Research Journal of Applied Sciences, Engineering and Technology 4(13): 1883-1887, 2012
ISSN: 2040-7467
© Maxwell Scientific Organization, 2012
Submitted: January 12, 2012
Accepted: February 09, 2012
Published: July 01, 2012
Prediction and Simulation the Breakthrough of Residual Chlorine
Removal by Granular Activated Carbon Adsorbent Using
Artificial Neural Networks
Rusul Naseer, 2Ala!a Abdulrazaq Jassim, 1Lü Xi-Wu and 1,3Saad Abualhail
1
School of Energy and Environment, Department of Environmental Science and Engineering,
Southeast University, Nanjing 210096, China
2
Department of Chemical Engineering,
3
Department of Civil Engineering, Faculty of Engineering, Basrah University, Basra, Iraq
1,2
Abstract: This study has included two parts. The first part has dealt with carbon production whereas the date
Palm was used to produce Granular Activated Carbon (GAC) with specific physical characteristics. The new
produced of GAC is used to adsorbate the Residual chlorine from water by deep bed filter column. In the
second part, the experimental results of the breakthrough of residual chlorine curves is predicted and simulated
using artificial neural network with back propagation algorithm whereas the optimum number of neuron was
investigated based on RMSE. The removal of residual chlorine has been used as target function in ANN while
the other properties of adsorption process such as operation conditions, chlorine concentration in raw water and
GAC characteristics has been used as input parameters. The results showed that ANN with back propagation
algorithm is a good tool that can be used to predict the best operating parameter for designing GAC layer in
multimedia filter whereas 35 neuron gave the best fitting with experimental data. In addition to that, the
simulation result was showed that the predictions of breakthrough curve model has been coincided well with
the measured values which explained that the depth 25 cm with grain size 1.5 mm of GAC filter bed will be
give the optimum removal of residual chlorine from chlorinated water.
Key words: Activated carbon, Artificial Neural Network (ANN), breakthrough curve, chlorine adsorption
INTRODUCTION
Activated carbon is extensively used to remove
pollutants from gaseous and liquid process streams. It is
produced by chemical or physical activation of
carbonaceous materials. The features of carbons such as
porosity, surface area, density and mechanical stability
govern the use of activated carbons as adsorbents (Salame
and Bandosz, 2000). Wastewater utilities have used
activated carbon to remove harmful chlorine residual
before discharge to the environment. The adsorption
mechanism of activated carbon provides a highly efficient
way to remove chlorine, although reaction rates may vary
with chlorine concentration, type of residual, carbon
particle size, pH and absorbed organic matter (Snoeyink
and Suidan, 1975). Activated carbon is very effective in
removing free chlorine from water (Potwora, 2009)
whereas the mechanism of removal employed by activated
carbon for dechlorination is not the adsorption phenomena
that occur for organic compound removal. Adsorption in
a fixed bed column such as deep bed filter generally
depends on the adsorption isotherm and the transport
mechanisms in adsorbent particles as well as the operating
conditions of the column, such as, adsorbate inlet
concentration, adsorbate flow rate, height of bed and
adsorbent particle diameter. So, numerous models have
been developed to predict the breakthrough time curve
which is knowledge based on transient material balance
and mass transfer coefficient such as Langmuir equation
which is based on a kinetic approach and assumes a
uniform surface (Chilton et al., 2002) and Freundlich
model which is consider an empirical equation based on
the Experimental parameter and the distribution of solute
between the solid phase and aqueous phase, in addition to
that, the adsorption equations frequently solved
numerically using finite element or finite difference
methods (Varadarajan and Badri, 1996 ) and weighted
residual techniques (Tien, 1994). Some studies have
showed that those approaches are acceptable for modeling
the binary dynamic adsorption, but they are not
appropriate in the case of complex solutions such as the
residual chlorine adsorbate in water on the GAC
adsorbent which is form multi component. Recently,
Artificial Neural Networks (ANN) have been applied
Corresponding Author: Lü Xi-Wu, School of Energy and Environment, Department of Environmental Science and Engineering,
Southeast University, Nanjing 210096, China
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Res. J. Appl. Sci. Eng. Technol., 4(13): 1883-1887, 2012
Fig. 1: Schematic diagram for fixed bed adsorption column in multimedia filter (Rusul et al., 2010)
Table 1: Experimental operating
adsorption
Parameter
Initial chlorine concentration
Feed flow rate
Height of bed
Grain diameter of GAC
Porosity of GAC
parameters for residual chlorine
Range
2-3
500-1300
5-30
1.5-2.36
0.51-0.44
Table 2: Basic compositions of palm-date pit
Component
Carbohydrates
Crude fibber
Oil
Protein
Ash
Moisture
Units
ppm
cm3/min
cm
mm
-
MATERIALS AND METHODS
An Experimental work was carried out in Basra city
(Qarma water treatment plant) from January 2010 through
August 2011.
Experimental set up: In the present work, deep bed filter
was used to study the adsorption process in multimedia
filter (Rusul et al., 2011). Schematic diagram of Deep bed
filter column has been shown in Fig. 1. Several
experiments were implemented by change the grain
diameter of a new production of GAC, operation
conditions and the inlet chlorine concentration whereas
Table 1 shows the range of operating parameters that have
been investigated in this study.
%
53-67
12-21
6-11
4.5-7
1.1-2.3
4-11
successfully a wide variety of domains (Abualhail et al.,
2011) which it is a combination of parameterized
functions called neurons (Hornik et al., 1989). Neurons
are organized into layers which are mutually connected by
highly parallel synaptic weights. The objective of the
neural network is to transform the input into meaningful
outputs with minimum error (Howard et al., 2006). In this
study, artificial neural network back propagation
algorithm was used to simulate and predict the
breakthrough curve of residual chlorine adsorbent by
GAC whereas a series of experiments was conduct with
different operating conditions to examine the effect of
GAC adsorbent on residual chlorine removal to predict
the performance of adsorption processing in multimedia
filter.
Adsorbents: In this study, Date Palm pits were used to
predict the granular activated carbon throughout the
experimental work whereas this part of study involved six
steps: washing, drying, crushing, sieving, carbonization
and activation. Basic Compositions of date palm pits is
given in Table 2 which is showed that the palm-date pits
contain approximately 53-67% of carbohydrates.
Data set for training and validating the FNN: The
Input parameters of ANN model are six parameters which
are GAC height, initial adsorbate concentration (inlet
concentration of the residual chlorine), grain size of GAC,
flow rate, porosity of GAC, time. Output parameter is the
breakthrough curve of adsorption process. Three values
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Res. J. Appl. Sci. Eng. Technol., 4(13): 1883-1887, 2012
0.54
2.5
0.51
2.0
Porosity
Inlet concentration (ppm)
3.0
1.5
1.0
0.48
0.45
0.5
0
0
500
1000
0.42
1500
0
500
Sample no.
1000
(a)
(b)
1200
Diameter of grain (GAC)
(mm)
2.5
Flow rate (cm 3 /min)
1000
2.0
1.5
1.0
800
600
400
200
0.5
0
1500
Sample no.
0
0
500
1000
1500
0
500
1000
1500
Sample no.
Sample no.
(c)
(d)
Hight of GAC(cm)
35
30
25
20
15
10
5
0
0
200
400
600
800 1000 1200 1400 1600
Sample no.
(e)
Fig. 2: Input parameters in ANN of residual chlorine removal on GAC in multimedia filter
was used for grain size of GAC, inlet concentration and
porosity, similarly GAC height, flow rate, where
expressed by (4, 3) values respectively. The time of
breakthrough is different value of all data set in the
training depending on the experimental work as shown in
Fig. (2a-e).
ANN-architectures investigation for residual chlorine
removal: To build ANN network, it is very important to
select the number of hidden neurons that gave best
training, validation and test results where our study will
investigate the number of hidden neurons in hidden layer
of ANN network and the root mean square generalization
error. In this study, 1407 measurements were randomly
divided into a training data base of 844 values for training
and model selections and a test data best 563 values for
the final assessment of the generalization performance of
validation and testing. The data are organized in
sequences, each sequence corresponding to one
experimental of breakthrough curve. The performance of
ANN was evaluated in terms of Root Mean Square
(RMSE) criterion to minimize the error and coefficient of
determination (R2) or regression analysis, between
predicated and observed date, it is provides a measure of
a strength of the correlation and the coefficient of
efficiency.
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Res. J. Appl. Sci. Eng. Technol., 4(13): 1883-1887, 2012
0.20
GAC-Adsorption
0.18
RMSE
0.16
0.14
0.12
Best no. of neurons
0.10
0.08
0.06
1
6
11
16 21
26
31
36
Number hidden neurons
41
46
(a) n = 2
Fig. 4: Selection of number of neurons in the hidden layer for
adsorption process
(b) n = 35
Fig. 3: Regression analysis between predicted outputs and the
experimental values adsorption process in multimedia
filter
Bias =
A
Different number of neurons from 2 to 40 was
investigated depending on RMSE, R2. The results are
shown in Fig. 3 that illustrate the effect number of
neurons on a regression analysis. Thirty five values of
hidden neurons were selected for the FNN model because
it presented greater values of (R2) and lower value of
RMSE than other neurons as shown in The number of
hidden neurons for adsorption process in the hidden layers
was predicted according to lowest value of RMSE, which
showed in Fig. 4. The final optimized architecture of the
breakthrough of residual chlorine removal on GAC as
shown in Fig. 5 corresponding to 35 neurons and 216
connection in the feed forward-ANN architecture.
B
C
D
E
F
00 0 0
Neuron hidden
Output
C/Co
A. Height of GAC
B. Grain size
C. Inlet concentration
D. Flow rate
E. Porosity
F. Time
Fig. 5: Final optimized architecture of ANN for the breakthrough curve of GAC adsorption
RESULTS AND DISCUSSION
ANN simulation results of residual chlorine on GAC
adsorbent: The simulation result of adsorption process
with respect to sample number for each neuron can be
obtained as shown in Fig. 6 whereas the testing data is
60% from observed data. The simulation results showed
that the figure of 35 neurons coincided very well with
experimental data. Figure 6 represents a design chart for
the fast prediction of residual chlorine removal by
adsorption method of GAC.
CONCLUSION
In this study, ANN with back propagation model was
used to predict and simulate the breakthrough curve of
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Res. J. Appl. Sci. Eng. Technol., 4(13): 1883-1887, 2012
multimedia filter whereas 35 neuron give the best fitting
with experimental data. In addition to that, the simulation
result showed that 25 cm depth with small grain size
which is 1.5 mm of GAC filter bed can be give the
optimum removal of residual chlorine from chlorinated
water.
Experimental data
Predication by FNN-static mode
No. of hidden neurons = 2
Testing data = 60% from observed data
1.2
1.0
C/Co
0.8
0.6
ACKNOWLEDGMENTS
0.4
Sample no.
This study was supported by National Natural
Science Foundation (51078074).
1407
1125
1266
985
844
704
563
422
282
141
0
1
0.2
REFERENCES
(a) n = 2
Experimental data
Predication by FNN-static mode
No. of hidden neurons = 20
Testing data = 60% from observed data
1.3
1.1
C/Co
0.9
0.7
0.5
0.3
0.1
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563
422
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282
141
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Fig. 6: ANN simulation results of residual chlorine
removal by GAC adsorbent
Residual chlorine removal in multimedia filter by
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the concentration of inlet chlorine. length of GAC bed,
flow rate, used as input parameters of ANN. The results
showed that ANN with back propagation algorithm is a
good tool that can be used to predict the best operating
parameter that can be used to design GAC layer in
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Transmitted by F.E. Udwadia, © Elsevier Science
Inc.
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