Efficiency and biological toxicity of iron-carbon microelectrolysis in treatment of the dye wastewater

doi:10.16085/j.issn.1000-6613.2019-0797

Abstract

Abstract:

In order to improve the efficiency of the actual dye wastewater treatment by the iron-carbon micro-electrolysis process, the response surface method was used to optimize the process conditions. A response surface model was constructed and the significance of the model was analyzed when removal efficiency of COD was used as response value and initial pH, iron dosage, iron-carbon mass ratio and reaction time were used as experimental factors. The results showed that when the initial pH was 3.53, the iron dosage was 83.92g/L, the iron-carbon mass ratio was 0.82 and the reaction time was 78.48min, the predicted COD removal efficiency was 75.25%. The deviation was 0.23% (＜2%) compared with the measured value. The model can be used to predict the change of COD removal efficiency. The biotoxicity of the influent and effluent in the iron-carbon microelectrolysis process was tested by E.coli. Compared with the influent group, the release of lactate dehydrogenase (LDH) in the effluent group decreased from 2.13 times to 1.64 times in the control group, while the production of reactive oxygen species (ROS) decreased from 19.26 times to 4.81 times in the control group. The cell death rate decreased from 98.1% to 61.5%, and the growth phase was extended from 5h to 9h, and the BOD₅/COD rose from 0.151 to 0.416. Therefore, the iron-carbon microelectrolysis process has the effect of reducing the biological toxicity of dye wastewater.

Key words: ferric-carbon micro-electrolysis, waste water, model, optimal design, biological toxicity

摘要：

为了提高铁碳微电解工艺处理实际印染废水的效率，采用响应面法进行工艺条件优化。以COD去除率为响应值，初始pH、铁投加量、铁碳质量比及反应时间为实验因素，构建响应面模型，分析模型的显著性。结果表明：当初始pH为3.53、铁投加量为83.92g/L、铁碳质量比为0.82及反应时间为78.48min时，COD去除率的预测值为75.25%，与实测值相差0.23%（＜2%），可以利用该模型预测COD去除率的变化。同时采用大肠杆菌对铁碳微电解工艺进出水的生物毒性进行检测，与进水组相比，出水组中乳酸脱氢酶（LDH）释放量由对照组的2.13倍下降至对照组的1.64倍，同时活性氧物质（ROS）产生水平由对照组的19.26倍下降至对照组的4.81倍，细胞死亡率由98.1%下降至61.5%，对数期由5h延长至9h，且BOD₅/COD从0.151升至0.416，因此铁碳微电解工艺具有降低印染废水生物毒性的作用。

关键词: 铁碳微电解, 废水, 模型, 优化设计, 生物毒性

CLC Number:

X523

Yanping JIA,Zhen ZHANG,Zhenhao BI,Jian ZHANG,Lanhe ZHANG. Efficiency and biological toxicity of iron-carbon microelectrolysis in treatment of the dye wastewater[J]. Chemical Industry and Engineering Progress, 2020, 39(2): 790-797.

贾艳萍,张真,毕朕豪,张健,张兰河. 铁碳微电解处理印染废水的效能及生物毒性变化[J]. 化工进展, 2020, 39(2): 790-797.

Figures/Tables 13

水质	颜色	COD/mg·L^-1	TOC	$N H 4 + - N$ /mg·L^-1	浊度/NTU	色度/倍	BOD₅/COD	pH	味道
印染废水	鲜红色	1288±100	107.8±10	10.9±4	112.4±3.2	345.2±15	0.151	4.58±1	刺激性酸臭味

水质	颜色	COD/mg·L^-1	TOC	$N H 4 + - N$ /mg·L^-1	浊度/NTU	色度/倍	BOD₅/COD	pH	味道
印染废水	鲜红色	1288±100	107.8±10	10.9±4	112.4±3.2	345.2±15	0.151	4.58±1	刺激性酸臭味

编码	因素	水平
编码	因素	-1	0	1
A	初始pH	2	4	6
B	铁投加量/g·L^-1	60	80	100
C	铁碳质量比	0.6	0.8	1
D	反应时间/min	60	90	120

序号	变量取值				COD去除率/%
序号	A	B	C	D	COD去除率/%
1	-1	1	0	0	69.83
2	0	1	0	1	62.27
3	0	0	0	0	75.28
4	0	0	-1	1	68.28
5	-1	0	-1	0	71.91
6	0	1	-1	0	69.70
7	1	0	-1	0	67.29
8	0	-1	0	-1	66.49
9	0	0	0	0	75.35
10	1	-1	0	0	67.60
11	1	0	1	0	69.97
12	0	0	-1	-1	70.20
13	1	1	0	0	66.51
14	0	0	1	-1	73.37
15	1	0	0	1	66.61
16	-1	0	1	0	71.05
17	-1	0	0	1	68.91
18	0	0	0	0	74.40
19	1	0	0	-1	68.65
20	0	-1	-1	0	66.25
21	0	1	0	-1	71.93
22	0	1	1	0	68.55
23	-1	-1	0	0	65.96
24	0	0	0	0	74.68
25	-1	0	0	-1	72.05
26	0	0	1	1	70.55
27	0	0	0	0	73.98
28	0	-1	0	1	65.96
29	0	-1	1	0	68.21

变差来源	平方和	自由度	均方和	F	P	显著性
模型	280.22	14	20.02	19.12	＜0.0001	极显著
A	14.26	1	14.26	13.62	0.0024	极显著
B	5.77	1	5.77	5.51	0.0341	显著
C	3.07	1	3.07	2.93	0.1088	不显著
D	27.33	1	27.33	26.11	0.0002	极显著
AB	6.15	1	6.15	5.88	0.0295	显著
AC	3.13	1	3.13	2.99	0.1056	不显著
AD	0.30	1	0.30	0.29	0.5993	不显著
BC	4.22	1	4.22	4.03	0.0643	不显著
BD	25.65	1	25.65	24.51	0.0002	极显著
CD	0.90	1	0.90	0.86	0.3689	不显著
A²	53.52	1	53.52	51.12	＜0.0001	极显著
B²	146.80	1	146.80	140.23	＜0.0001	极显著
C²	14.57	1	14.57	13.92	0.0022	极显著
D²	49.27	1	49.27	47.07	＜0.0001	极显著
残差	14.66	14	1.05
失拟项	13.30	10	1.33	3.91	0.1004	不显著
误差	1.36	4	0.34
合计	294.88	28
标准偏差（Std. Dev.）		1.02		相关系数（R-Squared）		0.9503
平均值（Mean）		69.82		校正决定系数（Adj R-Squared）		0.9006
变异系数（C.V. %）		1.47		预测相关系数（Pred R-Squared）		0.7331
压力系数（PRESS）		78.71		信噪比（Adeq Precision）		14.761

References 23

1	FATEMEH S F, CAVUS F. Zero valent nano-sized iron/clinoptilolite modified with zero valent copper for reductive nitrate removal[J]. Process Safety and Environmental Protection, 2013, 91(4): 304-310.
2	SAMARGHANDI M R, ZARRABI M, AMRANE A, et al. Kinetic of degradation of two azo dyes from aqueous solutions by zero iron powder: determination of the optimal conditions[J]. Desalination and Water Treatment, 2012, 40(1/2/3): 137-143.
3	ZHU X Y, CHEN X J, YANG Z M, et al. Investigating the influences of electrode material property on degradation behavior of organic wastewaters by iron-carbon micro-electrolysis[J]. Chemical Engineering Journal, 2018, 338(15): 46-54.
4	CHE J G, WAN J B, HUANG X P, et al. Pretreatment of piggery digestate wastewater by ferric-carbon micro-electrolysis under alkalescence condition[J]. Korean Journal of Chemical Engineering, 2017, 34(9): 1-9.
5	卢亮, 陈军昊, 王树荣. 模拟生物油分子蒸馏的响应面法工况优化[J]. 化工进展, 2018, 37(7): 2605-2612.
	LU L, CHEN J H, WANG S R. Condition optimization of simulated bio-oil molecular distillation via response surface method[J]. Chemical Industry and Engineering Progress, 2018, 37(7): 2605-2612.
6	李亿, 张红岩, 朱婧, 等. 响应面优化木糖母液发酵产丁二酸[J]. 化工进展, 2018, 37(1): 252-259.
	LI Y, ZHANG H Y, ZHU J, et al. Optimization of succinic acid fermentation from xylose mother liquor by response surface methodology[J]. Chemical Industry and Engineering Progress, 2018, 37(1): 252-259.
7	王哲, 张思思, 黄国和, 等. 高炉水淬渣对电镀废水中重金属和COD吸附的响应面优化[J]. 化工进展, 2016, 35(11): 3669-3676.
	WANG Z, ZHANG S S, HUANG G H, et al. Application of response surface methodology to optimize adsorption conditions for heavy metals and COD in electroplating waste water by water-quenched blast furnace slag[J]. Chemical Industry and Engineering Progress, 2016, 35(11): 3669-3676.
8	张东升, 余丽胜, 焦纬洲, 等. 基于响应面法的超声强化铁碳微电解处理硝基苯废水工艺优化研究[J]. 含能材料, 2018, 26(2): 178-184.
	ZHANG D S, YU L S, JIAO W Z, et al. Treatment of nitrobenzene wastewater via ultrasonic enhanced iron-carbon micro-electrolysis with response surface methodology[J]. Chinese Journal of Energetic Materials, 2018, 26(2): 178-184.
9	余若祯, 穆玉峰, 王海燕, 等. 排水综合评价中的生物毒性测试技术[J]. 环境科学研究, 2014, 27(4): 390-397.
	YU R Z, MU Y F, WANG H Y, et al. Review on the aquatic organisms toxicity test in the whole effluent assessment[J]. Research of Environmental Sciences, 2014, 27(4): 390-397.
10	戴迪楠. 基于生物毒性检测的污水水质安全评价及污水深度处理生物毒性削减特性的研究[D]. 西安: 西安建筑科技大学, 2017.
	DAI D N. A toxicity-based method for water safety assessment and biological toxicity reduction characteristics of deep treatment[D]. Xi’an: Xi’an University of Architecture and Technology, 2017.
11	何勤聪, 成国飞, 任源, 等. 精细化工废水有机成分分析及其生物降解性与毒性的拓扑研究[J]. 化工进展, 2009, 28(6): 1080-1085.
	HE Q C, CHENG G F, REN Y, et al. Organic compounds in fine chemical wastewater, their composition analysis and topological study in biodegradability and toxicity[J]. Chemical Industry and Engineering Progress, 2009, 28(6): 1080-1085.
12	王学江, 王虹, 赵建夫, 等. 基于大肠杆菌的CellSense生物传感器毒性分析性能研究[J]. 环境科学, 2009, 30(4): 1210-1214.
	WANG X J, WANG H, ZHAO J F, et al. Acute toxicity analysis performance of CellSense biosensor with E. coli[J]. Environmental Science, 2009, 30(4): 1210-1214.
13	钱俊, 李久铭, 只金芳, 等. 基于大肠杆菌的全细胞微生物传感器的构建及其在急性生物毒性检测中的应用[J]. 分析化学, 2013, 41(5): 738-743.
	QIAN J, LI J M, ZHI J F, et al. Development of a whole cells microbial biosensor based on E. coli and its application to acute biotoxicity determination[J]. Chinese Journal of Analytical Chemistry, 2013, 41(5): 738-743.
14	曹强, 签孝琳, 张澍, 等. 大气细颗粒物水溶成分和非水溶成分的细胞毒性[J]. 环境科学学报, 2008, 28(6): 1167-1172.
	CAO Q, QIAN X L, ZHANG S, et al. Cytotoxicity of soluble and insoluble components of atmospheric fine particles[J]. Acta Soientiae Circumstantiae, 2008, 28(6): 1167-1172.
15	MIRJANA B, VESNA M, NATAŠA S, et al. In vitro evaluation of neurotoxicity potential and oxidative stress responses of diazinon and its degradation products in rat brain synaptosomes[J]. Toxicology Letters, 2015, 233(1): 29-37.
16	胡立新. 基于傅里叶变换红外光谱的生物毒性测试方法及咪唑类离子液体毒性作用机制研究[D]. 北京: 中国科学院大学, 2018.
	HU L X. Preliminary investigation into biological toxicity based on FTIR and the toxic mechanism of imidazolium based ionic liquids[D]. Beijing: University of Chinese Academy of Sciences, 2018.
17	李媛媛. 反应型活性氧荧光探针的构建及其在环境水样和生物体中的应用[D]. 镇江: 江苏大学, 2017.
	LI Y Y. Construction of reaction-based fluorescent probes for reactive oxygen species of water samples and living organisms[D]. Zhenjiang: Jiangsu University, 2017.
18	张鑫, 王旗. 细胞中活性氧的荧光探针检测法研究进展[J]. 现代预防医学, 2010, 37(22): 4316-4318.
	ZHANG X, WANG Q. Progress in measurement of reactive oxygen species in cells by using fluorescent probes[J]. Modern Preventive Medicine, 2010, 37(22): 4316-4318.
19	CABISCOL E, TAMARIT J, ROS J. Oxidative stress in bacteria and protein damage by reactive oxygen species[J]. International Microbiology, 2000, 3(1): 3-8.
20	石柳. 焦化废水及氧化石墨烯对水生生物的毒性研究[D]. 大连: 大连理工大学, 2017.
	SHI L. Toxicity of coking wastewater and graphene oxide on aquatic organisms[D]. Dalian: Dalian University of Technology, 2017.
21	王学, 李勇超, 李铁龙, 等. 零价纳米铁对大肠杆菌的毒性效应[J]. 生态毒理学报, 2012, 7(1): 49-56.
	WANG X, LI Y C, LI T L, et al. Toxicity effects of nano-Fe⁰ on Escherichia coli[J]. Asian Journal of Ecotoxicology, 2012, 7(1): 49-56.
22	KEENAN C R, SEDLAK D L. Factors affecting the yield of oxidants from the reaction of nanoparticulate zero-valent iron and oxygen[J]. Environmental Science and Technology, 2008, 42(4): 1262-1267.
23	王静, 王晓燕, 水中和, 等. 玻璃载银抗菌材料的Ag⁺溶出性质及与大肠杆菌作用机理[J]. 材料导报, 2018, 32(16): 2709-2714, 2727.
	WANG J, WANG X Y, SHUI Z H, et al. Properties of silver ions release and antibacterial mechanism against E. coli of Ag-doped glass antimicrobial material[J]. Materials Reports, 2018, 32(16): 2709-2714, 2727.

[1]	XU Chenyang, DU Jian, ZHANG Lei. Chemical reaction evaluation based on graph network [J]. Chemical Industry and Engineering Progress, 2023, 42(S1): 205-212.
[2]	XU Chunshu, YAO Qingda, LIANG Yongxian, ZHOU Hualong. Research progress on functionalization strategies of covalent organic frame materials and its adsorption properties for Hg(Ⅱ) and Cr(Ⅵ) [J]. Chemical Industry and Engineering Progress, 2023, 42(S1): 461-478.
[3]	SUN Yuyu, CAI Xinlei, TANG Jihai, HUANG Jingjing, HUANG Yiping, LIU Jie. Optimization and energy-saving of a reactive distillation process for the synthesis of methyl methacrylate [J]. Chemical Industry and Engineering Progress, 2023, 42(S1): 56-63.
[4]	WANG Chen, BAI Haoliang, KANG Xue. Performance study of high power UV-LED heat dissipation and nano-TiO₂ photocatalytic acid red 26 coupling system [J]. Chemical Industry and Engineering Progress, 2023, 42(9): 4905-4916.
[5]	ZHANG Fan, TAO Shaohui, CHEN Yushi, XIANG Shuguang. Initializing distillation column simulation based on the improved constant heat transport model [J]. Chemical Industry and Engineering Progress, 2023, 42(9): 4550-4558.
[6]	LIU Xuanlin, WANG Yikai, DAI Suzhou, YIN Yonggao. Analysis and optimization of decomposition reactor based on ammonium carbamate in heat pump [J]. Chemical Industry and Engineering Progress, 2023, 42(9): 4522-4530.
[7]	ZHANG Zhen, LI Dan, CHEN Chen, WU Jinglan, YING Hanjie, QIAO Hao. Separation and purification of salivary acids with adsorption resin [J]. Chemical Industry and Engineering Progress, 2023, 42(8): 4153-4158.
[8]	WU Zhenghao, ZHOU Tianhang, LAN Xingying, XU Chunming. AI-driven innovative design of chemicals in practice and perspective [J]. Chemical Industry and Engineering Progress, 2023, 42(8): 3910-3916.
[9]	ZHANG Zhichen, ZHU Yunfeng, CHENG Weishu, MA Shoutao, JIANG Jie, SUN Bing, ZHOU Zichen, XU Wei. Research advances on runaway decomposition of high pressure polyethylene: Reaction mechanism, initiation system and model [J]. Chemical Industry and Engineering Progress, 2023, 42(8): 3979-3989.
[10]	LI Lanyu, HUANG Xinye, WANG Xiaonan, QIU Tong. Reflection and prospects on the intelligent transformation of chemical engineering research [J]. Chemical Industry and Engineering Progress, 2023, 42(7): 3325-3330.
[11]	XUE Kai, WANG Shuai, MA Jinpeng, HU Xiaoyang, CHONG Daotong, WANG Jinshi, YAN Junjie. Planning and dispatch of distributed integrated energy systems for industrial parks [J]. Chemical Industry and Engineering Progress, 2023, 42(7): 3510-3519.
[12]	ZHAO Yi, YANG Zhen, ZHANG Xinwei, WANG Gang, YANG Xuan. Molecular simulation of self-healing behavior of asphalt under different crack damage and healing temperature [J]. Chemical Industry and Engineering Progress, 2023, 42(6): 3147-3156.
[13]	LU Shijian, ZHANG Yuanyuan, WU Wenhua, YANG Fei, LIU Ling, KANG Guojun, LI Qingfang, CHEN Hongfu, WANG Ning, WANG Feng, ZHANG Juanjuan. Health risk assessment of nitrosamine pollutant diffusion in a million ton CO₂ capture project [J]. Chemical Industry and Engineering Progress, 2023, 42(6): 3209-3216.
[14]	YANG Hongmei, GAO Tao, YU Tao, QU Chengtun, GAO Jiapeng. Treatment of refractory organics sulfonated phenolic resin with ferrate [J]. Chemical Industry and Engineering Progress, 2023, 42(6): 3302-3308.
[15]	GU Shiya, DONG Yachao, LIU Linlin, ZHANG Lei, ZHUANG Yu, DU Jian. Design and optimization of pipeline system for carbon capture considering intermediate nodes [J]. Chemical Industry and Engineering Progress, 2023, 42(6): 2799-2808.