Least square-support vector (LS-SVM) method for modeling of methylene blue dye adsorption using copper oxide loaded on activated carbon: Kinetic and isotherm study

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JIEC-1504; No. of Pages 9 Journal of Industrial and Engineering Chemistry xxx (2013) xxx–xxx

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Least square-support vector (LS-SVM) method for modeling of methylene blue dye adsorption using copper oxide loaded on activated carbon: Kinetic and isotherm study M. Ghaedi a,*, A.M. Ghaedi b, M. Hossainpour b, A. Ansari b, M.H. Habibi c, A.R. Asghari d a

Chemistry Department, Yasouj University, Yasouj 75914-35, Iran Department of Chemistry, Gachsaran Branch, Islamic Azad University, P.O. Box 75818-63876, Gachsaran, Iran Nanotechnology Laboratory, Department of Chemistry, University of Isfahan, Isfahan 81746-73441, Iran d Chemistry Department, Semenan University, Semnan, Iran b c

A R T I C L E I N F O

Article history: Received 1 July 2013 Accepted 7 August 2013 Available online xxx Keywords: Methylene blue Copper oxide loaded on activated carbon (CuO-NP-AC) Kinetic model Isotherm Least square-support vector (LS-SVM)

A B S T R A C T

A multiple linear regression (MLR) model and least square support vector regression (LS-SVM) model with principal component analysis (PCA) was used for preprocessing to predict the efficiency of methylene blue adsorption onto copper oxide nanoparticle loaded on activated carbon (CuO-NP-AC) based on experimental data set achieved in batch study. The PCA-LSSVM model indicated higher predictive capability than linear method with coefficient of determination (R2) of 0.97 and 0.92 for the training and testing data set, respectively. Firstly, the novel nanoparticles including copper oxide as low cost, non-toxic, safe and reusable adsorbent was synthesized in our laboratory with a simple and routine procedure. Subsequently, this new material properties such as surface functional group, homogeneity and pore size distribution was identified by FT-IR, SEM and BET analysis. The methylene blue (MB) removal and adsorption onto the CuO-NP-AC was investigated and the influence of variables such as initial pH and MB concentration, contact time, amount of adsorbent and pH, and temperature was investigated. The results of examination of the time on experimental adsorption data and fitting the data to conventional kinetic model show the suitability of pseudo-second order and intraparticle diffusion model. Evaluation of the experimental equilibrium data by Langmuir, Tempkin, Freundlich and Dubinin Radushkevich (D-R) isotherm explore that Langmuir is superior to other model for fitting the experimental data in term of higher correlation coefficient and lower error analysis. ß 2013 The Korean Society of Industrial and Engineering Chemistry. Published by Elsevier B.V. All rights reserved.

1. Introduction Dyes widely applied in textiles, food and beverage industries and printing processes [1]. These materials are important pollutants that lead to producing serious hazards to the human and other animals and organisms. Dyes present in wastewater generate high toxicity following possible accumulation in the various ecosystem and environment. Therefore, their removals from industrial effluents before discharge into the environment require extreme and great attention. Various dyes removal pathways are based on sole or combination of the physical, chemical and biological processes. Among versatile protocol for wastewater treatment versatile, the adsorption methods are widely used to remove classes of chemical pollutants from wastewater. These wide application concern due to high efficiency,

* Corresponding author. Tel.: +98 741 2223048; fax: +98 741 2223048. E-mail addresses: [email protected], [email protected] (M. Ghaedi).

capacity adsorbent usable for large scale application, while the adsorbent are regenerable, safe and non-toxic and/or lower toxicity [2–7]. Nano-particles as sorbents for separation, removal and/or preconcentration are applicable for enrichment of trace elements are its effective protocol [8]. Dyes have diverse and versatile structure and are classified according to several criterion including [9] chemical structure, application and may be classified as soluble and insoluble family. (MB) (3,7-bis (dimethylamino)phenothiazin-5-iumchloride) belong to thiamine cationic dye commonly used for coloring paper, temporary hair colorant, dyeing cottons, wools and so on. MB as eye burns agent is responsible for permanent injury to the human and animals eyes [10]. Generally, MB readily absorb by fish tissue as wide application human nutrient constituents. This point makes its quantification an urgent task [11]. Extensive uses of dyes allow achieving huge amount of colored wastewater that lead to generation of problems and hazards for diverse organism. Dyes with complicated structure are not degraded and/or removed by conventional separation processes [12–14] and lead to design of an

1226-086X/$ – see front matter ß 2013 The Korean Society of Industrial and Engineering Chemistry. Published by Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.jiec.2013.08.011

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efficient and economic procedure for their treatment and removal [15]. Currently, wide and extensive application of activated carbon emerge from its high porosity, large surface area and high mechanical and chemical stability, with at least cost that act as mild reducing agent and catalyst [16–18] and also is applicable medically to adsorb a wide variety of toxins. Guava seeds are agricultural by-products without economic value, while the seeds are consider as waste product during guava juice processing (seeds represent about 5% of the fresh fruit) [19]. The seeds have high amount of lignocellulosic materials which make it suitable for the preparation of activated carbons [20,21]. Therefore, it is necessary to investigate and develop low-cost effective carbons usable for water pollution treatment and wide variety of such material exploited for contaminants removal from aqueous solutions. Consequently, this paper aims to evaluate the potential usage of novel low cost and green low-cost activated carbon from Cydonia oblonga wood and subsequent activation by HNO3 and/or HCl. This material surface subsequently loaded with copper oxide nanoparticles to improve its efficiency and ability for MB removal. The properties of proposed CuO-nanoparticle loaded AC are investigated through SEM, XRD, BET and FTIR studies. The dependency of adsorption performances to effective variables such as contact time, shaking rate, temperature and pH and ratio of CuO to AC are systematically studied at various scenarios. Modeling is useful and is a proven technique in science and engineering for simplifying complex systems to solve the problems. A good model includes those variables that have broad impact and ignores those minor variables (irrelevant) to simulated future conditions based on current conditions and the build models is a key factor to accurately predict the best output (removal percentage in present study). Often applied statistical liner models including multiple linear regression (MLR), stepwise regression and partial least squares regression (PLSR) which are based on linear algebra reduce the norm of a residual vector and nonlinear models namely artificial neural network (ANN), fuzzy inference system (FIS), adaptive neuro fuzzy inference system (ANFIS) and support vector regression (SVR) which their estimation is based on searching optimization method to reduce the norm of a residual vector [22– 25]. The statistical models often suffer from various limitations such as large dimension of the input data, while large numbers of them are irrelevant. Therefore, it is suitable in this situation to minimize the dimension of the input data. There are various methods such as genetic algorithm (GA), nonnegative matrix factorization, factor analysis and principal component analysis (PCA) to minimize the dimensionality of large data sets [26,27]. In this work PCA is selected as the feature transformation method to achieve new predictor variables and PCA-MLR and PCA-LSSVM have been applied for inspection of linear and nonlinear relationships exist among variables. The adsorption rates were evaluated by fitting the experimental data to traditional well knows kinetic models (pseudo first and second-order and intraparticle diffusion models). Following examination of the effect of temperature on the adsorption isotherms the calculated thermodynamic parameters of adsorption such as changes in free energy (DG0), enthalpy (DH0) and entropy (DS0) at various temperatures. The show it’s usefulness and cost effectives for quantitative sorption/desorption of MB as for the treatment of wastewater. The proposed nanoparticles show the highest sorption capacities (10.5 mg g1) for 0.09 g of CuO-NP-AC. 2. Experimental 2.1. Instruments and reagents Methylene blue (MB) (Fig. 1) (chemical formula of C27H34N2O4S, CI = 42,040; FW = 482.64, nature = basic green 4 and lmax of

Fig. 1. Chemical structure of MB.

623 nm) removal was studied in batch experiments using 100 mL glass beaker. The stock solution (100 mg L1) was prepared by dissolving 50.0 mg of MB in double distilled water and the test solutions were prepared by diluting stock solution to the desired concentrations daily. The pH was adjusted by addition of dilute HCl and/or KOH using pH/Ion meter model-686 and absorption studies were carried out using Jasco UV-Visible spectrophotometer model V-570. Chemicals with the highest purity available are purchased from Merck, Darmstadt, Germany. X-ray diffraction (XRD) patterns were recorded on a Bruker D8 advance X-ray diffractometer. Morphology and film thickness were measured by Philips XL-30 scanning electron microscopy. 2.2. Preparation of adsorbent from C. oblonga wood In order to separate shell from main part, about 2 kg of C. oblonga wood was cooked in a 1.0 L glass beaker for 2 h. Afterwards, the C. oblonga wood were immersed in 2 L of deionized water and heated to boil for two more hours in order to remove the water soluble phenolic compounds and to avoid from their releases during the adsorption experiments. Subsequently, the C. oblonga wood wastes were washed with distilled water and dried at 70 8C in an air-supplied oven for 8 h and was grounded thereafter in a disk-mill and sieved (20–30 mesh) subsequently. Sieved mass were then performed carbonization process in argon atmosphere at the temperature increase rate of 5 8C/min to the final temperature of 500 8C and kept for 1 h. The mass was then cooled and washed thoroughly several times by distilled water and dried. 2.3. Synthesis of CuO nanoparticles by sol–gel method Isopropanol solvent and monoethanolamine (MEA) was used to dissolve (CH3COO)2CuH2O. The solution was heated under magnetic stirring to 75 8C temperature for 1 h to form a homogeneous sol solution. When formed sol was obtained stable after 1 day aging at room temperature, the obtained stable sol was slowly heated under magnetic stirring up to 82 8C temperature until evaporate the solvent and form a hard homogeneous gel. After 1 day aging of gel at room temperature, the pyrolysis of the final gel was performed at temperature of 350, 450 and 550 8C for 2 h. 2.4. Measurements of dye uptake Concentrations of MB were estimated using the linear regression equations (obtained by plotting its calibration curve). The dye adsorption capacity of adsorbent were determined at the time intervals in the range of 1–35 min for 15 and 20 mg L1 at room temperatures and it was found that equilibrium was established after 12 and 27 min for 15 and 20 mg L1. The effect of initial pH in the range of 1–9 on MB adsorption on to CuO-NP-AC was studied at 15 mg L1, while isotherm studies was recorded in the range of 5– 40 mg L1. The amount of adsorbed MB by adsorbent (qe (mg g1)) was calculated by the following mass balance relationship: qe ¼ ðC 0  C e ÞV=W

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(1)

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where C0 and Ce are the initial and equilibrium dye concentrations in solution, respectively (mg L1), V the volume of the solution (L) and W is the mass (g) of the adsorbent used. 2.5. PCA-LSSVM There are various methods such as genetic algorithm (GA), nonnegative matrix factorization, factor analysis and principal component analysis (PCA) applicable to minimize the dimensionality of large data sets and create new predictor variables [26,27]. PCA was applied as an effective method for preprocessing and the designation of input data. It was seen that there was no redundancy in the input data set and size of the transformed data set after the computation (Fig. 2). Support Vector Machines (SVM) is a powerful tool and supervised learning method that can be used to classification or regression in nonlinear models. The SVM algorithm is based on the statistical learning theory and the structural risk minimization and typically achieves the convex optimization problems by solving the quadratic programs. SVM was introduced by Vapnik [28,29]. The SVM according to minimize an upper bound of generalization error with ability to avoid overfiting has achieved increasing popularity in many scientific studies. LS-SVM is an alternative method of SVM introduced by a short delineation of SVR is exhibited. The theory and more details of SVM and LS-SVM can be found in the literature [30]. LS-SVM utilizes a set of linear equations instead of quadratic programming problem to minimize the complex nature of optimization processes [25]. The constrained optimization problems can be shown using Lagrange multipliers. The LS-SVM equation can be indicated as follows: yðxÞ ¼

N X ða  a ÞKðX; X i Þ þ b;

0  ai ;

a  g

(2)

i¼1

where a. And a* are the Lagrange multipliers, K(X,Xi) is the kernel function, b is the bias value and g is the regularization parameter, determining the trade-off between the fitting error minimization and smoothness of the estimated function. Many kernel functions namely, radial basis function (RBF) kernel, linear kernel, sigmoid kernel and polynomial kernel have been presented in literature, the selection of the suitable kernel function to map the nonlinear input space into a linear feature space depends on the distribution of the training data in the feature space [25,30]. For regression models, the RBF kernel is often applied because of its influence and speed in training process [25]. The equation of the radial basis function kernel is as following: ðjjXX i jj2 =2s 2 Þ

KðX; X i Þ ¼ e

Fig. 2. Principal component versus variance explained (%).

(3)

3

where s2 is the kernel width and can be applied to adjust the degree of generalization. To make an LS-SVM model with RBF kernel, two parameters of s2 and g should be optimized. The obtained optimal combination of parameters is utilized to build multivariate models. 2.6. Data set and software The data set was divided into two groups, a training set consisted of 32 data and a test set with 9 data. The training and testing sets were applied for the making of the models and to evaluate the predictive authority of the constructed models, respectively. The free LS-SVM toolbox (LS-SVM V-1.8, Suykens, Leuven, Belgium) was applied with MATLAB Version R2011a to gather all the LS-SVM models. The multiple linear regression models were created using SPSS version 15.0. 2.7. Evaluation of models The output is normalized between 0 and 1 to avoid numerical overflows due to very large or very small weights. The normalization equation used is of the following: y ¼ xi  xmin =xmax  xmin

(4)

where y is the normalized value of xi. The maximum and minimum value of xi, (xmax and xmin) respectively is used in mathematical relationship. The quality of the resulting presented models can be evaluated by statistical means such as the mean squared error (MSE), the coefficient of determination (R2) and Q2 value (the cross-validated) which can be indicated as follows: MSE ¼

N 1X ðjyprd;i  yexp;i jÞ2 N i¼1

PN

R2 ¼ 1  Pi¼1 N

ðyprd;i  yexp;i Þ

i¼1 ðy prd;i

 ym Þ

Ptest ðyexp;i  yprd;i Þ2 Q 2 ¼ 1  Pi¼1 test 2 i¼1 ðyexp;i  ytrn Þ

(5)

(6)

(7)

where yprd,i is the predicted value by presented models, yexp,i is the experimental value, N is the number of data, ym is the average of the experimental value and ytrn is the averaged value of the training data set. 3. Result and discussion 3.1. Characterization of the CuO-NP-AC The BET surface area measurement of AC prepared from was made by nitrogen adsorption at 196 8C using Sorptomatic 1990 (Thermo Fisher Scientific, USA). Before the measurement, the carbon sample was out gassed under a reduced atmosphere for 4 h at room temperature, 8 h at 110 8C and finally 12 h at 200 8C. Cumulative pore volume and area for mesopores were calculated using Barret–Joyner–Halenda method. Morphology and microstructure of the CuO nanoparticles according to SEM studies (Fig. 3) shows approximate size of CuO nanoparticles around 45 nm. The scanning electron microscopic (SEM) is the primary tool uses for characterization of the surface morphology and fundamental physical properties of photocatalyst surface. It is useful for determination of the particle size, shape, porosity (Suzuki 2002). The SEM photograph was recorded by using Philips Netherland (Model-SEM-EDAX XL-30). The surface texture was found rough and heterogeneous porous in nature after treatment. CuO has

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Fig. 3. SEM image of pure CuO nanoparticles annealed at 5508. Fig. 5. BJH adsorption cumulative pore volume (larger) Halsey: FAAS correction.

Fig. 4. XRD pattern of pure CuO annealed at (a) 350 8C, (b) 450 8C and (c) 550 8C.

considerable number of pores where is suitable for trapping and adsorption of dyes into these pores. SEM is widely used to study the morphological features and surface characteristics and also to determine particle shape, size porosity and appropriate size distribution of the adsorbent materials. XRD analyses as powerful tools was used to study the crystal structures of the CuO nanoparticles and result is shown in Fig. 4, as synthesized and

annealed at 350, 450 and 550 8C for 2 h. The XRD patterns show that annealing causes an increase in the intensities of the peaks at planes (1 1 1) and (2 0 0). The FT-IR spectrum of present adsorbent show high intensity of OH vibrations and asymmetric and symmetric stretching vibrations correspond to CH2 and CH3. Two strong peaks observed in the range between 2963 and 2853 cm1 are assigned to asymmetric C–H bands and symmetric C–H bands, respectively. Stretching vibration band around 1700 cm1 is assigned to carbonyl C5 5O group present in aldehyde, ester, ketone and acetyl derivatives, while the strong band at 1600 cm1 is due to C5 5C band. Presence of such reactive atoms and copper center increase the number of vacant active sites for adsorptions [31]. The iodine value could be another means of assessing information on the surface area and adsorption capacity of (IV) material. Determination of IV is usually a complimentary test to the N2/77 K adsorption isotherms and assumed to measure the surface area in microspores within pore sizes of material. Increase in IV values of an adsorbent show high and reasonable capacity of material [32]. From Table 1 and Figs. 5 and 6, it can be observed that sorbent seems to have appreciable narrow microporosity unavailable to iodine while the adsorbed amount of iodine is higher for CuONP-AC. The surface area of CuO-NP-AC was found to be 82.7 m2/g. Total pore volume is 0.026 cm3/g and average pore diameter is less than 72.5 nm. Total surface properties of adsorbent are presented in Table 1. The morphological features and surface characteristics of adsorbent materials are widely studied by using SEM method. The SEM image of CuO-NP-AC (Fig. 3) shows a coarse porous surface with irregular pores. It occupies a volume and inhibits the contraction of the particle during the carbonization, finally leaving a porosity structure after being washed with deionized water [33].

Table 1 Summary report of adsorbent properties. Summary report Surface area BET surface area BJH adsorption cumulative surface area of pores between 17.000 A˚ and 3000.000 A˚ width BJH desorption cumulative surface area of pores between 17.000 A˚ and 3000.000 A˚ width Pore volume BJH adsorption cumulative volume of pores between 17.000 A˚ and 3000.000 A˚ width BJH Desorption cumulative volume of pores between 17.000 A˚ and 3000.000 A˚ width Pore size Adsorption average pore width (4V/A by BET) BJH Adsorption average pore width (4V/A) BJH Desorption average pore width (4V/A) Nanoparticle size Average particle size

82.723 m2/g 65.56 m2/g 69.65 m2/g

0.026356 cm3/g 0.026364 cm3/g

11.17095 A˚ 16.0811 A˚ 15.1409 A˚ 725.3078 A˚

Fig. 6. Isotherm linear plot.

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Table 2 Influence RPM on the MB recovery (%). Time (min)

Removal (%) 100 rpm

200 rpm

300 rpm

400 rpm

2 3 4 5 6 7 8 10 12 14

66 78.4 83.5 87.3 90.4 93.8 95.9 97.8 98.4 98.8

83.8 85.8 88.7 90.4 92.9 93.7 96.2 98.3

87.5 92.3 97.3 98.8 99.0 99.4

91.3 95.3 99.2

Fig. 7. Effect of contact time (20 ppm) of removal of MB on CuO-NP-AC.

3.3. Effect of pH 3.2. Effect of contact time and initial dye concentration and effect RPM on removal (%) and time The adsorption of MB onto CuO-NP-AC at various initial concentrations was studied as a function of contact time to determine the necessary adsorption equilibrium time. A large number of vacant surface sites are available for adsorption during the initial stage and subsequently the remaining vacant surface sites decrease and difficult to be occupied due to repulsive forces between the solute molecules on the solid and bulk phases [34– 36]. Figs. 7 and 8 show the effects of contact time until 15 min on the adsorption of 20 ppm of MB by CuO-NP-AC. As it can be seen, the removal at all investigated dye concentrations is rapid at the initial stages and gradually decreases with the progress of adsorption until reaching equilibrium. At the initial contact time due to high amount of available adsorbent surface the rate of adsorption is fast. The equilibrium contact time strongly depend to initial dye concentration and the system reach equilibrium at 10 and 20 mg L1 following stirring at 400 rpm for 20 and/or 30 min. The adsorption capacity at equilibrium (qm) decrease from 10.54 to 9.72 mg g1 with an increase in the initial MB concentrations from 15 to 20 mg L1 using 0.09 and 0.11 g of adsorbent. In despite of decrease in MB adsorption rate the actual amount of adsorbed MB significantly increased. As it is well known, the rate of diffusion and convection significantly depend to the stirring rate. The influence of stirring rate in the range of 100–500 rpm (Table 2) show that by raising the RPM until 400 rpm by enhance in convection and diffusion on increase in mass transfer occur that simultaneously decrease in equilibrium time. Further increases in mixing rate do not change significantly the removal percentage.

Fig. 8. The effect of initial concentration ration (M) on fractional conversion (XA)– time curve: (slopes indicating of rate of initial adsorption).

The efficiency of sorption depend to the solution pH (variation in the degree of ionization and the surface properties of the adsorbent and MB charge and MB structure (MB) [37,38]. Therefore, as set of similar removal experiments over the pH range of 1.0–9.0 was conducted to obtain the optimum pH for efficient and quantitative MB adsorption. The result show (Fig. 9) that the MB sorption increased following rising initial pH from 1 to 6 and in the pH range of 6–9 marginally decreased. Therefore, all subsequent studies were carried out at pH 7 as optimum pH. Following strong adsorption of H+ on the adsorbent surface, a total positive charge provide an electrostatic repulsion between the adsorbent surface and the cationic MB molecules that cause a decrease in adsorption and removal percentage. On the other hand, at pH above 3 due to deprotonation of the adsorbent functional group, get surface neutral charge that enhance the interaction between dye and adsorbent and the removal percentage significantly increased. At this pH range, due to presence of copper atom and oxygen in nano scale with high abundance make possible to observe strong interaction between nitrogen atoms of MB with adsorbent. This interaction enhanced by hydrogen bonding of MB with adsorbent or wander walls force as extra interaction bond. 3.4. Effect of adsorbent dose Vacant adsorption site of adsorbent limit the rate and amount of migration of dye molecule to the adsorbent surface. The percent of adsorption of MB on CuO-NP-AC was studied at different adsorbent doses (0.025, 0.045, 0.05, 0.07, 0.09 and 0.11 g, respectively) keeping MB concentration (50 mg L1), pH (7) and room temperature at different contact time (1–35 min). It was seen that adsorbent dosage has significant contribution on the removal percentage and by increase in the adsorbent dosage at fixed time at all initial dye concentrations, the removal percentage significantly

Fig. 9. Effect of pH on the removal of MB at room temperature CuO-NP-AC.

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3.5.2. Pseudo-second-order equation The pseudo second-order model [45], which is based on equilibrium adsorption and expressed as 2

t=qt ¼ 1=k qe2 þ t=qe 1

where

h ¼ k2 qe2

(9)

1

where h (mg g min ) show initial adsorption rate and k2 (g mg1 min1) is the pseudo second-order rate constant. The values of qe and k2 can be obtained from the slopes and the intercepts of the t/qt versus t plots. The Elovich equation is used for the description of chemisorptions and represented as linear from as follow [46]: qt ¼ 1=blnðabÞ þ 1=blnðtÞ Fig. 10. Effect of amount of adsorbent (g) on MB removal at room temperature CuONP-AC.

increased. On the other hand, it was seen that raising adsorbent dose (from 0.025 to 0.11 g) cause increase in removal percentage from 23.4% to 99.2% at equilibrium (Fig. 10). This was attributed to an increase in available surface area and adsorption sites that significantly improved by increasing the amount of adsorbent [39,40]. 3.5. Adsorption kinetics Several models can be used to express the mechanism of solute sorption onto a sorbent. In order to design a fast and effective model, investigations were made on adsorption rate. For the examination of the controlling mechanisms of adsorption process, such as chemical reaction, diffusion control and mass transfer, several kinetics models are used to test the experimental data [41– 44]. 3.5.1. Pseudo-first-order equation Lagergren pseudo-first-order kinetic equation is described as linear form follows [42,43]: logðqe  qt Þ ¼ logðqe Þ  k1 =2:303t

(8)

where k1 (min1) is the equilibrium rate constant of pseudo-firstorder equation. The slope and intercept of the graph of log (qe  qt) versus t show the value of constants k1 and qe that their values are presented in Table 3. Table 3 Kinetic parameters of MB removal using 0.09 g adsorbent over concentration in the range of 15 and 20 mg L1 at optima condition. Parameter values: amount of adsorbent Models

Parameters

First order kinetic model: log (qe  qt) = log(qe)  (K1/2.303)t

K1

Second order kinetic model: t/qt = 1/k2qe2 + (1/qe)t

Intraparticle diffusion qt = Kid t1/2 + C

Elovich qt = 1/b ln(ab) + 1/b ln (t) Experimental date

15 ppm

(10)

1

where a (mg g min ) is the initial sorption rate and b (g mg1) express desorption constant and usually calculated from the slopes and intercepts of the qt versus ln (t) plots. The validity and accuracy of each model, tested according to following normalized standard deviation Dq (%) equation sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi P jðqexp  qcal Þ=qexp j2 Dqð%Þ ¼ 100 (11) N1 In this equation, the qexp and qcal (mg g1) are the experimental and theoretical value of adsorption capacity. The lower value of Dq (%) shows suitability of this model. The best fit able isotherms and kinetic models are recognized by analyzing the data set using Dq (%) and the values of determination coefficient (R2). All kinetic parameters, correlation coefficients and Dq (%) at various dye concentration is presented in Table 3 show that the pseudo firstorder kinetic curves do not give good fit to the experimental kinetic data (low R2 and high Dq (%)). On the contrary, the results present ideal fit to the pseudo second-order model with the extremely high R2 value (0.9970–0.9989) and acceptable agreement between the calculated and experimental qe values. The pseudo-second-order model as another alternative adapt to approach fitting the experimental data based on its relatively low Dq (%) value (7.03–12.57) that show significant contribution of chemical sorption mechanism [46]. Judgment about the validity and applicability of the exploited models is verified according to the correlation coefficient (R2) and agreement between experimental and theoretical qe value. High R2 value indicates that the predominant mechanism of adsorption is chemisorptions based on sharing the electrons between adsorbate/adsorbent and hydrogen bonding and/or p–p interaction. Chemisorptions restricted to one layer molecules adsorption following additional physically adsorbed layers [47]. k2 is the rate constant of pseudosecond-order adsorption (g mg1 min1) and qe, is the adsorption capacity at equilibrium obtained from the pseudo-second-order kinetic model (mg g1).

20 ppm

0.0345

0.0300

qe (cal) R2 Dq (%) K2*102

2.48 0.9421 70.01 0.157

2.88 0.9631 74.05 0.182

qe (cal) R2 Dq(%) h Kdif

7.23 0.9970 12.57 8.24 0.39

10.32 0.9989 7.03 19.34 0.358

4.978 0.9391 2.02 0.9459 8.27

8.25 0.9564 2.21 0.9542 11.1

C R2 B R2 qe (exp)

1

3.5.3. Intraparticle diffusion model In the pore-diffusion model, intra-particle diffusion is estimated by the following equation [48] qt ¼ kid t 1=2 þ C

(12)

where kid (mg g1 min1/2) is the intra-particle diffusion rate constant, and always determined from the slope of the linear plot of qt versus t1/2. The value of C traditionally evaluated from the intercept and give idea about thickness of the boundary layer. The higher value of intercept shows the greater boundary layer effect and the resistance toward mass transfer by the rate of adsorption that lead to increase in contact time. Generally, the behavior that intra-particle diffusion is the only stage limit the adsorption, confirmed by C value equals to zero that need the intra-particle plot passes through the origin [49]. The enhance in the

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intra-particle rate constants (kid) with rising the initial dye concentrations (Table 3) may attributed and related to the increase in mass transfer forces that is important for the adsorption process. The higher value of kid1 compares to kid2 show the predominant and more contribution of bulk diffusion for the adsorption of MB on CuO-NP-AC. In this system, MB sorption is governed as liquid phase mass transport and through the intra-particle mass transport. 3.6. Adsorption isotherms The adsorption isotherm equation is empirically obtained and related the partition of target compound between the adsorbent and liquid phase at equilibrium versus its initial concentration. The equilibrium data was fitted by conventional and traditionally known models such as Langmuir, Freundlich and Tempkin. The linear form of Langmuir isotherm equation is given as: C e =qe ¼ 1=Q m b þ C e =Q m

(13)

where Ce (mg L1) is the equilibrium concentration of the adsorbate; qe and Qm (mg g1) is the conventional and maximum adsorption capacity b (L mg1) is the Langmuir adsorption constant. The favorable nature of adsorption tested generally based on calculation of a dimensionless constant known as separation factor (RL) according to well known below equation [50]: RL ¼ 1=ð1 þ ka C 0 Þ

(14)

where ka (L mg1) is the Langmuir isotherm constant and C0 (mg L1) is the target initial concentration. The RL value indicates whether the type of the isotherm is favorable (0 < RL < 1), unfavorable (RL > 1), linear (RL = 1), or irreversible (RL = 0). The Langmuir constants can be obtained from the plot of Ce/qe versus Ce. The isotherm constants, correlation coefficients, and Dq (%) are summarized in Table 4. The present removal process well fitted to the Langmuir isotherm model with high R2 (0.9896). This result may be due to the homogeneous distribution of active sites on the surface of adsorbent. Furthermore, Langmuir R2 value between 0 and 1 and the 1/n value lower than 1 simultaneously shows favorable adsorption process. 3.6.1. The Freundlich isotherm The Freundlich isotherm model [51] has constants such as kf that show information about the bonding energy and know as the adsorption or distribution coefficient and represents the quantity

7

of dye adsorbed onto adsorbent. 1/n show adsorption intensity of dye onto the adsorbent (surface heterogeneity). The value closer to zero by rising heterogeneous nature of surface (1/n < 1 indicates normal Langmuir isotherm while 1/n above 1 indicate bimechanism and cooperative adsorption). The applicability of the Freundlich adsorption isotherm was assessed by plotting log (qe) versus log (Ce) and respective values for this model constant at various amount of adsorbent is shown in Table 4. The correlation coefficients (0.90–0.91) and higher error value of this model show that the Freundlich model has lower efficiency compare to the Langmuir model. 3.6.2. The Tempkin isotherm Judgment for suitability of each model for the representation of methods applicability for explanation of experimental data is according to R2 value and lower value concern to error analysis. Although, Langmuir and even Freundlich model have reasonable and acceptable R2 value, but the applicability of other models such as Tempkin isotherm has commonly been applied in the following linear form [52]. The Tempkin isotherm Eq. (15) can be simplified to the following equation: qe ¼ blna þ blnC e

(15)

where b = (RT)/b is related to the heat of adsorption, T is the absolute temperature in Kelvin and R is the universal gas constant, 8.314 (J mol1 K1) [53]. The adsorption data were analyzed according to the linear form of the Tempkin isotherm Eq. (15). Examination of the data shows that the Tempkin isotherm is efficiently applicable for fitting the MB adsorption onto CuO-NPAC. The linear isotherm constants and coefficients of determination are presented in Table 4. The heat of MB adsorption onto CuONP-AC was found to decrease from 2.12 to 2.03 kJ mol1 with decrease in CuO-NP-AC dose from 0.09 to 0.11 g. The correlation coefficients R2 obtained from Tempkin model were comparable to that obtained for Langmuir and Freundlich equations, which explain the applicability of Tempkin model to the adsorption of MB onto CuO-NP-AC. 3.7. Comparison of adsorbents for MB Many MB removal process using various adsorbent were reported in the literature [10,54–56] and their performance for MB removal was compared in term of low-cost adsorbents adsorption capacity, initial concentration and contact time. It

Table 4 Comparison of the coefficients isotherm parameters for MB adsorption onto CuO-NP-AC. Isotherm

Equation

Langmuir

Ce/qe = 1/KaQm + Ce/Qm

Freundlich

Ln qe = ln KF + (1/n)ln Ce

Tempkin

qe = Bl ln KT + Bl ln Ce

Dubinin and Radushkevich

Ln qe = ln Qs  Be2

Parameters

Qm (mg g1) Ka (L mg1) RL X2 R2 1/n KF (L mg1) X2 R2 Bl KT (L mg1) X2 R2 Qs (mg g1) B E (kJ/mol) = 1/(2K)1/2 X2 R2

Adsorbent (g) 0.09

0.11

10.548 0.706 0.13–0.20 1.450 0.9896 0.360 1.83 4.545 0.9127 2.120 7.633 11.7 0.8982 2.620 1E-07 4.082 106.125 0.7035

9.72 0.72 0.15–0.22 0.967 0.9846 0.390 3.622 3.12 0.9023 2.030 7.332 6.25 0.845 6.710 9E-08 4.082 117.7 0.6653

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JIEC-1504; No. of Pages 9 M. Ghaedi et al. / Journal of Industrial and Engineering Chemistry xxx (2013) xxx–xxx

8

Table 6 Comparison for the removal of dyes by different adsorbents. Contact time

Adsorbent

Adsorbate

Adsorption capacity (mg g1)

Concentration (mg L1)

Ag-NP-AC Pd-NP-AC Natural palygorskite Tea waste Cotton stalk CuO-NP-AC

MB MB MB

71.43 75.40 48.39

7.0 5.0 100

15 15 20

MB MB MB

85.16 4.5–15.7 10.55

50 50 20

360 90 15

Reference

[9] [9] [41] [42] [43] This work

models [24]. The statistical parameters achieved for PCA-MLR and PCA-LSSVM as linear and/or nonlinear model (Table 5) show that the coefficient of determination and mean square error values of training and testing set for PCA-LSSVM model are better than PCAMLR model. A valid and predictive model should have satisfied Q2 values (higher than 0.5 for testing data set) [57–61]. The obtained values of Q2 for PCA-LSSVM and PCA-MLR models were 0.868 and 0.853, respectively. It was found that the relationship between inputs (pH, dye concentration, amount of nanoparticles, time and amount of carbon active) and removal (%) was nonlinear. 4. Conclusion

Fig. 11. Plot of experimental versus fitted and predicted normalized removal values (a) the PCA-MLR model and (b) the PCA-LSSVM model.

can be seem that MB removal by CuO-NP-AC (Table 6) is superior to previously reported literature in term of higher adsorption capacity (10.55 mg g1) shorter required time (15 min) and using initial concentration (20 mg L1). 3.8. PCA-LSSVM The PC scores were utilized as inputs for the making of PCALSSVM and PCA-MLR models. LS-SVM model evaluation is based on optimization of two parameters such as s2 and g. To achieve the optimized values of the parameters, specifications such as optimization routine, cost function and kernel function were selected simplex, cross-validation and RBF kernel, respectively. The optimized obtained values of s2 and g were 0.49 and 4.17, respectively. Fig. 11a displays the presence of good agreement between the experimental values and the predicted values. The residuals of the offered models calculated values of normalized removal are plotted versus the predicted values (Fig. 11b). The presence of residuals at both sides of the zero line show the lack of any systematic error exists in the creation of the introduced Table 5 The statistical parameters of suggested models. PCA-LSSVM

PCA-MLR

R2 MSE Q2

Training set

Testing set

Training set

Testing set

0.90 0.0068 –

0.88 0.0047 0.85

0.97 0.0031 –

0.92 0.0043 0.87

Removal of MB by has a great advantage of independence of process against a wide range of pH values. Considering the amount of adsorbent low adsorbent dosage (0.11 g). It was shown that by increasing the adsorbent dosage, higher removal percentage is achieved which can be attributed to increase in surface area and the activated nano eshel and nano pore carbon adsorbent sites available. The adsorption of MB depend to adsorbent surface and amount MB concentration, contact time and pH of the solution. The percentage adsorption is maximal at pH value of around 9 and decreases with acidic strength of the dye solution. The results show the applicability of model for best representation of experimental data. The equilibrium data are analyzed using Langmuir, Freundlich and Tempkin isotherm equations. The result shows that the experimental data are best correlated by Langmuir isotherm. In summary, CuO-NP-AC with high adsorption capacity, acceptable removal percentage using a routine and simple synthesis with easy separation of nanoparticles after adsorption is an alternative applicable industrial candidate for MB removal. This method due to unique advantages such as safe, rapid and inexpensive methodology for removal MB as toxic dye is superior to the other previous troublesome methods. On the basis of batch adsorption experiments, a significant aim was to achieve a suitable model that could construct valid prediction on the percentage of methylene blue dye removal. A model using PCA-MLR and PCA-LSSVM were introduced to predict the efficiency of methylene blue dye removal. The results displayed that the performance of PCALSSVM model was better than the PCA-MLR model. References [1] A. Mittal, J. Mittal, A. Malviya, V.K. Gupta, J. Colloid Interface Sci. 340 (2009) 16. [2] K.K.H. Choy, J.F. Porter, G. McKay, Chem. Eng. J. 103 (2004) 133. [3] T. Robinson, G. Muc-Mullan, R. Marchant, P. Nigam, Bioresour. Technol. 77 (2001) 247. [4] M. Tuzen, K.O. Saygi, C. Usta, M. Soylak, Bioresour. Technol. 99 (2008) 1563. [5] N. Hoda, E. Bayram, E. Ayranci, J. Hazard. Mater. B 137 (2006) 344. [6] M. Ghaedi, H. Tavallali, M. Sharifi, S. Nasiri Kokhdan, A. Asghari, Spectrochim. Acta. A. Mol. Biomol. Spectrosc. 83 (2011). [7] S.J. Hosseini, S. Nasiri Kokhdan, A.M. Ghaedi, S.S. Moosavian, Fresenius Environ. Bull. 20 (2011) 219. [8] L. Zhang, Y. Zhu, H.M. Li, Rare Metals 29 (2010) 16. [9] K. Mahapatra, D.S. Ramteke, L.J. Paliwal, J. Anal. Appl. Pyrolysis 95 (2012) 79.

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