Process parameters optimization for degradation of benzene by non-thermal plasma

doi:10.16085/j.issn.1000-6613.2019-0686

Abstract

Abstract:

Non-thermal plasma technology was used to purify benzene with benzene removal efficiency as the evaluation indicator. According to the single factor experiments, the range of specific energy density, initial concentration and oxygen content was determined. The influence of interaction and interaction on the removal efficiency of benzene was investigated by Design-Expert response surface method. The composition of by-products was analyzed by Fourier transform-infrared spectroscopy(FTIR), gas chromatography/mass spectrometry (GC-MS) and scanning electron microscopy(SEM). According to the simulation of the quadratic polynomial model, the individual variables, the interaction of the specific energy density and the oxygen content had a significant effects on the removal efficiency of benzene. The results showed that the optimal parameters were determined as the specific energy density of 5.98kJ/L, initial concentration of 452.08mg/m³ and the oxygen volume fraction of 1.66%. The removal efficiency model-predicted was 96.63%, the average value measured was 95.23% and the relative error between the two results was 1.40%, which indicated that the quadratic polynomial model was reliable. The solid phase by-products mainly contained long-chain alkanes, long-chain olefins, phenols, esters, ketones and amides. The overall morphology was cluster-like with obvious spherical morphology. The undegraded benzene, ethylene oxide, benzonitrile, 4-cyanopyridine were detected in the liquid phase product. In addition to the CO₂ produced by mineralization and undegraded benzene, the gas phase product also had benzonitrile and ester.

Key words: benzene, pollution, non-thermal plasma, oxidation, removal efficiency, response surface methodology, parameter optimization, by-product

摘要：

采用低温等离子体技术净化苯，以降解后的苯去除效率为评价指标。根据单因素实验，确定能量密度、初始浓度、氧气含量的取值范围；采用Design-Expert响应曲面法，考察单独变量作用及交互作用对苯去除率的影响；通过傅里叶变换红外光谱仪（FIIR）、气相色谱-质谱联用仪（GC-MS）及扫描电子显微镜（SEM）对反应产物组成进行分析。根据二次多项式模型模拟可知，单因素变量、能量密度和氧气含量的交互项均对苯去除率具有显著影响；优化结果显示，低温等离子体降解苯的最佳工艺条件为能量密度5.98kJ/L，初始浓度452.08mg/m³，氧气体积分数1.66%，模型预测苯去除效率为96.63%，实验验证平均值为95.23%，测定值与预测值之间相对误差为1.40%，证明该模型具有可靠性。固相副产物中主要含长链烷烃、长链烯烃、酚类、酯类、酮类、酰胺类，整体形貌属于团簇状，拥有较明显的球形形貌；液相产物中检测到未降解的苯、环氧乙烷、苯腈、4-氰基吡啶；气相产物除了含有矿化生成的CO₂和未降解的苯之外，还含有苯腈和酯。

关键词: 苯, 污染, 低温等离子体, 氧化, 去除效率, 响应曲面法, 参数优化, 副产物

CLC Number:

X964

Chunyu WANG,Ling ZHU,Danyun XU,Qingyue LUO. Process parameters optimization for degradation of benzene by non-thermal plasma[J]. Chemical Industry and Engineering Progress, 2020, 39(1): 402-412.

王春雨,朱玲,许丹芸,罗清月. 低温等离子体降解苯的工艺参数优化[J]. 化工进展, 2020, 39(1): 402-412.

Figures/Tables 21

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项目	影响因素	水平
项目	影响因素	-1	0	1
能量密度（X₁）	能量密度/kJ?L^-1	3	4.5	6
初始浓度（X₂）	初始浓度/mg?m^-3	320	480	640
氧气体积分数（X₃）	氧气体积分数/%	0	2	4

项目	影响因素	水平
项目	影响因素	-1	0	1
能量密度（X₁）	能量密度/kJ?L^-1	3	4.5	6
初始浓度（X₂）	初始浓度/mg?m^-3	320	480	640
氧气体积分数（X₃）	氧气体积分数/%	0	2	4

序号	编码			去除率/%
序号	X₁	X₂	X₃	实验值	预测值
1	-1	-1	0	86.65	85.69
2	1	-1	0	96.00	95.74
3	-1	1	0	82.30	82.56
4	1	1	0	86.35	87.31
5	-1	0	-1	69.89	70.98
6	1	0	-1	83.95	84.34
7	-1	0	1	82.23	81.85
8	1	0	1	84.39	83.30
9	0	-1	-1	71.32	71.20
10	0	1	-1	68.39	67.04
11	0	-1	1	76.38	77.73
12	0	1	1	70.21	70.33
13	0	0	0	90.37	89.23
14	0	0	0	90.25	89.23
15	0	0	0	89.88	89.23
16	0	0	0	88.31	89.23
17	0	0	0	87.36	89.23

序号	编码			去除率/%
序号	X₁	X₂	X₃	实验值	预测值
1	-1	-1	0	86.65	85.69
2	1	-1	0	96.00	95.74
3	-1	1	0	82.30	82.56
4	1	1	0	86.35	87.31
5	-1	0	-1	69.89	70.98
6	1	0	-1	83.95	84.34
7	-1	0	1	82.23	81.85
8	1	0	1	84.39	83.30
9	0	-1	-1	71.32	71.20
10	0	1	-1	68.39	67.04
11	0	-1	1	76.38	77.73
12	0	1	1	70.21	70.33
13	0	0	0	90.37	89.23
14	0	0	0	90.25	89.23
15	0	0	0	89.88	89.23
16	0	0	0	88.31	89.23
17	0	0	0	87.36	89.23

方差来源	平方和	自由度	均方	F	P	显著性
模型	1109.89	9	123.32	55.87	<0.0001	显著
X₁	109.67	1	109.67	49.68	0.0002	显著
X₂	66.70	1	66.70	30.22	0.0009	显著
X₃	48.31	1	48.31	21.89	0.0023	显著
X₁X₂	7.02	1	7.02	3.18	0.1177	不显著
X₁X₃	35.40	1	35.40	16.04	0.0052	显著
X₂X₃	2.62	1	2.62	1.19	0.3117	不显著
X₁²	53.53	1	53.53	24.25	0.0017	显著
X₂²	104.19	1	104.19	47.20	0.0002	显著
X₃²	677.46	1	677.46	306.89	<0.0001	显著
残差	15.45	7	2.21	—	—	—
失拟项	8.35	3	2.78	1.57	0.3292	不显著
误差	7.11	4	1.78	—	—	—
总和	1125.34	16	—	—	—	—
R²值	0.9863	—	—	—	—	—
R²_Adj值	0.9686	—	—	—	—	—
R²_Pred值	0.8751	—	—	—	—	—