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Frontiers of Environmental Science & Engineering

ISSN 2095-2201

ISSN 2095-221X(Online)

CN 10-1013/X

邮发代号 80-973

2018 Impact Factor: 3.883

Frontiers of Environmental Science & Engineering  2019, Vol. 13 Issue (4): 51   https://doi.org/10.1007/s11783-019-1132-5
  本期目录
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.

Key wordsResponse surface methodology    Fluoride removal    Electrodialysis    Box-Behnken design
收稿日期: 2019-01-08      出版日期: 2019-05-10
Corresponding Author(s): Zhun Ma,Xueli Gao   
 引用本文:   
. [J]. Frontiers of Environmental Science & Engineering, 2019, 13(4): 51.
Xiaomeng Wang, Ning Li, Jianye Li, Junjun Feng, Zhun Ma, Yuting Xu, Yongchao Sun, Dongmei Xu, Jian Wang, Xueli Gao, Jun Gao. Fluoride removal from secondary effluent of the graphite industry using electrodialysis: Optimization with response surface methodology. Front. Environ. Sci. Eng., 2019, 13(4): 51.
 链接本文:  
https://academic.hep.com.cn/fese/CN/10.1007/s11783-019-1132-5
https://academic.hep.com.cn/fese/CN/Y2019/V13/I4/51
Fig.1  
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  
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  
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  
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  
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  
Fig.2  
Fig.3  
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?107 4 3.906?108
Cor total 0.84 16
Tab.6  
Fig.4  
Fig.5  
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