-52-4
0.98
0.97
0.91
2-11-4
0.98
0.97
0.95
2-14-4
0.98
0.98
0.93
2-41-4
0.97
0.98
0.91
2-38-4
0.98
0.98
0.91
2-51-4
0.97
0.98
0.90
2-52-4
0.98
0.98
0.90
Epochs
132
120
72
151
182
58
63
120
123
133
Activation function
Hidden layer
Output layer
Tanh
Logistic
Tanh
Exponential
Tanh
Logistic
Logistic
Exponential
Logistic
Tanh
Tanh
Logistic
Tanh
Logistic
Logistic
Exponential
Tanh
Exponential
Tanh
Exponential
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Advances in Environmental Biology, 8(16) Special 2014, Pages: 172-178
Fig. 2: Matching of the experimental and ANN model values for testing sets of moisture contentwhite
mulberrymicrowave drying (power is 200W)
As it can be seen in Figure 2, the created ANN model can predict the moisture content very good.
Fig. 3: Matching of the experimental and ANN model values for testing sets ofmoisture ratio (MR) of white
mulberry microwave drying (power is 200W).
The time required to dry white mulberry samples from initial moisture content of 78.5±1.5% (w.b.) to the
final moisture content of 4±1% (w.b.) was 24.5, 14, 10.5 and 7 min at 200, 300, 400 and 500 W, respectively.
The results indicated that mass transfer within the sample was more rapidly during higher microwave power
heating because more heat was generated within the sample creating a large vapor pressure difference between
the center and the surface of the product due to characteristic microwave volumetric heating.
Fig. 4: Matching of the experimental and ANN model values for testing sets ofdrying rate (DR) of white
mulberry microwave drying (power is 200W)
Figure 4 shows how the drying rate of white mulberry samples. The maximum drying rates were
approximately 0.342, 0.527, 0.568 and 0.719 kg [H2O]/kg dry mater .min, when the microwave powers of 200,
300, 400 and 500W were applied, respectively. The moisture content of the material was very high during the
initial phase of the drying, which resulted in a higher absorption of microwave power and higher drying rates
due to the higher moisture diffusion. As the drying progressed, the loss of moisture in the product caused a
decrease in the absorption of microwave power and resulted in a fall in the drying rate.
As it can be seen in this figure, although the ability of ANN model in prediction of drying rate is satisfying, it
isnot as good as two above parameters prediction (R2=0.88 Vs. 0.99 and 0.98). This may be due to a larger value
of variation in this parameter and therefore poor training processes.
Figure 5 shows the variation of energy efficiency whit drying time for microwave drying of white mulberry
samples. The energy efficiency was very high during the initial phase of the drying which resulted in a higher
absorption of microwave power. Following moisture reduction, the energy absorbed by the product decreased
and reflected power increased.
There is a larger variation in the initial seconds of drying process respect to the other part of this process.
Therefore, the ANN model has predicted the smooth region of this curve (the last seconds) better than the other
region. However, the ability of ANN model in prediction of this parameter is satisfactory.
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Advances in Environmental Biology, 8(16) Special 2014, Pages: 172-178
Fig. 5: Matching of the experimental and ANN model values for testing sets of energy efficiency of white
mulberry microwave drying (power is 200W).
Conclusion:
Characteristics of the microwave drying of white mulberrywere determined. Microwave drying period of
samples lasted between 24.5 and 7 min at the microwave powers at 200 and 500 W, respectively.It can be
concluded that 500 W is the optimum microwave power level in the microwave drying of white mulberrywith
respect to drying time and energy efficiency. The values of effective diffusivity for microwave drying of white
mulberryranged from 1.43×10-6 to 4.88×10-6m2/s and activation energy was found 8.922 W/g. Energy efficiency
increases with the increase of microwave drying power and moisture content. In addition,ANN model has been
used in prediction of different drying parameters and it showed a good ability in prediction of these parameters.
The coefficient of correlations for these parameters were 0.98, 0.99, 0.88 and 0.94 for moisture content,
moisture ratio (MR) and drying rate (DR) and energy efficiency, respectively.
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