化工学报 ›› 2019, Vol. 70 ›› Issue (3): 1099-1110.DOI: 10.11949/j.issn.0438-1157.20181054
张千1(),刘向阳1,陈旺1,吴恒1,肖芃颖1,吉芳英2,李宸3,念海明3
收稿日期:
2018-09-19
修回日期:
2018-11-07
出版日期:
2019-03-05
发布日期:
2019-03-05
通讯作者:
张千
作者简介:
及第一作者:张千(1986—),男,博士研究生,讲师,<email>zhangqianswu2005@163.com</email>
基金资助:
Qian ZHANG1(),Xiangyang LIU1,Wang CHEN1,Heng WU1,Pengying XIAO1,Fangying JI2,Chen LI3,Haiming NIAN3
Received:
2018-09-19
Revised:
2018-11-07
Online:
2019-03-05
Published:
2019-03-05
Contact:
Qian ZHANG
摘要:
为解决现有除磷吸附剂粒径小造成的材料易流失和系统压降过大等问题,以实现吸附除磷工艺在实际工程中的应用,以聚氨酯填料为载体,水溶性聚氨酯为介质,将水化硅酸钙负载到聚氨酯填料上制成负载型除磷填料。研究了制备条件对除磷填料除磷效果的影响,采用扫描电子显微镜(SEM)和傅里叶变换红外光谱仪(FTIR)观察分析了负载前后水化硅酸钙微观结构及化学基团的变化;利用除磷填料作为除磷吸附床的滤料,研究了运行条件对吸附床除磷效果的影响。在此基础上,利用响应曲面法研究了除磷吸附床磷酸盐去除率和各变量之间的关系,并对工艺参数进行了优化。结果表明,水性聚氨酯溶液的浓度和用量分别为100 g/L和50 ml,水化硅酸钙的质量为12 g的条件下所制备的除磷填料除磷效果最好;SEM和FTIR分析结果显示,水化硅酸钙负载前后其孔隙结构和化学基团没有明显的变化;预测模型的方差分析结果表明,HRT(X 1)、进水ρ(PO4 3--P)(X 2)、温度(X 3)、初始pH(X 4)以及X 1 X 2,X 1 X 4,X 2 X 3,X 2 X 4的交互作用均对磷酸盐的去除具有显著影响 (P<0.05),但X 1 X 3的交互作用对磷酸盐的去除影响不显著。通过预测模型获得的最佳运行条件为:HRT为79.77 min,进水ρ(PO4 3--P)为1.70 mg/L, 温度为34.04℃,pH为9.68。在该条件下,反应器对磷酸盐的去除率可以达到93.46%。
中图分类号:
张千, 刘向阳, 陈旺, 吴恒, 肖芃颖, 吉芳英, 李宸, 念海明. 新型除磷填料的制备及除磷吸附床运行参数的优化[J]. 化工学报, 2019, 70(3): 1099-1110.
Qian ZHANG, Xiangyang LIU, Wang CHEN, Heng WU, Pengying XIAO, Fangying JI, Chen LI, Haiming NIAN. Preparation of a novel phosphorus removal filler and optimization of phosphate removal adsorption bed process[J]. CIESC Journal, 2019, 70(3): 1099-1110.
Factor | Level | |||
---|---|---|---|---|
HRT/min | Influent ρ( | Tempera-ture/℃ | Initial pH | |
HRT/min | 40—90 | 6 | 30 | 9 |
influent ρ( | 70 | 0.5—2.5 | 30 | 9 |
temperature/℃ | 70 | 6 | 17—40 | 9 |
initial pH | 70 | 6 | 30 | 6—11 |
表1 单因素试验中的运行参数
Table 1 Operation parameters in each single factor experiment
Factor | Level | |||
---|---|---|---|---|
HRT/min | Influent ρ( | Tempera-ture/℃ | Initial pH | |
HRT/min | 40—90 | 6 | 30 | 9 |
influent ρ( | 70 | 0.5—2.5 | 30 | 9 |
temperature/℃ | 70 | 6 | 17—40 | 9 |
initial pH | 70 | 6 | 30 | 6—11 |
Code | Variable | Code level | ||||
---|---|---|---|---|---|---|
-α | -1 | 0 | 1 | +α | ||
X 1 | HRT/min | 50 | 60 | 70 | 80 | 90 |
X 2 | influent ρ( | 0.5 | 1 | 1.5 | 2 | 2.5 |
X 3 | temperature/℃ | 16 | 23 | 30 | 37 | 44 |
X 4 | initial pH | 7 | 8 | 9 | 10 | 11 |
表2 响应曲面模型的变量及其范围
Table 2 Variables and corresponding range of response surface methodology model
Code | Variable | Code level | ||||
---|---|---|---|---|---|---|
-α | -1 | 0 | 1 | +α | ||
X 1 | HRT/min | 50 | 60 | 70 | 80 | 90 |
X 2 | influent ρ( | 0.5 | 1 | 1.5 | 2 | 2.5 |
X 3 | temperature/℃ | 16 | 23 | 30 | 37 | 44 |
X 4 | initial pH | 7 | 8 | 9 | 10 | 11 |
Sample | ρ(waterborne polyurethane)/(g/L) | Volume of waterborne polyurethane/ ml | Dosage of hydrated calcium silicate/g | Phosphate removal rate/% |
---|---|---|---|---|
1 | 100 | 50 | 4 | 95.7 |
2 | 100 | 100 | 8 | 96.6 |
3 | 100 | 150 | 12 | 97.9 |
4 | 100 | 200 | 16 | 98.3 |
5 | 200 | 50 | 8 | 95.9 |
6 | 200 | 100 | 4 | 91.5 |
7 | 200 | 150 | 16 | 97.6 |
8 | 200 | 200 | 12 | 96.6 |
9 | 300 | 50 | 12 | 99.6 |
10 | 300 | 100 | 16 | 99.2 |
11 | 300 | 150 | 4 | 72.2 |
12 | 300 | 200 | 8 | 88.0 |
13 | 400 | 50 | 16 | 91.6 |
14 | 400 | 100 | 12 | 92.8 |
15 | 400 | 150 | 8 | 74.7 |
16 | 400 | 200 | 4 | 44.1 |
K 1 | 388.5 | 382.8 | 303.5 | |
K 2 | 381.6 | 380.1 | 355.2 | |
K 3 | 359.0 | 342.4 | 386.9 | |
K 4 | 303.2 | 327 | 386.7 | |
k 1 | 97.1 | 95.7 | 75.9 | |
k 2 | 95.4 | 95.0 | 88.8 | |
k 3 | 89.8 | 85.6 | 96.7 | |
k 4 | 75.8 | 81.8 | 96.7 | |
R | 21.3 | 13.9 | 20.8 | |
major-minor order of test factor | A>C>B | |||
optimal level | A1 | B1 | C3 | |
optimal combination | A1B1C3 |
表3 制备除磷填料的正交试验结果
Table 3 Orthogonal test results of phosphorus removal filter
Sample | ρ(waterborne polyurethane)/(g/L) | Volume of waterborne polyurethane/ ml | Dosage of hydrated calcium silicate/g | Phosphate removal rate/% |
---|---|---|---|---|
1 | 100 | 50 | 4 | 95.7 |
2 | 100 | 100 | 8 | 96.6 |
3 | 100 | 150 | 12 | 97.9 |
4 | 100 | 200 | 16 | 98.3 |
5 | 200 | 50 | 8 | 95.9 |
6 | 200 | 100 | 4 | 91.5 |
7 | 200 | 150 | 16 | 97.6 |
8 | 200 | 200 | 12 | 96.6 |
9 | 300 | 50 | 12 | 99.6 |
10 | 300 | 100 | 16 | 99.2 |
11 | 300 | 150 | 4 | 72.2 |
12 | 300 | 200 | 8 | 88.0 |
13 | 400 | 50 | 16 | 91.6 |
14 | 400 | 100 | 12 | 92.8 |
15 | 400 | 150 | 8 | 74.7 |
16 | 400 | 200 | 4 | 44.1 |
K 1 | 388.5 | 382.8 | 303.5 | |
K 2 | 381.6 | 380.1 | 355.2 | |
K 3 | 359.0 | 342.4 | 386.9 | |
K 4 | 303.2 | 327 | 386.7 | |
k 1 | 97.1 | 95.7 | 75.9 | |
k 2 | 95.4 | 95.0 | 88.8 | |
k 3 | 89.8 | 85.6 | 96.7 | |
k 4 | 75.8 | 81.8 | 96.7 | |
R | 21.3 | 13.9 | 20.8 | |
major-minor order of test factor | A>C>B | |||
optimal level | A1 | B1 | C3 | |
optimal combination | A1B1C3 |
Standard order | X 1 | X 2 | X 3 | X 4 | | |
---|---|---|---|---|---|---|
Measured value | Predicted value | |||||
22 | 70 | 1.5 | 44 | 9 | 91.00 | 90.37 |
17 | 50 | 1.5 | 30 | 9 | 78.12 | 78.79 |
8 | 80 | 2 | 37 | 8 | 88.84 | 89.47 |
5 | 60 | 1 | 37 | 8 | 86.58 | 85.90 |
9 | 60 | 1 | 23 | 10 | 85.87 | 85.23 |
2 | 80 | 1 | 23 | 8 | 86.82 | 86.90 |
18 | 90 | 1.5 | 30 | 9 | 92.48 | 91.31 |
16 | 80 | 2 | 37 | 10 | 92.59 | 92.73 |
3 | 60 | 2 | 23 | 8 | 73.58 | 72.73 |
12 | 80 | 2 | 23 | 10 | 88.94 | 89.61 |
24 | 70 | 1.5 | 30 | 11 | 91.12 | 90.85 |
19 | 70 | 0.5 | 30 | 9 | 86.02 | 85.54 |
28 | 70 | 1.5 | 30 | 9 | 91.02 | 91.01 |
25 | 70 | 1.5 | 30 | 9 | 91.01 | 91.01 |
13 | 60 | 1 | 37 | 10 | 88.99 | 89.76 |
14 | 80 | 1 | 37 | 10 | 88.59 | 88.67 |
7 | 60 | 2 | 37 | 8 | 78.54 | 79.26 |
30 | 70 | 1.5 | 30 | 9 | 90.95 | 91.01 |
23 | 70 | 1.5 | 30 | 7 | 83.96 | 83.73 |
10 | 80 | 1 | 23 | 10 | 86.99 | 87.54 |
1 | 60 | 1 | 23 | 8 | 80.99 | 81.37 |
4 | 80 | 2 | 23 | 8 | 86.59 | 86.34 |
6 | 80 | 1 | 37 | 8 | 87.09 | 88.03 |
27 | 70 | 1.5 | 30 | 9 | 91.03 | 91.01 |
29 | 70 | 1.5 | 30 | 9 | 91.02 | 91.01 |
21 | 70 | 1.5 | 16 | 9 | 82.59 | 82.71 |
26 | 70 | 1.5 | 30 | 9 | 91.00 | 91.01 |
20 | 70 | 2.5 | 30 | 9 | 80.99 | 80.97 |
15 | 60 | 2 | 37 | 10 | 86.59 | 85.75 |
11 | 60 | 2 | 23 | 10 | 78.89 | 79.22 |
表4 中心复合设计与试验结果
Table 4 Factors and levels for central composite design
Standard order | X 1 | X 2 | X 3 | X 4 | | |
---|---|---|---|---|---|---|
Measured value | Predicted value | |||||
22 | 70 | 1.5 | 44 | 9 | 91.00 | 90.37 |
17 | 50 | 1.5 | 30 | 9 | 78.12 | 78.79 |
8 | 80 | 2 | 37 | 8 | 88.84 | 89.47 |
5 | 60 | 1 | 37 | 8 | 86.58 | 85.90 |
9 | 60 | 1 | 23 | 10 | 85.87 | 85.23 |
2 | 80 | 1 | 23 | 8 | 86.82 | 86.90 |
18 | 90 | 1.5 | 30 | 9 | 92.48 | 91.31 |
16 | 80 | 2 | 37 | 10 | 92.59 | 92.73 |
3 | 60 | 2 | 23 | 8 | 73.58 | 72.73 |
12 | 80 | 2 | 23 | 10 | 88.94 | 89.61 |
24 | 70 | 1.5 | 30 | 11 | 91.12 | 90.85 |
19 | 70 | 0.5 | 30 | 9 | 86.02 | 85.54 |
28 | 70 | 1.5 | 30 | 9 | 91.02 | 91.01 |
25 | 70 | 1.5 | 30 | 9 | 91.01 | 91.01 |
13 | 60 | 1 | 37 | 10 | 88.99 | 89.76 |
14 | 80 | 1 | 37 | 10 | 88.59 | 88.67 |
7 | 60 | 2 | 37 | 8 | 78.54 | 79.26 |
30 | 70 | 1.5 | 30 | 9 | 90.95 | 91.01 |
23 | 70 | 1.5 | 30 | 7 | 83.96 | 83.73 |
10 | 80 | 1 | 23 | 10 | 86.99 | 87.54 |
1 | 60 | 1 | 23 | 8 | 80.99 | 81.37 |
4 | 80 | 2 | 23 | 8 | 86.59 | 86.34 |
6 | 80 | 1 | 37 | 8 | 87.09 | 88.03 |
27 | 70 | 1.5 | 30 | 9 | 91.03 | 91.01 |
29 | 70 | 1.5 | 30 | 9 | 91.02 | 91.01 |
21 | 70 | 1.5 | 16 | 9 | 82.59 | 82.71 |
26 | 70 | 1.5 | 30 | 9 | 91.00 | 91.01 |
20 | 70 | 2.5 | 30 | 9 | 80.99 | 80.97 |
15 | 60 | 2 | 37 | 10 | 86.59 | 85.75 |
11 | 60 | 2 | 23 | 10 | 78.89 | 79.22 |
Source | Sum of squares | df | Mean square | F value | P value |
---|---|---|---|---|---|
Model | 691.57 | 13 | 53.20 | 101.47 | <0.0001 |
X 1 | 235.25 | 1 | 235.25 | 448.70 | <0.0001 |
X 2 | 31.33 | 1 | 31.33 | 59.75 | <0.0001 |
X 3 | 88.01 | 1 | 88.01 | 167.87 | <0.0001 |
X 4 | 76.11 | 1 | 76.11 | 145.17 | <0.0001 |
X 1 X 2 | 65.21 | 1 | 65.21 | 124.37 | <0.0001 |
X 1 X 3 | 11.56 | 1 | 11.56 | 22.05 | 0.0002 |
X 1 X 4 | 10.37 | 1 | 10.37 | 19.78 | 0.0004 |
X 2 X 3 | 3.98 | 1 | 3.98 | 7.59 | 0.0141 |
X 2 X 4 | 6.89 | 1 | 6.89 | 13.14 | 0.0023 |
X 1 2 | 60.86 | 1 | 60.86 | 116.08 | <0.0001 |
X 2 2 | 103.05 | 1 | 103.05 | 196.56 | <0.0001 |
X 3 2 | 34.15 | 1 | 34.15 | 65.14 | <0.0001 |
X 4 2 | 23.70 | 1 | 23.70 | 45.21 | <0.0001 |
residual | 8.39 | 16 | 0.52 | ||
lack of fit | 8.38 | 11 | 0.76 | 918.35 | <0.0001 |
pure error | 4.150×10-3 | 5 | 8.300×10-4 |
表5 回归模型的方差分析
Table 5 Analysis of variance(ANOVA) for regression model
Source | Sum of squares | df | Mean square | F value | P value |
---|---|---|---|---|---|
Model | 691.57 | 13 | 53.20 | 101.47 | <0.0001 |
X 1 | 235.25 | 1 | 235.25 | 448.70 | <0.0001 |
X 2 | 31.33 | 1 | 31.33 | 59.75 | <0.0001 |
X 3 | 88.01 | 1 | 88.01 | 167.87 | <0.0001 |
X 4 | 76.11 | 1 | 76.11 | 145.17 | <0.0001 |
X 1 X 2 | 65.21 | 1 | 65.21 | 124.37 | <0.0001 |
X 1 X 3 | 11.56 | 1 | 11.56 | 22.05 | 0.0002 |
X 1 X 4 | 10.37 | 1 | 10.37 | 19.78 | 0.0004 |
X 2 X 3 | 3.98 | 1 | 3.98 | 7.59 | 0.0141 |
X 2 X 4 | 6.89 | 1 | 6.89 | 13.14 | 0.0023 |
X 1 2 | 60.86 | 1 | 60.86 | 116.08 | <0.0001 |
X 2 2 | 103.05 | 1 | 103.05 | 196.56 | <0.0001 |
X 3 2 | 34.15 | 1 | 34.15 | 65.14 | <0.0001 |
X 4 2 | 23.70 | 1 | 23.70 | 45.21 | <0.0001 |
residual | 8.39 | 16 | 0.52 | ||
lack of fit | 8.38 | 11 | 0.76 | 918.35 | <0.0001 |
pure error | 4.150×10-3 | 5 | 8.300×10-4 |
图6 HRT、进水 P O 4 3 - -P浓度、温度以及初始pH之间的交互效应对磷酸盐去除率影响的等高线图和响应曲面图
Fig.6 Contour and response surface plots for interaction effects of HRT, influent P O 4 3 - -P concentration, temperature and initial pH on phosphate removal efficiency
Solution No. | HRT/min | Influent ρ( | Temperature/℃ | Initial pH | Phosphate removal efficiency/% | Desirability |
---|---|---|---|---|---|---|
1 | 79.77 | 1.70 | 34.04 | 9.68 | 93.46 | 1.000 |
2 | 79.75 | 1.70 | 33.98 | 9.68 | 93.4579 | 1.000 |
3 | 79.90 | 1.70 | 34.01 | 9.67 | 93.4578 | 1.000 |
4 | 79.97 | 1.71 | 34.01 | 9.67 | 93.4576 | 1.000 |
5 | 79.48 | 1.70 | 34.15 | 9.69 | 93.4573 | 1.000 |
6 | 76.75 | 1.70 | 33.87 | 9.69 | 93.4572 | 1.000 |
7 | 80.00 | 1.69 | 32.80 | 9.59 | 93.4221 | 0.998 |
8 | 80.00 | 1.70 | 33.80 | 9.27 | 93.3107 | 0.992 |
表6 磷酸盐去除率的优化结果
Table 6 Optimization results for phosphate maximum removal efficiency
Solution No. | HRT/min | Influent ρ( | Temperature/℃ | Initial pH | Phosphate removal efficiency/% | Desirability |
---|---|---|---|---|---|---|
1 | 79.77 | 1.70 | 34.04 | 9.68 | 93.46 | 1.000 |
2 | 79.75 | 1.70 | 33.98 | 9.68 | 93.4579 | 1.000 |
3 | 79.90 | 1.70 | 34.01 | 9.67 | 93.4578 | 1.000 |
4 | 79.97 | 1.71 | 34.01 | 9.67 | 93.4576 | 1.000 |
5 | 79.48 | 1.70 | 34.15 | 9.69 | 93.4573 | 1.000 |
6 | 76.75 | 1.70 | 33.87 | 9.69 | 93.4572 | 1.000 |
7 | 80.00 | 1.69 | 32.80 | 9.59 | 93.4221 | 0.998 |
8 | 80.00 | 1.70 | 33.80 | 9.27 | 93.3107 | 0.992 |
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