Modeling the Removal of Endosulfan from Aqueous Solution by

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Modeling the removal of Endosulfan from aqueous solution by electrocoagulation process using artificial neural network (ANN) Seyed Mohammad Mirsoleimani-azizi, Ali Akbar Amooey, Shahram Ghasemi, and Saeid Salkhordeh-panbechouleh Ind. Eng. Chem. Res., Just Accepted Manuscript • DOI: 10.1021/acs.iecr.5b02846 • Publication Date (Web): 23 Sep 2015 Downloaded from http://pubs.acs.org on September 24, 2015

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Industrial & Engineering Chemistry Research

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Modeling the removal of Endosulfan from aqueous solution by

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electrocoagulation process using artificial neural network (ANN)

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Seyed Mohammad Mirsoleimani-azizi1, Ali Akbar Amooey1*, Shahram Ghasemi2, Saeid

4

Salkhordeh-panbechouleh1

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1

Department of Chemical Engineering, University of Mazandaran, Babolsar, Iran.

6

2

Faculty of Chemistry, University of Mazandaran, Babolsar, Iran.

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Abstract

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Electrocoagulation (EC) is an electrochemical method to treatpolluted wastewaters and aqueous

9

solutions. In this research, EC was used to remove Endosulfan from aqueous solution. The

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results show that the best conditions that obtained in this study are: pH=4, current density=6.2

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mA/cm2, initial concentration of Endosulfan =30mg/L and electrolysis time=60 min. The

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solution conductivity seems to has no significant effect on the removal efficiency. Artificial

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neural network (ANN) was utilized to model the experimental data. The model was developed

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by using three layer feed-forward neural network with eight neuron in the hidden layer for

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modeling of EC process. A comparison between the predicted results and experimental data gave

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high correlation coefficient ( = 0.976) and showed that the model is able to predict the

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removal efficiency.

18 19

Key words:

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Artificial neural network; Electrocoagulation; Endosulfan; Removal efficiency *

Corresponding author: Ali Akbar Amooey, University of Mazandaran, Babolsar, Iran; Tel: +981135302903

E-mail address :[email protected]

1 ACS Paragon Plus Environment


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1. Introduction

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Endosulfan has been used in agriculture around the world to control insect pests. The water

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which is contaminated with Endosulfan can bring about serious environmental problem and also

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threat human health.Endosulfan is one of the most toxic pesticides on the market today, which

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isresponsible for many fatal pesticide poisoning incidents around the world. The excessive

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concentration of this insecticide, causesreproductive and developmental damages in both animals

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and humans, and also can promote proliferation of human breast cancer cell1. So, the amount of

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usage should be controlled lest the toxicant contaminate ground or sea water.

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To find out the suitable treatment for removal of toxicant from water for both environmental and

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economic reasons, several processes like treatment by ion exchange2, advanced oxidation

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process3, photochemical degradation

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developed. Electrochemical technology can be applied for the treatment of wide range of

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wastewaters. One of the electrochemical methods is electrocoagulation (EC) that can compete

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with the conventional chemical coagulation process in the treatment of wastewaters. The EC

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process is characterized by low investment cost, need low space, simple equipment requirement,

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sludge stability, operational simplicity, no need to chemical materials, rapid sedimentation, low

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sludge production and environmental compatibility. EC is an effective and credible method for

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treating different wastewaters including phenol

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wastewaters from chicken industry 11, cheese whey 12, hospital wastewater13, baker’s yeast 14 and

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heavy metal containing solution 15, 16.

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The EC process is based on in situ generation of coagulant through electrodissolution of

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aluminum electrodes. In EC process, the aluminum electrodes produce their hydroxides (Al

4

5

and by adsorption on zero-valent zinc

6

, arsenic7, fluoride8 , oil


9

have been

, diazinon10,

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(OH)3) in the contaminated water. In this process, when the aluminum electrodes are used as an

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anode and acathode, the main reactions can be summarized as follows:

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a. Anodic reactions:

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Al → Al3+ +3e- (1)

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2H2O → O2 (gas) + 4H+ (aq) + 4e- (2)

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b. Cathodic reactions:

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2H2O +2e- → H2 (gas) + 2(OH)-(aq) (3)

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Due to the complexity of the reactions which occur in the EC process, it is difficult to determine

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the kinetic parameters, thus it causes uncertainties in the design and scale upof chemical reactors

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for industries. EC process is generally complicated and depends on several parameters, so the

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modeling of this process has many problems which cannot be solved by simple linear

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correlation.

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The artificial neural network (ANN) has ability to recognize and reproduce cause and effect

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relationships through training, for multiple input/output system, which makes it efficient to

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represent and up-scaling even the most complex systems like electrocoagulation

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robust and reliable characteristic in finding the non-linear relationships between variables

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(input/output) in complex systems, numerous application of ANN have been successfully

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conducted to solve environmental engineering problems 18, 19.

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According to literature, modeling of EC process has been little investigated. Hu et al. applied

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Langmuir equation to specify the kinetic of the fluoride removal reactionby EC 20. Emamjomeh

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et al. developed empirical model for fluoride removal by electrocoagulation/flotation (ECF)

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process 21. Recently Valente et al. predict chemical oxygen demand in dairy industry treated by

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EC with ANN 22.


17, 18

. ANN has


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In this paper, the removal efficiency of Endosulfan in aqueous solution by EC treatment was

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investigated. The effect of several parameters such as initial pH, electrolysis time,initial

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concentration of Endosulfan and current density on the removal efficiency wasstudied. An

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important aim of this study is removal of Endosulfan from aqueous solution by EC and

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presentation of an ANN model that provide reliable and robust prediction of the efficiency of this

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process.

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2. Experimental

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2.1. Materials and instruments

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Endosulfan solution was prepared by dissolving Endosulfan (Merck, Germany) in distilled water.

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The chemical structure and other characteristics of Endosulfan are shown in Table 1.Initial pH of

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solutions was adjusted by 0.5 M NaOH (Merck, Germany) and HCl (Merck, Germany) solutions

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and determined by pH meter (Metrohm 826, Switzerland). The initialconductivity of

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solutionwasadjusted by addition of NaCl. Also the conductivity measurementswere carried out

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by conducto meter (JENWAY, 4020, U.K). Four aluminum plates were used as anodes and

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cathodes. Dimensions of electrodes were 100 × 50 × 2 mm and the distance between two

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electrodes in EC cell is 10 mm in all experiments. The outer electrodes were connected to the DC

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power source (Sanjesh, Iran) with galvanostatic operational option to control the current density.

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2.2. Procedure

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The experimental setup is shown in Fig. 1.Experiments were conducted with four aluminum

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electrodes connected in bipolar mode to a glass pipe. Before each experiment, the electrodes

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were polished by sandpapers with different mesh and then dipped in 0.5 M HCl solution to


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dissolve any oxide from their surface.Then electrodes were rinsed with distilled water, and

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finally dried at oven at 75℃ for 15 minutes. All runs were performed at 25±3 ℃. In each

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experiment, 400 mL of Endosulfan solution (with specified concentration) was transferred into

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the electrolytic cell. After the current density was adjusted to the desired value, the operation

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wasstarted. The contaminant concentration varies between 10 to 80 mg/L. In this study, the

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voltage of cell was between 10 to 30 V and current density was in the range of 2.5 to 12 mA/cm2.

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The conductivity of solution was between 2.62 to 7.7 mS/cm. During the process, the solution

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was agitated at 200 rpm and the sampling of solution was carried out at each 15 minute to

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determine the residual concentration of Endosulfan. Also the total time of process was 60

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minutes. The concentration of Endosulfan in solution was analyzed using UV–Vis

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spectrophotometer (Braic-2100, China). Absorbance was measured at the wavelength of 250 nm

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and spectral bandwidth of 0.2 nm. The removal efficiency (Re) was calculated using Eq. 4 where

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C0 is the initial concentration of Endosulfan in aqueous solution and C is concentration of it at

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time t.

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Re = (1- ) ×100

(4)

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2.3. ANN method

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Artificial neural network (ANN) is a branch of Artificial Intelligence (AI) which can model any

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kind of data sets even in cases where available data are complex. ANN is a mathematical

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modeling technique which applies numerical analysis to provide reliable models23. The

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inspiration of using neural network came from the biology of human brain

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neurons are interconnected to process different information.


24

where billions of


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An ANN, at least, consists of two layers: input and output layers. The input layer represents the

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independent variables while the output layer represents the dependent variable. Between the

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input layer and the output layer there are layers called the hidden layers. Number of neurons in

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hidden layers should be optimized. Each layer is composed of some neurons which are

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connected to neurons located in previous and next layers. Information in an ANN is divided

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between multiple cells (nodes) and connection between cells (weights) 18. The number of input

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and output neurons is fixed by the nature of problems.

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Data points should be divided into two major sets. First set is used to train and validate the ANN

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and the other data set is used to test the network. Training procedure optimizes network

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parameters (weights and biases). When training of ANN via proper propagation method is

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finished, second set of data which is completely new to ANN is used to test the trained

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network23.

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In an ANN, transfer function is a mathematical representation of the relation between the input

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and output layer. There are several transfer function such as radbas,purelin,hardlim,satlin, poslin

126

and etc. One of the most commonly used function is sigmoidal transfer function and is given by

127

19

:

=

1 5 1 +

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Where f(x) is the hidden neuron output. This function is used in the next section to normalize the

129

experimental data.

130 131

3. Results and discussion

132

3.1. Neural network modeling


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The topology of an ANN is determined by the number of layers, the number of nodes in each

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layer and the nature of the transfer functions. Optimization of ANN is an important step in the

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development of a model

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transfer function with backpropagation algorithm was used. A linear transfer function (purelin)

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was used at the output layer. The training function was “train scaled conjugate gradient

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backpropagation” (trainscg). All calculations were carried out with MATLAB mathematical

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software with ANN toolbox.

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To apply one network for EC process, five neurons such as time of electrolysis, current density,

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pH, solution conductivity and initial concentration of Endosulfan in aqueous solution are

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required for input layer and one neuron for output layer (removal efficiency of Endosulfan). The

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range of studied variables is summarized in Table 2. To optimize neurons number in hidden

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layer, 70% of data points were used to train the ANN and deviations were considered to make

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decision about optimized number of neurons in hidden layer. The results of experiments

146

indicated that 5-8-1 network could be applicable for this process. Fig. 2 shows architecture of 5-

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8-1 network.

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70 experiments were conducted to develop ANN model. The samples were allocated to training

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and test set that each of them contains 50 and 20 samples, respectively.

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Since the transfer function used in the hidden layer was sigmoid, all samples were normalized in

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0.1 - 0.9 range. So, all of data (xi) were converted to norm values (xnorm) as follows 17, 18: = 0.8

25

. In this research, multilayer feed-forward ANN with sigmoidal

− ! + 0.1 6 −

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Where and

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between the experimental and predicted values using the ANN for all of data used for training

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and testing. It shows that the points are well distributed around X=Y line in narrow area. A

are the extreme values of variable . Fig. 3 demonstrates a comparison



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correlation coefficient of = 0.976 for the line plotted using experimental and predicted data,

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illustrates the reliability of model.

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The results demonstrate that ANN is fast and has the prediction capability. Assume that no

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experimental data is available for a condition in EC, 5-8-1 network can predict the removal

159

efficiency accurately via available data of similar system while other techniques do not have this

160

capability.

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3.2. Effect of initial pH

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The dependences of removal efficiency on initial pH values were investigated over initial pH

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range of 2-10. Vik et al. observed that the pH of solution changes during the EC process. They

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reported that pH increment occurs when the initial pH is lower than 7

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increase in pH tohydrogen evolution at cathode. However, Chen et al. explained this by the

166

release of CO2 from wastewater. Actually in low pH, CO2 releases during the H2 evolution and

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causes to pH increment

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the final pH doesn’t vary significantly and only a short drop occurs 28. The experimental values

169

of Endosulfan removal percent from aqueous solution at different initial pH values as well asthat

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obtained by ANN are shown in Fig. 4. Each experiment replicate (n) five times (n=5) and relative

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standard deviation (RSD) was 2.1%

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As it can be observed in Fig. 4, the optimum efficiency was occurred in pH = 4. By decreasing

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pH of the solution, the probability of dissolving the aluminum hydroxide and the conversion of it

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into other types of aluminum species increases. When pH increases, aluminum hydroxide

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converts to the negative aluminum hydroxide complexes according to following equations 10:

176

Al (OH) 3 + OH-→ Al(OH)4-

(7)

177

Al (OH)4- + OH- → Al(OH)52-

(8)

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Al (OH)5- + OH- → Al(OH)63-

(9)

27

26

. They ascribed this

. Also Bazrafshan et al. demonstrated that when pH is higher than 8,


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Also, the negatively charged aluminate ions may be formed through the following reaction:

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Al (OH)3 + OH- → AlO2- + 2H2O

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There is an optimum pH for adequate adsorption of Endosulfan and an increase or a decrease of

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pH can affect on the removal efficiency of adsorbed Endosulfan. In basic media, as a result of

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the formation of aluminum species which their acidic sites are filled with hydroxide ions,

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Endosulfan cannot be adsorbed by the precipitate. In high acidic media, the aluminum hydroxide

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coagulant is solved, so the absorbed Endosulfan releases in the solution.

(10)

186 187

3.3. Effect of current density

188

Fig. 5 shows a comparison between experimental (n=5, RSD=1.7) and calculated values of

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removal efficiency of Endosulfan as a function of current density.

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In an EC process, current density determines the coagulant production rate and the removal

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efficiency depends on aluminum concentration. The theoretical amounts of Al dissolution (mtheo)

192

in the EC cell can be expressed by Faraday’s law as follows 10: "#$% =

&'( 11 )*

193

where I is the current density (A), t is the time of electrolysis (s), m is the amount of dissolved

194

aluminum (g), M is the atomic weight of the aluminum (g/mol), Z is the metal valance (3 for Al),

195

and F is Faraday’s constant (F = 96,487 C/mol). As the results indicate, the removal efficiency

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increases with current density increment. By increasing the current density from 2.5 to 12

197

mA/cm2, the removal efficiency rises from 74.6 % to 92.6 %. According to Faraday’s law, the

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amount of anodic dissolution of Al grows by increasing the current density. The higher amounts

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of generated coagulant can enhance the EC removal efficiency. At variance, less aluminum is



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releases from the anode when lower current densities are applied and due to it, the removal

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efficiency of Endosulfan from aqueous solution diminishes.

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3.4. Effect of electrolysis time

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Electrolysis time is an important parameter which affects on the removal efficiency and controls

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the reaction rate. To explore the effect of operating time, a series of experiments (n=5,

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RSD=2.3%) were carried out on solution containing constant Endosulfan concentration (50

207

mg/L) with initial pH = 7 under constant current density (6.6 mA/cm2) at different electrolysis

208

times. According to Faraday’s law, the amount of aluminum released from anode depends on the

209

electrolysis time and current density, so by increasing the time of reaction, more aluminum is

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releases from anode surface and the Endosulfan removal from solution enhances. Fig.6

211

illustrated that the removal efficiency increases from 66.6 % after 15 minutes to 84.57 % after 60

212

minutes of process. Also, it can be observed in Fig.6 that predicted values from proposed ANN

213

model are in good agreement with the experimental data.

214 215

3.5. Effect of the initial concentration of Endosulfan

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Effect of the initial concentration of Endosulfan was investigated on the removal efficiency of

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EC cell in the range of 10 mg/L to 80 mg/L. The experimental data (n=5, RSD=2.15)and ANN

218

predicted values of Endosulfan removal efficiency against initial concentration of it is depicted

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in Fig. 7. As presented in Fig. 7, the removal efficiency of Endosulfan decreases with increasing

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in its initial concentration. For example, after 60 minutes of EC process at pH=7, current density

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(6.6 mA/cm2) and at initial Endosulfan concentration of 10, 30, 50, 70 and 80 mg/L, about

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91.23, 88, 84.57, 65.3 and 51.75% of removal efficiencies were obtained, respectively. This can


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be related to the fact that the amount of Al ions is constant at the same constant current density

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and time for all initial concentration of contaminate (according to Faraday’s law). As aresult, the

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Al ions produced at high initial concentration of Endosulfan are insufficient to reduce all of

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contaminates. Similar results were observed previously in other studies18, 29.

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3.5. Effect of solution conductivity

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To investigate the influence of solution conductivity, different solution of Endosulfan (50 mg/L

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at pH=7) were prepared with conductivity in the range of 2.6 to 7.7 mS/cm. The current density

230

of 6.6 mA/cm2 was applied to all solution. Fig. 8 show the relation between the removal

231

efficiency and solution conductivity. As it can be seen the solution conductivity has no

232

significant effect on the removal efficiency of Endosulfan.Also, it can be dedicated from Fig. 8

233

that ANN predicts the removal efficiency correctly.

234 235

4. Conclusions

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The removal

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electrocoagulation using aluminum electrodes. The effects of various operational parameters

238

such as initial concentration of contamination, current density, pH, electrolysis time, and solution

239

conductivity have been investigated on removal efficiency. It was observed that in initial

240

concentration of Endosulfan, current density and electrolysis time have significant effect on the

241

removal efficiency. The results revealed that pH = 4 is optimum condition and by increasing the

242

pH of solution, the removal efficiency decreases. Also the results illustrated that the performance

243

of EC process in removal of Endosulfan can be successfully predicted by applying a multilayer

244

feed-forward ANN (using back propagation algorithm) with 8 hidden layers.

efficiency of Endosulfan from aqueous solution was examined by

245



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Acknowledgments

247

We gratefully acknowledge thefinancial support received fromthe University of Mazandaran.

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337 338 339 340

List of Figuresand Tables

341 342


Page 16 of 23

Page 17 of 23

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Fig. 1

The setup of EC experiment: 1: DC power supply; 2: Stirrer; 3: Magnetic bar; 4: Cathode electrode; 5: Anode electrode.

Fig. 2

The ANN optimized structure.

Fig. 3

Comparison of the experimental results with those calculated via neural network modeling

Fig. 4

The effect of pH on the removal efficiency of Endosulfan: current density (6.2 mA/cm2), Endosulfan concentration (50 mg/L), solution conductivity (2.62 mS/cm).

Fig. 5

The effect of current density on the removal efficiency of Endosulfan: pH=7, Endosulfan concentration (50 mg/L), solution conductivity (2.62 mS/cm).

Fig. 6

The effect of electrolysis time on the removal efficiency of Endosulfan: current density (6.2 mA/cm2), Endosulfan concentration (50 mg/L), solution conductivity (2.62 mS/cm), pH=7.

Fig. 7

The effect of initial concentration on the removal efficiency of Endosulfan: current density (6.2 mA/cm2), pH=7, solution conductivity (2.62 mS/cm).

Fig. 8

The effect of solution conductivity on the removal efficiency of Endosulfan: current density (6.2 mA/cm2), Endosulfan concentration (50 mg/L), pH=7

Table 1

Characteristic of Endosulfan

Table 2

Model variables and their ranges

343 344 345 17 ACS Paragon Plus Environment


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Page 18 of 23

346

Fig. 1

347

Fig. 2.

348 349 18 ACS Paragon Plus Environment

Page 19 of 23

350

100

Removal Efficiency (perdicted)

90

y = 1.0104x - 0.1055 R² = 0.9768

80 70 60 50 40 30 20 10 0 0

20

40

60

80

100

Removal Efficiency (Experimental)

351 352

Fig. 3.

353

354

120 100 Removal Efficiency (%)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


80 60 40 predicted 20 Experimental 0 0

355

2

4

6 Initial pH

8

356 19 ACS Paragon Plus Environment

10

12


357

Fig. 4

358

100 90

Removal Efficiency (%)

80 70 60 50 40 30 Predicted 20 Experimental

10 0 0

2

4

6

8

10

12

14

Current density (mA/cm2)

359

Fig. 5

360 100 90 Removal Efficiency (%)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 20 of 23

80 70 60 50 40 Predicted

30 20

Experimental

10 0 0

10

20

30

40

50

Time of Electrolysis (min)

361

Fig. 6

362 363 20

ACS Paragon Plus Environment

60

70

Page 21 of 23

100


90 80 70 60 50 40 30 Experimental

20 10

Predicted

0 0

20

364

40 60 Initial Concentration (mg/L)

80

100

Fig. 7.

365 366 367

100 90


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


80 70 60 50 40 30 Predicted

20

Experimental

10 0 0

2

4

6

Solution Conductivity (mS/cm)

368

Fig. 8.

369


8

10


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370

Page 22 of 23

Table 1 Structural

Commercial name

Chemical class

Mw(g/mol)

Density(g/cm3)

Solubility in water(mg/L)

Thiodan, Endocide

Organochlorine

406.93

371 372 373

Table 2 Variable

Range

Input layer Initial PH

2 - 10

Current density

2.51 - 12 mA/cm

Electrolysis time

0 - 60 min

Initial Endosulfan concentration

10 - 80 mg/L

Solution Conductivity

2.62 - 7.71 mS/cm

Output layer Residual concentration

0 - 100%

374

375 376


1.745

0.33

Page 23 of 23

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377 378 379


Modeling the Removal of Endosulfan from Aqueous Solution by

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