In this study, response surface methodology (RSM) based on three-variable-five-level central composite rotatable design was used to analyze the effects of combined and individual operating parameters (biomass dose, initial concentration of Cr(VI) and pH) on the Cr(VI) adsorption capacity of dried Bacillus cereus. A quadratic polynomial equation was obtained to predict the adsorbed Cr(VI) amount. Analysis of variance showed that the effect of biomass dose was the key factor in the removal of Cr(VI). The maximum adsorbed Cr(VI) amount (30.93 mg g−1) was found at 165.30 mg L−1, 2.96, and 3.01 g L−1 for initial Cr(VI) concentration, pH, and biosorbent dosage, respectively. The surface chemical functional groups and microstructure of unloaded and Cr(VI)-loaded dried Bacillus cereus were identified by Fourier transform infrared spectroscopy (FTIR) and scanning electron microscopy (SEM), respectively. Besides, the results gained from these studies indicated that Langmuir isotherm and the second-order rate expression were suitable for the removal of Cr(VI) from wastewater. The results revealed RSM was an effective method for optimizing biosorption process, and dried Bacillus cereus had a remarkable performance on the removal of Cr(VI) from wastewater.

INTRODUCTION

The release of large quantities of heavy metals from industries into the environment is posing serious risks to human health and environment (Saraswat & Raj 2010; Flouty & Estephane 2012; Li et al. 2013). Chromium, which is extensively used in electroplating, steel production, wood preservation and leather tanning, is a very common pollutant in industrial effluents, and has become an environmental problem around the world (Das 2010). Due to its non-biodegradability, toxicity, mobility in natural water ecosystems and bioaccumulation in living tissues, the concentration of chromium ion in the solution exceeding its critical level has an adverse impact on the environment and public health (Rubio et al. 2002; Ngah & Hanafiah 2008; Landaburu-Aguirre et al. 2010; Choppala et al. 2013). Besides, the discharge standards of heavy metals approved by national or international agencies become more and more stringent. All those reasons have accelerated the search for a cost-effective and environment friendly method for the removal of chromium to an environmentally safe level.

The conventional methods, such as chemical precipitation, solvent extraction, coagulation–flocculation, photocatalysis, flotation, membrane separation and adsorption, are used to remove metal ions from wastewaters and effluents (Demirbas 2008; Wang & Chen 2009; Suazo-Madrid et al. 2011). However, due to the feasibility of economical and technical factors, and the avoidance of chemical sludge, the implementation of these methods is limited (Al-Rub et al. 2006). Biosorption, which uses biomass or natural substances as a sorbent to remove the toxic heavy metals from wastewater, has been considered as an attractive alternative to the traditional methods (Demirbas 2008). The mechanisms of biosorption are based on physicochemical interactions between metal and functional groups of the cell wall (Çelekli &Bozkurt 2011). Biological biomasses, such as bacteria, yeast, fungi, and algae, have been used for their biosorptive potential (Çelekli & Bozkurt 2011). Numerous studies have reported that due to its easy handling, no nutrient requirements, low costs and no effect from the toxicity of the metal ions, the use of nonliving biomass is more convenient and practical than that of living biomass. Besides, many studies have shown that biosorption of metal ions by biosorbents is strongly dependent upon the initial concentration of metal ions and adsorption conditions (Al-Rub et al. 2006; Demirbas 2008). Hence, an effective method to assess the effect (single effect and interactive effects) of variables on the removal of heavy metals is very important. The traditional methods used to assess the influence of factors usually change one independent variable parameter, while maintaining all others at a constant level. However, because of the extra chemical consumption, excessive time and human power requirements, the traditional methods are not valid. Response surface methodology (RSM) design is a collection of statistical and mathematical techniques, which are useful for designing experiments, building models, and analyzing the effects of several independent variables on the response design and optimization process (Amini et al. 2008, 2009). The main advantage of RSM is the decreased number of experimental trials required to interpret multiple parameters and their interactions. In order to determine a suitable polynomial equation for describing the response surface, RSM can be employed to optimize the process.

Nevertheless, few studies have been found in the literature for optimization of adsorption of Cr(VI) ions by dried Bacillus cereus. Therefore, in this research, the objective was to optimize the adsorption conditions of Cr(VI) ions by dried Bacillus cereus using RSM. The effects of variables including biomass dose, initial concentration of Cr(VI) ions and initial pH were investigated by three-variable-three-level central composite design (CCD). This process was analyzed using Matlab in RSM. An empirical model correlating response to the three variables was then developed. The biosorbent was characterized through Fourier transform infrared (FTIR: Nicolet 5700 spectrometer, USA) and scanning electron microscope (SEM: Quanta 200, FEI, China) to know the physico-chemical properties of the biosorbent. In this work, we also applied common methods that employ various kinetics models and isotherm parameters calculation to determine the adsorption capacity.

MATERIALS AND METHODS

Preparation of adsorbent

Bacillus cereus individuals were isolated from the sediment from a sequencing batch reactor. The isolated bacteria were cryopreserved in solid medium at 4 °C. At the beginning of the experiment, the bacteria were transferred to a 500 mL conical flask containing 300 mL broth medium (1.2 g beef extract, 4.0 g peptone and 2.0 g NaCl), and shaken at 150 rpm and 30 °C for 36 h, then harvested by centrifugation at 9,000 rpm and 4 °C for 6 min. The precipitate was washed with distilled water, dried at 60 °C for 12 h then ground and sieved into different fractions. The 0.56- to 0.85-mm particle size fraction was used in the experiments.

Cr(VI) solution standards

The Cr(VI) ion solution was prepared from potassium dichromate (K2Cr2O7). Standard stock solutions were prepared in distilled water, slightly acidified with 1 N HCl and 1N NaOH, and sterilized at 121 °C for 15 min, then kept at 28 °C. The glassware was leached in 3 N HCl and rinsed several times with distilled water before use to avoid metal contamination.

Analysis and measurements

The concentrations of residual Cr(VI) ions in the supernatant solutions were determined using UV-VIS spectrophotometry (SP-1920, China). The metal uptake yield from the liquid phase was determined at the beginning (mg L−1) and at the end (mg L−1). The following equation was used to compute biosorbent uptake yield (mg g−1), where v is the volume of the solution (mL) and w is the mass of the biosorbent (g) (Şahan & Öztürk 2014) 
formula
1

Experimental design

The optimum condition for the adsorption of Cr(VI) by dried Bacillus cereus was determined by RSM. RSM is a statistical technique used for multiple regression analysis of quantitative data obtained from statistically designed experiments by solving the multivariable equations simultaneously (Amini et al. 2008). Due to its suitability to fit a quadratic surface, which usually works well for process optimization, CCD is most frequently used for RSM design. The adsorbed Cr(VI) amount was statistically modeled and designed by RSM, and CCD was used to optimize the biosorption process.

This design consists of full factorial or fractional factorial design to determine effects of variables and interactions, an additional design, often a star design in which experimental points are at a α distance from its center which can be used to determine quadratic terms and central point to determine curvature of response. A central composite rotable design (CCRD) for three factors was employed for this experimental design to provide data to model the effects of the independent variables, such as pH, biomass concentration (g L−1) and initial lead concentration (mg L−1), and the three-variable-five-level design as shown in Table 1 was used for the optimization procedure. The second-order quadratic equation to predict the maximum adsorbed Cr(VI) amount is given below: 
formula
2
Table 1

Independent variables and levels used for CCRD in Cr(VI) biosorption process

  Levels
 
Variables Symbols −1.682 (-α) −1 1.682 (α) 
pH X1 1.00 1.88 3.00 4.12 5.00 
Biomass concentration (g L−1X2 2.40 2.65 3.00 3.36 3.60 
Initial Ni2+ concentration (mg L−1X3 100.00 120.27 150.00 179.73 200.00 
  Levels
 
Variables Symbols −1.682 (-α) −1 1.682 (α) 
pH X1 1.00 1.88 3.00 4.12 5.00 
Biomass concentration (g L−1X2 2.40 2.65 3.00 3.36 3.60 
Initial Ni2+ concentration (mg L−1X3 100.00 120.27 150.00 179.73 200.00 

where Y is the predicted response, , , …, are the input variables, which affect the response Y, , …, are the square effects, and are the interaction effects, is the intercept term, (i = 1, 2,…, k) is the linear effect, (i = 1, 2, …, k) is the squared effect, (i = 1, 2, …, k; j = 1, 2, …, k) is the interaction effect and e is a random error (Şahan & Öztürk 2014; Turkyilmaz et al. 2014).

The analysis of variance (ANOVA) data were computed by Matlab in order to obtain the interaction between the processed variables and the response. The quality of the fit of the polynomial model was expressed by the coefficient of determination (R2) and the statistical significance was checked by the F test using the same program.

Adsorption kinetics and isotherms

Adsorption kinetics were determined in the following experiment: 3.01 g L−1 of adsorbent mass and 50 mL of aqueous Cr(VI) solution with initial concentration in the range from 50 to 150 mg L−1 were added to a 250 mL flask and incubated in a water bath set to room temperature (28 °C), with shaking at pH 2.9 and the speed of 150 rpm for 60 min.

Various adsorption kinetic models have been used to investigate the controlling mechanism of the adsorption process. The adsorption data were analyzed using first- and second-order kinetic models, which are shown in linear form by the following equations, respectively (Buntić et al. 2013): 
formula
3
 
formula
4

where and (mg g−1) are the amounts of the metal ions adsorbed at t (min) and equilibrium, respectively, and (1/min) and (g/mg min) are the rate constants of the pseudo-first-order and second-order, respectively.

Isotherm studies were performed by preparing Cr(VI) solutions with the initial concentration of Cr(VI) solution in the range from 50 to 150 mg L−1, and mixing the solutions with 3.01 g L−1 g of the adsorbent followed by agitating the mixture (150 rpm, at 28 °C) at pH 2.9 until equilibrium. In this study, the most common adsorption isotherm equations, including Langmuir and Freundlich, were tested to understand the nature of the adsorption mechanism.

The Langmuir isotherm theory assumes monolayer coverage of adsorbate over a homogeneous adsorbent surface (Güzel et al. 2015). The linearized Langmuir isotherm equation is represented by Equation (5): 
formula
5

where (mg g−1) is the adsorbed Cr(II) amount at equilibrium, (mg L−1) is the supernatant concentration at equilibrium, and (mg g−1) and b (L mg−1) are constants representing the maximum adsorption capacity and the Langmuir constant related to the heat of adsorption, respectively.

The Freundlich isotherm is an empirical equation based on sorption on a heterogeneous surface or surface supporting sites of varied affinities (Güzel et al. 2015). The linear form of the Freundlich isotherm is given by the following equation: 
formula
6
where and n are the Freundlich constants characteristic of the system.
Langmuir isotherm can be expressed in terms of a dimensionless separation factor, , which describes the type of isotherm: 
formula
7

where is the initial concentration of Cr(VI). The magnitude of determines the feasibility of the adsorption process. If , adsorption is unfavorable; if , adsorption is linear; if , adsorption is favorable and if , adsorption is irreversible (Sarkar & Santra 2015).

Characterization of the biosorbent

Characterization of biosorbent surface and structure hold keys to understand the metal binding mechanism onto biomass. In this study, the infrared spectrum of raw and metal laden adsorbents was obtained by the use of a FTIR spectrometer (Perkin–Elmer Spectrum 100 FTIR Spectrometer) equipped with an Attenuated Total Reflection accessory (Perkin Elmer) to identify the functional groups responsible for the sorption. SEM was used to show the morphology of the dried Bacillus cereus biomass. The samples were dried by liquefied nitrogen, coated with gold and observed with a microscope. Finally, images of the samples were taken under SEM.

RESULTS AND DISCUSSION

Experimental design

Experimental design of CCRD with the corresponding results and the adsorbed Cr(VI) amount (experimental and predicted) for the process response are presented in Table 2. The second-order equation for the estimation of the adsorbed Cr(VI) amount in terms of pH , biosorbent dosage and initial concentration of Cr(VI) was represented in terms of coded factors (, −1,0 and +1, ) as follows:

Table 2

CCRD arrangement and responses for biosorption process

  Factors
 
  Cr(VI) ion removal (mg g−1)
 
Run Point type X1 X2 X3 Adsorbed amount of Cr ion (mg L−1Experimental Predicted 
Fact 1.88 2.64 120.27 27.37 10.37 11.93 
Fact 1.88 3.36 179.73 95.74 28.49 27.96 
Fact 4.12 3.36 179.73 89.38 26.60 25.54 
Fact 4.12 3.36 120.27 45.19 13.45 15.18 
Fact 1.88 3.36 120.27 43.91 13.07 15.35 
Fact 4.12 2.64 120.27 36.28 13.74 14.78 
Fact 1.88 2.64 179.73 79.20 30.00 28.61 
Fact 4.12 2.64 179.73 80.47 30.48 28.88 
Axial 3.00 3.00 200.00 79.10 26.37 29.34 
10 Axial 3.00 2.40 150.00 54.56 22.73 23.21 
11 Axial 3.00 3.00 100.00 31.28 10.43 6.74 
12 Axial 1.00 3.00 150.00 69.83 23.28 22.55 
13 Axial 3.00 3.60 150.00 87.64 24.35 23.14 
14 Axial 5.00 3.00 150.00 68.55 22.85 22.94 
15 Center 3.00 3.00 150.00 91.46 30.49 30.39 
16 Center 3.00 3.00 150.00 90.19 30.06 30.39 
17 Center 3.00 3.00 150.00 94.01 31.34 30.39 
18 Center 3.00 3.00 150.00 88.92 29.64 30.39 
  Factors
 
  Cr(VI) ion removal (mg g−1)
 
Run Point type X1 X2 X3 Adsorbed amount of Cr ion (mg L−1Experimental Predicted 
Fact 1.88 2.64 120.27 27.37 10.37 11.93 
Fact 1.88 3.36 179.73 95.74 28.49 27.96 
Fact 4.12 3.36 179.73 89.38 26.60 25.54 
Fact 4.12 3.36 120.27 45.19 13.45 15.18 
Fact 1.88 3.36 120.27 43.91 13.07 15.35 
Fact 4.12 2.64 120.27 36.28 13.74 14.78 
Fact 1.88 2.64 179.73 79.20 30.00 28.61 
Fact 4.12 2.64 179.73 80.47 30.48 28.88 
Axial 3.00 3.00 200.00 79.10 26.37 29.34 
10 Axial 3.00 2.40 150.00 54.56 22.73 23.21 
11 Axial 3.00 3.00 100.00 31.28 10.43 6.74 
12 Axial 1.00 3.00 150.00 69.83 23.28 22.55 
13 Axial 3.00 3.60 150.00 87.64 24.35 23.14 
14 Axial 5.00 3.00 150.00 68.55 22.85 22.94 
15 Center 3.00 3.00 150.00 91.46 30.49 30.39 
16 Center 3.00 3.00 150.00 90.19 30.06 30.39 
17 Center 3.00 3.00 150.00 94.01 31.34 30.39 
18 Center 3.00 3.00 150.00 88.92 29.64 30.39 

 
formula
8
Equation (8) represents the quantitative effect of the process variables (, , and ) and their interactive effects on the response . A positive sign in the equation represents a synergistic effect of the variables, while a negative sign indicates an antagonistic effect of the variables. The results of the ANOVA for the proposed statistical model and their model coefficients are given in Tables 3 and 4, respectively.
Table 3

Regression coefficients of predicted quadratic polynomial model for adsorbed Cr(VI) amount

Terms Regression coefficients 
Intercepts 
 −377.2206 
Linear term 
 19.4754 
 138.9367 
 2.0399 
Quadratic 
 −1.6652 
 −0.0194 
 −0.0910 
Interaction 
 −1.9122 
 −20.0578 
 −0.0049 
Terms Regression coefficients 
Intercepts 
 −377.2206 
Linear term 
 19.4754 
 138.9367 
 2.0399 
Quadratic 
 −1.6652 
 −0.0194 
 −0.0910 
Interaction 
 −1.9122 
 −20.0578 
 −0.0049 
Table 4

ANOVA for adsorbed Cr(VI) amount model

  Sum of squares df Mean square F value P value R2   
Model 929.433 929.433 339.059 0.000 0.955 Significant 
Residual 43.859 16 2.741     
Lack-of-fit 42.276 3.252 6.160 0.180   
Pure error 1.583 13 0.528     
Total 433.942 17      
  Sum of squares df Mean square F value P value R2   
Model 929.433 929.433 339.059 0.000 0.955 Significant 
Residual 43.859 16 2.741     
Lack-of-fit 42.276 3.252 6.160 0.180   
Pure error 1.583 13 0.528     
Total 433.942 17      

The F value, which is a ratio of the mean square due to regression to the mean square due to error, was converted into its corresponding P value. From a statistical point of view, if the P value < 0.05, then the model is statistically significant. As the P value decreases, it becomes less likely that the effect is due to chance, and more likely that there was a real cause. In this case, the P value of model was 0.00001 and it was significant. Besides, R2 = 0.955 for Equation (2) indicated a good fitting for the experimental data and predicted values.

A plot of observed removal of Cr(VI) versus those obtained from Equation (7) is shown in Figure 1. The figure proves that the predicted response from the empirical model was in good agreement with the observed data.
Figure 1

The observed Cr(VI) uptake versus predicted Cr(VI) uptake capacity of adsorbent.

Figure 1

The observed Cr(VI) uptake versus predicted Cr(VI) uptake capacity of adsorbent.

Effect of process variables

To gain better understanding of the effects of the three independent variables and their interactions on the Cr(VI) amount adsorbed by dried Bacillus cereus, three-dimensional response surface plots were constructed based on the quadratic model. Figure 2 shows the results of fitting experimental Cr(VI) adsorption data to the response model represented by Equation (7).
Figure 2

Response surfaces for combined effect of: (a) pH and biosorbent dosage at constant initial Cr(VI) concentration of 150 mg L−1; (b) pH and initial Cr(VI) concentration at constant biosorbent dosage 3 mg g−1; and biosorbent dosage and initial Cr(VI) concentration at constant pH of 3.0; on the amount of Cr(VI) adsorbed by dried Bacillus cereus.

Figure 2

Response surfaces for combined effect of: (a) pH and biosorbent dosage at constant initial Cr(VI) concentration of 150 mg L−1; (b) pH and initial Cr(VI) concentration at constant biosorbent dosage 3 mg g−1; and biosorbent dosage and initial Cr(VI) concentration at constant pH of 3.0; on the amount of Cr(VI) adsorbed by dried Bacillus cereus.

Figure 2(a)(c) are drawn at constant values of 150 mg L−1 initial Cr(VI) concentration , 3 g L−1 biomass concentration and pH , respectively. Figure 2(a)(c) show the simultaneous effects of pH and biosorbent dosage, the simultaneous effects of pH and initial Cr(VI) concentration, and the simultaneous effects of biosorbent dosage and initial Cr(VI) concentration on the adsorbed Cr(VI) amount, respectively.

Effect of biosorbent dosage

The effect of biosorbent dosage was found to be the most significant among the studied variables on the adsorbed Cr(VI) amount. Figure 2(a) and (c) show the combined effect of biosorbent dosage with pH and the initial concentration of Cr(VI) on the adsorbed Cr(VI) amount. According to Figure 2(a) and (c), the adsorbed Cr(VI) amount was highly dependent on the increase in biosorbent dosage; the maximum biosorption (30.93 mg g−1) was obtained when the biosorbent dosage was 3.01 g L−1. Biosorption of Cr(VI) is proportional to the binding sites. A higher dose of biosorbent in the solution leads to greater availability of exchangeable sites for the ions, so the adsorbed Cr(VI) amount increased with increasing biosorbent dosage. However, the adsorbed Cr(VI) amount decreased when biomass dosage was above 3.01 g L−1. This behavior was attributed to a partial aggregation of biomass, which reduced availability of exchangeable sites for the biosorption (Ibrahim 2011).

Effect of pH

The pH of the medium affects the solubility of metals and the ionization state of functional groups present in the biosorbent(Subbaiah & Yun 2013). Figure 2(a) and (b) show the interactive effect of pH with biosorbent dosage and initial concentration of Cr(VI) on the adsorbed Cr(VI) amount. It can be seen from the response graphs that the adsorption capacity of Cr(VI) increased as the pH increased, reached a maximum (30.93 mg g−1) at pH 2.96, then decreased as the pH continued increasing. The lower adsorbed amount was apparent at low pH, which could be attributed to the presence of a higher concentration of H+ in the solution, which could compete with Cr(VI) ions for the adsorption sites on the surface of dried Bacillus cereus. The decrease of the biosorption efficiency when pH was beyond 2.95 might be attributed to the reduction of H+ concentration and the formation of anionic hydroxide complexes which decreased the dissolved metal concentration in solution, and their competition with the active sites, which led to decreased Cr(VI) uptake (Ibrahim 2011).

Effect of initial concentration of Cr(VI)

The combined effects of initial Cr(VI) concentration with pH and biosorbent dosage are shown in Figure 2(b) and (c). Because a driving force provided by the initial concentration of metal ions could overcome mass transfer resistance between the biosorbent and the biosorption medium, a higher initial metal concentration would have a beneficial effect on the dried Bacillus cereus sorption capacity. Such an effect was clearly shown in Figure 2(b) and (c): the adsorbed Cr(VI) amount rapidly increased with initial Cr(VI) concentration increasing from 100 to 165.30 mg L−1, and roughly reached a maximum at 165.30 mg L−1. However, when the initial ion concentration continued to increase, the metal uptake reached equilibrium and all sites were almost saturated with metals. Hence, the rate of increment of adsorption capacity gradually became slower.

Confirmation experiments

The objective of the experimental design was to optimize the conditions to maximize the adsorbed Cr(VI) amount. On the basis of RSM, the optimum levels of factors for adsorbed Cr(VI) amount were 165.30 mg L−1, 2.96, and 3.01 g L−1 for initial Cr(VI) concentration, pH, and biosorbent dosage, respectively. Under the optimum conditions, the adsorbed amount was 30.93 mg g−1 and the corresponding removal efficiency of Cr(VI) was 59.28%. Furthermore, to support the optimized data as given by numerical modeling under optimized conditions, confirmatory experiments were conducted with the parameters attained by the model, and the removal efficiency for Cr(VI) was found to be 31.34 mg g−1. Based on these results, it could be said that the dried Bacillus cereus could be used for the removal of Cr(VI) from wastewater.

Adsorption kinetics study

In order to understand sorption kinetics of Cr(VI) by dried Bacillus cereus, the pseudo-first- and second-order kinetics models were applied to experimental data. As shown in Figure 3(a), the adsorption rate was very rapid within the first 10 min, and the adsorption process was basically completed and reached equilibrium after 15 min. This probably reflects instantaneous adsorption or external surface adsorption, as previously reported (Han et al. 2010).
Figure 3

Cr(VI) adsorption isotherms of dried Bacillus cereus: (a) at different initial Cr(VI) concentration; (b) Langmuir isotherm; (c) Freundlich isotherm.

Figure 3

Cr(VI) adsorption isotherms of dried Bacillus cereus: (a) at different initial Cr(VI) concentration; (b) Langmuir isotherm; (c) Freundlich isotherm.

The parameters of both kinetic models and R2 values are shown in Table 5. Figure 3(c) clearly indicates that the pseudo-second-order model gave a better prediction of the equilibrium uptake values for all initial Cr(VI) concentrations, and the correlation coefficients of pseudo-second-order kinetics were above 0.999 for all initial concentrations of Cr(VI).

Table 5

Kinetic parameters for the removal of Cr(VI) by dried Bacillus cereus

  Pseudo-first-order kinetic model Pseudo-second-order kinetic model 
150 mg L−1 0.9215 0.998 
120 mg L−1 0.8261 0.999 
100 mg L−1 0.8494 0.998 
80 mg L−1 0.7842 0.999 
50 mg L−1 0.9785 0.999 
  Pseudo-first-order kinetic model Pseudo-second-order kinetic model 
150 mg L−1 0.9215 0.998 
120 mg L−1 0.8261 0.999 
100 mg L−1 0.8494 0.998 
80 mg L−1 0.7842 0.999 
50 mg L−1 0.9785 0.999 

Adsorption isotherm models

In order to further investigate the biosorption mechanism of Cr(VI), the biosorption isotherm models (Langmuir and Freundlich) were used to characterize the interaction of concentrations of metal ions in solution (; mg L−1) with the amount of adsorbed metal ions on adsorbent (; mg·g−1) at equilibrium. Adsorption equation parameters were obtained from experimental data by using Equations (5) and (6). The results and correlation coefficients are presented in Figure 4 and Table 6. By comparing the constants and correlation coefficients R2 (Table 1), it can be seen that the Langmuir model was more suitable for the experimental equilibrium sorption data than the Freundlich model. In this situation, the value was calculated as 0.0025 for Cr(VI) adsorption onto dried Bacillus cereus. So the adsorption of Cr(VI) ion onto dried Bacillus cereus was favorable.
Table 6

Langmuir and Freundlich adsorption model constants for Cr(VI) adsorption

Model qmax (mg g−1b (L mg−1RL Kf 1/n R2 
Langmuir 30.01 7.0226 0.0025 – – 0.9975 
Freundlich       0.0035 2.2213 0.9948 
Model qmax (mg g−1b (L mg−1RL Kf 1/n R2 
Langmuir 30.01 7.0226 0.0025 – – 0.9975 
Freundlich       0.0035 2.2213 0.9948 
Figure 4

Plot of (a) pseudo-first-order kinetic equation and (b) second-order kinetic equation.

Figure 4

Plot of (a) pseudo-first-order kinetic equation and (b) second-order kinetic equation.

SEM analysis

Figure 5 shows the SEM images of dried Bacillus cereus biomass before and after Cr(VI) biosorption process at a magnification of 30,000. It is observed that the surface morphology of the biomass changed obviously after metal adsorption. Before Cr(VI) biosorption (Figure 5(a)), the material had a fluffy surface texture, and a highly irregular surface format was observed, which increased the contact area and favored sorption. SEM micrographs of the Cr(VI)-loaded biosorbent (Figure 5(b)) show a surface that is less fluffy and porous. This may be due to the adsorption of Cr(VI) on the outer surface of dried Bacillus cereus biomass, making the surface of dried Bacillus cereus biomass less fluffy and porous than its original form.
Figure 5

The SEM micrograph of dried Bacillus cereus before (a) and after (b) Cr(VI) adsorption.

Figure 5

The SEM micrograph of dried Bacillus cereus before (a) and after (b) Cr(VI) adsorption.

Infrared spectroscopy study

As shown in Figure 6, the spectrum of unloaded biomass shows a broad absorption peak at 3,288 cm−1, corresponding to the overlapping of −OH and −NH peaks (Gao et al. 2010). A peak at about 2,922 cm−1 is assigned to the stretching vibrations of C–H bond of methylene groups. The peak at about 1,654 cm−1 is caused by the stretching band of carboxyl groups. The weak band at 1,540 cm−1 could be due to a combination of the stretching vibration of C–N and deformation vibration of N–H peptidic bond of protein (Amide II) (Gao et al. 2011). The weak peaks in the region of wave numbers 1,400–1,460 cm−1 are representative of amino substituted alkyl groups. Besides, the weak band at 1,246 cm−1 indicates the presence of carboxylic acids (Yadav 2005). The band at 1,062 cm−1 could be attributed to the stretching vibration of O–H of polysaccharides. Meanwhile, some bands in the fingerprint region could be related to the phosphate groups. After Cr(VI) was loaded, a change of peak positions was observed at 1,062, 1,242, 1,400, 1,456, 1,539, 1,654 , 2,923 and 3,288 cm−1. The significant changes in the wave number of these peaks after loading of Cr(VI) indicated that the functional groups (C–N, N–H, carboxyl groups, C–H, hydroxyl and O–H) were involved in the biosorption of Cr(VI) on the surface of dried Bacillus cereus.
Figure 6

FTIR spectra of unloaded (a) and Cr(VI)-loaded (b) dried Bacillus cereus.

Figure 6

FTIR spectra of unloaded (a) and Cr(VI)-loaded (b) dried Bacillus cereus.

CONCLUSION

To optimize the Cr(VI) biosorption by the dried Bacillus cereus, RSM was used to study the combined and individual effects of operating parameters (pH, biomass dosage, and initial Cr(VI) concentration) on Cr(VI) adsorption capacity. The experimental results showed the effect of biosorbent dosage was the most significant. The second-order polynomial equation model whose validity was agreed upon was estimated which was suitable for the experimental data. The optimum adsorption conditions for removal of Cr(VI) were 165.30 mg L−1, 2.96, and 3.01 g L−1 for initial Cr(VI) concentration, pH, and biosorbent dosage, respectively. Under these conditions, maximum adsorbed amount and removal efficiency of Cr(VI) were 30.93 mg g−1 and 59.28%, respectively. This study also revealed that the Langmuir isotherm was more suitable than the Freundlich isotherm for biosorption by dried Bacillus cereus, and the kinetics of Cr(VI) adsorption followed the pseudo-second-order rate equation. Based on the above experiments, it could be concluded that RSM was a powerful statistical method for optimization of experimental conditions, and dried Bacillus cereus as an eco-friendly biosorbent showed a remarkable performance in the removal of Cr(VI).

ACKNOWLEDGEMENTS

This research was financially supported by the open fund of the National Science and Technology Pillar Program (2014BAL04B04), Natural Science Foundation of Hubei Province, China (no. 2013CFB289; 2013CFB308) and Major Science and Technology Program for Water Pollution Control and Treatment (no. 2009ZX07317-008-003).

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