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† †

Department of Chemical Engineering and ‡Faculty of Chemistry, University of Mazandaran, Babolsar 47416-95447, Iran ABSTRACT: Electrocoagulation (EC) is an electrochemical method to treat polluted wastewaters and aqueous solutions. In this research, EC was used to remove Endosulfan from aqueous solution. The results show that the best conditions that obtained in this study are pH = 4, current density = 6.2 mA/cm2, initial concentration of Endosulfan = 30 mg/L, and electrolysis time = 60 min. The solution conductivity seems to have no significant effect on the removal efficiency. Artificial neural network (ANN) was utilized to model the experimental data. The model was developed by using three layer feed-forward neural network with eight neurons in the hidden layer for modeling of EC process. A comparison between the predicted results and experimental data gave high correlation coefficient (R2 = 0.976) and showed that the model is able to predict the removal efficiency.

1. INTRODUCTION Endosulfan has been used in agriculture around the world to control insect pests. Water that is contaminated with Endosulfan can bring about serious environmental problems and also threaten human health. Endosulfan is one of the most toxic pesticides on the market today and is responsible for many fatal pesticide poisoning incidents around the world. The excessive concentration of this insecticide, causes reproductive and developmental damages in both animals and humans, and also can promote proliferation of human breast cancer cells.1 Therefore, the amount of usage should be controlled lest the toxicant contaminate ground or seawater. To find out the suitable treatment for removal of toxicant from water for both environmental and economic reasons, several processes such as treatment by ion exchange,2 advanced oxidation process,3 photochemical degradation,4 and adsorption on zerovalent zinc5 have been developed. Electrochemical technology can be applied for the treatment of wide range of wastewaters. One of the electrochemical methods is electrocoagulation (EC) that can compete with the conventional chemical coagulation process in the treatment of wastewaters. The EC process is characterized by low investment cost, low space need, simple equipment requirement, sludge stability, operational simplicity, no need for chemical materials, rapid sedimentation, low sludge production, and environmental compatibility. EC is an effective and credible method for treating different wastewaters including phenol,6 arsenic,7 fluoride,8 oil,9 diazinon,10 wastewaters from chicken industry,11 cheese whey,12 hospital wastewater,13 baker’s yeast,14 and heavy metal containing solution.15,16 The EC process is based on in situ generation of coagulant through electrodissolution of aluminum electrodes. In the EC process, the aluminum electrodes produce their hydroxides (Al(OH)3) in the contaminated water. In this process, when the aluminum electrodes are used as an anode and a cathode, the main reactions can be summarized as follows:

(1)

2H 2O → O2(g) + 4H+(aq) + 4e−

(2)

(b) Cathodic reactions: 2H 2O + 2e− → H 2(g) + 2(OH)−(aq)

(3)

Because of the complexity of the reactions that occur in the EC process, it is difficult to determine the kinetic parameters; thus it causes uncertainties in the design and scale-up of chemical reactors for industries. The EC process is generally complicated and depends on several parameters, so the modeling of this process has many problems that cannot be solved by simple linear correlation. The artificial neural network (ANN) has the ability to recognize and reproduce cause and effect relationships through training, for multiple input/output systems, which makes it efficient to represent and up-scale even the most complex systems such as EC.17,18 ANN has robust and reliable characteristic in finding the nonlinear relationships between variables (input/output) in complex systems; numerous applications of ANN have been successfully conducted to solve environmental engineering problems.18,19 According to the literature, modeling of EC process has been little investigated. Hu et al. applied the Langmuir equation to specify the kinetics of the fluoride removal reaction by EC.20 Emamjomeh et al. developed an empirical model for fluoride removal by electrocoagulation/flotation (ECF) process.21 Recently, Valente et al. predicted chemical oxygen demand in the dairy industry treated by EC with ANN.22 In this paper, the removal efficiency of Endosulfan in aqueous solution by EC treatment was investigated. The effect Received: Revised: Accepted: Published:

(a) Anodic reactions: © 2015 American Chemical Society

Al → Al3 + + 3e−

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August 3, 2015 September 19, 2015 September 23, 2015 September 23, 2015 DOI: 10.1021/acs.iecr.5b02846 Ind. Eng. Chem. Res. 2015, 54, 9844−9849

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Industrial & Engineering Chemistry Research Table 1. Characteristics of Endosulfan

between 10 and 30 V, and current density was in the range of 2.5−12 mA/cm2. The conductivity of solution was between 2.62 and 7.7 mS/cm. During the process, the solution was agitated at 200 rpm, and the sampling of solution was carried out at each 15 min to determine the residual concentration of Endosulfan. Also, the total time of process was 60 min. The concentration of Endosulfan in solution was analyzed using a UV−vis spectrophotometer (Braic-2100, China). Absorbance was measured at the wavelength of 250 nm and spectral bandwidth of 0.2 nm. The removal efficiency (Re) was calculated using eq 4 where C0 is the initial concentration of Endosulfan in aqueous solution, and C is the concentration of it at time t:

of several parameters such as initial pH, electrolysis time, initial concentration of Endosulfan, and current density on the removal efficiency was studied. An important aim of this study is removal of Endosulfan from aqueous solution by EC and presentation of an ANN model that provides reliable and robust prediction of the efficiency of this process.

2. EXPERIMENTAL SECTION 2.1. Materials and Instruments. Endosulfan solution was prepared by dissolving Endosulfan (Merck, Germany) in distilled water. The chemical structure and other characteristics of Endosulfan are shown in Table 1. Initial pH of solutions was adjusted by 0.5 M NaOH (Merck, Germany) and HCl (Merck, Germany) solutions and determined by pH meter (Metrohm 826, Switzerland). The initial conductivity of the solution was adjusted by addition of NaCl. Also, the conductivity measurements were carried out by conducto meter (JENWAY, 4020, U.K). Four aluminum plates were used as anodes and cathodes. Dimensions of electrodes were 100 × 50 × 2 mm3, and the distance between two electrodes in EC cell was 10 mm in all experiments. The outer electrodes were connected to the DC power source (Sanjesh, Iran) with galvanostatic operational option to control the current density. 2.2. Procedure. The experimental setup is shown in Figure 1. Experiments were conducted with four aluminum electrodes

⎛ C⎞ R e = ⎜1 − ⎟ × 100 C0 ⎠ ⎝

(4)

2.3. ANN Method. ANN is a branch of artificial intelligence (AI) that can model any kind of data sets even in cases where available data are complex. ANN is a mathematical modeling technique, which applies numerical analysis to provide reliable models.23 The inspiration of using neural network came from the biology of the human brain24 where billions of neurons are interconnected to process different information. An ANN, at least, consists of two layers: input and output layers. The input layer represents the independent variables, while the output layer represents the dependent variable. Between the input layer and the output layer, there are layers called the hidden layers. The number of neurons in hidden layers should be optimized. Each layer is composed of some neurons, which are connected to neurons located in previous and next layers. Information in an ANN is divided between multiple cells (nodes) and connection between cells (weights).18 The number of input and output neurons is fixed by the nature of problems. Data points should be divided into two major sets. The first set is used to train and validate the ANN, and the other data set is used to test the network. A training procedure optimizes network parameters (weights and biases). When training of ANN via proper propagation method is finished, the second set of data, which is completely new to the ANN, is used to test the trained network.23 In an ANN, transfer function is a mathematical representation of the relation between the input and output layer. There are several transfer functions such as radbas, purelin, hardlim, satlin, poslin, etc. One of the most commonly used function is sigmoidal transfer function and is given by19

Figure 1. Setup of EC experiment: (1) DC power supply; (2) stirrer; (3) magnetic bar; (4) cathode electrode; (5) anode electrode.

connected in bipolar mode to a glass pipe. Before each experiment, the electrodes were polished by sandpapers with different mesh and then dipped in 0.5 M HCl solution to dissolve any oxide from their surfaces. Then electrodes were rinsed with distilled water and finally dried at oven at 75 °C for 15 min. All runs were performed at 25 ± 3 °C. In each experiment, 400 mL of Endosulfan solution (with specified concentration) was transferred into the electrolytic cell. After the current density was adjusted to the desired value, the operation was started. The contaminant concentration varies between 10 and 80 mg/L. In this study, the voltage of cell was

f (x ) =

1 1 + e −x

(5)

Where f(x) is the hidden neuron output. This function is used in the next section to normalize the experimental data. 9845

DOI: 10.1021/acs.iecr.5b02846 Ind. Eng. Chem. Res. 2015, 54, 9844−9849

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Industrial & Engineering Chemistry Research

3. RESULTS AND DISCUSSION 3.1. Neural Network Modeling. The topology of an ANN is determined by the number of layers, the number of nodes in each layer, and the nature of the transfer functions. Optimization of ANN is an important step in the development of a model.25 In this research, multilayer feed-forward ANN with sigmoidal transfer function with backpropagation algorithm was used. A linear transfer function (purelin) was used at the output layer. The training function was “train scaled conjugate gradient backpropagation” (trainscg). All calculations were carried out with MATLAB mathematical software with ANN toolbox. To apply one network for EC process, five neurons such as time of electrolysis, current density, pH, solution conductivity, and initial concentration of Endosulfan in aqueous solution are required for input layer and one neuron for output layer (removal efficiency of Endosulfan). The range of studied variables is summarized in Table 2. To optimize neurons

Seventy experiments were conducted to develop the ANN model. The samples were allocated to training and test sets, and each of them contains 50 and 20 samples, respectively. Since the transfer function used in the hidden layer was sigmoid, all samples were normalized in 0.1−0.9 range. Therefore, all of the data (xi) were converted to norm values (xnorm) as follows:17,18 ⎛ x − xmin ⎞ xnorm = 0.8⎜ i ⎟ + 0.1 ⎝ xmax − xmin ⎠

(6)

Where xmin and xmax are the extreme values of variable xi. Figure 3 demonstrates a comparison between the experimental and

Table 2. Model Variables and Their Ranges variable input layer initial pH current density electrolysis time initial Endosulfan concentration solution conductivity output layer residual concentration

range 2−10 2.51−12 mA/cm 0−60 min 10−80 mg/L 2.62−7.71 mS/cm 0−100%

Figure 3. Comparison of the experimental results with those calculated via neural network modeling.

number in the hidden layer, 70% of data points were used to train the ANN, and deviations were considered to make decision about optimized number of neurons in the hidden layer. The results of experiments indicated that 5−8−1 network could be applicable for this process. Figure 2 shows the architecture of 5−8−1 network.

predicted values using the ANN for all of data used for training and testing. It shows that the points are well distributed around X = Y line in narrow area. A correlation coefficient of R2 = 0.976, for the line plotted using experimental and predicted data, illustrates the reliability of the model. The results demonstrate that ANN is fast and has the prediction capability. Assuming that no experimental data are available for a condition in EC, 5−8−1 network can predict the removal efficiency accurately via available data of a similar system, while other techniques do not have this capability. 3.2. Effect of Initial pH. The dependences of removal efficiency on initial pH values were investigated over initial pH range of 2−10. Vik et al. observed that the pH of solution changes during the EC process. They reported that pH increment occurs when the initial pH is lower than 7.26 They ascribed this increase in pH to hydrogen evolution at the cathode. However, Chen et al. explained this by the release of CO2 from wastewater. Actually, in low pH, CO2 releases during the H2 evolution and causes pH to increase.27 Also, Bazrafshan et al. demonstrated that when pH is higher than 8, the final pH does not vary significantly, and only a short drop occurs.28 The experimental values of Endosulfan removal percent from aqueous solution at different initial pH values as well as that obtained by ANN are shown in Figure 4. Each experiment was replicated (n) five times (n = 5), and relative standard deviation (RSD) was 2.1%. As can be observed in Figure 4, the optimum efficiency occurred at pH = 4. By decreasing pH of the solution, the probability of dissolving the aluminum hydroxide and the conversion of it into other types of aluminum species increased.

Figure 2. ANN optimized structure. 9846


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Figure 4. 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).

Figure 5. Effect of current density on the removal efficiency of Endosulfan: pH = 7, Endosulfan concentration (50 mg/L), solution conductivity (2.62 mS/cm).

When pH increases, aluminum hydroxide converts to the negative aluminum hydroxide complexes according to the following equations:10 Al(OH)3 + OH− → Al(OH)4 −

(7)

Al(OH)4 − + OH− → Al(OH)52 −

(8)

Al(OH)5−

(9)

−

+ OH → Al(OH)6

3−

the anode when lower current densities are applied, and because of this, the removal efficiency of Endosulfan from aqueous solution diminishes. 3.4. Effect of Electrolysis Time. Electrolysis time is an important parameter, which affects on the removal efficiency and controls the reaction rate. To explore the effect of operating time, a series of experiments (n = 5, RSD = 2.3%) were carried out on solution containing constant Endosulfan concentration (50 mg/L) with initial pH = 7 under constant current density (6.6 mA/cm2) at different electrolysis times. According to Faraday’s law, the amount of aluminum released from anode depends on the electrolysis time and current density, so by increasing the time of reaction, more aluminum is released from the anode surface, and the Endosulfan removal from solution enhances. Figure 6 illustrated that the removal

Also, the negatively charged aluminate ions may be formed through the following reaction: Al(OH)3 + OH− → AlO2− + 2H 2O

(10)

There is an optimum pH for adequate adsorption of Endosulfan, and an increase or a decrease of pH can effect on the removal efficiency of adsorbed Endosulfan. In basic media, as a result of the formation of aluminum species, whose acidic sites are filled with hydroxide ions, Endosulfan cannot be adsorbed by the precipitate. In highly acidic media, the aluminum hydroxide coagulant is solved, so the absorbed Endosulfan releases in the solution. 3.3. Effect of Current Density. Figure 5 shows a comparison between experimental (n = 5, RSD = 1.7) and calculated values of removal efficiency of Endosulfan as a function of current density. In an EC process, current density determines the coagulant production rate, and the removal efficiency depends on aluminum concentration. The theoretical amounts of Al dissolution (mtheo) in the EC cell can be expressed by Faraday’s law as follows:10

ItM (11) ZF where I is the current density (A), t is the time of electrolysis (s), m is the amount of dissolved aluminum (g), M is the atomic weight of the aluminum (g/mol), Z is the metal valence (3 for Al), and F is Faraday’s constant (F = 96,487 C/mol). As the results indicate, the removal efficiency increases with current density increment. By increasing the current density from 2.5 to 12 mA/cm2, the removal efficiency rises from 74.6% to 92.6%. According to Faraday’s law, the amount of anodic dissolution of Al grows by increasing the current density. The higher amounts of generated coagulant can enhance the EC removal efficiency. At variance, less aluminum is released from mtheo =

Figure 6. 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.

efficiency increases from 66.6% after 15 min to 84.57% after 60 min of process. Also, it can be observed in Figure 6 that predicted values from proposed ANN model are in good agreement with the experimental data. 3.5. Effect of the Initial Concentration of Endosulfan. Effect of the initial concentration of Endosulfan was investigated on the removal efficiency of EC cell in the range of 10−80 mg/L. The experimental data (n = 5, RSD = 2.15) and ANN predicted values of Endosulfan removal efficiency 9847


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it can be dedicated from Figure 8 that ANN predicts the removal efficiency correctly.

against initial concentration of it are depicted in Figure 7. As presented in Figure 7, the removal efficiency of Endosulfan

4. CONCLUSIONS The removal efficiency of Endosulfan from aqueous solution was examined by electrocoagulation using aluminum electrodes. The effects of various operational parameters such as initial concentration of contamination, current density, pH, electrolysis time, and solution conductivity have been investigated on removal efficiency. It was observed that in initial concentration of Endosulfan, current density and electrolysis time have significant effects on the removal efficiency. The results revealed that pH = 4 is optimum condition, and by increasing the pH of solution, the removal efficiency decreases. Also, the results illustrated that the performance of EC process in removal of Endosulfan can be successfully predicted by applying a multilayer feed-forward ANN (using backpropagation algorithm) with eight hidden layers.

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

AUTHOR INFORMATION

Corresponding Author

*Phone: +981135302903. E-mail: [email protected]. decreases with increasing in its initial concentration. For example, after 60 min of EC process at pH = 7, current density (6.6 mA/cm2) and at initial Endosulfan concentrations of 10, 30, 50, 70, and 80 mg/L, about 91.23, 88, 84.57, 65.3, and 51.75% of removal efficiencies were obtained, respectively. This can be related to the fact that the amount of Al ions is constant at the same constant current density and time for all initial concentrations of contaminate (according to Faraday’s law). As a result, the Al ions produced at high initial concentration of Endosulfan are insufficient to reduce all of contaminates. Similar results were observed previously in other studies.18,29 3.6. Effect of Solution Conductivity. To investigate the influence of solution conductivity, different solutions of Endosulfan (50 mg/L at pH = 7) were prepared with conductivity in the range of 2.6−7.7 mS/cm. The current density of 6.6 mA/cm2 was applied to all solutions. Figure 8 show the relation between the removal efficiency and solution conductivity. As can be seen, the solution conductivity has no significant effect on the removal efficiency of Endosulfan. Also,

Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS We gratefully acknowledge thefinancial support received from the University of Mazandaran.

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REFERENCES

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Figure 8. Effect of solution conductivity on the removal efficiency of Endosulfan: current density (6.2 mA/cm2), Endosulfan concentration (50 mg/L), pH = 7. 9848


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