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Frontiers of Environmental Science & Engineering

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Front. Environ. Sci. Eng.    2019, Vol. 13 Issue (4) : 51    https://doi.org/10.1007/s11783-019-1132-5
RESEARCH ARTICLE
Fluoride removal from secondary effluent of the graphite industry using electrodialysis: Optimization with response surface methodology
Xiaomeng Wang1, Ning Li1, Jianye Li2, Junjun Feng1, Zhun Ma1(), Yuting Xu1, Yongchao Sun1,3, Dongmei Xu1, Jian Wang4, Xueli Gao3(), Jun Gao1
1. College of Chemical and Environmental Engineering, Shandong University of Science and Technology, Qingdao 266590, China
2. School of Chemical and Environmental Engineering, Weifang University of Science & Technology, Shouguang 262700, China
3. Key Laboratory of Marine Chemistry Theory and Technology (Ministry of Education); College of Chemistry and Chemical Engineering, Ocean University of China, Qingdao 266100, China
4. The Institute of Seawater Desalination and Multipurpose Utilization, SOA, Tianjin 300192, China
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Abstract

RSM was utilized to optimize and model influential parameters on fluoride removal.

Regression models involving independent variables and main response were developed.

Interactive effects and optimum of process factors were illuminated and determined.

Fluoride removal efficiency of 99.69% was observed in optimal process conditions.

Response surface methodology was utilized to model and optimize the operational variables for defluoridation using an electrodialysis process as the treatment of secondary effluent of the graphite industry. Experiments were conducted using a Box-Behnken surface statistical design in order to evaluate the effects and the interaction of the influential variables including the operational voltage, initial fluoride concentration and flow rate. The regression models for defluoridation and energy consumption responses were statistically validated using analysis of variance (ANOVA); high coefficient of determination values (R2 = 0.9772 and R2 = 0.9814; respectively) were obtained. The quadratic model exhibited high reproducibility and a good fit of the experimental data. The optimum values of the initial fluoride concentration, voltage and flow rate were found to be 13.9 mg/L, 13.4 V, 102.5 L/h, respectively. A fluoride removal efficiency of 99.69% was observed under optimum conditions for the treatment of the secondary effluent of the graphite industry.
Keywords Response surface methodology      Fluoride removal      Electrodialysis      Box-Behnken design     
Corresponding Author(s): Zhun Ma,Xueli Gao   
Issue Date: 10 May 2019
 Cite this article:   
Xiaomeng Wang,Ning Li,Jianye Li, et al. Fluoride removal from secondary effluent of the graphite industry using electrodialysis: Optimization with response surface methodology[J]. Front. Environ. Sci. Eng., 2019, 13(4): 51.
 URL:  
https://academic.hep.com.cn/fese/EN/10.1007/s11783-019-1132-5
https://academic.hep.com.cn/fese/EN/Y2019/V13/I4/51
Fig.1  The schematic diagram of the ED process.
Item Thickness (μm) Burst strength (KPa) Resistance
(Wcm2)		 Transport number
Cation exchange membrane 120 200 3.0 > 0.96
Anion exchange membrane 120 200 2.8 > 0.96
Tab.1  Main performance parameters of the ion exchange membrane
Item Description
Type of membrane CH-0
Size of the membrane 160 mm?280 mm
Distance between the membranes 0.75 mm
Effective volume of the membrane 120 mm?175 mm
Effective area of the membrane 0.021 m2
Number of membranes 25 pairs
Storage tank volume 5 L
Plates of electrodialyzer Cathode plate and anode plate
Tab.2  Parameters of the dialyzer
Experimental run Factor 1
A: C (mg/L)		 Factor 2
B: v (L/h)		 Factor 3
C: U (V)		 Y (%)
1 20.00 135 10.00 98.34
2 20.00 70 5.00 50.59
3 30.00 135 15.00 99.69
4 10.00 135 15.00 98.97
5 20.00 200 15.00 99.45
6 10.00 135 5.00 55.45
7 30.00 200 10.00 96.99
8 20.00 135 10.00 98.39
9 20.00 135 10.00 97.93
10 10.00 200 10.00 98.89
11 30.00 135 5.00 78.73
12 20.00 135 10.00 97.91
13 20.00 70 15.00 99.39
14 20.00 135 10.00 99.46
15 20.00 200 5.00 78.65
16 30.00 70 10.00 84.04
17 10.00 70 10.00 84.09
Tab.3  BBD details and the experimental results of fluoride removal
Experimental run Factor 1
A*: U (V)		 Factor 2
B*: v (L/h)		 Factor 3
C*: t (min)		 Y1 (%) E (Wh/L)
1 15.00 200 40.00 99.45 0.78
2 15.00 135 20.00 88.05 0.64
3 5.00 70 40.00 31.18 0.10
4 5.00 200 40.00 59.38 0.14
5 10.00 135 40.00 80.66 0.42
6 10.00 70 20.00 23.05 0.27
7 10.00 200 20.00 34.11 0.41
8 10.00 200 60.00 96.99 0.58
9 5.00 135 20.00 15.56 0.10
10 5.00 135 60.00 78.73 0.16
11 10.00 135 40.00 80.54 0.42
12 15.00 70 40.00 90.24 0.81
13 10.00 70 60.00 84.04 0.46
14 10.00 135 40.00 80.53 0.42
15 15.00 135 60.00 99.69 0.76
16 10.00 135 40.00 80.57 0.42
17 10.00 135 40.00 80.7 0.42
Tab.4  BBD matrix for three variables and the observed results for the energy consumption response variable
Source Sum of squares df Mean squares F-value Prob>F
Model 3808.92 9 423.21 33.26 <0.0001
A 60.80 1 60.80 4.78 0.0651
B 390.09 1 390.09 30.66 0.0009
C 2247.37 1 2247.37 176.64 <0.0001
AB 0.86 1 0.86 0.067 0.8028
AC 127.33 1 127.33 10.01 0.0159
BC 195.96 1 195.96 15.40 0.0057
A2 40.63 1 40.63 3.19 0.1171
B2 77.68 1 77.68 6.11 0.0428
C2 615.51 1 615.51 48.38 0.0002
Residual 89.06 7 12.72
Lack of fit 87.47 3 29.16 73.18 0.0006
Pure error 1.59 4 0.40
Cor total 3897.98 16
Tab.5  ANOVA results of the RSM of the reduced quadratic model
Fig.2  Normal probability plot (a) and residual error (b) between the predicted response and actual response.
Fig.3  Response surface plot: (a) the influence of the initial fluoride concentration and flow rate on the fluoride removal at a voltage of 10 V; (b) the influence of the initial fluoride concentration and voltage on the fluoride removal at a flow rate of 135 L/h; (c) the influence of the flow rate and voltage on the fluoride removal at an initial fluoride concentration of 20 mg/L.
Source Sum of squares df Mean squares F-value Prob>F
Model 0.83 9 0.092 41.07 <0.0001
A-voltage 0.78 1 0.78 347.00 <0.0001
B-flow rate 0.0093 1 0.0093 4.18 0.0802
C-running time 0.035 1 0.035 15.66 0.0055
AB 0.0012 1 0.0012 0.54 0.4866
AC 0.00083 1 0.00083 0.37 0.5608
BC 0.00012 1 0.00012 0.053 0.8248
A2 0.00027 1 0.00027 0.12 0.7368
B2 0.0025 1 0.0025 1.13 0.3221
C2 0.0015 1 0.0015 0.65 0.4455
Residual 0.016 7 0.0022
Lack of fit 0.016 3 0.0052 133455.78 <0.0001
Pure error 1.562?10−7 4 3.906?10−8
Cor total 0.84 16
Tab.6  ANOVA results of the RSM of the energy consumption of the reduced quadratic model
Fig.4  Comparison between (a) normal % probability and residual error (b) predicted response and actual response.
Fig.5  Response surface plot: (a) the influence of the flow rate and voltage on the energy consumption for a running time of 40 min; (b) the influence of the running time and voltage on the energy consumption at a flow rate of 135 L/h; (c) the influence of the running time and the flow rate on the energy consumption at a voltage of 10 V.
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