# Removal of reactive blue 19 from aqueous solution by pomegranate residual-based activated carbon: optimization by response surface methodology

- Elham Radaei
^{1}, - Mohammad Reza Alavi Moghaddam
^{1}Email author and - Mokhtar Arami
^{2}

**12**:65

https://doi.org/10.1186/2052-336X-12-65

© Radaei et al.; licensee BioMed Central Ltd. 2014

**Received: **27 December 2012

**Accepted: **5 March 2014

**Published: **28 March 2014

## Abstract

### Background

In this research, response surface methodology (RSM) was applied to optimize Reactive Blue 19 removal by activated carbon from pomegranate residual. A 2^{4} full factorial central composite design (CCD) was applied to evaluate the effects of initial pH, adsorbent dose, initial dye concentration, and contact time on the dye removal efficiency.

### Methodology

The activated carbon prepared by 50 wt.% phosphoric acid activation under air condition at 500°C. The range of pH and initial dye concentration were selected in a way that considered a wide range of those variables. Furthermore, the range of contact time and adsorbent dose were determined based on initial tests. Levels of selected variables and 31 experiments were determined. MiniTab (version 16.1) was used for the regression and graphical analyses of the data obtained.

### Results

It was found that the decrease of initial dye concentration and the increase of initial pH, adsorbent dose, and contact time are beneficial for improving the dye removal efficiency. Analysis of variance (ANOVA) results presented high R^{2} value of 99.17% for Reactive Blue 19 dye removal, which indicates the accuracy of the polynomial model is acceptable.

### Conclusions

Initial pH of 11, adsorbent dose of 1.025 g/L, initial dye concentration of 100 mg/L, and contact time of 6.8 minutes found to be the optimum conditions. Dye removal efficiency of 98.7% was observed experimentally at optimum point which confirmed close to model predicted (98.1%) result.

### Keywords

Reactive dye Adsorption Pomegranate residual Response surface methodology## Introduction

Many industries, especially textile and food industries often use dyes and pigments to color their products. As a result, these industries often discharge large amounts of colored effluents due to unfixed dyes on fibres or food during coloring and washing steps [1]. Due to the disposal of these effluents into the receiving water body may cause severe damage to aquatic biota and humans due to mutagenic and carcinogenic effects [2–4], it is of great importance to provide waste-treatment facilities for minimizing these substances in the effluents before discharge.

There are several methods available for treatment of dye-containing wastewater, such as membrane [5], electrochemical [6], coagulation/flocculation [7], and biological [8]. The adsorption technique has been found not only to be effective, but also practical in application for the dye-containing wastewater treatment, due to its high efficiency, simplicity, ease of operation, and the availability of a wide range of adsorbents [9, 10]. Activated carbons (ACs) are widely used as the most efficient adsorbents. The chemical activation is often used to produce ACs and it involves mixing the feedstock with a chemical activating agent such as H_{3}PO_{4}, ZnCl_{2}, and KOH [11–13].

Conventional and classical methods of studying a process by maintaining other factors involved at an unspecified constant level does not depict the combined effect of all the factors involved. Response Surface Methodology (RSM) is a collection of mathematical and statistical techniques useful for developing, improving, and optimizing processes and can be used to evaluate the relative significance of several affecting factors even in the presence of complex interactions. The main objective of RSM is to determine the optimum operational conditions for the system or to determine a region that satisfies the operating specifications [14]. Many research groups applied this method for removal of different pollutants by adsorption process [14–19].

The aim of the present study is to optimize and model the removal of Reactive Blue 19 from aqueous solution by activated carbon derived from pomegranate residual using RSM. The relationship between dye removal efficiency and four main independent parameters including initial pH, initial dye concentration, adsorbent dose, and contact time were evaluated by applying central composite design (CCD).

## Methods

### Materials and activated carbon preparation

In this study, pomegranate residual was collected from Meykhosh juice industry in Yazd/Iran. Pomegranate residual has been dried in an oven for 2 h at 100°C until a constant weight was reached. It was then ground in a ball mill and passed through sieve No.8. They were soaked for 24 h in the ratio of 1:1 (w/v) with 50 wt.% phosphoric acid at room temperature. The sample is then decanted, dried in a muffle furnace for 1 h at 500°C. Then the samples were washed sequentially several times with hot distilled water, until pH of the washing solution became neutral. In the last step, activated carbon (AC) was powder and sieved by the No. 200 mesh.

_{2}adsorption/desorption isotherms at 77 K using an Autosorb 1 analyzer (Quantachrome Corporation, USA). The specific surface area (S

_{BET}) was calculated by Brun- auer–Emmett–Teller (BET) method. The textural characteristics of AC are shown in Table 2. The pore size distribution was determined by using the Barrett–Joyner– Halenda (BJH) method (Figure 1).

**Textural properties obtained by N2 adsorption/desorption studies**

Parameters | Values | |
---|---|---|

BET surface area (m | BET | 825.46 |

Pore volume (cm | BJH adsorption | 0.3455 |

Pore diameter (Å) | BJH adsorption | 14.35 |

### Experimental design

A central composite design (CCD) was employed for determining the optimum condition for the dye removal. A total of 31 experiments were carried out according to a 2^{4} full factorial CCD, consisting of 16 factorial experiments (coded to the usual ± 1 notation), 8 axial experiments (on the axis at a distance of ± α from the center), and 7 replicates (at the center of the experimental domain).

where N_{F} is the number of points in the cube portion of the design (N_{F} =2^{k}, k is the number of factors). Therefore, α is equal to (2^{4})^{1/4} = 2 according to equation (1).

_{i}(the real value of an independent variable) were coded as x

_{i}(dimensionless value of an independent variable) according to equation (2):

_{0}is the value of X

_{i}at the center point and ΔX represents the step change.

**Experimental range and levels of the independent variables**

Parameters | Levels | |||||
---|---|---|---|---|---|---|

-α | -1 | 0 | 1 | α | ||

Initial pH | x | 3 | 5 | 7 | 9 | 11 |

Adsorbent dose (g/L) | x | 0.75 | 1.00 | 1.25 | 1.50 | 1.75 |

Initial dye concentration (mg/L) | x | 100 | 200 | 300 | 400 | 500 |

Contact time (min) | x | 1 | 3 | 5 | 7 | 9 |

where Y is the predicted response (dye removal efficiency), b_{0} the constant coefficient, b_{i} the linear coefficients, b_{ii} the quadratic coefficients, b_{ij} the interaction coefficients and x_{i}, x_{j} are the coded values of the variables. MiniTab (version 16.1) was used for the regression and graphical analyses of the data obtained. The reliability of the fitted model was justified through ANOVA and the coefficient of R^{2}.

## Results

### Statistical analysis

**RSM design and its observed and predicted values**

Run | Initial pH (x | Adsorbent dose (x | Initial dye concentration (x | Contact time (x | Dye removal (%) | |
---|---|---|---|---|---|---|

Experimental | Predicted | |||||

1 | 5 | 1 | 200 | 3 | 70.46 | 70.99 |

2 | 9 | 1 | 200 | 3 | 71.57 | 72.39 |

3 | 5 | 1.5 | 200 | 3 | 89.35 | 87.55 |

4 | 9 | 1.5 | 200 | 3 | 91.64 | 92.28 |

5 | 5 | 1 | 400 | 3 | 49.39 | 47.68 |

6 | 9 | 1 | 400 | 3 | 51.40 | 52.31 |

7 | 5 | 1.5 | 400 | 3 | 64.34 | 64.06 |

8 | 9 | 1.5 | 400 | 3 | 72.00 | 72.01 |

9 | 5 | 1 | 200 | 7 | 73.06 | 73.08 |

10 | 9 | 1 | 200 | 7 | 74.41 | 74.71 |

11 | 5 | 1.5 | 200 | 7 | 90.54 | 89.65 |

12 | 9 | 1.5 | 200 | 7 | 92.84 | 94.59 |

13 | 5 | 1 | 400 | 7 | 50.87 | 50.25 |

14 | 9 | 1 | 400 | 7 | 53.28 | 55.11 |

15 | 5 | 1.5 | 400 | 7 | 67.43 | 66.63 |

16 | 9 | 1.5 | 400 | 7 | 75.32 | 74.80 |

17 | 3 | 1.25 | 300 | 5 | 75.98 | 78.77 |

18 | 11 | 1.25 | 300 | 5 | 91.19 | 88.35 |

19 | 7 | 0.75 | 300 | 5 | 45.68 | 44.66 |

20 | 7 | 1.75 | 300 | 5 | 79.95 | 80.92 |

21 | 7 | 1.25 | 100 | 5 | 93.52 | 92.87 |

22 | 7 | 1.25 | 500 | 5 | 49.17 | 49.78 |

23 | 7 | 1.25 | 300 | 1 | 63.94 | 64.40 |

24 | 7 | 1.25 | 300 | 9 | 69.80 | 69.29 |

25 | 7 | 1.25 | 300 | 5 | 69.27 | 69.33 |

26 | 7 | 1.25 | 300 | 5 | 70.50 | 69.33 |

27 | 7 | 1.25 | 300 | 5 | 69.27 | 69.33 |

28 | 7 | 1.25 | 300 | 5 | 68.30 | 69.33 |

29 | 7 | 1.25 | 300 | 5 | 71.27 | 69.33 |

30 | 7 | 1.25 | 300 | 5 | 67.47 | 69.33 |

31 | 7 | 1.25 | 300 | 5 | 69.27 | 69.33 |

_{1}

^{2}and x

_{2}

^{2}(P values of 0.000) were insignificant to the response. ANOVA for the selected dye removal is also listed in Table 6. In this case, the P-value of 0.000 (P < 0.05) for regression model equation implies that the second-order polynomial model fitted to the experimental results well. The lack-of-fit was also calculated from the experimental error (pure error) and residuals. “F-value of Lack-of-fit” of 2.24 implies the significance of model correlation between the variables and process response for dye removal. Additionally, the value of R

^{2}= 99.17% and R

^{2}(adj) =98.44% confirm the accuracy of the model. Furthermore, parity plot for the experimental and predicted value of RB19 removal efficiency (%) is demonstrated in Figure 3.

**Statistical regression coefficients for RB19 removal efficiency (%) in coded units**

Term | Coefficient | SE Coefficient | T | P |
---|---|---|---|---|

Constant | 69.3371 | 0.6393 | 108.465 | 0.000 |

x | 2.3943 | 0.3452 | 6.935 | 0.000 |

x | 9.0655 | 0.3452 | 26.259 | 0.000 |

x | −10.7734 | 0.3452 | −31.206 | 0.000 |

x | 1.2223 | 0.3452 | 3.541 | 0.003 |

x | 3.5562 | 0.3163 | 11.244 | 0.000 |

x | −1.6346 | 0.3163 | −5.168 | 0.000 |

x | 0.4977 | 0.3163 | 1.573 | 0.135 |

x | −0.6212 | 0.3163 | −1.964 | 0.067 |

x | 0.8284 | 0.4228 | 1.959 | 0.068 |

x | 0.8070 | 0.4228 | 1.909 | 0.074 |

x | 0.0549 | 0.4228 | 0.130 | 0.898 |

x | −0.0465 | 0.4228 | −0.110 | 0.914 |

x | −0.0007 | 0.4228 | −0.002 | 0.999 |

x | 0.1210 | 0.4228 | 0.286 | 0.778 |

**ANOVA for RB19 removal efficiency (%)**

Source | DF | Seq SS | Adj SS | Adj MS | F | P |
---|---|---|---|---|---|---|

Regression | 14 | 5455.27 | 5455.27 | 389.66 | 136.22 | 0.000 |

Linear | 4 | 4931.44 | 4931.44 | 1232.86 | 430.99 | 0.000 |

Square | 4 | 502.12 | 502.12 | 125.53 | 43.88 | 0.000 |

Interaction | 6 | 21.72 | 21.72 | 3.62 | 1.27 | 0.327 |

Residual error | 16 | 45.77 | 45.77 | 2.86 | ||

Lack-of-fit | 10 | 36.11 | 36.11 | 3.61 | 2.24 | 0.168 |

Pure error | 6 | 9.66 | 9.66 | 1.61 | ||

Total | 30 | 5501.04 |

### Response surface and counter plotting for evaluation of operational parameters

### Process optimization

In order to determine the optimum conditions by the adsorption process, the desired aim in terms of RB19 removal efficiency was defined as target to achieve 98% removal efficiency. The optimum values of the process parameters were calculated in coded units (x_{i}) and then converted into uncoded units (X_{i}) using Equation ([1]). Initial pH of 11, adsorbent dose of 1.025 g/L, initial dye concentration of 100 mg/L and contact time of 6.8 minutes found to be the optimum conditions by the model. The optimum condition was repeated three times and dye removal efficiencies of 98.4, 98.6, and 99.1% were resulted. The average of 98.7% dye removal efficiency was found close to the model prediction of 98.1%.

## Discussion

According to the results, contact time is the least important parameter, which is reported by other research groups [20, 21]. Furthermore, by increasing of initial pH and adsorbent dose, and decreasing of initial dye concentration, the dye removal efficiency improved. These results are in good agreement with of previous studies [22, 23].

The effect of the four selected independent parameters and interactions among the RSM were analyzed which was shown that some interactions like (x_{1}^{2} and x_{2}^{2}) influenced the adsorption performance as well as all selected parameters. ANOVA showed a high R^{2} value of regressions model equation (R^{2} = 0.9917), thus ensuring a satisfactory adjustment of the second-order regression model with the experimental data. The optimum RB19 removal efficiency were found at initial pH of 11, adsorbent dose of 1.025 g/L, initial dye concentration of 100 mg/l and contact time of 6.8 min. An experiment was performed in optimum conditions which confirmed that the model and experimental results are in close agreement (98.7% compared to 98.1% for the model).

## Conclusions

In this research, response surface methodology was applied as an experimental design to explore the optimal conditions for RB19 dye removal from aqueous solutions by activated carbon prepared by pomegranate residual. The effect of four operating variables of adsorption process including initial pH, adsorbent dose, initial dye concentration, and contact time were examined. The BET method showed that the average S_{BET} of AC was 825.46 m^{2} g^{−1}. The results of this investigation presented that RSM is a powerful statistical optimization and modeling tool for RB19 removal using adsorption process.

## Declarations

### Acknowledgements

The authors are grateful to the Amirkabir University of Technology (AUT) research fund for the financial support. In addition, the authors wish to express thanks to Ms. Armineh Azizi and Ms. Shabnam Sadri Moghaddam (PhD students of environmental engineering), and Ms. Lida Ezzedinloo for their assistance during experiments.

## Authors’ Affiliations

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