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Frontiers of Environmental Science & Engineering

ISSN 2095-2201

ISSN 2095-221X(Online)

CN 10-1013/X

Postal Subscription Code 80-973

2018 Impact Factor: 3.883

Front. Environ. Sci. Eng.    2016, Vol. 10 Issue (4) : 6    https://doi.org/10.1007/s11783-016-0845-y
RESEARCH ARTICLE
Wastewater-nitrogen removal using polylactic acid/starch as carbon source: Optimization of operating parameters using response surface methodology
Yan GUO,Chuanfu WU,Qunhui WANG(),Min YANG,Qiqi HUANG,Markus MAGEP,Tianlong ZHENG
School of Civil and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China
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Abstract

The use of PLA/starch blends for nitrogen removal was achieved.

The influence of different operating parameters on responses was verified using RSM.

The conditions for desired responses were successfully optimized simultaneously.

Blends material may have a promising application prospect in the future.

Nitrogen removal from ammonium-containing wastewater was conducted using polylactic acid (PLA)/starch blends as carbon source and carrier for functional bacteria. The exclusive and interactive influences of operating parameters (i.e., temperature, pH, stirring rate, and PLA-to-starch ratio (PLA proportion)) on nitrification (Y1), denitrification (Y2), and COD release rates (Y3) were investigated through response surface methodology. Experimental results indicated that nitrogen removal could be successfully achieved in the PLA/starch blends through simultaneous nitrification and denitrification. The carbon release rate of the blends was controllable. The sensitivity of Y1, Y2, and Y3 to different operating parameters also differed. The sequence for each response was as follows: for Y1, pH>stirring rate>PLA proportion>temperature; for Y2, pH>PLA proportion>temperature>stirring rate; and for Y3, stirring rate>pH>PLA proportion>temperature. In this study, the following optimum conditions were observed: temperature, 32.0°C; pH 7.7; stirring rate, 200.0 r·min-1; and PLA proportion, 0.4. Under these conditions, Y1, Y2, and Y3 were 134.0 μg-N·g-blend-1·h-1, 160.9 μg-N·g-blend-1·h-1, and 7.6 × 103 μg-O·g-blend-1·h-1, respectively. These results suggested that the PLA/starch blends may be an ideal packing material for nitrogen removal.

Keywords Nitrogen removal      Polylactic acid      Starch      Carbon source      Response surface methodology     
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Corresponding Author(s): Qunhui WANG   
Issue Date: 12 May 2016
 Cite this article:   
Yan GUO,Chuanfu WU,Qunhui WANG, et al. Wastewater-nitrogen removal using polylactic acid/starch as carbon source: Optimization of operating parameters using response surface methodology[J]. Front. Environ. Sci. Eng., 2016, 10(4): 6.
 URL:  
https://academic.hep.com.cn/fese/EN/10.1007/s11783-016-0845-y
https://academic.hep.com.cn/fese/EN/Y2016/V10/I4/6
coded values actual values
A/°C B C/ (r·min-1) D
-1 25.0 6.5 50 0.4
0 30.0 7.5 125 0.5
+ 1 35.0 8.5 200 0.6
Tab.1  Experimental range and levels of the independent variables
experimentNo. variables responses
A/°C B C/(r·min-1) D Y1a Y2b Y3c
1 30.0(0) 6.5(-1) 200.0(+1) 0.5(0) -3.2 95.3 5.8
2 30.0(0) 8.5(+1) 200.0(+1) 0.5(0) 99.3 127.7 9.0
3 35.0(+1) 7.5(0) 125.0(0) 0.4(-1) 64.3 82.7 3.2
4 35.0(+1) 7.5(0) 200.0(+1) 0.5(0) 120.0 117.7 6.9
5 25.0(-1) 7.5(0) 125.0(0) 0.6(+1) 57.7 50.3 2.9
6 35.0(+1) 8.5(+1) 125.0(0) 0.5(0) 64.3 65.3 3.2
7 35.0(+1) 7.5(0) 125.0(0) 0.6(+1) 52.0 54.0 4.3
8 25.0(-1) 7.5(0) 125.0(0) 0.4(-1) 78.0 80.3 5.9
9 25.0(-1) 7.5(0) 50.0(-1) 0.5(0) 59.0 61.0 3.8
10 25.0(-1) 6.5(-1) 125.0(0) 0.5(0) 0.0 -1.1 2.3
11 30.0(0) 8.5(+1) 50.0(-1) 0.5(0) 71.0 169.3 6.8
12 25.0(-1) 7.5(0) 200.0(+1) 0.5(0) 107.0 59.3 5.6
13 35.0(+1) 6.5(-1) 125.0(0) 0.5(0) 0.0 -3.7 2.3
14 25.0(-1) 8.5(+1) 125.0(0) 0.5(0) 67.0 57.7 3.3
15 30.0(0) 6.5(-1) 125.0(0) 0.4(-1) 0.0 0.8 3.0
16 30.0(0) 7.5(0) 50.0(-1) 0.6(+1) 39.0 74.0 5.0
17 30.0(0) 7.5(0) 50.0(-1) 0.4(-1) 25.3 66.7 4.6
18 30.0(0) 8.5(+1) 125.0(0) 0.6(+1) 107.7 57.3 4.3
19 30.0(0) 8.5(+1) 125.0(0) 0.4(-1) 98.0 115.3 5.4
20 35.0(+1) 7.5(0) 50.0(-1) 0.5(0) 39.0 149.0 6.1
21 30.0(0) 6.5(-1) 125.0(0) 0.6(+1) 0.0 -3.7 3.7
22 30.0(0) 7.5(0) 125.0(0) 0.5(0) 59.7 83.7 4.3
23 30.0(0) 7.5(0) 200.0(+1) 0.6(+1) 89.0 111.7 6.1
24 30.0(0) 6.5(-1) 50.0(-1) 0.5(0) -34.0 60.0 5.1
25 30.0(0) 7.5(0) 200.0(+1) 0.4(-1) 126.0 201.7 9.7
Tab.2  Central composite arrangement for independent variables and their responses
coefficients of the regression model F-values coefficient values (coded) prob>F
Y1 Y2 Y3 Y1 Y2 Y3 Y1 Y2 Y3
model 7.69 3.14 5.22 59.67 83.67 4.31 0.0013 0.0374 0.0062
X1 0.18 1.73 0.36 -2.42 13.12 0.18 0.6799 0.2183 0.5606
X2 63.66 13.79 7.60 45.38 37.07 0.81 <0.0001 0.0040 0.0202
X3 24.65 1.24 11.04 28.24 11.11 0.98 0.0006 0.2918 0.0077
X4 0.46 2.89 2.47 -3.86 -16.99 -0.46 0.5126 0.1197 0.1472
X1X2 4.580e-003 0.022 5.424e-003 -0.67 2.57 -0.037 0.9474 0.8850 0.9427
X1X3 0.7 0.18 0.17 8.25 -7.42 -0.21 0.4219 0.6771 0.6876
X1X4 0.041 3.72e-04 3.86 2 0.33 1.00 0.8432 0.9850 0.0777
X2X3 4.026e-003 1.24 0.49 -0.63 -19.25 0.36 0.9507 0.2917 0.4986
X2X4 0.06 0.6 0.82 2.42 -13.37 -0.46 0.8112 0.4572 0.3876
X3X4 1.65 1.98 3.70 -12.67 -24.33 -0.98 0.2275 0.1897 0.0832
X12 0.086 1.17 2.01 3.44 -22.29 -0.86 0.7754 0.3043 0.1865
X22 5.04 1.63 0.25 -26.33 -26.31 -0.30 0.0485 0.2300 0.6276
X32 0.36 4.32 13.59 7.08 42.8 2.23 0.5592 0.0643 0.0042
X42 0.36 0.13 0.097 7.02 -7.47 0.19 0.5626 0.7242 0.7619
R2 0.92 0.81 0.88
adjusted R2 0.89 0.86 0.81
CV/% 35.53 44.75 20.73
adeq precision 9.984 6.867 9.067
Tab.3  Regression coefficients of the model for the response functions in coded level
Fig.1  Ammonium, total nitrogen, and COD concentration change vs. time
Fig.2  Response surface plot for nitrification rate as a function of two factors (the third factor kept constant at central point)
Fig.3  Response surface plot for denitrification rate as a function of two factors (the third factor kept constant at central point)
Fig.4  Response surface plot for COD release rate as a function of two factors (the third factor kept constant at central point)
case No. importance variables responses desirability
r1 r2 r3 A/°C B C/(r·min-1) D Y1a Y2b Y3c DY
1 5 5 5 32.0 8.0 157.8 0.4 107.4 120.5 5.1 0.69
2 5 5 4 32.0 8.0 169.7 0.4 115.5 129.5 5.7 0.70
3 5 5 3 32.0 7.9 182.0 0.4 124.1 141.3 6.5 0.72
4 5 5 2 32.0 7.8 187.1 0.4 126.0 145.5 6.7 0.75
5 5 5 1 32.0 7.7 200.0 0.4 134.0 160.9 7.6 0.81
6 5 4 1 32.0 7.6 200.0 0.4 131.0 158.6 7.5 0.81
7 5 3 1 32.0 7.7 190.6 0.4 126.3 147.9 6.9 0.81
8 5 2 1 32.0 7.9 184.9 0.4 126.0 144.3 6.7 0.81
9 5 1 1 32.0 8.0 183.0 0.4 126.0 143.3 6.6 0.81
Tab.4  Optimized results at different cases of reassignment of importance
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