Modeling the Removal of Endosulfan from ... - ACS Publications

Subscriber access provided by University of Otago Library

Article

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

Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a free service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are accessible to all readers and citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.

Industrial & Engineering Chemistry Research is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

Page 1 of 23

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

Industrial & Engineering Chemistry Research

1

Modeling the removal of Endosulfan from aqueous solution by

2

electrocoagulation process using artificial neural network (ANN)

3

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

4

Salkhordeh-panbechouleh1

5

1

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

6

2

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

7

Abstract

8

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

10

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

11

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

12

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

13

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

14

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

15

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

16

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

17

removal efficiency.

18 19

Key words:

20 21

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


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 2 of 23

22

1. Introduction

23

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

24

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

25

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

26

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

27

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

28

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

29

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

30

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

31

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

32

process3, photochemical degradation

33

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

34

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

35

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

36

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

37

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

38

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

39

treating different wastewaters including phenol

40

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

41

heavy metal containing solution 15, 16.

42

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

43

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,

Page 3 of 23

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


44

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

45

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

46

a. Anodic reactions:

47

Al → Al3+ +3e- (1)

48

2H2O → O2 (gas) + 4H+ (aq) + 4e- (2)

49

b. Cathodic reactions:

50

2H2O +2e- → H2 (gas) + 2(OH)-(aq) (3)

51

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

52

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

53

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

54

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

55

correlation.

56

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

57

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

58

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

59

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

60

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

61

conducted to solve environmental engineering problems 18, 19.

62

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

63

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

64

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

65

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

66

EC with ANN 22.


17, 18

. ANN has


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

67

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

68

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

69

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

70

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

71

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

72

process.

73 74

2. Experimental

75

2.1. Materials and instruments

76

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

77

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

78

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

79

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

80

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

81

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

82

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

83

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

84

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

85 86

2.2. Procedure

87

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

88

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

89

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


Page 4 of 23

Page 5 of 23

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


90

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

91

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

92

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

93

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

94

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

95

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

96

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

97

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

98

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

99

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

100

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

101

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

102

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

103

time t.

104

Re = (1- ) ×100

(4)

105 106

2.3. ANN method

107

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

108

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

109

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

110

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

111

neurons are interconnected to process different information.


24

where billions of


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

112

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

113

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

114

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

115

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

116

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

117

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

118

and output neurons is fixed by the nature of problems.

119

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

120

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

121

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

122

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

123

network23.

124

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

125

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 +

128

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


Page 6 of 23

Page 7 of 23

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


133

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

134

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

135

development of a model

136

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

137

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

138

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

139

software with ANN toolbox.

140

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

141

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

142

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

143

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

144

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

145

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-

147

8-1 network.

148

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

149

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

150

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

151

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 −

152

Where and

153

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

154

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



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 8 of 23

155

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

156

illustrates the reliability of model.

157

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

158

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.

161

3.2. Effect of initial pH

162

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

163

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

164

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

165

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

167

causes to pH increment

168

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

170

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

171

standard deviation (RSD) was 2.1%

172

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

173

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

174

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

175

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)

178

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,


Page 9 of 23

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


179

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

180

Al (OH)3 + OH- → AlO2- + 2H2O

181

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

182

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

183

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

184

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

185

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

189

removal efficiency of Endosulfan as a function of current density.

190

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

191

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

196

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

198

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

199

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



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

200

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

201

efficiency of Endosulfan from aqueous solution diminishes.

Page 10 of 23

202 203

3.4. Effect of electrolysis time

204

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

205

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

206

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

210

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

216

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

217

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

219

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

220

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

221

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

222

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


Page 11 of 23

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


223

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

224

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

225

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

226

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

227

3.5. Effect of solution conductivity

228

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

229

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

236

The removal

237

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



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

246

Acknowledgments

247

We gratefully acknowledge thefinancial support received fromthe University of Mazandaran.

Page 12 of 23

248 249 250

5. References

251

(1)

252

Aurrekoetxea, J. J.; Exposito, J.; Lorenzo, M.; Torne, P.; Villalobos, M.; Pedraza, V.; Sasco, A.

253

J.; Olea, N., Breast Cancer Risk and the Combined Effect of Environmental Estrogens. Cancer

254

Causes Control 2004, 15, 591.

255

(2)

256

From Water by Hydrophobic Zeolites. An Isothermal Study.J. Hazard. Mater.2012, 203, 357.

257

(3)

258

D. D., Efficient Removal of Endosulfan From Aqueous Solution by UV-C/Peroxides: A

259

Comparative Study. J. Hazard. Mater.2013, 263,584.

260

(4)

261

Comparative Studies of Various Iron-Mediated Oxidative Systems for the Photochemical

262

Degradation of Endosulfan in Aqueous Solution. J. Photochem. Photobiol., A. 2015, 306, 80

263

(5)

264

Valent Zinc in Water and Soil. J. Environ. Manage. 2015, 150, 451.

265

(6)

266

Mixture by Electrocoagulation Using Al, Cu, Fe, Pb, and Zn as Anode Materials. Ind. Eng.

267

Chem. Res.2014, 53, 18339.

Ibarluzea Jm, J.; Fernandez, M. F.; Santa-Marina, L.; Olea-Serrano, M. F.; Rivas, A. M.;

Yonli, A. H.; Batonneau-Gener, I.; Koulidiati, J., Adsorptive Removal of α-Endosulfan

Shah, N. S.; He, X.; Khan, H. M.; Khan, J. A.; O'Shea, K. E.; Boccelli, D. L.; Dionysiou,

Shah, N. S.; He, X.; Khan, J. A.; Khan, H. M.; Boccelli, D. L.; Dionysiou, D. D.,

Cong, L.; Guo, J.; Liu, J.; Shi, H.; Wang, M., Rapid Degradation of Endosulfan by Zero-

Fajardo, A. S.; Martins, R. C.; Quinta-Ferreira, R. M., Treatment of a Synthetic Phenolic


Page 13 of 23

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


268

(7)

Song, P.; Yang, Z.; Xu, H.; Huang, J.; Yang, X.; Wang, L., Investigation of Influencing

269

Factors and Mechanism of Antimony and Arsenic Removal by Electrocoagulation Using Fe–Al

270

Electrodes. Ind. Eng. Chem. Res.2014, 53, 12911.

271

(8)

272

Drinking Water by Electrocoagulation in a Continuous Filter Press Reactor Coupled to a

273

Flocculator and Clarifier. Sep. Purif. Technol.2014, 134, 163.

274

(9)

275

Electrocoagulation Technique.Alexandria Eng. J.2014, 53, 199.

276

(10)

277

Removal of Diazinon From Aqueous Solution by Electrocoagulation Process Using Aluminum

278

Electrodes.Korean J. Chem. Eng.2014, 31, 1016.

279

(11)

280

Parameters in Electrocoagulation Treating Chicken Industry Wastewater to Recover Hydrogen

281

Gas With Pollutant Reduction. Renewable Energy.2015, 80, 101.

282

(12)

283

Cheese Whey Wastewater: An Application of Response Surface Methodology.J. Environ.

284

Manage.2014, 146, 245.

285

(13)

286

Aqueous Solution and Hospital Wastewater by Electrocoagulation. Sci. Total Environ.2013, 443,

287

351.

288

(14)

289

Yeast Wastewater Using Response Surface Methodology by Electrocoagulation. Desalination

290

2012, 286, 200.

Sandoval, M. A.; Fuentes, R.; Nava, J. L.; Rodríguez, I., Fluoride Removal from

Fouad, Y. O., Separation of Cottonseed Oil From Oil–Water Emulsions Using

Amooey, A.; Ghasemi, S.; Mirsoleimani-azizi, S.; Gholaminezhad, Z.; Chaichi, M.,

Thirugnanasambandham, K.; Sivakumar, V.; Prakash Maran, J., Optimization of Process

Tezcan Un, U.; Kandemir, A.; Erginel, N.; Ocal, S. E., Continuous Electrocoagulation of

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



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 14 of 23

291

(15)

Al-Shannag, M.; Al-Qodah, Z.; Bani-Melhem, K.; Qtaishat, M. R.; Alkasrawi, M., Heavy

292

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

293

and Process Performance. Chem. Eng. J. (Amsterdam, Neth.)2015, 260, 749.

294

(16)

295

Aluminum Electrocoagulation: A Study on Back Mixing and Utilization Rate of Electro-

296

Generated Al Ions. Chem. Eng. J. (Amsterdam, Neth.)2015, 267, 86.

297

(17)

298

Decolorization by UV/H2O2Using Artificial Neural Networks. Dyes Pigm.2008, 77, 288.

299

(18)

300

Solutions by Electrocoagulation: Modeling of Experimental Results Using Artificial Neural

301

Network.J. Hazard. Mater.2009, 171, 484.

302

(19)

303

Neural Networks for Modeling of the Mreatment of Wastewater Contaminated with Methyl Tert-

304

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

305

(20)

306

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

307

(21)

308

Monopolar Electrocoagulation/Flotation (ECF) Process. J. Hazard. Mater.2006, 131, 118.

309

(22)

310

Network Prediction of Chemical Oxygen Demand in Dairy Industry Effluent Treated by

311

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

312

(23)

313

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

Lu, J.; Li, Y.; Yin, M.; Ma, X.; Lin, S., Removing Heavy Metal Ions With Continuous

Aleboyeh, A.; Kasiri, M. B.; Olya, M. E.; Aleboyeh, H., Prediction of Azo Dye

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

Emamjomeh, M. M.; Sivakumar, M., An Empirical Model for Defluoridation by Batch

Valente, G. F. S.; Mendonça, R. C. S.; Pereira, J. A. M.; Felix, L. B., Artificial Neural

Safamirzaei, M.; Modarress, H., Hydrogen Solubility in Heavy n-Alkanes; Modeling and


Page 15 of 23

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


314

(24)

Prakash, N.; Manikandan, S. A.; Govindarajan, L.; Vijayagopal, V., Prediction of

315

Biosorption Efficiency for the Removal of Copper(II) Using Artificial Neural Networks. J.

316

Hazard. Mater.2008, 152, 1268.

317

(25)

318

(ANN) for Modeling of Decolorization of Textile Dye Solution Containing C. I. Basic Yellow 28

319

by Electrocoagulation Process. J. Hazard. Mater.2006, 137, 1788.

320

(26)

321

Water. Water Research 1984, 18, 1355.

322

(27)

323

Electrocoagulation. Sep. Purif. Technol.2000, 19, 65.

324

(28)

325

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

326

Chem2012, 9, 2297.

327

(29)

328

Solution by Electrocoagulation Process Using Aluminum Electrodes. Desalination 2011, 279,

329

121.

Daneshvar, N.; Khataee, A. R.; Djafarzadeh, N., The Use of Artificial Neural Networks

Vik, E. A.; Carlson, D. A.; Eikum, A. S.; Gjessing, E. T., Electrocoagulation of Potable

Chen, X.; Chen, G.; Yue, P. L., Separation of Pollutants From Restaurant Wastewater by

Bazrafshan, E.; Ownagh, K. A.; Mahvi, A. H., Application of Electrocoagulation Process

Shafaei, A.; Pajootan, E.; Nikazar, M.; Arami, M., Removal of Co (II) From Aqueous

330 331 332 333 334 335 336



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

337 338 339 340

List of Figuresand Tables

341 342


Page 16 of 23

Page 17 of 23

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


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


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 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


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

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

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


377 378 379


Modeling the Removal of Endosulfan from ... - ACS Publications

Recommend Documents