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Technical Journal of Engineering and Applied Sciences Available online at www.tjeas.com ©2016 TJEAS Journal-2016-6-1/39-44 ISSN 2051-0853 ©2016 TJEAS

Comparison of Artificial Neural Network and NeutralFuzzy Inference System for photocatalytic removal of reactive red dye Mohammad Reza Rrezaei Kahkha1*, Jamshid Piri2 1. Departement of environmental health engineering,zabol university of medical sciences,Zabol.Iran. 2. Department of Soil &Water Faculty of Irrigation and Drainage,University of Zabol.Iran. *Corresponding Author email: [email protected] ABSTRACT: In this study, non-linear models artificial neural networks (ANNs), adaptive neuro-fuzzy inference system (ANFIS), and hybrid methods ANN have been used to estimate the photocatalytic oxidation of reactive red dye onto ZnO photocatalyst. Important parameters such pH , amount of ZnO and initial dye concentration have been used as the inputs of the network, while the output is the percentage removal of dye after adsorption. Different algorithms and transfer functions for hidden layer have been tested to find the most suitable and reliable network. Results indicated that both methods are suitable for prediction of removal efficiency of reactive red dye. Key words: Removal Dye, ZnO photocatalyst, Artificial neural network,prediction, adaptive neuro-fuzzy inference system(ANFIS) INTRODUCTION 7

There are over than 100,000 commercially available dyes with more than 7 x10 tons of dyestuff produced annually worldwide. These dyes are widely used in a number of industries, such as textiles, food,cosmetics and paper printing, with the textile industry being the largest consumer of dyes (Paul et al., 2011). Considerable attention has been given to issues associated with the presence of colored compounds in aqueous wastewater generated from textile industries, since water is the only efficient carrier for dyes and other compounds that are used in the dyeing and finishing processes. Dyes are classified according to their application and chemical structure, and are composed of a group of atoms known as chromophores, responsible for the dye color. These chromophore-containing centers are based on diverse functional groups, such as azo, anthraquinone, methine, nitro, arilmethane, carbonyl and others. In addition, electrons withdrawing or donating substituents so as to generate or intensify the color of the chromophores are denominated as auxochromes.(Royer et al., 2009. Lima et al., 2008,Royer et al., 2010) Textile wastewaters are characterized by extreme fluctuations in many parameters such as chemical oxygen demand (COD), biochemical oxygen demand (BOD), pH, color and salinity. In general, the treatment of dye–containing effluents is being undertaken by biological, adsorption, membrane, coagulation– flocculation,oxidation–ozonation, and Advanced Oxidation Processes (AOPs) (Alves de lima et al., 2007). AOPs have been developed to degrade the nonbiodegradable contaminants of drinking water and industrial effluents into harmless species (e.g. CO2, H2O, etc). Heterogeneous photocatalysis via combination of TiO2 or ZNO and UV light is considered one of the most promising AOPs for destruction of water–soluble organic pollutants. Artificial neural network (ANN) has been widely used to model the effect of parameters influencing adsorption or degradation of dyes (Khataee et al., 2010,Yang et al., 2012,Zarei et al., 2010). ANN is one of the artificial intelligence (AI) techniques that mimic the behavior of human brains. ANN is a nonlinear, powerful computational tool which is highly capable of solving classification, modeling and association problems by simulating the basic functions of the mammalian brain (Zurada, 1992) There are two phases in utilizing the neural network in a real-world problem: The first phase is called training/learning in which the connection strengths among the neurons, called weights, are tuned to model complex relationships between inputs and outputs. The second phase is testing phase in which the weights determined in the learning phase are used to produce approximately

Tech J Engin & App Sci., 6 (1): 39-44, 2016 correct results for new input values that are not used in the training phase.(Haykin, 2008) ANFIS model is the type of functions that considered for the model inputs. These selected functions are Gaussian, circular, and sigmoid. In this research and for the first time both ANN and ANFIS models were used for prediction of photocatalytic degradation of Reactive Red (RR) dye from aqueous solutions. Affective parameters on photacatalytic process such as pH, initial dye concentration and dosage of ZnO nano particles were tested and optimized. MATERIAL AND METHODS Batch experimental procedure All the experiment were performed in a batch photoreactor system that equipped by 1 UV lamp (125w) at center of reactor. For enhance of removal efficiency the reactor was covered with aluminum sheets. Appropriate dose of ZnO nanoparticle were mixed with different amount of dye concentration and solution stirred at 300 rpm for 30 minutes while UV lamp at 3800 W was irradiate solution. After completion of experiment 30 ml of the sample was taken and in order to separate the zinc oxide nanoparticles, the sample was centrifuged at 5000 rpm and filtered. Measuring of remind concentration was done by Cecil spectrophotometer at 530 nm. The removal percentage of RR dye (%removal) was calculated as follow: (1) -1 Where Ce and C0 are initial and final dye concentration (mg l ) of reactive dye, respectively. ANN Model The Neural Network code of MATLAB (R2013a) mathematical software was used to predict removal percentage. A three layer ANN (Fig. 1) with a tangent sigmoid transfer function (tansig) at hidden layer, a linear transfer function (purelin) at output layer and Levenberg–Marquardt algorithm with 5000 iterations were implemented. The data were randomly divided into three groups (60% for training i.e. 30, 15% i.e. 9 data for cross validation and 15% i.e. 9 for testing set). -1 In this study three neurons (initial dye concentration (30–100 mg.L ), adsorbent dosage (0.3– 2 g) and pH( 2-12) as input layer ,1–23 neurons in the hidden layer and one neuron (removal percentage) in the output layer were applied. All the data (input and output) for ANN models were normalized between 0 and 1 to avoid numerical overflows due to very large or small weights. The normalization equation applied is as follows: (2) Where y is the normalized value of xi, the xmax and xmin are the maximum and minimum value of xi, respectively. Figure 1. Input, hidden and output layers in neutral network. All calculations related to this research using EXCEL software and network architecture was done by coding in MATLAB. Adaptive neural-fuzzy inference system (ANFIS) A popular teaching method in neuro-fuzzy systems is the fuzzy inference system, which uses the hybrid learning algorithms to identify the fuzzy system parameters and teach the model as well (Piri et al., 2013. Tang et al., 2010). As seen in Figure 2, ANFIS model is a five-layer structure, which is the result of adding the fuzzy logical models to the artificial neural net: Layer 1 or input layer: In this layer, the membership degree of the nodes entering different fuzzy periods is determined by using the membership functions. There are numerous kinds of membership functions such as trapezoids, sigmoid, Gaussian, and bell-shaped functions. Two fuzzy sets are considered for each input. The shape of the membership function and the amount of their overlapping are optional, and determined by equation 3:

 A ( x) 

1 x  ci 1 ai

2 bi

(3)

In this equation, x is input, and a, b, and c are comparative parameters and the non-linear coefficients of this equation, which determine the shape of the membership function. The set of the fuzzy variables coefficients is 40

Tech J Engin & App Sci., 6 (1): 39-44, 2016 called S1 set or the left- handed set (LHS). The output amounts of the first layer (e.g. solar radiation data) show the membership amount for each input regarding the different membership functions of the inputs. Layer 2: This layer is the result of multiplying the input amounts by the nodes, and finally the firing strength. For example, for the first node, we have: (4)

wi   A1 ( x1 )  B1 ( x 2 )

Layer 3: Its nodes normalize the firing strength:

w1

W1 

n

w

, i  1,2,..., n

(5)

i

i 1

In this equation n is the number of nodes in each layer. Layer 4 is the terms layer in which terms are achieved. These terms are the results of operation on the input signals into this layer: (6) Z1  w1 f1  w1 ( p1 x1  q1 x2  r1 ) In this equation r1, q1, and p1 are the consequent parameters. This set is generally called the set of RHS parameters. Layer 5 is the last layer of the net which includes only one node and in calculated via adding all input amounts into its total output: n

w f i 1

i

(7)

i

Figure 2. The structure of an ANFIS net One of the features of every ANFIS model is the type of function considered for the model inputs. (Rehman et al., 2008). These selected functions are Gaussian, circular, and sigmoid. The process of selecting the membership functions is studied for each of the functions separately, and ANFIS model is trained individually for each of these membership functions. Afterwards, the amounts of error are compared and the function which has the least amount of error in the shortest period of training is chosen as the best membership function.(Piri et al., 2009) Eventually, the results of analyze of each model obtained by, applying different architectures and algorithms, are compared with the measurements and the best model is obtained. The statistical criteria used for the evaluation of the models include root mean square error (RMSE) and mean absolute error (MAE), as following:  N 2  ( Fi  Oi )   RMSE   i 1   N    



 N Fi  Oi  i 1  MAE   N  



     

1

2

(8)

(9)

Where Fi and Oi are the ith simulated and observed amounts, respectively and N is number of observations. The model with the lowest RMSE and MAE is the most accurate model. (Piri et al., 2009) RESULTS AND DISCUSSION Effect of pH on dye removal efficiency The pH has an important role on dye oxidation since the pH of the aqueous solution will control the magnitude of the electrostatic charges that are imparted by ionized dye molecules and adsorbate (Elemen et al., 2012. Tomczak et al., 2011). Figures 3 and 4 depict the comparison of actual and predicted values for effect of pH on removal efficiency for ANN and ANFIS model respectively developed for photocatalytic degradation. As can be seen from Fig.1, the photocatalytic removal of RR dye dependent on pH of solution. Removal efficiency of RR dye 41

Tech J Engin & App Sci., 6 (1): 39-44, 2016 increases with increasing solution pH from 2 to 4 and decreases slowly when solution pH is above 4. Acidic conditions could be favorable for the oxidation of reactive red because a significantly high electrostatic attraction could exist between the positively charged surface of the ZnO nanoparticle under acidic conditions and the anionic dye. Therefore, the initial pH=4 was selected as optimized pH. Similar results were reported by other researchers. (Shirmardi et al., 2012. Absalan et al., 2011). Also, figure 1 and 2 showed the comparision of actual data and predicted data obtained by ANN and ANFIS models for evaluation effect of pH on removal efficiency, respectively. 2 Accuracy of the prediction for both models is fairly good and acceptable. The amounts of R , RMSE and MAE of ANFIS and ANN models for are presented in table.1 2

Table 1. The R , RMSE and MAE statistics of ANN and ANFIS models for investigation effect of pH on removal efficiency in validation period Model

Training algorithm

Stimulating function

ANN ANFIS

LM LM

Sigmoid Gaussmf

No. of neurons in the hidden layer 6 9

R2

RMSE

MAE

0.94 0.9617

0.570222222 0.792679439

0.121 0.057

Figure 3. ANN model for comparison of actual results with predicted data for evaluation of pH effect on removal dye -1 efficiency. (Initial dye concentration=50 mg l ,Zno dosage=1g,time=30min) Figure 4.ANFIS model for comparison of actual results with predicted data for evaluation of pH effect on removal -1 dye efficiency. (Initial dye concentration=50 mg l ,Zno dosage=1g,time=30min) Effect of ZnO dosage on dye removal efficiency Amount of ZnO nanoparticle is an important parameter in photocatalytic removal of dyes because this determines the capacity of the adsorbent for a given initial dye concentration. (Kangamani et al., 2007) In order to attain the optimal amount of ZnO nanoparticle for the adsorption of RR dye, 0.3–2 g adsorbent was used for adsorption experiments at optimized pH (pH 4), initial dye concentration (50 mg/L), for 30 min. In Figures 5, 6 illustrated comparission of actual and predicted values using ANN and ANFIS models for investigation effect of ZnO dosage on removal efficiency as an output parameters,respectively. As it can be seen from these figures, it was found that the adsorption percentage increased with the increasing amount of ZnO nanoparticle, and when the amount exceeded 1.8 g, the removal efficiency reached maximum amount. An increase in adsorption rate with adsorbent dosage can be attributed to increased surface area of ZnO nanoparticle and the availability of more adsorption sites.(Tariq et al., 2008) Similar results were reported by other researchers. (Tariq et al., 2008, Aber et al., 2007) The results indicated that neuro-fuzzy model is the most accurated method for removal dye efficiency. 2 The amounts of R , RMSE and MAE of ANFIS and ANN models are presented in table.2 2

Table 2. The R , RMSE and MAE statistics of ANN and ANFIS models for investigation effect of ZnO dosage on removal efficiency in validation period. Model

Training algorithm

Stimulating function

ANN ANFIS

LM LM

Sigmoid Gaussmf

No. of neurons in the hidden layer 6 9

R2

RMSE

MAE

0.917 0.927

0.820813705 0.701835451

0.600815028 0.498875

Figure 5. ANN model for comparison of actual results with predicted data for evaluation of ZnO dosage on removal -1 dye efficiency. (Initial dye concentration=50 mg l ,pH=4,time=30min) Figure 6. ANFIS model for comparison of actual results with predicted data for for evaluation of ZnO dosage on -1 removal dye efficiency. (Initial dye concentration=50 mg l ,pH=4,time=30min). Effect of dye concentration on removal efficiency Initial dye concentration is very important factor for investigation of photocatalytic experiments. Since increase of dye concentration blocked transmission of UV light and therefore decreased removal efficiency.(Bahnemann et al., 2004) In Figures 7, 8 illustrated comparission of actual and predicted values using ANN and ANFIS models for investigation effect of dye concentration on removal efficiency as an output -1 parameters,respectively. Consideration of this graph a maximum efficiency obtained at 50 mg l reactive red dye.

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Tech J Engin & App Sci., 6 (1): 39-44, 2016 2

The results indicated that ANN model is the most accurate method for removal efficiency. The amounts of R , RMSE and MAE of ANFIS and ANN models are presented in table.3 2

Table 3. The R , RMSE and MAE statistics of ANN and ANFIS models for investigation effect of initial dye concentration on removal efficiency in validation period. Model

Training algorithm

Stimulating function

ANN ANFIS

LM LM

Sigmoid Gaussmf

No. of neurons in the hidden layer 6 9

R2

RMSE

MAE

0.917 0.927

0.285599115 0.527696755

0.157801683 0.352611111

Figure 7. ANN model for comparison of actual results with predicted data for evaluation of initial dye concentration on removal efficiency. (ZnO dose=1.8 g ,pH=4,time=30min). Figure 8. ANFIS model for comparison of actual results with predicted data for evaluation of initial dye concentration on removal efficiency. (ZnO dose=1.8 g ,pH=4,time=30min). CONCLUSION In this paper for the first time photocatalytic removal of reactive red dye using ZnO nanoparticle was studied. A batch reactor designated and applied to perform experimants. Three important parameters that affected removal efficiency were tested and optimized. Nowadays, due to the development of artificial intelligence in simulating various parameters, models such as neuro and fuzzy nets have gained more attention regarding their capability in predicting unknown parameters. In this research, an adaptive neuro-fuzzy inference system(ANFIS) and a neural network model with exogenous inputs (ANN) were used for removal of reactive red dye prediction. Results indicated that both models are favorable for simulation and prediction of results. By investigating effect of adsorbent (ZnO nanoparticle) dosage on removal efficiency and lowest amount of MAE, RMSE and highest 2 amount of R obtained from ANFIS model found that ANFIS model is more accurate for this parameters. By 2 studying effect of dye concentration on removal efficiency and amount of MAE, RMSE and R obtained from ANN model found that ANN more matches with actual data for these parameters. REFRENCES Aber S., Daneshvar N., Soroureddin S.M., Chabok A., Asadpour-Zeynali K.2007. Study of acid orange 7 removal from aqueous solutions by powdered activated carbon and modeling of experimental results by artificial neural network. Desalination, 211: 87–95. Absalan G, Asadi M, Kamran S, Sheikhian S, and Goltz D. M. 2011. Removal of reactive red-120 and 4-(2-pyridylazo) resorcinol from aqueous samples by Fe3O4 magnetic nanoparticles using ionic liquid as modier. J. Hazard. Mater..192,476–484. Alves de Lima R. S,Bazo A. P,Salvadori D. M. F, Rech C. M, Oliveira D. P, and Umbuzeiro G. A, 2007. Mutagenic and carcinogenic potential of a textile azo dye processing plant effluent that impacts a drinking water source,” Mutat. Res. 626, 53–60. Bahnemann D. 2004. Photocatalytic Water Treatment: Solar Energy Applications. J. Sol. Energy.77: 445-459. Brookstein D. S, 2009. Factors associated with textile pattern dermatitis caused by contact allergy to dyes, nishes, foams, and preservatives. Dermatol. Clin. 27. 309–322. Elemen S, Emriye Perrin Akçakoca Kumbasar E, Yapar S. 2012. Modeling the adsorption of textile dye on organoclay using an Artificial Neural network. Dyes Pigm. 95:102-111. Kangamani K. S, Sathishkumar M, Sameena Y. 2007. Utilization of modifed silk cotton hull waste as an adsorbent for the removal of textile dye (reactive blue MR) from aqueous solution,” Bioresour. Technol., 981265–1269. Khataee A. R, Dehghan G, Ebadi A, Zarei M, Pourhassan M (2010) Biological treatment of a dye solution by Macroalgae Chara spe:effect of operational parameters, intermediates identification and artificial neural network modeling. Bioresour. Technol. 101: 2252–2258. Lima E. C,Royer B, Vaghetti J .C. P. .2012. Application of Brazilian pine-fruit shell as a biosorbent to removal of reactive red 194 textile dye from aqueous solution. Kinetics and equilibrium study. J. Hazard. Mater, 155536–550. Paul J, Rawat K. P, Sarma K. S. S, Sabharwal S. 2011. Decoloration and degradation of Reactive Red-120 dye by electron beam irradiation in aqueous solution,” Appl. Radiat. Isot., 69. 982–987. Piri, J., Amin, S., Moghaddamnia, A., Han, D.andD.Remesun. 2009. Daily pan evaporation modelling is hot and dry climate, journal of hydrologic engineering, 14: 803-811. Rehman, S. and M. Mohandes. 2008. Artificial neural network estimation of global solar radiation using air temperature and relative humidity. Energy Policy 36: 571- 576. Royer B, Cardoso N. F,Lima E. C, Macedo T. R, Airoldi C, 2010. A useful organofunctionalized layered silicate for textile dye removal J. Hazard. Mater. 181. 366–374. Royer B, Cardoso N. F,Lima E. C. 2009. Applications of Brazilian pine-fruit shell in natural and carbonized forms as adsorbents to removal of methylene blue from aqueous solutions-Kinetic and equilibrium study,” J. Hazard. Mater. 164. 1213–1222. Shirmardi M, Mesdaghinia A. R.,Mahvi A. H., Nasseri S., and Nabizadeh R. 2012. Kinetics and equilibrium studies on adsorption of acid red 18 (Azo-Dye) using multiwall carbon nanotubes (MWCNTs) from aqueous solution. E-Journal of Chemistry. 9. 2371–2383. Tang, W., Yang, K., He, J., and Qin, J. 2010. Quality control and estimation of global solar radiation in China J. Sol. Energy, 84: 466–475.

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