Response surface methodology for optimization of the adsorption capability of ball-milled pomegranate peel for different pollutants

Journal of Molecular Liquids 250 (2018) 433–445

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Response surface methodology for optimization of the adsorption capability of ball-milled pomegranate peel for different pollutants Mohammed Eid M. Ali a,⁎, Hazem Abdelsalam b, Nabila S. Ammar a, Hanan S. Ibrahim a a b

Water Pollution Research Department, National Research Center, El-Buhouth St., Dokki, Cairo P.O. 12622, Egypt Theoretical Physics Department, National Research Center, El-Buhouth St., Dokki, Cairo P.O. 12622, Egypt

a r t i c l e

i n f o

Article history: Received 6 August 2017 Received in revised form 27 November 2017 Accepted 5 December 2017 Available online 8 December 2017 Keywords: Adsorption RSM CCD Metal Dye Ball-milled pomegranate peel

a b s t r a c t Pomegranate peel waste was milled using ball milling to get particles of size of 90 μm and used for adsorption of toxic pollutants. The response surface methodology was used for modeling of the adsorption capacity of copper ions and C.I. Reactive Yellow 145 (3RS) from wastewater using ball-milled pomegranate peel (BMPP). The predicted removal capacities obtained from the model are in a good agreement within the experimental results with correlation regression of 0.94 for dye adsorption and 0.92 for metal adsorption. The maximum removal capacity over adsorbent biomass is expected to be 209.7103 mg g− 1 for dye and 103 mg g− 1 for metal at optimal values of the independent variables. The analogue experimental results are 224 mg g− 1 for dye and 106 mg g− 1 for metal. Conclusively, the obtained results confirm the applicability of used model for predicting the adsorptive capacity of ball-milled pomegranate peel of dye and metals. As well ball-milled pomegranate peel is considering a powerful, cost effective and anionic/cationic adsorbent for decontamination of wastewater. © 2017 Elsevier B.V. All rights reserved.

1. Introduction Deterioration of water quality is mostly originated from untalented utilization of natural water, and extensive urbanization and industrialization. It has a harmful impact on human health and environment. Direct/indirect discharge of wastewater containing non-degradable dyes and toxic metals into water bodies are the most serious environmental problems [1–3]. Hence, the principal crux target of environmental researchers is development of the feasible, green and economic remediation procedures for decontamination of pollutants from environment devoid of any harmful impact on human health [4–6]. Various remediation techniques are implemented for control of aquatic pollution [7]. Most of them have some drawbacks; such as high cost, low efficiency for removal of emanating pollutants and generation of risky sludge [8–10]. The immediate challenge is finding low-cost treatment methods for removal of dye and metals from wastewater. Adsorption process was applied for decontamination of these pollutants using biomass. Optimization of treatment process is the needed fundamental mission for researchers. However, minimizing the

⁎ Corresponding author at: 33 El Buhouth St., Dokki, Cairo 12622, Egypt. E-mail addresses: [email protected], [email protected] (M.E.M. Ali).

https://doi.org/10.1016/j.molliq.2017.12.025 0167-7322/© 2017 Elsevier B.V. All rights reserved.

number of experiments is required for decreasing the cost, the consumed period, and the environmental effect. The response surface method (RSM) is a commanding procedure which could accomplish to optimize the treatment process. RSM is a combination of statistical and mathematical models for creating, improving and optimizing the processes by employing a set of designed experiments. It is an illustration of the variations of the response (i.e. efficiency or removal) as a function of two independent variables at the central value of the other variables. While, the contour plot was a 2D plane graph where each line in the 2D contour plot accounts for infinite combinations of two independent variables at the constant value of surface response. The contour plot is very useful for estimation of the interaction between different input variables and identification their optimal levels [11–14]. The design of the experiments (DoE) is an essential part of the RSM for evaluation of the factors that have significant effect on the response. Moreover, DoE select the number of levels for each factor and there combinations with other factors at which the response should be evaluated [15]. Herein, pomegranate peel is milled using ball milling to enhance the adsorption process of Cu2 + ions and dye from wastewater. As well, the current work will construct models for treatment of non-degradable dyes and toxic copper ions using RSM and the central composite design.

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N9 Na 4O 16S 5 and Molecular Weight: 1026.25 were obtained from local textile factory (Cairo, Egypt). Copper (II) sulphate pentahydrate (CuSO 4 ·5H2O) from Merck (Merck, Germany) was used for preparation of stock solution of copper ions solution in deionized water. 2.2. Ball milling of pomegranate peel

Fig. 1. C.I. reactive yellow 145 (3RS).

Table 1 Input variables and there levels employed in the 24 central composite design. Variables

Range of values and levels −2

−1

0

1

2

X1, time (min) X2, pH X3, dose (g L−1) X4, init. concentration (mg L41)

30 1 0.2 25

60 2 0.5 50

90 3 0.8 75

120 4 1.1 100

150 5 1.4 125

2. Materials and experimental design 2.1. Materials used Materials used in the study are including the following; C.I. Reactive Yellow 145 (3RS) (Fig. 1) with Molecular Formula: C28H20Cl

Pomegranate peels were obtained from the waste of juice manufacturing Factory. These peels were washed to remove any impurities, and dried at 60 °C. Then they were crushed with up to grain size of 3–6 mm and then washed again several times with deionized water until no color was observed. Then, it was dried and mechanically milled using a planetary ball mill (Retsch PM 100 CM, Germany). In a typical procedure, 10 g of starting preliminary material was loaded together with several stainless steel balls of 10 mm diameters (powder/ball = 1/10 weight ratio) placed in a 500 mL stainless steel container then start grinding process at 50 rpm for 1 h. The fine powder was collected and passed through suitable sieve with pore size 90 μm. 2.3. Characterization of ball milled-pomegranate peel FTIR spectrophotometer (630-JASCO, JASCO, Japan) was used to determine the differences in functional groups before and after the sorption process on BMPP. Pellets were prepared with 2 mg BMPP biomass and 100 mg KBr. The spectra were recorded in the wave number range of 400 to 4000 cm − 1. The specific surface area, pore size and pore volume of the samples were measured using nitrogen sorption (NOVA touch instrument, Quantachrome, USA) at 77 K. Prior to the sorptometric experiment, the samples were degassed at 423 K for 12 h. Spectrum acquisition and analysis

Table 2 Central composite design for the experiment and the response results. Run

X1

X2

X3

X4

Time (min)

pH (−)

Dose (g L−1)

Initial conc. (mg L−1)

Adsorbed dye (mg g−1) Observed

Predicted

Observed

Predicted

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

−1 −1 −1 −1 −1 −1 −1 −1 1 1 1 1 1 1 1 1 −2 2 0 0 0 0 0 0 0 0 0 0 0 0

−1 −1 −1 −1 1 1 1 1 −1 −1 −1 −1 1 1 1 1 0 0 −2 2 0 0 0 0 0 0 0 0 0 0

−1 −1 1 1 −1 −1 1 1 −1 −1 1 1 −1 −1 1 1 0 0 0 0 −2 2 0 0 0 0 0 0 0 0

−1 1 −1 1 −1 1 −1 1 −1 1 −1 1 −1 1 −1 1 0 0 0 0 0 0 −2 2 0 0 0 0 0 0

60 60 60 60 60 60 60 60 120 120 120 120 120 120 120 120 30 150 90 90 90 90 90 90 90 90 90 90 90 90

2 2 2 2 4 4 4 4 2 2 2 2 4 4 4 4 3 3 1 5 3 3 3 3 3 3 3 3 3 3

0.5 0.5 1.1 1.1 0.5 0.5 1.1 1.1 0.5 0.5 1.1 1.1 0.5 0.5 1.1 1.1 0.8 0.8 0.8 0.8 0.2 1.4 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8

50 100 50 100 50 100 50 100 50 100 50 100 50 100 50 100 75 75 75 75 75 75 25 125 75 75 75 75 75 75

71.92 105.26 35.90 36.65 41.72 97.64 18.40 20.17 88.23 86.83 40.10 53.82 44.45 103.10 18.00 23.00 29.01 24.27 33.20 26.11 128.46 22.99 29.00 81.83 42.80 37.77 41.50 40.23 42.80 42.80

67.4493 98.5008 33.2510 32.9844 47.9922 97.7762 8.4001 26.8659 69.9874 97.0864 40.2205 36.0013 48.3717 94.2032 13.2110 27.7243 30.5863 33.9827 49.1625 21.4284 131.7081 31.0310 38.2745 83.8393 41.3161 41.3161 41.3161 41.3161 41.3161 41.3161

5 9.4 2.73 1.36 39 58 17.73 23.64 7 15.8 2.18 2.45 38 60 18.64 24.09 23.75 23.75 2.5 37.5 45.6 15.71 18.38 53.75 31.25 32.5 33.13 31.88 30.63 32.5

6.5850 17.2392 6.7208 6.3900 37.9425 58.6617 17.4733 27.2075 8.2142 21.0133 6.4750 8.2892 37.9267 60.7908 15.5825 27.4617 17.9392 19.8225 0 40.3958 42.3825 9.1892 19.9292 42.4625 31.9817 31.9817 31.9817 31.9817 31.9817 31.9817

Adsorbed metal (mg g−1)

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of energy dispersive X-ray (EDX) peaks are carried out by SEM imaging using JEOL Scanning Electron Microscopes (JEOL, USA) and analyses (TIA) software.

2.4. Experimental design The central composite design (CCD) was selected to create the set of batch experiments for optimizing the adsorption process using mechanical shaker (Stuart, UK) at speed of 250 rpm and temperature of 25 °C. Full factorial and fractional factorial designs with three or more levels for each variable was used for calculation of the coefficient of the quadratic terms in the model [12,16] but these designs require more experimental runs than necessary for the accurate estimation of the model parameters. CCD is much more efficient for calibrating quadratic models than factorial design because it accurately estimate the model parameters but with fewer number of runs.

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The total number of the experimental runs required to build the CCD are 2k factorial design points with center at the origin (at the corner of a cube), 2 k points at a fixed distance(α) from the center that allow for estimation of quadratic terms and replicate runs at the center. The experimental runs number at central points provides an estimation of the pure error and information about the existence of curvature in the response surface for four variables; six points used at the center [17]. Therefore, the total number of experimental runs required for four independent variables is 24 + 2 × 4 + 6 = 30 [18]. In order to define the range of values of the independent variables (Xi) and code them as xi according to the Eq. (1): xi ¼

X i −X 0 ΔX

ð1Þ

where, xi is the value of the “i” independent coded variable, X0 is the central value of Xi , and Δ X is the difference between nearest

A

B

Fig. 2. FTIR of ball-milled pomegranate peels before and A) after adsorption of Cu, B) after adsorption of reactive yellow dye.

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M.E.M. Ali et al. / Journal of Molecular Liquids 250 (2018) 433–445 Table 3 ANOVA test results for the responses y1 and y2. Response

Source of variance

Degree of freedom

Sum of squares

Mean square

F-value

P-value

y1 dye removal

Model Residual Lack of fit Pure error Total Model Residual Lack of fit Pure error Total

14 15 10 5 29 14 15 10 5 29

24,713.1 1724.8 1704.41 20.4 26,437.9 7623.3 626.2 622 4.229 8249.5

1765.2 115 170.4 4.1 – 544.5 41.8 62.2 0.846 –

15.4

0.0001

41.8 – – 13

0.0013 – – 0.0001

73.5 – –

0.0001 – –

y2 metal removal

neighbor Xi values. In this work, four independent variables are selected to study their effect on the removal process. According to Table 1 these coded variables are X1 , X2 , X3 , and X4 for time, pH, dose, and initial concentration, respectively. Thirty separate experimental runs were conducted to study the effect of the mentioned independent variables on the removal capacity of Cu2 + ions and dye over BMPP. A second order polynomial is then used for fitting the response y1 (dye removal capacity) and y2 (metal removal capacity) obtained Fig. 3. Nitrogen adsorption/desorption of ball-milled pomegranate peels (a), pore size distribution (b).

Fig. 4. SEM image of pure BBPM (a) and after Cu2+ adsorption (b), and energy-dispersive X-ray (EDX) analysis of BBPM after adsorption (c).

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Fig. 5. The relation between the experimental and the predicted values of the removal capacity of dye (a) and metal (b).

from the experimental runs. The quadratic polynomial is given by Eq. (2): ym ¼ β0 þ

k X i

βi xi þ

k X i

βi x2i þ

k X i¼1

k

∑ j¼iþ1 βij xij þ ε

ð2Þ

where, ym is The response variable (y1 or y2), xi is the coded independent variables, and β's are the unknown parameters. The CCD experimental design is given in Table 2, which is presented the relation between the independent variables and the corresponding observed response. Afterward, at the specified values of the independent variables, the measured response values are then substituted in Eq. (2) to obtain a set of equations which in matrix form can be written as Eq. (3): Y ¼ Xβ þ ε

ð3Þ

where, Y is the matrix representing the measured response values and X is the matrix of the input of coded independent variables. The solution of Eq. (3) is obtained by using the method of

least square (MLS). According to the MLS, it is assumed that ɛ has a normal distribution with zero mean and variance. The coefficients β should minimize the sum of squares of residual [19,20], applying this condition to Eq. (3) to obtain the coefficients as Eq. (4): β ¼ ðX0X Þ−1 X 0 Y

ð4Þ

where, X′ being the transpose of the matrix; X. The regression coefficients are estimated numerically using the Matlab software through performing a multilinear regression of the response in Y on the input variables in X. For removal of copper ions, all samples were measured using inductively coupled plasma optical emission spectrometry (Agilent ICP-OES 5100, Australia) according to Standard Methods [21]. Also for dye removal, the samples were measured by spectra scanning using spectrophotometer (630-JASCO, JASCO, Japan). All experiments were repeated three times with relative standard deviation percent b1%.

Table 4 Estimation of the regression coefficients of the quadratic polynomials y1 and y2. Coefficient

β0 β1 β2 β3 β4 β12 β13 β14 β23 β24 β34 β11 β22 β33 β44

Response (y1)

Response (y2)

Estimated values

t-Value

P-value

Estimated values

t-Value

P-value

41.3161 0.8491 −6.9335 −25.1693 11.3912 −0.5396 1.1078 −0.9881 −1.3484 4.6831 −7.8295 −2.2579 −1.5052 10.0133 4.9352

9.4378 0.3879 −3.1676 −11.4988 5.2042 −0.2013 0.4133 −0.3686 −0.5030 1.7469 −2.9206 −1.1028 −0.7351 4.8906 2.4104

0.0000 0.7035 0.0064 0.0001 0.0001 0.8432 0.6853 0.7176 0.6223 0.1011 0.0105 0.2875 0.4736 0.0002 0.0292

31.9817 0.4708 12.6325 −8.2983 5.6333 −0.4113 −0.4687 0.5362 −5.1513 2.5163 −2.7462 −3.2752 −4.2127 −1.5490 −0.1965

12.1246 0.3570 9.5783 −6.2920 4.2713 −0.2546 −0.2902 0.3320 −3.1891 1.5578 −1.7002 −2.6548 −3.4147 −1.2555 −0.1592

0.0001 0.7261 0.0001 0.0001 0.0007 0.8025 0.7756 0.7445 0.0061 0.1401 0.1097 0.0180 0.0038 0.2285 0.8756

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3. Results and discussion 3.1. Ball-milled pomegranate peel characterization FTIR spectra of ball-milled pomegranate peel (BMPP) were analyzed to identify the functional active groups on the surface. The detected IR bands were illustrated in Figs. 2A and 1B. Previous studies transacted with the IR analysis of pomegranate peels, where the recorded IR bands are somewhat similar to the obtained IR spectra of BMPP [22]. A broad IR band is detected between 3200 and 3600 cm− 1 which is attributed to OH stretching vibration which has many origins (water, phenolic etc.…). The medium band observed between 2800 and 3000 cm − 1 that related to C\\H stretching vibration. The existence of the C_O group vibration stretching can be confirmed by IR absorption bands at 1733 and 1619 cm− 1 . The absorption bands with moderate intensity at 1445, 1356, and 1231, and 1024 cm− 1 referred to the bending vibration of C\\O\\H, bending vibration of O\\H (in plane bending), stretching vibration of C\\O group and C_O group out of plane bending vibration, respectively. Also, the presence of absorption bands assigned to out of plane bending vibrations of O\\H and

C_O and O\\H are located at 766, 877 and 617 cm− 1, respectively. A band appeared at around 1021 cm− 1 is assigned to C\\O stretching vibration. While that appeared at around 420 cm− 1 is ascribed to Si\\O that present in wall of BMPP. Also, after adsorption process of dye and metal on surface of BMPP, FTIR spectra were also analyzed. Fig. 2A showed IR spectra of BMPP that adsorbed Cu2 + ions, it was found that decrease in intensity of IR bands for bare BMPP which they might involve in the biosorption process. Moreover, strong IR band appears at 531 cm− 1 that may be ascribed to Cu\\O which confirms the adsorption Cu ions on the surface of BMPP. Meanwhile, Fig. 2B showed IR absorption bands of BMPP that adsorbed dye molecules. Noted that new absorption IR bands at 2132, 1324 and 1162 cm− 1, which could be related to stretching vibration of N_C_O or N_C_N, and N_O groups and bending vibration of C\\N, the existence of these bands confirm the bonding of dye to BMPP surface and verifies the proceeding of adsorption of dye on surface of adsorbent. Furthermore, N2 adsorption/desorption analysis of BMPP material was investigated for finding out porosity and surface area. Adsorption/desorption isotherm is resemble to IUPAC isotherm III with H4 hysteresis loop that is characteristic to nanoporous material. This confirmed by pore size distribution

Fig. 6. The response surfaces representing the modeled dye removal capacity (a, c) and their corresponding contour plots (b, d) as a function of: time and pH (a, b) time and dose (c, d) at central point values of other parameters.

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(Fig. 3B), where average pore diameter is 5.6 nm. The calculated BET surface area, and pore volume is 8.59 m 2 g − 1, and 0.0072 cm3 g− 1, respectively. Scanning electron microscope (SEM) image of raw BMPP and after adsorption of Cu2 + are illustrated in Fig. 4. It was observed filamentous appearance of raw BMPP (see Fig. 4a). After copper ions adsorption, filament of biosorbent blocked with Cu2 + ion (see Fig. 4b). Fig. 4c shows the typical EDX pattern for BBPM, before and after the sorption of Cu2 + ions. The EDX pattern shows the characteristic signal of Cu2 + ions that verifies the sorption of copper ions on BBPM surface.

3.2. Adsorption optimization CCD design of 30 experimental runs was done to examine the involvement of four prominent factors; time, pH, BMPP dose, and pollutant concentration in the adsorption process of dye and metal (response). CCD matrix for experimental and predictable values of removal capacity of dye and metal was listed in Table 2. It could be detected that the capacities of adsorption for reactive yellow dye and metal fluctuate strongly with the values of the influential factors. However, the results confirmed almost 75%

439

elimination of dye, while, the removal of Cu2 + ions was lower at around 40–60%. Performing a multiple regression analysis of the experimental data gives the subsequent second order polynomial equations which represent the relation between the response y1 (the dye adsorption capacity), the response y2 (adsorption capacity of metal) and the input coded variables x1, x2, x3, and x4: Eqs. (5) & (6). The Fischer's statistical test for analysis of variance (ANOVA) is employed to perform the statistical testing of the model. The probability (P-value) of the model terms was calculated at 95% of confidence level. The ANOVA results for modeled adsorption capacity are given in Table 3 for reactive dye and copper ions removals. The Fvalue in the ANOVA test represents the ratio of the model mean square to the error mean square. Consequently, the large value of this ratio implies that the mean square contributed by the model is larger than error mean square. Thus, according to the variance analysis, the high F-values (F = 15.4 for dye removal and 13 for metal) in addition to the very low probability value (P b 0.0001) insure the high significance of the model. As well the comparison between the experimental and the predicted values of the removal capacity of dye removal and metal removal were investigated and represents in Fig. 5a, and Fig. 5b for dye and metal, respectively. It

Fig. 7. Predicted dye removal response surface under the influence of: time-initial concentration (a), pH-dose (c), and the related contour plots in (b) and (d) at central point values of other parameters.

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provides a satisfactory approximation between the modeled predicted results and the observed results with high correlation coefficient (R 2). Furthermore, there is a good agreement between the model expected values and the experimental values of the response with R2 = 0.94 for dye removal and R2 = 0.92 for metal removal. Accordingly it gives a strengthening for the application of this proposed model for prediction the removal capacity of studied pollutants. This value is close to its corresponding value of R 2 that assures adequate adjustment of the model independent variables with respect to the number of experimental observations. As a result, the second-order regression models sufficiently explain the removal process. y1 ¼ 41:32 þ 0:85x1 −6:93x2 −25:17x3 þ 11:39x4 −0:54x1 x2 þ 1:11x1 x3 −0:99x1 x4 −1:35x2 x3 þ 4:68x2 x4 −7:83x3 x4 −2:26x21 −1:51x22 þ 10:01x23 þ 4:94x24

ð5Þ

y2 ¼ 31:98 þ 0:47x1 þ 12:63x2 −8:29x3 þ 5:63x4 −0:41x1 x2 −0:47x1 x3 þ 0:54x1 x4 −5:15x2 x3 þ 2:52x2 x4 −2:75x3 x4 −3:28x21 −4:21x22 −1:55x23 −0:2x24 :

ð6Þ

The model coefficients have been calculated to assess its significance, where the corresponding values; t-values and P-values are shown in Table 4. The smaller P-value (P b 0.05) and the higher tvalue insure the high significance of the corresponding coefficient

[18]. Thus, the variables that have the highest effect on the reactive dye removal capacity (y1) are the dose and initial concentration. Meanwhile, the variations of pH, dose, and initial concentration have a considerable influence on copper ions removal capacity (y2).

3.3. Response surfaces and counter plots for dye adsorption (y1) Three-dimensional (3D) response surfaces and the corresponding two-dimensional (2D) contour plots are presented for response y1 that represent the modeled dye adsorption capacity as a function of the independent factors; time, pH, dose, and initial concentration. The response surfaces due to the effects of (time and pH), and (time and dose) on the response y1 are illustrated in Fig. 6(a & c), at the mean value of the other variables. The corresponding contour plots are shown in Fig. 6(b & d). It is also noted in the plots that the coded values of these variables (Eq. (2), Eq. (3)) are converted to their natural values using Eq. (1). As noticed in Fig. 4(a–c), the effect of time and pH on the response shows a maximal removal capacity (y1 ca 45 mg L − 1) at time of 100 min and lower value of the solution pH. In addition, the elliptical nature of the contour lines in Fig. 6b implies that there is a significant interaction between the time and the pH. The interactions between the independent variables in the contour plots have an important effect

Fig. 8. Effect of initial concentration and pH (a, b), initial concentration and dose (c, d) on the response (y1) at central point values of other parameters.

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on the response because high interactions means the existence of maximum, minimum, or saddle points in the response surface which help in estimating the optimization process. Fig. 6(c) shows that the adsorption capacity of dye (y1 ) decreases significantly with the increase in the applied dose. The maximum removal of dye achieved after 120 min at lower value of dose. The higher dose results in decrease of adsorption which could be ascribed to the overlying of adsorbent active sites [23]. Also from the contour plot, the nearly linear contour lines imply that interaction between the time and the dose is weak. Fig. 7 illustrates the behavior of the response as a function of initial concentration and time (Fig. 7a, b) and dose and pH (Fig. 7c, d). The obtained maximum removal of dye is 80 mg g− 1, at initial concentration of 125 mg L− 1, and time of 100 min. In order to obtain the maximum value of adsorption capacity the values of pH and dose should be minimized, as illustrated in (Fig. 7c, d). The mutual effect of the (initial concentration – pH) and (initial concentration– dose) on the modeled removal capacity (y1) are illustrated in Fig. 7. In contrast to previously, the minimization of pH increased the removal capacity, here to obtain the maximum removal we need to maximize the pH value (Fig. 8a, b). Since the wastewater pH has an important impact on the adsorption of organic pollutants i.e. dyes over biomass. It affects the charged status of dyes and the surface charge of adsorbing material [23,24]. For the second part

441

(Fig. 8c, d) the maximum y 1 can be achieved by minimizing the dose and increasing the initial concentration. 3.4. Response surfaces and counter plots for metal adsorption (y2) The removal of toxic metals using eco-friendly materials such as low cost biosorbent which have capability for metals decotamination from wastewater. Moreover, there is a growing interesting for application these materials for adsorption [25]. Previously, the removal of metals investigated using classical experiment design, where the factors affecting adsorption is studied individually but the current study used orthogonal experiment using CCD to obtain the quadratic RSM and optimal operating condition. For instance, due to the interaction terms in RSM, the adsorption capacity does not increase linearly with time (see Fig. 9a, b), where the previous reported study showed a linear relation between removal capacity and time [25]. Moreover, the CCD provides a set of experiments, with purposive changes to the input variables for optimization the adsorption capacity. Therefore, response surfaces and counter plots of removal capacity of Cu2 + ions as a function of the same independent factors were established to clarify the impact of factors on the adsorption of metal using BMPP. Fig. 9 presents the surface plots and the corresponding contour plots of the predictable metal removal capacity (y2) a function of

Fig. 9. 3D plots of the modeled metal adsorption capacity and the contour plots as a function of time-pH and time-dose at central point values of other parameters.

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(i) time (30–150 min) and pH (1–5) (Fig. 9a, b) and (ii) time (30–150 min) and dose (0.2–1.4 g L − 1) (Fig. 9c, d) with keeping other variables constant at central point values. It is worth noting that the second order polynomial model for the copper removal capacity gives some negative responses which correspond to the small response observed from the experiment. We set these negative values to zero in the sense that the mnimum removal value is zero. These zero values appear at one value in Table 2 and will apear here in the 3D and the contour plots for responses calculated as a function of pH and any other variable. According to Fig. 9b, the correlation of time and pH has a significant influence on the metal removal. The optimal operating condition are ranged from 80 to 100 min for time and from 4.0 to 5.0 for solution pH at central values of the other variables. In term of the effect of time and dose, the removal capacity of metals increased with decreasing dose and increasing time as observed from Fig. 9d. Figs. 10 and 11 illustrates the effect of the remaining combinations of time and pH, dose and initial concentration on the modeled metal adsorption capacity. Generally, the behavior of the response y2 is similar to the previously discussed response surfaces only the interaction between the independent variables is not significant like in previous plots, as shown in Fig. 10b, d where contour line is almost linear.

3.5. The collective effect of all variables on the removal capacity It is very difficult to visualize n-dimensional surface in n + 1 dimensional space for n N 2. Therefore, the removal capacity could not be plotted in three dimensional graph as a function of the four variables; time, pH, dose, and initial concentration. The optimization of graphical user interface (GUI) provided in Matlab makes this visualization more perceptive as shown in Fig. 12. For each adsorption capacity, four plots are presented to demonstrate the effect of the individual variables on the adsorption capacity, as Fig. 12a for dye adsorption capacity and Fig. 12b for metal adsorption capacity. Each plot indicates the predicted response as a function of one independent variable at the mean values of the other three variables shown in a box below each axis. By dragging the dotted blue line in any plot or by changing the value in the box below it, the predicted response updated simultaneously. Therefore GUI can be used to calculate the optimal value, in general any required value, of the response and the corresponding values of the inputs. As shown in Fig. 11a showed that the dye removal capacity has maximum value at the central point of time (90 min) and the response decrease in both directions away from it. The response (y1) almost decreases in a linear fashion with increasing the pH and this behavior changes with varying the values of other variables. The effect of the

Fig. 10. 3D plots (a, c) and 2D contour plots (b, d) of the response (y2) indicating the effect of the interaction between time and initial concentration, at the mean value of pH and dose, pH and dose at the mean value of time and initial concentration at central point values of other parameters.

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dose on the response shows a minimum in the reactive dye removal capacity at BMPP dose of 1.2 g L− 1. From the GUI curves, the obtained optimal dye removal capacity is 209.7 mg g−1 at time of 53.9 min, adsorbent dose of 0.2 g L−1, pH of 2.0, and initial concentration of 125 mg L−1. Meanwhile, the modeled metal adsorption capacity illustrated in Fig. 12b shows a maximum at the mean value of time and decrease in both directions away from the optimal period. The response (y2) increases by increasing the pH to reach a maximum value at pH 4.5. The influence of the last three variables on the removal capacity is almost linear decrease with increasing the dose and increase with increasing the pH and initial concentration. The maximum metal removal capacity is 103 mg g− 1 at time 97.4 min, pH 5.0, dose of 0.2 g L−1 and initial Cu2+ ions concentration 125 mg L−1 as shown Fig. 12b. In order to check the predicted optimal values of the removal capacities, three experimental runs are conducted at the setting optimal values of the independent factors. It was found that the removals capacities are 224 mg g− 1 for dye and 106 mg g − 1 for Cu (II) ions over BMPP biomass, these values are very close to the predicted ones that emphasized the suitability of applied model for prediction of adsorptive capacity of BMPP for metals and dyes. It is exigent for comparison the present experimental results with previous adsorption because the recorded removal varies with

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respect to waste source, dose and concentration of pollutant. The pomegranate peels and carbonized pomegranate peels waste were previously applied adsorbent for different dyes and metal [26–33]. Table 5 presented the comparison of BMPP material with different pomegranate peels waste for adsorption dyes and metals. It was found that BMPP has a high capacity for dye and metal rather than other pomegranate peels.

4. Conclusion In this work, the response surface methodology with CCD design was applied for modeling and optimizing the removal capacity of metal and dye from wastewater using BMPP. The ANOVA test with the very low P-value (P b 0.0001) and the high F-value (F = 15.2 for dye, F = 13 for metal). The high determination coefficients (R2 0.94 for dye and R 2 0.92 for metal) indicated that the current model is highly significant for modeling the experimentally calculated adsorption capacities as a function of time, pH, dose, and initial copper concentration. The optimal removal capacity obtained from the GUI is 209.7 mg g − 1 for dye and 103 mg g − 1 for metal. Moreover, the applicability of modeling using RSM incorporation with CCD is envisaging adsorptive capacity of BMPP of dye and

Fig. 11. The response surfaces of the modeled metal removal capacity (a, c) and their corresponding contour plots for (b, d) as a function of time and pH, and time and dose at central point values of other parameters.

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Fig. 12. (a) GUI with four plots illustrate the variations in the modeled dye removal (y1) as a function of time, pH, dose, initial concentration. (b) The same as in (a) but for metal removal capacity.

Table 5 Comparison of maximum of adsorption capacities of dyes and metal ions over different pomegranate peel materials as adsorbents. Adsorbent

Pollutant

Maximum adsorption capacity (mg g−1)

Reference

Untreated pomegranate peel (PGP) Pomegranate peel waste Raw pomegranate peel Chemical activated Carbon/pomegranate peel (AC1) Chemical activated Carbon/pomegranate peel (AC2) Chemical activated Carbon/pomegranate peel (AC3) Raw pomegranate peel Chemical activated Carbon/pomegranate peel (AC1) Chemical activated Carbon/pomegranate peel (AC2) Chemical activated Carbon/pomegranate peel (AC3) Punica granatum L. peels Ball-milled pomegranate peel (BBMP) Magnetic pomegranate waste (PW) Punica granatum L. peels Carbonized pomegranate peel (CPP). microwave-assisted Pomegranate peel-AC Activated carbons-pomegranate peel Ball-milled pomegranate peel (BBMP)

Cu ions Ni ions Cu ions Cu ions Cu ions Cu ions Pb ions Pb ions Pb ions Pb ions Pb ions Cu ions Congo red dye Acid Blue 40 dye Malachite Green dye Remazol brilliant blue reactive Direct blue-106 dye C.I. Reactive Yellow 145 (3RS)

30.12 mg g−1 69.4 mg g−1 1.32 mg g−1 18.1 mg g−1 19.2 mg g−1 21.98 mg g−1 13.87 mg g−1 13.99 mg g−1 17.6 mg g−1 17.91 mg g−1 193.9 103–106 mg g−1 86.96 mg g−1 138.1 mg g−1 31.45 mg g−1 33.2 mg g−1 42.59 mg g−1 210–224 mg g−1

Ben-Ali et al., 2017 Bhatnagar and Minocha, 2010 El-Ashtoukhy et al., 2008 El-Ashtoukhy et al., 2008 El-Ashtoukhy et al., 2008 El-Ashtoukhy et al., 2008 El-Ashtoukhy et al., 2008 El-Ashtoukhy et al., 2008 El-Ashtoukhy et al., 2008 El-Ashtoukhy et al., 2008 Ömerŏglu Aya et al., 201 The current study Sayğılı, 2015 Ömerŏglu Aya et al., 2012

metals. Therefore, BMPP is a promising low cost biomass for decontamination of wastewater pollutants.

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