Mn强化Fe/C微电解工艺条件优化及降解油墨废水机理

doi:10.11949/0438-1157.20220006

摘要/Abstract

摘要：

为了提高Fe/C微电解工艺对油墨废水的处理效率，以金属锰改变传统铁碳填料的成分，采用响应面法优化微电解工艺条件，通过三维荧光光谱、紫外可见光谱、气-质联用色谱等分析处理前后油墨废水的有机物成分及填料表面结构的变化，探究絮凝和降解机理。结果表明：在初始pH为2.79，反应时间为1.58 h，Fe/Mn质量比为3.11，填料总投加量为93.36 g/L的条件下，COD去除率达到87.9%，预测值(87.8%)与实测值相差0.1%，采用响应面法可准确预测COD去除率的变化。经Fe/Mn/C微电解工艺处理后，油墨废水Zeta电位上升，絮凝作用增强。Fe/Mn/C微电解工艺可破坏苯环及共轭双键结构，对类溶解性微生物代谢产物、类芳香族蛋白质类物质以及类腐殖酸类物质的降解效果显著，微电解过程中填料表面生成了铁、锰氧化物，部分氧化物附着在活性炭表面。

关键词: Fe/Mn/C, 微电解, 响应面, 优化, 油墨废水, 降解机理

Abstract:

In order to improve the treatment efficiency of ink wastewater using Fe/C microelectrolysis process, the compositions of conventional Fe/C fillers were changed by using metallic manganese, and the process conditions of microelectrolysis was optimized by using response surface methodology. The changes of organic matters in the wastewater and surface structure of the filler before and after the treatment of ink wastewater were analyzed by using three-dimensional fluorescence spectra, UV-Vis spectra and GC-MS, respectively. The flocculation and degradation mechanisms of ink wastewater were explored. The results showed that under the conditions of initial pH 2.79, reaction time of 1.58 h, Fe/Mn molar ratio of 3.11 and the filler dosage of 93.36 g/L, removal efficiency of COD reached 87.9%. The difference between the predicted value (87.8%) and the measured value was 0.1%. The response surface methodology could accurately predict the changes of the removal efficiency of COD. After being treated by Fe/Mn/C micro-electrolysis process, the Zeta potential of ink waste water increases, and the flocculation effect is enhanced. Fe/Mn/C microelectrolysis process could destroy the structure of benzene ring and conjugated double bonds, and it could efficiently degrade soluble microbial metabolites, aromatic protein substances and humic acids. During the microelectrolysis process, iron and manganese oxides were formed on the surface of the filler and some of the oxides adhered to the surface of the activated carbon.

Key words: Fe/Mn/C, microelectrolysis, response surface, optimization, ink wastewater, degradation mechanism

中图分类号:

X 523

贾艳萍, 丁雪, 刚健, 佟泽为, 张海丰, 张兰河. Mn强化Fe/C微电解工艺条件优化及降解油墨废水机理[J]. 化工学报, 2022, 73(5): 2183-2193.

Yanping JIA, Xue DING, Jian GANG, Zewei TONG, Haifeng ZHANG, Lanhe ZHANG. Optimization of process conditions for Mn enhanced Fe/C microelectrolysis and degradation mechanism of ink wastewater[J]. CIESC Journal, 2022, 73(5): 2183-2193.

图/表 13

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编码	因素	单位	水平
编码	因素	单位	-1	0	1
A	初始pH	—	2	3	4
B	反应时间	h	1	1.5	2
C	Fe/Mn质量比	—	2	3	4
D	填料总投加量	g/L	80	100	120

编码	因素	单位	水平
编码	因素	单位	-1	0	1
A	初始pH	—	2	3	4
B	反应时间	h	1	1.5	2
C	Fe/Mn质量比	—	2	3	4
D	填料总投加量	g/L	80	100	120

变差来源	平方和	自由度	均方和	F值	P值	显著性
模型	320.45	14	22.89	22.76	<0.0001	极显著
A	14.39	1	14.39	14.31	0.0020	极显著
B	5.77	1	5.77	5.74	0.0312	显著
C	5.35	1	5.35	5.32	0.0370	显著
D	33.70	1	33.70	33.51	<0.0001	极显著
AB	6.15	1	6.15	6.11	0.0268	显著
AC	3.24	1	3.24	3.22	0.0943	不显著
AD	0.30	1	0.30	0.30	0.5921	不显著
BC	2.42	1	2.42	2.40	0.1433	不显著
BD	20.84	1	20.84	20.72	0.0005	极显著
CD	0.20	1	0.20	0.20	0.6605	不显著
A²	60.04	1	60.04	59.69	<0.0001	极显著
B²	174.37	1	174.37	173.36	<0.0001	极显著
C²	23.88	1	23.88	23.74	0.0002	极显著
D²	65.74	1	65.74	65.36	<0.0001	极显著
残差	14.08	14	1.01
失拟项	12.67	10	1.27	3.59	0.1149	不显著
误差	1.41	4	0.35
合计	334.53	28
标准偏差		1.00		相关系数		0.9579
平均值		81.76		校正决定系数		0.9158
变异系数		1.23		预测相关系数		0.7753
压力系数		75.18		信噪比		16.129

变差来源	平方和	自由度	均方和	F值	P值	显著性
模型	320.45	14	22.89	22.76	<0.0001	极显著
A	14.39	1	14.39	14.31	0.0020	极显著
B	5.77	1	5.77	5.74	0.0312	显著
C	5.35	1	5.35	5.32	0.0370	显著
D	33.70	1	33.70	33.51	<0.0001	极显著
AB	6.15	1	6.15	6.11	0.0268	显著
AC	3.24	1	3.24	3.22	0.0943	不显著
AD	0.30	1	0.30	0.30	0.5921	不显著
BC	2.42	1	2.42	2.40	0.1433	不显著
BD	20.84	1	20.84	20.72	0.0005	极显著
CD	0.20	1	0.20	0.20	0.6605	不显著
A²	60.04	1	60.04	59.69	<0.0001	极显著
B²	174.37	1	174.37	173.36	<0.0001	极显著
C²	23.88	1	23.88	23.74	0.0002	极显著
D²	65.74	1	65.74	65.36	<0.0001	极显著
残差	14.08	14	1.01
失拟项	12.67	10	1.27	3.59	0.1149	不显著
误差	1.41	4	0.35
合计	334.53	28
标准偏差		1.00		相关系数		0.9579
平均值		81.76		校正决定系数		0.9158
变异系数		1.23		预测相关系数		0.7753
压力系数		75.18		信噪比		16.129

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