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Sep 23, 2015 - In this research, EC was used to remove Endosulfan from aqueous solution. The results show that the best conditions that obtained in th...
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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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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

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Salkhordeh-panbechouleh1

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

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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

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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.

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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]

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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

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and by adsorption on zero-valent zinc

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, arsenic7, fluoride8 , oil

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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.

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. 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.

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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

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

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:

 =

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

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experimental data.

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3. Results and discussion

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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

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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 

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. 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

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

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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

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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

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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:

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Al (OH) 3 + OH-→ Al(OH)4-

(7)

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Al (OH)4- + OH- → Al(OH)52-

(8)

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Al (OH)5- + OH- → Al(OH)63-

(9)

27

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. 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)

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3.3. Effect of current density

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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)

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

&'( 11 )*

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

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

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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

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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

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

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

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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

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

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

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model are in good agreement with the experimental data.

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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

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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

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

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

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

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that ANN predicts the removal efficiency correctly.

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4. Conclusions

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The removal

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electrocoagulation using aluminum electrodes. The effects of various operational parameters

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

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

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

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

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

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

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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

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We gratefully acknowledge thefinancial support received fromthe University of Mazandaran.

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5. References

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Arsand, D. R.; Kümmerer, K.; Martins, A. F., Removal of Dexamethasone From

Gengec, E.; Kobya, M.; Demirbas, E.; Akyol, A.; Oktor, K., Optimization of Baker's

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Metal Ions Removal From Metal Plating Wastewater Using Electrocoagulation: Kinetic Study

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Butyl Ether (MTBE) by UV/H2O2Process. J. Hazard. Mater.2005, 125, 205.

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Electrocoagulation (EC) Process Using Aluminum Electrodes. J. Hazard. Mater.2007, 145, 180.

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Electrocoagulation. Sep. Purif. Technol.2014, 132, 627.

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Prediction by Artificial Neural Network. Fluid Phase Equilib.2011, 310, 150.

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Aber, S.; Amani-Ghadim, A. R.; Mirzajani, V., Removal of Cr(VI) From Polluted

Salari, D.; Daneshvar, N.; Aghazadeh, F.; Khataee, A. R., Application of Artificial

Hu, C.-Y.; Lo, S.-L.; Kuan, W.-H., Simulation the Kinetics of Fluoride Removal by

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Using Iron and Aluminum Electrodes for Fluoride Removal From Aqueous Environment. E- J.

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337 338 339 340

List of Figuresand Tables

341 342

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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

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346

Fig. 1

347

Fig. 2.

348 349 18 ACS Paragon Plus Environment

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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

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80 60 40 predicted 20 Experimental 0 0

355

2

4

6 Initial pH

8

356 19 ACS Paragon Plus Environment

10

12

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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 (%)

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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

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60

70

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100

Removal Efficiency (%)

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

Removal Efficiency (%)

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80 70 60 50 40 30 Predicted

20

Experimental

10 0 0

2

4

6

Solution Conductivity (mS/cm)

368

Fig. 8.

369

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8

10

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370

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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

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1.745

0.33

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377 378 379

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