Journal of Chemistry
Volume 2019 (2019), Article ID 3017163, 11 pages
https://doi.org/10.1155/2019/3017163

Research Article

Pineapple Bark Performance in Dyes Adsorption: Optimization by the Central Composite Design

Ahlam Fegousse,¹ Abdelali El Gaidoumi,² Youssef Miyah,² Rabea El Mountassir,¹ and Anissa Lahrichi¹

¹Laboratory of Biochemistry, Faculty of Medicine and Pharmacy, Sidi Mohamed Ben Abdellah University, 30000 Fez, Morocco
²Laboratory of Catalysis, Materials and Environment, Higher School Technology, Sidi Mohamed Ben Abdellah University, 30000 Fez, Morocco

Correspondence should be addressed to Ahlam Fegousse; ahlam.fegousse@gmail.com

Received 24 November 2018; Revised 31 December 2018; Accepted 15 January 2019; Published 14 February 2019

Academic Editor: Nicolas Roche

Copyright © 2019 Ahlam Fegousse et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

This work is concerned with the study of the adsorption in aqueous medium of a three-dye mixture which contains Methylene Blue, Brilliant Green, and Congo Red on the pineapple bark. This adsorbent material has been characterized by scanning electron microscopy (SEM) and Fourier transform infrared spectroscopy (FTIR). The experimental design methodology, based on the response surface methodology (RSM) by the central composite design (CCD), has been applied for the optimization of the parameters, namely, the temperature, dose of the adsorbent, and pH. The yield reached 98.91% under optimal conditions (T = 30°C; adsorbent dose = 2.5 g·L⁻¹; pH = 9.8) at an initial concentration of 20 mg·L⁻¹.

1. Introduction

Water is an essential element for the survival of all living organisms. Nowadays, the water sources contaminated by a wide variety of pollutants coming from industrial effluents are a subject of several researchers [1, 2]. Dyes have a large amount of pollutants and can be identified even by the human eye [3]. In this respect, dyes are used in various industries such as textiles, food, paper, rubber, plastics, and cosmetics [4]. Hence, the discharge of wastewater from these industries to water resources poses unavoidable problems. The presence of dyes in trace water is undesirable because most of them are toxic, mutagenic, and carcinogenic [5]. Furthermore, dyes not only prevent the penetration of light and reduce the photosynthetic activities of rivers but also disturb the aquatic balance. Thus, the elimination of dyes from wastewater before discharge is an indispensable task. In this case, several techniques such as flocculation, adsorption, oxidation, electrolysis, biodegradation, ion exchange, and photocatalysis have been used for the removal of dyes in wastewater [6]. Among the several techniques, adsorption has received considerable attention because of its many advantages in terms of cost, ease of use, flexibility and simplicity of design, and insensitivity to toxic pollutants [7, 8]. Similarly, of the various adsorbents, activated carbon may be the most effective adsorbent for the removal of dyes due to its excellent adsorption capacity [9]. However, its use is limited on account of its high cost [10]. Thus, attention has shifted to finding cheaper and more effective alternatives. For this reason, natural materials, agricultural and industrial waste, and biosorbents represent potential alternatives. Additionally, a large number of unconventional and low-cost adsorbents have been proposed by many researchers for the removal of dyes [11, 12] including agricultural waste such as sawdust [13], bark [14], and orange peel [15] and industrial waste, namely, metal hydroxide sludge [16], red mud [17], ashes flying [18], clays [19–21], diatomites [22, 23], zeolites [24, 25], siliceous materials [26, 27], biosorbents [28–31], and others (cyclodextrin [32, 33], starch [34], cotton [35], etc.).

The purpose of this work was to study the adsorption of a three-dye mixture that contained Methylene Blue, Brilliant Green, and Congo Red on the pineapple bark according to the surface response method using the central composite design that consists of optimizing physicochemical parameters such as pH, temperature, and mass of the adsorbent.

2. Materials and Methods

2.1. Preparation of the Adsorbate and Adsorbent

The studied dyes have been supplied by Sigma-Aldrich and used without prior treatment. Congo Red (CR) is an anionic dye, part of the class of azo, with chemical formula C₃₂H₂₂N₆O₆S₂Na₂ and molar mass equal to 696.66 g·mol⁻¹. Its molecular structure is illustrated in Figure 1(a). Methylene Blue (MB) and Brilliant Green (BG) which are cationic dyes, with chemical formulas of C₁₆H₁₈ClN₃S and C₂₇H₃₄N₂O₄S and molar masses equal to 319.85 g·mol⁻¹ and 482.63 g·mol⁻¹, respectively. Their molecular structures are shown schematically in Figures 1(b) and 1(c). The colored solution (DM) has been precisely prepared by mixing the three dyes with same proportion in ultrapure water.

Figure 1: Chemical structure of dyes: (a) Congo Red, (b) Methylene Blue, and (c) Brilliant Green.

The pineapple bark (PB) was washed with ultrapure water followed by free-air drying. After that, it was crushed and sieved to a fraction less than 125 nm. The resulting powder was then washed with ultrapure water to remove impurities and then oven-dried at a temperature of 60°C.

2.2. Adsorption Procedure

The experiments have been performed in a batch system with constant stirring in Pyrex beakers using a volume of 50 mL of colored solution. Moreover, 4 mL samples have been taken using a 0.45 μm diameter syringe filter and then analyzed by a Jasco V-750 UV-Vis spectrophotometer at a wavelength λmax = 624 nm.

The capacity of adsorption was calculated according to

where qt is the amount of adsorbed dye per unit adsorbent at instant t (mg/g), m is the used mass of adsorbent (g), C₀ is the initial concentration of dye in solution (mg/L), Ct is the concentration of dye in solution at instant t (mg/L), and V is the used volume of solution (L).

The concentrations and removal percentages were calculated taking into account the dyes’ purity: ≥35%, ≥82%, and ≥90% for CR, MB, and BG, respectively.

2.3. Characterization Techniques

Morphological analysis was observed by scanning electron microscopy (SEM) using a Quanta 200 FEI microscope. Fourier transform infrared spectroscopy (FTIR) analysis was performed in the 400–4000 cm⁻¹ interval using a Bruker Vertex 70 spectrometer to determine the functional analysis of the PB powder.

The point zero of charge (pHPZC) was determined by a classical method [36], which consists in preparing 50 mL solutions of NaCl (0.01 M) and adjusting their pH to precise values ranging from 2 to 12 by addition of NaOH or HCl (0.1 M). Next, 0.5 g of adsorbent was introduced into each solution. The suspensions were stirred at room temperature for 24 h before determining the pHfinal. The pHPZC was calculated based on the curve pHfinal − pHinitial = f(pHinitial).

3. Results and Discussions

3.1. Characterizations of the Adsorbent

The use of the SEM technique makes it possible to visualize the morphology of the surface of the adsorbent. The SEM micrographs of PB are shown in Figure 2. The obtained images display not only the amorphous nature and the heterogeneous morphology of PB but also indicate the presence of both a grainy structure (Figure 2(b)) close to the appearance of the grapes and a filamentous structure and cellulosic fibers of various sizes and shapes (Figure 2(c)), which promote the fixation of dyes. Consequently, these results are similar to those obtained by Miyah et al. [37].

Figure 2: SEM micrographs of PB: magnification (a) ×2000; (b, c) ×4000 (reproduced from Fegousse et al. [38]).

FTIR spectrum analysis (Figure 3) shows a band around 3444.03 cm⁻¹ which is characteristic of the presence of a band O-H group, a small band around 2925.38 cm⁻¹ which attributes to C-H elongation vibrations, a band at 2361.24 cm⁻¹ which could be due to the nitrile triple bond C≡N, a band at 1633.21 cm⁻¹ which originates from the vibration of the C=O groups, a band at 1398.56 cm⁻¹ which coincides with the OH band aromatic alcohol, a 1255.45 cm⁻¹ band which corresponds to the CO bond, a 1037.19 cm⁻¹ band which is assigned to the primary alcohol function (C-OH), and a band at 668.09 cm⁻¹ which characterizes the function ≡CH [39].

Figure 3: FTIR spectra of PB (reproduced from Fegousse et al. [38]).

The pHPZC corresponds to the pH value for which the net electric charge of the surface of the material is neutral [40]. pHPZC is very important in adsorption phenomena, especially when electrostatic forces are involved in the mechanisms. Figure 4 shows that the pHPZC of PB is 6.7. This point illustrates an amphoteric behavior of PB. In fact, the surface of PB is positively charged at pH < pHPZC and negatively charged at pH > pHPZC. As pH decreases, the number of negatively charged sites decreases, and the number of positively charged sites increases and vice versa when pH increases [41–43].

Figure 4: The point zero of charge of PB.

3.2. Effect of Contact Time

As illustrated in Figure 5, the equilibrium time is reached in the first five minutes for a concentration range varying between 20 mg·L⁻¹ and 50 mg·L⁻¹. This can be explained by the fact that the available number of active sites on the surface of adsorbent becomes saturated after five minutes [44]. In this case, the adsorption capacity (Qe) remains constant after equilibrium time, which denotes that the adsorption is rapid and confirms strong chemical interactions between the dye molecules and adsorbent. In this respect, these outcomes allow us to have a quick overview on the effectiveness of PB.

(a)

(b)

Figure 5: Effect of contact time on (a) adsorption efficiency and (b) adsorption capacity at different concentrations. m = 2.g·L⁻¹; [DM] = 20–50 mg·L⁻¹; T = 20°C, pH = 6; Vag = 400 rpm; t = 60 min.

3.3. Effect of Adsorbent Dose

Figure 6(a) shows the monitoring of the adsorption capacity as a function of time at an initial concentration of 20 mg·L⁻¹; a large decrease in the DM adsorbed amount with the increase of the adsorbent dose was observed. This phenomenon is due to the decrease of number of DM molecules (decrease in concentration) and the difficulty of adsorbent-adsorbate interaction, which explains the saturation of the active sites of the adsorbent. Additionally, while the curve which is a function of the adsorption efficiency (Figure 6(b)) has a proportionally inverse appearance to that of the adsorbed quantity, it is obviously noticed that the adsorption efficiency increases with the increase in the adsorbent dose. Hence, 2 g·L⁻¹ of the adsorbent constitutes an optimal dose to remove 97.93% of DM which corresponds to an adsorption capacity of 14.6 mg·g⁻¹.

(a)

(b)

Figure 6: Effect of PB dose on (a) adsorption capacity and (b) adsorption efficiency. m = 0.5–3 g·L⁻¹; [DM] = 20 mg·L⁻¹; T = 20°C; pH = 6; Vag = 400 rpm; t = 60 min.

3.4. Effect of Temperature

The outcomes of the evolution of the adsorption capacity as a function of temperature at an initial concentration of 20 mg·L⁻¹ are displayed in Figure 7. In this respect, the analysis of the curve shows that the temperature has a negative effect on the adsorption capacity; it affects the chemical potential of the material: the mobility of the system increases but the interactions decrease. This suggests that the interaction of PB and DM may be exothermic. Thus, the adsorption capacity reaches 9.87 mg·g⁻¹ for a temperature of 20°C which corresponds to a removal efficiency of 98.97%.

(a)

(b)

Figure 7: Effect of temperature on (a) adsorption efficiency and (b) adsorption capacity. m = 2 g·L⁻¹; [DM] = 20 mg·L⁻¹; T = 20–50°C; pH = 6; Vag = 400 rpm; t = 60 min.

The adsorption may result in decreasing or increasing the surface energy of the adsorbent materials. The parameters that can describe the adsorption thermodynamics due to the transfer of the solute to the solid-liquid interface are the standard enthalpy (ΔH°), the standard entropy (ΔS°), and the free standard enthalpy (ΔG°). The values of ΔH° and ΔS° were calculated using equation (2) [4] by plotting ln Kd as a function of 1/T (Figure 8).

where R is the ideal gas constant (R = 8.314 J·mol⁻¹·K⁻¹); T is the absolute temperature of solution (K); and Kd is the distribution coefficient equal to Qe/Ce (Qe and Ce represent the capacity of adsorption and concentration at equilibrium, respectively).

Figure 8: ln Kd versus 1/T for the adsorption of DM on PB.

The values of ΔG° were calculated using the following formula [4]:

The obtained results show that the standard enthalpy value is negative (Table 1), which confirms that the adsorption of the dye molecules at the sites of the PB adsorbent is exothermic. The negative values of the free standard enthalpy and standard entropy indicate that the process of adsorption of the DM on PB is spontaneous and follows a disorder, respectively.

Table 1: Thermodynamic parameters of the adsorption process of DM on PB.

Temperature (°C)
ΔH° (KJ·mol⁻¹)
ΔS° (J·mol⁻¹·K⁻¹)
ΔG° (J·mol⁻¹)
R²

20
−22.44
−51.68
−37591.20
0.9993

30
−38108.04

40
−38624.88

50
−39141.72

3.5. Effect of pH

The evolution of DM adsorption as a function of pH at an initial concentration of 20 mg·L⁻¹and dose of 2 g·L⁻¹ is illustrated in Figure 9. The adsorption is favorable at acid pH due to the positive charge of the adsorbent which promotes the adsorption of the negatively charged anionic dye (RC). On the contrary, at basic pH, the adsorption is stable even in the presence of cationic dyes (MB and BG). This indicates that the adsorbent is selective for the anionic dye and only at acidic pH. Furthermore, the adsorption of the cationic dyes is owing to the activation of the surface of the adsorbent by the anionic dye which makes the surface negative and, therefore, active in front of the cationic dyes. Indeed, for acid pH values, the cationic dyes are in competition with the H⁺ ions presented in high concentration in the solution [45,46]. These ions, positively charged, are more adsorbed than cationic dyes and then beneficial for adsorption of anionic dye with negative charge. Additionally, when the pH increases, there is a decrease in the H⁺ ions and increase in OH⁻, which explains the favorability of the adsorbent to absorb the cationic dyes. Based on the obtained results, pH = 6 is the optimum value for the adsorption of DM.

Figure 9: pH effect on PB adsorption capacity. m = 2 g·L⁻¹; [DM] = 20 mg·L⁻¹; T = 20°C; pH = 2–12; Vag = 400 rpm; t = 30 min.

3.6. Optimization of Adsorption

The aim of this study is to optimize the adsorption of the mixture of dyes by the response surface methodology (RSM). The design used for the optimization, belonging to RSM, is the central composite design (CCD); its equation is polynomial of second order [47]:

where Y represents the percentage of adsorption, Xi represents the independent variable (temperature, pH or dose of the adsorbent), XiXj demonstrates the interactions between the variables, and bi represents the coefficient of the effect resulted by Xi.

The number of experiments to be performed is determined by the following formula:

where K is the number of independent variables and f is the number of experiments performed at the center of the experimental scope.

To realize 17 experiments according to the computation of the formula (3), each variable is represented by 5 levels: −1.68; −1; 0; +1; 1.68.

3.7. Table of Variables

Table 2 represents the three studied variables and their corresponding levels. The reaction time is 5 min, and the concentration of DM was fixed at 20 mg·L⁻¹.

Table 2: Description of the variables and their levels.

Parameters
Variables
Levels

−1.68
−1
0
+1
+1.68

T (°C)
X₁
20
30
40
50
60

pH
X₂
2
4
6
8
10

m (adsorbent dose) (g·L⁻¹)
X₃
0.5
1
1.5
2
2.5

3.8. Experimental Matrix and Postulated Mathematical Model

Table 3 summarizes the 17 performed experiments according to the CCD design. Experiments 15, 16, and 17 represent the experiments at the center. According to the results, the percentage of adsorption varies between 75.55% and 97.18%. The mathematical model generated by the Design-Expert software version 11 is

Table 3: Experimental matrix.

Experience
Variables
Response

X₁
X₂
X₃
Y

1
−1
−1
−1
77.01

2
1
−1
−1
76.81

3
−1
1
−1
98.75

4
1
1
−1
98.45

5
−1
−1
1
85.42

6
1
−1
1
90.13

7
−1
1
1
90.75

8
1
1
1
99.70

9
−1.68
0
0
85.15

10
1.68
0
0
92.18

11
0
−1.68
0
75.55

12
0
1.68
0
99.52

13
0
0
−1.68
90.25

14
0
0
1.68
95.70

15
0
0
0
97.30

16
0
0
0
97.18

17
0
0
0
97.25

3.9. ANOVA Analysis

Analysis of variance (ANOVA) has been used, and the results are given in Table 4. Generally, the adaptation of the postulated model is confirmed by a high Fisher value (F) with as low a probability (P) as possible. From the ANOVA test, the obtained value of 0.0001 is less than 0.05 value evaluated by the Fischer relation which confirms that the model terms are significant [48,49]. The resulting F value of 230.5 is greater than the Fisher value of F9.7 = 3.8 for a 95% confidence level. So, the model is well suited to experimental data [50–52].

Table 4: Results of ANOVA for the fitted model.

Source
Degree of freedom
Sum of squares
Mean square
value
value

Model
9
1103.07
122.56
230.5
<0.0001

Residual
7
3.72
0.53

Total
16
1106.7916

The fit quality of the model can be justified by the correlation coefficient (R²) which indicates the proportion of the response variability taken into account by the model [53]. The best correlation is obtained for which R² = 0.80 is suggested [54]. The obtained R value of 0.9966 (Table 5) implied that a large proportion of the variability of the response is considered by the model; 0.34% of the variations are not taken into account.

Table 5: Correlation of model.

R²

Standard deviation
Mean
Coefficient of variation (%)

0.9966
0.9923
0.73
91.01
0.8

3.10. Estimation of Coefficients

The determination of the coefficients makes it possible to judge the significance of the effects of the variables and their interactions. Thus, Table 6 summarizes the results found.

Table 6: Estimated regression coefficients and their significance.

Coefficients
Estimated values
Standard deviation
value
value

b₀
97.22
0.42
230.5
<0.0001

b₁
1.83
0.2
85.95
<0.0001

b₂
7.22
0.2
1338.61
<0.0001

b₃
1.77
0.2
80.29
<0.0004

b₁₁
−2.95
0.22
184.92
0.0847

b₂₂
−3.35
0.22
238.34
0.0002

b₃₃
−1.43
0.22
43.32
<0.0001

b₁₂
0.52
0.26
4.03
<0.0001

b₁₃
1.77
0.26
47.14
<0.0001

b₂₃
−3.56
0.26
190.68
<0.0001

Generally, interaction coefficients that have a value less than 0.05 consider themselves as coefficients having a significant effect on the response. According to the previous table, all values are less than 0.05; so, all coefficients are considered to have a very crucial effect on the response, except in the case of b₁₁ for which is equal to 0.0847.

3.11. Graphic Study

The graphical representations of three response surfaces are illustrated in Figure 10. In this respect, the optimum point of the adsorption yield has been achieved based on the response surface of the X₂X₃, interaction between pH and adsorbent dose (Figure 10(c)), which have already shown a positive effect on yield (Figures 6 and 9). Besides, the maximum yield that is not acquired according to the X₁X₂ and X₁X₃ interactions (Figures 10(a) and 10(b)) is justified by the negative effect of temperature (X₁) on the adsorption process (Figure 7). Hence, the optimal conditions are summarized in Table 7.

(a)

(b)

(c)

Figure 10: Response surfaces showing adsorption efficiency as function of (a) temperature and pH, (b) temperature and adsorbent dose, and (c) pH and adsorbent dose.

Table 7: Established optimal conditions.

Optimal conditions
Adsorption (%)

Temperature (°C)
pH
Adsorbent dose (g·L⁻¹)

30
9.8
2.5
99.96

3.12. Evolution of Adsorption under Optimal Conditions

To confirm the validity of the theoretical values of the optimization, a follow-up of the evolution of the adsorption under the optimal conditions has been carried out (Figure 11). In this case, we notice that the system leads to a rapid adsorption of 98.91% after 5 min close to the theoretical value.

Figure 11: Evolution of the adsorption of DM under optimal conditions.

From what have been stated the above, CCD has shown great satisfaction in optimizing the parameters influencing the adsorption of the studied dyes mixture since the statistical tests are well adapted to the experimental results. These outcomes are in total agreement with the various publications, which show the validation of the CCD plane in the study of adsorption of dyes on different types of adsorbents [55–57]. In addition, the yield obtained at optimal conditions is very high, which confirms the performance of the pineapple bark as an adsorbent for the studied mixture.

4. Conclusions

The performance of the pineapple bark in the adsorption of the dyes mixture Methylene Blue, Brilliant Green, and Congo Red has been studied in a batch system. In this respect, the parameters the mass of the adsorbent, the concentration of the dyes mixture, the pH, and the temperature have indeed influenced the adsorption process. Besides, adsorption at a concentration of 20 mg·L⁻¹ is rapid during the first five minutes of contact for a mass of 2 g·L⁻¹. Moreover, the adsorption is favorable in acidic medium and is of the exothermic type. Furthermore, through the experimental design methodology, CCD plan, we have optimized the physicochemical parameters such as the mass of the adsorbent, the pH, and the temperature influencing the adsorption of the dyes mixture into pineapple bark. During 5 min of the adsorption, under the optimum conditions pH = 9.8, T = 30°C, and adsorbent dose = 2.5 g·L⁻¹, the output reaches 98.91% at an initial concentration of 20 mg·L⁻¹ and for a volume of 50 mL.

Data Availability

No data were used to support this study.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Acknowledgments

The authors are grateful to the Laboratory of Biochemistry of the Faculty of Medicine and Pharmacy and the Innovation Center, Sidi Mohamed Ben Abdellah University of Fez (Morocco), for the financial support given to Dr. Ahlam Fegousse.

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