Modeling drying kinetics of paneer using Artificial Neural Networks (ANN)
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JOURNAL OF FOOD RESEARCH AND TECHNOLOGY
Journal homepage: www.jakraya.com/journa/jfrt
ORIGINAL ARTICLE
Modeling drying kinetics of paneer using Artificial Neural Networks (ANN)
Shivmurti Srivastav* and BK Kumbhar1
*Associate Professor, Department of Food Processing Technology, A D Patel Institute of Technology, New V V
nagar, Anand, Gujarat-388121, India.
1
G B Pant University of Agriculture and Technology, Pantnagar, Uttarakhand-263145, India.
Abstract
Empirical model of low pressure superheated steam of Paneer were
developed. Effect of steam pressure and temperature on drying time was
*Corresponding author: evaluated. Page model showed higher R2 (0.997) and lower SD (0.0043)
compared to generalized exponential and logarithmic models. The average
Shivmurti Srivastav (PhD) value of parameter k and n for 1cm-cube paneer varied from 0.0041 to
0.0067 and 1.1552 to 1.2209, respectively while that for 1.5 cm-cube were
Email: shivmurtis@gmail.com from 0.0021 to 0.0044 and 1.1987 to 1.3032, respectively. Moisture
content, drying rate and moisture ratio data were generated by conducting
the experiments in low pressure superheated steam dryer. Optimized ANN
Received: 20/01/2014 models were developed for rapid and more accurate prediction of moisture
content with one hidden layers and nine nurons having R2 0.9993, drying
Revised: 02/03/2014 rate with one hidden layers and five neurons having R2 0.9977 and moisture
ratio with two hidden layers and seven neurons having R2 0.9997 in drying.
Accepted: 02/03/2014 ANN modeling can be used for predicting moisture content, drying rate and
moisture ratio.
Keywords: Artficial Neural Networks, Superheated steam, Drying,
Paneer.
Introduction (ANN) to food processing have opened up novel
Food industry is always looking for a drying possibilities for processing industries (Susan, 1998).
method which is rapid, energy efficient and yielding ANN has the capabilities of learning from the inputs
uniform, hygienic and safe products (Nema et al., and outputs and developing relationship between them
2013). Superheated steam drying is a potential method that can be used for further prediction of outputs for the
due to its several advantages. Product maintained given inputs. For nonlinear problems, ANN is a most
higher porosity and better rehydration. Absence of promising alternative technique (Banggard and
oxidative reactions (enzymatic browning, lipid Thodberg, 1992). The advantage of ANN over rule-
oxidation), high heat transfer coefficients, higher based model is that, if the process under analysis
drying rates, energy saving due to latent heat supplied changes, new examples can be added and ANN model
to the dryer can be recovered. This drying method is can be trained. This is much easier than determining
environment friendly since it is a closed system, no new models or rules.
explosion or fire hazard, combination of drying with During last few years, interest in using Artificial
other product treatment like pasteurization or Neural Networks (ANNs) as a modeling tool in food
sterilization of food stuffs (Devahastin et al, 2004; technology is increased. ANNs have been successfully
Deventer and Heijmans, 2001). The superheated steam used in several food applications like model for
acts both as heat source and as drying medium to take prediction of physical properties of dried carrot,
away the evaporated water. In process control, the main prediction of dryer performance, extrusion processing
objectives are food safety, high quality and yield at of wheat and wheat-black soybean, energy
minimal costs. To obtain high quality products, on-line requirements for size reduction of wheat, grain drying
control techniques are required. Most food processes process, dough rheological properties among others
are highly nonlinear, with time varying dynamics, (Islam et al., 2003, Kerdpiboon et al., 2006; Huang et
which complicates food automation. However, the al., 1993; Shihani et al., 2004; Fang et al., 2000; Farkas
recent developments in artificial intelligence based et al., 2000; Ruan et al., 1995). Artificial neural
advanced control tools such as artificial neural network networks are mathematical models which have the
capability of relating the input and output parameters,
Journal of Food Research and Technology | January-March, 2014 | Vol 2 | Issue 1 | Pages 39-45
©2014 Jakraya Publications (P) LtdSrivastav and Kumbhar………..Modeling drying kinetics of Paneer using Artificial Neural Networks (ANN)
learning from examples through iteration, without pipeline to increase the steam temperature to desired
requiring a prior knowledge of the relationships level of superheating. Temperature was controlled by
between the process parameters. Several neural dimmerstat.
network structures have been described, but the most The sample holder was made using thin
commonly used is the multilayer perception. This stainless steel sheet into a circular disc with 15 cm
network comprises an input and an output layer of diameter. It was connected to a balance by a thin rod
processing units interconnected via one or more hidden passing through a G.I. pipe. One side of the rod was
layers. attached to the sample holder and other side was rested
Keeping the advantages of ANN, the present on analytical digital balance (Model XB-320M, Adair
study was undertaken, to develop Artificial Neural Dutt and Co. (I) Pvt. Ltd., Kolkata). The balance was
Network (ANN) models for drying kinetics of paneer placed in a smaller chamber. The balance had a
using superheated steam drying methods. weighing capacity of 320g with a least count of 0.001g.
Size of balance was 210 × 340 × 89 mm. The operating
Materials and Methods temperature of the balance was 10°-50°C. The data
Paneer was kept at 4°C in a refrigerator until recorded by the balance was transferred through the
use. The initial moisture content of paneer was about serial cable by software to a computer (Remote Control
50% (wb). It was diced in to a cube of two different Version 1.0.38. Adair Dutt and Co. (I) Pvt. Ltd.,
sizes of 1×1×1cm3 and 1.5×1.5×1.5 cm3 with stainless Kolkata). Chromel - Alumel (K type) thermocouples
steel knife. Paneer was pretreated with solution were installed to measure temperature of superheated
containing 2.5% sodium chloride and 0.5% potassium steam at inlet of drying chamber, drying chamber,
sorbate at 50°C for 10 min. About 50g paneer cubes product and balance chamber continuously. These
were dipped into 500 mL solution. After pretreatment thermocouples were connected to the data logger
cubes were then removed, and drained using stainless (Model 1551C12, Digitech, Roorkee, India). The
steel mesh. The pretreatment was given to prevent fat temperature range of data logger was 0-900°C and
loss from the paneer and browning. Before drying accuracy better than ±0.5%. Scan rate was 1-99
samples were blotted to remove the surface moisture s/channel. Thermocouple signals multiplexed and
and put in the drying chamber. Drying experiments transferred to the computer through Terminal Software,
were conducted at 62°, 72° and 82°C temperatures and installed in PC. A vacuum pump (Make Kirloskar
10, 14 and 18 kPa absolute pressures with superheated Pneumatic Co. Ltd, Pune, India) was used to maintain
steam. the desired vacuum in the drying chamber.
Experimental setup Drying experiments
Experimental setup of low pressure superheated Prior to conducting the experiments, the steam
steam dryer with its accessories is shown schematically was generated in the autoclave and maintained at
in Fig 1. Main components of the experimental setup 103kPa. The drying chamber was heated using side
were steam generator, drying chamber, vacuum pump wall electric heaters to maintain drying chamber
and data acquisition system with computer. The drying temperature at 50°Cand to prevent condensation of
chamber consisted of a box insulated properly with steam in the drying chamber. Once the desired drying
rock wool. Inner dimensions of insulated chamber were chamber temperature obtained, approximately 50g of
40 × 45 × 45 cm. Two electric heaters of 1.5 kW pretreated sample was placed in the drying chamber.
capacities each were provided on opposite side walls of Vacuum pump was switched on to maintain to desired
the drying chamber. The temperature of drying operating pressure. After it steam inlet valve was
chamber was controlled by a temperature controller opened slowly to flash the steam into the drying
knob provided in front of drying chamber. Thermostat chamber. The steam temperature with respect to the
was provided to maintain the temperature inside the desired pressure was controlled by a heating tape
drying chamber. The drying chamber was connected by placed on the steam pipe. Ratio of the steam pressure in
a pipe from bottom to a chamber in which digital the steam reservoir to that in the drying chamber was
balance was kept. An autoclave was used as a steam rather high, the effect of adiabatic expansion of steam
generator and a steam reservoir. The pressure of steam introduced in to the drying chamber on the steam
was maintained using auto cut controls. A steam trap temperature was rather small. Therefore, required
was provided to reduce accumulation of steam steam temperature was achieved by controlling the
condensate in the reservoir. Steam was transported to electric heater placed in the drying chamber. Sample
the drying chamber through a pipe insulated with glass weight was recorded continuously in the computer at
wool. A heating tape, rated 1kw was mounted on steam 1min interval. Also, the temperature of drying
chamber, product, steam and balance chamber were
Journal of Food Research and Technology | January-March, 2014 | Vol 2 | Issue 1 | Pages 39-45
©2014 Jakraya Publications (P) Ltd
40Srivastav and Kumbhar………..Modeling drying kinetics of Paneer using Artificial Neural Networks (ANN)
recorded by data logger at 1min interval. The samples to produce the corresponding output that may be passed
were dried until the desired final moisture content of on to other neurons.
about 1% db was obtained. Experiments were
performed at 10, 14 and 18 kPa absolute pressure and Training and testing algorithms
62, 72 and 82°C steam temperature. All experiments MATLB software was used for Artificial Neural
were replicated thrice. Same procedure was followed to Networks (ANN) modeling. The networks were
dry all sizes of paneer cubes. simulated based on a multilayer feed forward neural
network. This type of network is very powerful in
Empirical modeling function optimization modeling (Kerdpiboon, 2006).
Moisture ratio data were fitted to Page, The input layer, hidden layers, and output layer
Generalized exponential and Logarithmic model in structures are shown in Fig 2. The inputs parameters
order to select the best predictive model for low selected for modeling other than drying time and
pressure superheated steam drying of paneer cubes. weights are given in Table 1:
The models are given below:
Table 1: Input parameters for modeling
Page model:
M − Mo Method of Back propagation
MR =
Mo − Me
=Exp −kθ
n
( ) (1) computation
Algorithm Levenberg -Marquardt
for minimization of
where
error
θ = Drying time
k, n = Model constants The network training Different size of epochs
Goal Minimum error
Generalized exponential model:
M − Mo Transfer functions Hyperbolic tangent,
MR = = A Exp ( − kθ ) (2) Linear sigmoid transfer
Mo − Me
function transfer function
Where
A, k = Drying constant The inputs included the time of drying and weight
changing with time. The output layer consisted of %db,
Logarithmic model: rate of drying (dm/dθ) and MR. The number of hidden
M − Mo layers were two and number of neurons in each hidden
MR = =a + b ln(θ) (3) layer varied from 1 to 9 (3, 5, 7, or 9). The networks
Mo − Me were simulated with the learning rate equal to 0.05. For
Where training and testing of ANN configuration different
ratio of data sets were examined. It was found that 50%
a,b =Dryingconstants of data set used for training and 50% for testing
predicted the best output.
ANN description of the drying process
The neural network model consisted of an input, Selection of optimal ANN configuration
a hidden and an output layer. The input layer has two The optimal configurations from training and
nodes, which corresponded to two processing testing for each neuron were selected based on neural
conditions or independent variables: time of drying and network predictive performance, which gave the
corresponding weight of the sample. The output layer minimum error from training process. The mean
consisted of three neurons or dependent variables, relative error (MAE), Standard deviation of MAE
representing the moisture content (% db), drying rate (STDA), Percentage of relative mean square error (%
and moisture ratio (Shrivastav and Kumbhar, 2009). MRE), and standard deviation of % MRE (STDR) were
The nodes and the neurons were connected to each used to compare the performances of various ANN
other by weighted links, wij , over which signals can moels (Kerdpiboon, 2006; Torrecilla, 2007).
pass. The arriving signals multiplied by the connection
weights are first summed (activation function) and then
passed through the sigmoid function (transfer function) Results and Discussion
Page, generalized exponential equation and
logarithmic that enable prediction of drying curves of
Journal of Food Research and Technology | January-March, 2014 | Vol 2 | Issue 1 | Pages 39-45
©2014 Jakraya Publications (P) Ltd
41Srivastav and Kumbhar………..Modeling drying kinetics of Paneer using Artificial Neural Networks (ANN)
For simulation it is generally not convenient to
Input layer Hidden layers Output
(i) (j) (k) use individual value of page’s equation parameters k
and n at selected experimental conditions. Therefore, it
Bi Bi was decided to develop a model relating the page
Time %db equation parameters to temperature and pressure. A
dm/dt
non linear regression analysis with second degree
polynomial equation was used to determine
Weight MR
dependence of k and n on temperature and pressure.
The polynomial relation for k and n is given in
equation 4. Detailed coefficients for drying constants k
and n are given in Table 4 at different operating
Fig 2: Theoretical architecture of multilayer neural conditions. These models were developed in MATLAB
network for prediction of moisture content, software.
drying rate and moisture ratio
paneer cubes undergoing low pressure superheated k 'or n' = AT
1
2
+ B1T + C1 (4)
2
steam drying were evaluated. The results of regression A1 = a1 + a2 P + a3 P
analysis are given in Table 2. It was found that Page
model had higher R2 and lower standard deviation
B1 = b1 + b2 P + b3 P 2
compared to other two models all experiments. C1 = c1 + c2 P + c3 P 2
Therefore, Page model was selected for further data A, B, C , a1 , a2 , a3 , b1 , b2 , b3 , c1 , c2 , c3 areconstants
analysis.
The values of k and n were obtained at different where P is pressure, kPa and T is temperature, 0C
drying conditions of temperature and pressure for 1.0
and 1.5 cm cube. The average values of k and n at each The ANN modeling thus obtained for moisture
condition are reported in Table 3. The average value of content, moisture ratio and drying rate summarized that
parameter k and n for 1cm cube paneer varied from the individual drying data set of temperatures i.e. 62,
0.0041 to 0.0067 and 1.1552 to 1.2209, respectively 72 and 820C for each pressure and size (Eighteen ANN
while that for 1.5 cm cube were from 0.0021 to 0.0044 models), combining drying data at all temperatures for
and 1.1987 to 1.3032, respectively. each pressure (Six ANN models), combining drying
Journal of Food Research and Technology | January-March, 2014 | Vol 2 | Issue 1 | Pages 39-45
©2014 Jakraya Publications (P) Ltd
42Srivastav and Kumbhar………..Modeling drying kinetics of Paneer using Artificial Neural Networks (ANN)
data at all pressures for each temperature (Six ANN hidden layer. Each combination of hidden layers and
models), combining all drying data for temperatures, neurons per hidden layer was trained.
pressures and size (Six ANN models). Each data set Relative mean square error (MAE), Standard
was divided in to two groups, consisting of 50% for deviation of MAE (STDA), Percentage of relative mean
training and 50% for testing. During training, the data square error (% MRE) and Standard deviation of
set was used to determine the optimum number of %MRE (STDR) and R2 along with number of hidden
hidden layers, neurons per hidden layer that gave the layers and neurons in each hidden layer are reported for
best predictive power. Architecture of artificial neural one set of data for illustration in Table 5.
network was hidden layers 1 and 2 and neurons 3-9 per
Table 2: Parameters of equations obtained from linear regression of drying data for different drying models
Standard Deviation between observed and
R2
Drying predicted moisture ratios
Size of Pressure
Temperature Generalized Page’s Generalized Logarithmic
Paneer (0C) (kPa) Page’s Logarithmic
exponential model exponential model
model model model
model
Superheated Steam Drying
10 0.9945 0.9686 0.9100 0.0010 0.0145 0.0119
62 14 0.9934 0.9563 0.9739 0.0015 0.0109 0.0111
18 0.9961 0.9665 0.9735 0.0011 0.0086 0.0100
10 0.9859 0.9656 0.9155 0.0030 0.0171 0.0109
1.0.cm-
14 0.9934 0.9839 0.9767 0.0020 0.0116 0.0126
cube 72
18 0.9879 0.9683 0.9767 0.0031 0.0222 0.0114
10 0.9845 0.9350 0.9776 0.0041 0.0275 0.0145
14 0.9869 0.9674 0.9771 0.0036 0.0209 0.0145
82
18 0.9772 0.9569 0.9771 0.0043 0.0284 0.0120
10 0.9782 0.8455 0.9100 0.0025 0.0228 0.2862
14 0.9972 0.9880 0.9156 0.0016 0.0035 0.2931
62
18 0.9934 0.9823 0.9161 0.0020 0.0043 0.2754
10 0.9888 0.9556 0.9082 0.0014 0.0113 0.3052
1.5 cm-
14 0.9873 0.9860 0.9173 0.0017 0.0044 0.3048
cube 72
18 0.9939 0.9929 0.9171 0.0013 0.0062 0.2864
10 0.9917 0.9205 0.9094 0.0010 0.0208 0.3229
14 0.9939 0.9629 0.9198 0.0015 0.0176 0.3219
82
18 0.9953 0.9656 0.9203 0.0011 0.0130 0.3005
Table 3: Parameters of Page Equation (LPSSD)
Temperature Pressure K N
(0C) (kPa) 1cm cube 1.5 cm cube 1cm cube 1.5 cm cube
10 0.0041 0.0021 1.2209 1.3032
14 0.0038 0.0032 1.2152 1.2226
62
18 0.0050 0.0028 1.1568 1.2336
10 0.0054 0.0026 1.1960 1.2772
14 0.0050 0.0035 1.1917 1.2162
72
18 0.0054 0.0034 1.2005 1.2115
10 0.0062 0.0031 1.1843 1.2618
14 0.0061 0.0044 1.1805 1.1987
82
18 0.0067 0.0039 1.1552 1.1957
Journal of Food Research and Technology | January-March, 2014 | Vol 2 | Issue 1 | Pages 39-45
©2014 Jakraya Publications (P) Ltd
43Srivastav and Kumbhar………..Modeling drying kinetics of Paneer using Artificial Neural Networks (ANN)
Table 4: Coefficients for Constants K' and N' of Paneer Dried With Low Pressure Superheated Steam
For K' For N'
Coefficients
1.0 cm-cube 1.5 cm-cube 1.0 cm-cube 1.5 cm-cube
a1 5.625E-06 -3.594E-05 -2.119E-03 1.180E-03
a2 -1.750E-06 5.625E-06 3.754E-04 -1.738E-04
a3 9.375E-08 -2.031E-07 -1.569E-05 6.109E-06
b1 -9.050E-04 5.134E-03 3.099E-01 -1.810E-01
b2 2.845E-04 -7.963E-04 -5.520E-02 2.642E-02
b3 -1.475E-05 2.878E-05 2.308E-03 -9.290E-04
c1 4.590E-02 -1.874E-01 -1.006E+01 8.594E+00
c2 -1.211E-02 2.915E-02 2.018E+00 -1.059E+00
c3 6.010E-04 -1.051E-03- -8.443E-02 3.697E-02
In table 5 the minimum MRE was found with required to lower the error if there are enough number
one hidden layers and nine neurons for moisture of neurons (Torrecilla et al., 2005). The best prediction
content (dry basis), one hidden layers and five neurons for most of the data set contained two hidden layers.
for drying rate and two hidden layers and seven ANN developed for combined drying data had slightly
neurons for moisture ratio at 620C and 10kPa for 1.0 higher error than individual conditions. Minimum and
cm-cube paneer. The results showed that the number of maximum error involved between actual and predicted
hidden layers, and neurons per hidden layer, that values were 2.1657 – 3.929, 0.0555 – 0.0692 and
yielded minimum error was different for each drying 0.0207 – 0.0416 for moisture content, drying rate and
technique. A large number of hidden layers is not moisture ratio respectively.
%db
MR
dm/dt
Fig 3: Correlation between predicted and experimental values
Journal of Food Research and Technology | January-March, 2014 | Vol 2 | Issue 1 | Pages 39-45
©2014 Jakraya Publications (P) Ltd
44Srivastav and Kumbhar………..Modeling drying kinetics of Paneer using Artificial Neural Networks (ANN)
Plots of experimentally determined moisture Neural Network Model were fitted in the drying data.
content, drying rate and moisture ratio versus ANN The empirical models were analyzed for best prediction
predicted values for all combined data are shown in Fig of moisture ratio. The best models were selected on the
3. The correlation coefficients were greater than 0.98 in basis of maximum coefficient of determination (R2)
all cases. For all combined data set with superheated and standard deviation (SD). ANN modeling was done
steam, the R2 was found 0.9975, 0.9934 and 0.9983 for at two hidden layers (1 and 2) and nine neurons (3, 5,
moisture content, drying rate and moisture ratio, 7, 9). The optimal ANN configuration was selected
respectively. This shows that the ability of ANN to after calculating the statistical parameters like mean
predict moisture content, drying rate and moisture ratio relative error (MAE), standard deviation of MAE
was very good. (STDA), percentage of relative mean square error
(%MRE) and standard deviation of %MRE (STDR).
Conclusions Thus, ANN modeling can be successfully used for
Empirical model namely page’s, generalized accurately predicting moisture content, drying rate and
exponential and logarithmic models and Artificial moisture ratio.
Table 5: Prediction of Drying Properties
Drying No No (%)db dm/dt MR
Conditions hidden neurons
layer R2 R 2
R2
MAE STDA MRE STDR (%) MAE STDAMRE STDR (%) MAE STDA MRE STDR (%)
1 3 1.091 0.991 18.512 0.389 99.96 14.644 0.734 30.298 0.749 74.90 0.012 0.011 28.892 0.625 99.60
1 5 0.363 0.377 7.815 0.154 99.99 0.015 0.020 7.830 0.119 99.77 0.004 0.004 9.256 0.183 99.99
1 7 0.418 0.407 6.053 0.121 99.98 0.050 0.193 22.660 0.515 91.99 0.007 0.013 6.895 0.136 99.90
1 9 0.567 1.088 2.875 0.037 99.93 0.058 0.235 23.302 0.518 89.98 0.006 0.012 6.728 0.157 99.93
17, SS, 62, 2
3 0.869 0.583 12.756 0.168 99.96 0.030 0.081 19.991 0.423 97.49 0.009 0.007 29.824 0.602 99.93
2 5 0.430 0.339 5.798 0.071 99.99 0.019 0.024 19.466 0.456 99.51 0.003 0.003 5.666 0.090 99.99
2 7 1.078 0.717 21.322 0.389 99.96 0.014 0.017 19.418 0.512 99.81 0.011 0.007 3.523 0.620 99.97
2 9 0.608 0.594 21.853 0.447 99.98 0.018 0.024 19.920 0.476 99.53 0.006 0.006 37.060 0.087 99.94
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