The adsorption of methylene blue (MB) by low cost biomass lotus seedpod (LSP) was optimized by a central composite design combined with response surface methodology in aqueous solution. Solution pH, initial dye concentration and adsorbent dosage were studied as independent variables at five levels each, respectively. Analysis of variance suggested the validity of the regression model. LSP was characterized by Fourier transform infrared spectra and energy dispersive spectroscopy. The kinetics revealed that the adsorption behavior followed the pseudo-second-order model. Langmuir and Freundlich isotherm models were used to evaluate the adsorption, and the experimental data were better fitted by the Langmuir isotherm than the Freundlich isotherm. The maximum monolayer adsorption capacity of the LSP was 157.98 mg g−1 at 30 °C for MB adsorption. In addition, 0.2 M HCl solution could be used for reusability of LSP via desorption tests. LSP was proven to be an available and effective biosorbent for MB removal from aqueous solution.

INTRODUCTION

Dyes from industrial effluents such as textile, paper, plastics and leather are great concerns due to their high visibility, undesirability and recalcitrance (Roosta et al. 2014), which can cause severe damage to both humans and the environment (Han et al. 2011). Methylene blue (MB) (3,7-bis(dimethylamino)-phenothiazin-5-ium chloride) has been widely used for coloring paper, temporary hair colorant, dyeing cottons and so on (Iriarte-Velasco et al. 2015). MB can cause eye burns in both human and animals, which may be responsible for permanent injury to the eyes (Kavitha & Namasivayam 2007). Therefore, it is necessary to find low-cost and effective ways of removing MB from industrial wastewater to reduce environmental problems.

Adsorption has been testified to be an available approach for MB removal due to the high efficiency, capacity and large scale ability of generable adsorbents, though many other technologies such as flocculation, coagulation, precipitation, membrane filtration, electrochemical techniques, and ozonation have also been applied (Bestani et al. 2008). The design of novel procedures based on non-toxic, low-cost and easily available adsorbents is the best choice for MB removal. Numerous researches have been conducted to utilize low-cost adsorbents for removal of MB, including bamboo (Mui et al. 2010), coffee beans (Franca et al. 2009), fruit shell (Noorimotlagh et al. 2014), and vine shoots (Ruiz-Fernandez et al. 2010) and so on. Lotus seedpod (Nelumbo nucifera, (LSP)), as a waste biomass from an agricultural byproduct, is widespread in China, especially in Hubei Province (Han et al. 2011). LSP contains abundant floristic fiber, protein and some functional groups that may contribute to the adsorption process, like other plant materials (Han et al. 2007).

However, the adsorption efficiency depends on several independent variables such as solution pH, initial dye concentration, adsorbent dosage and so on (Cao et al. 2014). Response surface methodology (RSM) with analysis of variance (ANOVA) as a mathematical statistical technique has long been used to optimize all the process independent variables (Auta & Hameed 2011; Pandey & Negi 2015), which can evaluate the relative significance of the variables even in the presence of complex factor-factor interactions (Ghaedi et al. 2015). Hence, central composite design (CCD) under RSM using ANOVA can be applied to maximize the criterion of the response. A comparison between the results from the present models and the experimental values is also conducted during the process.

In the present study, the objective was to investigate the adsorption of MB by low-cost biomass LSP from aqueous solution and to optimize the independent parameters for maximal removal efficiency by RSM using ANOVA. The LSP before and after adsorption was characterized by Fourier transform infrared (FTIR) spectra and energy dispersive spectroscopy (EDS). The adsorption mechanism was also determined by calculating the kinetics. Different isotherm models, including Langmuir and Freundlich, were utilized to fit the adsorption process. Desorption experiments were also conducted to investigate the reusability of LSP. This study reports for the first time on the availability and feasibility of low-cost biomass LSP as an alternative adsorbent for MB removal.

MATERIALS AND METHODS

LSP preparation

LSP was collected from the East Lake, Wuhan, China. Prior to experiments, the LSP was thoroughly washed with running water for several hours to remove dust and then washed with distilled water. The clean LSP was then oven-dried at 105 °C overnight. Dried materials were cut into small pieces, and then ground into powder. Only fine particles passing through a 50 meshsieve (corresponding to 297 μm in diameter) were used for the present study.

Batch adsorption experiments

Batch experiments were conducted in a series of 50 mL flasks by shaking 0.04 g LSP with 40 mL of predetermined concentrations of MB solution in a mechanical shaker at 180 rpm, 30 °C for 4 h. The initial pH (around 7.0) was not controlled otherwise for the RSM optimization tests. The adsorption kinetics were determined by shaking 0.04 g LSP with 40 mL of 100 mg L−1 MB solution at a constant temperature of 30 °C. The residual MB concentration in the supernatant was determined at certain intervals. Adsorption isotherms were performed by agitating 40 mL of MB solution at concentrations from 10 to 500 mg L−1 at 30 °C. After adsorption, the LSP was separated from the MB solution and the supernatants were analyzed according to the method by (Sun et al. 2015). All experiments were conducted in triplicate, and the average values were used for statistical analysis.

The adsorption capacity qe (mg g−1) and removal efficiency (%) were determined by Equations (1) and (2), respectively: 
formula
1
 
formula
2
where Co and Ce are the initial and equilibrium MB concentrations, respectively (mg L−1), V is the volume of MB solution (L), and M is the amount of LSP (g).

Desorption experiments

During the desorption process, 0.02 g of the MB-loaded LSP was separated, and was then reused and stirred with 40 mL of HCl solution at various concentrations (0, 0.2, 0.4, 0.6, 0.8 and 1.0 mol L−1) for 4 h. The desorption efficiency was determined by analyzing the MB concentration in the supernatant solution after centrifugation.

Optimization of the experimental design

A CCD under RSM was used to investigate the three independent variables: the initial pH (X1), MB concentration (X2) and adsorbent dosage (X3) on MB removal at five levels in aqueous solution (Table 1). The adsorption of MB onto LSP was explained by the following second-order polynomial model (Savasari et al. 2015): 
formula
3
where Y is the predicted response, the terms ,,, and are the offset term, linear effect, squared effect, interaction effect and residual term, respectively. and are the coded variables.
Table 1

Experimental variables and their coded levels for CCD

Factors Range and levels (coded)
 
− 2 − 1 
X1: pH 10 
X2: MB concentration (mg·L−110 50 100 200 500 
X3: Dosage (mg·L−10.5 
Factors Range and levels (coded)
 
− 2 − 1 
X1: pH 10 
X2: MB concentration (mg·L−110 50 100 200 500 
X3: Dosage (mg·L−10.5 

Design expert version 8 (Stat-Ease, USA) was used to optimize the levels of the independent variables, evaluate the interactions of the process factors, and for regression and graphical analysis of data. The removal efficiency was set as the output response.

RESULTS AND DISCUSSION

Characteristics of LSP

Figure 1 shows the FTIR spectra for LSP before (a) and after (b) adsorption of MB. Both samples displayed a broad and strong band with peaks at 3,423 and 3,427 cm−1, which was due to the stretch vibration of bonded –OH on the surface of the LSP (Pandey & Negi 2015). The asymmetric stretch vibration of –CH3 could be seen at 2,924 cm−1, while the symmetric stretch vibration of –CH2 led to the adsorption peak at 2,854 cm−1 (Han et al. 2011). The strong peak at 1,622 cm−1 corresponded to C=O stretching vibrations typical of carboxylate moieties, while the small peaks at 1,525 and 1,446 cm−1 were attributed to the aromatic rings and symmetric bending of –CH3, respectively (Cao et al. 2014). The asymmetric bending vibrations of –CH3 and –CH2 could be seen at peaks near 1,378–1,388 cm−1. The additional peak at 1,062 cm−1 indicated the S=O stretching vibration (Noorimotlagh et al. 2014). Upon MB loading, the FTIR spectrum had no significant change apart from some intensity increases and vibrations and bonds shifts. The results of FTIR analysis revealed some potential active sites for adsorption such as carbonyl and hydroxyl groups (Kavitha & Namasivayam 2007).
Figure 1

FTIR of the LSP before and after adsorption of MB.

Figure 1

FTIR of the LSP before and after adsorption of MB.

Figure 2(a) and 2(b) present the EDS results of LSP before and after adsorption of MB, respectively. The presence of typical elements in biomass, including carbon, oxygen, phosphorus magnesium, potassium, calcium and iron, was commonly detected relatively abundantly in both samples. The presence of cationic elements like magnesium, potassium, calcium and iron might contribute to the adsorption of the MB anionic dye. In addition, the strong peak of sulfur confirmed the high adsorption capacity of LSP, for this element was abundant only in MB. Meanwhile, the higher abundance of carbon and oxygen within the sample after adsorption also confirmed the adsorption of MB onto LSP. The EDS spectrum also provided evidence of a large number of carbonyl and hydroxyl groups on the surface of the LSP before and after adsorption (Kavitha & Namasivayam 2007).
Figure 2

EDS spectrum of LSP (a) before and (b) after adsorption of MB.

Figure 2

EDS spectrum of LSP (a) before and (b) after adsorption of MB.

Optimization of the MB adsorption process

CCD model fitting and statistical analysis

A 20-run CCD experimental design (pH 1–10, initial concentration 10–500 mg L−1, and dosage 0.5–5 mg L−1) was carried out, and the results are shown in Table 2. The following second-order polynomial model equation describes the adsorption removal (%) of MB. 
formula
4
Each actual value was compared with the predicted value from the above Equation (4), and the residual was calculated by subtraction (Table 2). It was apparent that the predicted values were highly consistent with the actual ones, while the residuals were dispersed around zero with no particular pattern within the range of experiments, indicating good validity of the regression model given above (Pandey & Negi 2015).
Table 2

Experimental design in terms of coded variables and results of the CCD

Run Coded variable
 
Response (%)
 
X1 X2 X3 Actual Predicted Residual 
200 81.08 82.20 1.12 
50 100.00 100.00 0.01 
100 95.17 94.31 0.86 
50 83.11 83.23 0.13 
100 95.37 94.31 1.06 
200 99.90 100.00 0.10 
100 95.17 94.31 0.86 
50 99.41 100.00 0.59 
10 100 93.72 92.27 1.45 
10 100 0.5 84.51 85.10 0.60 
11 50 78.97 79.02 0.05 
12 200 64.01 63.46 0.55 
13 100 56.41 57.07 0.66 
14 500 61.20 60.96 0.23 
15 10 98.69 98.73 0.04 
16 100 95.13 94.31 0.81 
17 100 95.00 94.31 0.69 
18 100 96.00 95.13 0.87 
19 200 84.04 83.40 0.64 
20 100 95.23 94.31 0.91 
Run Coded variable
 
Response (%)
 
X1 X2 X3 Actual Predicted Residual 
200 81.08 82.20 1.12 
50 100.00 100.00 0.01 
100 95.17 94.31 0.86 
50 83.11 83.23 0.13 
100 95.37 94.31 1.06 
200 99.90 100.00 0.10 
100 95.17 94.31 0.86 
50 99.41 100.00 0.59 
10 100 93.72 92.27 1.45 
10 100 0.5 84.51 85.10 0.60 
11 50 78.97 79.02 0.05 
12 200 64.01 63.46 0.55 
13 100 56.41 57.07 0.66 
14 500 61.20 60.96 0.23 
15 10 98.69 98.73 0.04 
16 100 95.13 94.31 0.81 
17 100 95.00 94.31 0.69 
18 100 96.00 95.13 0.87 
19 200 84.04 83.40 0.64 
20 100 95.23 94.31 0.91 

To further ensure the validity of the above RSM model for MB adsorption, ANOVA analysis was conducted. The results of the fitting model, in the form of ANOVA, are given in Table 3. The model F-value of 227.69 implied the model was significant, since there was only a 0.01% chance that a ‘model F-value’ this large could occur due to noise. Values of ‘prob>F’ less than 0.05 indicated model terms were significant. Therefore, all terms including X1, X2, X3, X1X2, X1X3, and X2X3 were significant model in this study. The ‘Lack of Fit F-value’ of 226.52 implied the Lack of Fit was significant, which revealed that some systematic variations were unaccounted for in the hypothesized model. The exact replicate values of the independent variables in the model that provided an estimate of pure error could be responsible (Pandey & Negi 2015). A good agreement between the predicated and actual results was obtained due to the high values of the determination coefficient (R2 = 0.995) and the adjusted determination coefficient (adj. R2 = 0.991).

Table 3

ANOVA of the response surface quadratic model for MB adsorption

Source Sum of squares Degrees of freedom Mean square F value P-value
prob > F 
Model 3389.56 376.62 227.69 <0.0001 
X1 685.72 685.72 414.55 <0.0001 
X2 58.86 58.86 35.58 0.0001 
X3 21.44 21.44 12.96 0.0048 
X1X2 0.00 0.00 0.00 0.9787 
X1X3 2.83 2.83 1.71 0.2200 
X2X3 94.00 94.00 56.83 <0.0001 
X12 759.84 759.84 459.36 <0.0001 
X22 31.94 31.94 19.31 0.0013 
X32 74.63 74.63 45.12 <0.0001 
Residual 16.54 10 1.65   
Lack of Fit 16.47 3.29 226.52 <0.0001 
Pure Error 0.073 0.015   
Cor Total 3406.10 19    
R2 = 0.995, adj. R2 = 0.991    
Source Sum of squares Degrees of freedom Mean square F value P-value
prob > F 
Model 3389.56 376.62 227.69 <0.0001 
X1 685.72 685.72 414.55 <0.0001 
X2 58.86 58.86 35.58 0.0001 
X3 21.44 21.44 12.96 0.0048 
X1X2 0.00 0.00 0.00 0.9787 
X1X3 2.83 2.83 1.71 0.2200 
X2X3 94.00 94.00 56.83 <0.0001 
X12 759.84 759.84 459.36 <0.0001 
X22 31.94 31.94 19.31 0.0013 
X32 74.63 74.63 45.12 <0.0001 
Residual 16.54 10 1.65   
Lack of Fit 16.47 3.29 226.52 <0.0001 
Pure Error 0.073 0.015   
Cor Total 3406.10 19    
R2 = 0.995, adj. R2 = 0.991    

Model interpretation and optimization of variables

Three-dimensional (3D) surface plots were generated to reveal the behavior of the experimental design system within two factors, holding the other factor at central level (Liu et al. 2010). The effects of the experimental variables on the responses are presented in the 3D plots in Figure 3.

Figure 3(a) and 3(b) reveal the influence of initial solution pH on the adsorption removal efficiency. It was obvious that the removal rate increased via a pH ranging from 3.0 to 7.0. Within the pH range from 1.0 to 10.0, the removal rate increased sharply from 50% at pH 1.0 to 95% at pH 7.5, and then decreased to 88% at pH 10.0. Solution pH was a vital factor due to its effects on both the LSP and the MB (Kavitha & Namasivayam 2007). An alkaline environment was more favorable for the adsorption process of MB onto LSP for the cationic dye reaction with OH, which would be suppressed by H+ in acidic conditions (Han et al. 2011). The slight decrease in removal rate after pH 7.5 indicated that the electrostatic mechanism was not the only mechanism for MB adsorption by LSP. Han et al. (2007) reported that the chemical reaction between the adsorbent and dye molecules was another possible mechanism. Given the above results, the batch experiments were carried out without pH adjustment of the original MB (pH near 7.0).
Figure 3

Response surface plots for combined effect of (a) pH and initial concentration, (b) pH and dosage, and (c) initial concentration and dosage.

Figure 3

Response surface plots for combined effect of (a) pH and initial concentration, (b) pH and dosage, and (c) initial concentration and dosage.

The effect of the initial MB concentration on the removal rate is shown in Figure 3(a) and 3(c). Although the removal rate decreased with the increase in MB concentration, the equilibrium adsorption capacity increased, and the trend was more apparent at appropriate pH values. This could be explained by the larger mass transfer provided by higher MB concentration, which could overcome various resistances from the aqueous solution (Yi & Zhang 2008). However, the amount of adsorbed MB stayed unchanged at high MB concentrations, for the adsorption sites available were limited on the surface of the LSP, thus making the adsorption capacity decrease to some degree (Dural et al. 2011). Considering the removal efficiency and adsorption capacity, an initial concentration of around 100 mg L−1 was recommended for this study.

Figure 3(b) and 3(c) show the influence of LSP dosage on the removal efficiency of MB. It was obvious that the removal rates at different dosages were largely affected by the initial concentration. Within the initial concentration range from 10 to near 80 mg L−1, the removal rate increased slowly; while an opposite trend was exhibited for removal efficiency at concentrations above 80 mg L−1, with 2.75 g/L as a turning point for LSP dosage. This was due to the overlapping of the active sites in competition for excess adsorbents (Weng & Pan 2007). Therefore, a dosage of about 2.75 g L−1 was recommended for high concentrations (above 200 mg L−1) of MB while at low concentrations (lower than 200 mg L−1) 1 g L−1 of adsorbent was enough.

Adsorption kinetics and isotherms

Adsorption kinetics

Pseudo-first-order and pseudo-second-order models were used to evaluate the kinetic mechanism that controlled the adsorption process. The linear forms of pseudo-first-order (Kannan & Sundaram 2001) and pseudo-second-order (Ho & McKay 1998) equations were given as follows: 
formula
5
 
formula
6
where q1, q2 and qt are the amount of MB adsorbed at equilibrium and at time t, in mg·g−1, k1 (min−1) and k2 (g mg−1 min−1) are the rate constants. The plots are presented in Figure 4, while the calculated kinetic models and related constants with linear regression coefficient (R2) are shown in Table 4.
Table 4

Kinetic parameters for adsorption process at 30 °C

Pseudo-first-order
 
Pseudo-second-order
 
k1 (min−1q1 (mg g−1R2 k2 × 104 (g mg−1 min−1q2 (mg g−1R2 
16.5165 99.9001 0.9906 5.8132 99.8004 0.9994 
Pseudo-first-order
 
Pseudo-second-order
 
k1 (min−1q1 (mg g−1R2 k2 × 104 (g mg−1 min−1q2 (mg g−1R2 
16.5165 99.9001 0.9906 5.8132 99.8004 0.9994 
Figure 4

Kinetic plots for adsorption of MB onto LSP at 30 °C: (a) pseudo-first-order and (b) pseudo-second-order model.

Figure 4

Kinetic plots for adsorption of MB onto LSP at 30 °C: (a) pseudo-first-order and (b) pseudo-second-order model.

The results showed that the experimental values were better fitted by the pseudo-second-order than the pseudo-first-order model due to the higher R2, though both models could well describe the adsorption process (0.9994 and 0.9906 for the pseudo-first-order and pseudo-second-order model, respectively). In addition, the theoretical q2 value (99.8004 mg g−1) agreed well with the experimental values at various initial concentrations. The kinetics indicated that chemical adsorption involving valency forces through the exchange or sharing of electrons between MB molecules and the LSP was the rate-limiting step of the adsorption mechanism (Hameed et al. 2007; Wang et al. 2015).

Adsorption isotherms

In this study, the Langmuir and Freundlich isotherms were employed to characterize the adsorption behavior. Both isotherms are stated below.

Langmuir isotherm: 
formula
7
Freundlich isotherm: 
formula
8
where qe is the equilibrium MB concentration onto the LSP (mg g−1), Ce is the equilibrium MB concentration in the solution phase (mg L−1), qmax is the monolayer adsorption capacity of the LSP (mg g−1), KL (L mg−1), KF (L mg−1) and n is the adsorption constant for the Langmuir and Freundlich isotherms, respectively. The plots of Ce/qe versus Ce, log qe versus log Ce are shown in Figure 5, and the isotherm parameters are given in Table 5.
Table 5

Isotherm parameters for the adsorption of MB onto LSP at 30 °C

Langmuir
 
Freundlich
 
qmax (mg·g−1KL (L·mg−1R2 KF (L·mg−1R2 
157.9779 0.6734 0.9995 42.5177 2.2592 0.9009 
Langmuir
 
Freundlich
 
qmax (mg·g−1KL (L·mg−1R2 KF (L·mg−1R2 
157.9779 0.6734 0.9995 42.5177 2.2592 0.9009 
Figure 5

Isotherm plots for adsorption of MB onto LSP: (a) Langmuir and (b) Freundlich isotherm.

Figure 5

Isotherm plots for adsorption of MB onto LSP: (a) Langmuir and (b) Freundlich isotherm.

The results indicated that the Langmuir model (R2 = 0.9995) fitted the adsorption better than the Freundlich model (R2 = 0.9009). Furthermore, the maximum adsorption capacity of MB by the LSP was 157.98 mg g−1 in the present conditions. Compared with the previous researches (Feng et al. 2012; Sun et al. 2013; Zhang et al. 2013; Sarat Chandra et al. 2015), the maximum monolayer adsorption of MB was at a relatively high level. Moreover, the KL value of 0.67 L mg−1 also suggested that the LSP was an ideal adsorbent for MB removal (Han et al. 2011).

Desorption experiment

The adsorption reversibility of the MB loaded LSP was investigated using various concentrations of HCl solutions and the results are shown in Figure 6. It can be seen from Figure 6 that the maximum desorption capacity of 28.5 mg g−1 was obtained at 0.2 mol L−1 HCl. It stayed unaffected at about 23 mg g−1 at a desorption solution concentration above 0.4 mol L−1 HCl. The mechanism of desorption was probably the ion-exchange of MB with the H+ on the surface of the LSP (Yi & Zhang 2008; Chen et al. 2015). Hence 0.2 mol L−1 HCl solution was best for desorption and reusability of LSP for MB adsorption.
Figure 6

Desorption performance at different concentrations of HCl solutions.

Figure 6

Desorption performance at different concentrations of HCl solutions.

CONCLUSIONS

The adsorption of MB onto low-cost biomass, LSP, from aqueous solution was investigated using RSM with initial pH, MB concentration and adsorbent dosage as independent variables. Good validity of the regression model was confirmed by analysis. The adsorption process was found to be consistent with the pseudo-second-order model. Both Langmuir and Freundlich isotherms were employed to evaluate the adsorption behavior with the maximum monolayer adsorption capacity of 157.98 mg g−1. Desorption performance suggested that 0.2 mol L−1 HCl solution could be used for desorption and reusability of LSP. The results of FTIR and EDS analysis confirmed the availability of LSP as an effective adsorbent for MB adsorption.

ACKNOWLEDGEMENTS

This work was financially supported by the CRSRI Open Research Program (SN: CKWV2016399/KY), the Open Project of State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology (QA201614), and the National Natural Science Foundation of China (NSFC) (No. 51378400).

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