Optimization of preparation of cassava alcohol sludge-based activated carbon by response surface methodology and its adsorption properties for gallic acid

doi:10.12034/j.issn.1009-606X.220204

Abstract

Abstract:

The dewatered sludge from the production process of cassava alcohol plant was used as raw material. Response surface methodology was applied to optimize the preparation of cassava alcohol sludge activated carbon. At the same time, a series of characterization analysis was carried out for the optimal product, which was applied to the treatment of gallic acid wastewater. The results showed that the optimal preparation conditions were: activation temperature of 489℃, impregnation time of 14 h, activation time of 51 min, zinc chloride concentration of 21.53%, the adsorption iodine value under this condition was 521.64 mg/g. The results of characterization to CASAC suggested that the BET surface was found as 441.86 m²/g and the average pore diameter as 2.50 nm. It was full of different sizes of small pores on the carbon surface. The activated carbon owned a low metal content but more oxygen-containing functional group after activation process. The effects of carbon dosage, pH, contact time and solution temperature on the removal of gallic acid from water were investigated. It is suggested that the sample carbon could remove gallic acid efficiently, and the removal rate of gallic acid increased with the increase of carbon dosage and the decrease of pH value. The adsorption of gallic acid by cassava alcohol sludgy-based activated carbon was in line with pseudo second-order kinetics model as well as Freundlich isothermal model. The maximum adsorption capacity was 126.72 mg/g. The diffusion mechanism showed that the adsorption process was influenced by the diffusion of liquid film in addition to the diffusion within particles. Thermodynamic analysis indicated that the adsorption of gallic acid was a spontaneous process of heat absorption and entropy increase. This study provided a theoretical basis for the preparation of high-performance activated sludge and the application of high concentration of natural organic wastewater treatment.

Key words: cassava alcohol sludge, activated carbon, response surface methodology, gallic acid, adsorption

摘要：

以木薯酒精厂生产过程中产生的脱水污泥为原料，采用响应曲面法 Box-Behnken模型优化了木薯酒精污泥基活性炭的制备工艺，同时对最优成品进行一系列表征分析，并将其应用于没食子酸废水的处理研究。活性炭的最优制备条件为活化温度489℃，浸渍时间14 h，活化时间51 min，氯化锌浓度21.53%，该条件下样品的碘吸附值达521.64 mg/g。表征分析显示其表面布有众多孔壁较薄、大小不一的孔洞，金属含量较小，BET比表面积达441.86 m²/g，平均孔径为2.50 nm，拥有丰富的微孔结构，表面富有较多的含氧官能团。考察了活性炭投加量、pH、接触时间、溶液温度对样品去除水中没食子酸的影响。结果表明，样品能够高效去除没食子酸，且随着投加量的增加和pH值降低，没食子酸的去除率呈增长趋势。木薯酒精污泥基活性炭对没食子酸的吸附符合pseudo second-order动力学模型和Freundlich等温模型，最大吸附量为126.72 mg/g。扩散机理显示除颗粒内扩散外也包含液膜扩散过程。热力学分析表明该吸附反应是自发进行的吸热且熵增的过程。本研究将为制备高性能污泥活性炭并应用于高浓度天然有机物废水处理提供理论基础。

关键词: 木薯酒精污泥, 活性炭, 响应曲面法, 没食子酸, 吸附

CLC Number:

X703.1

Zhilin ZHANG, Lei DING, Qiang ZHOU, Jian YU, Changjin GUO, Dewei ZHANG. Optimization of preparation of cassava alcohol sludge-based activated carbon by response surface methodology and its adsorption properties for gallic acid[J]. The Chinese Journal of Process Engineering, 2021, 21(7): 794-806.

张智霖, 丁磊, 周强, 余剑, 郭昌进, 张德伟. 响应曲面法优化木薯酒精污泥基活性炭制备及对没食子酸的吸附性能[J]. 过程工程学报, 2021, 21(7): 794-806.

Figures/Tables 18

Table 1 Independent variable levels and codified values for the Box-Behnken experimental design

Factor	Code	Level
Factor	Code	-1	0	1
Activation temperature/℃	A	400	500	600
Impregnation time/h	B	6	12	18
Activation time/min	C	30	60	90
Zinc chloride concentration/%	D	10	20	30

Table 2 Experimental Box-Behnken design matrix and measured and predicted results

Run	Activation temperature/℃, A	Impregnation time/min, B	Activation time/min, C	Zinc chloride concentration/%, D	Iodine values, R/(mg/g)
Run	Activation temperature/℃, A	Impregnation time/min, B	Activation time/min, C	Zinc chloride concentration/%, D	Experiment	Predict
1	500	12	90	10	490.21	490.45
2	500	6	60	30	503.17	503.89
3	500	12	60	20	525.24	521.49
4	500	6	90	20	506.38	503.06
5	500	12	90	30	447.16	456.53
6	400	18	60	20	493.57	490.82
7	400	12	30	20	454.21	465.91
8	500	18	90	20	472.93	478.86
9	500	18	30	20	515.08	516.46
10	400	12	60	10	470.93	468.77
11	500	12	60	20	515.63	521.49
12	500	18	60	30	502.18	493.55
13	500	6	60	10	480.93	491.19
14	600	18	60	20	458.98	462.14
15	400	6	60	20	492.63	489.8
16	500	6	30	20	502.60	494.74
17	500	12	30	30	508.62	508.69
18	600	12	60	30	445.74	445.95
19	400	12	90	20	484.65	482.49
20	500	18	60	10	498.15	499.05
21	600	12	90	20	434.93	424.85
22	600	12	30	20	466.93	470.71
23	600	12	60	10	447.05	446.89
24	500	12	30	10	476.64	467.57
25	400	12	60	30	478.70	476.91
26	500	12	60	20	518.76	521.49
27	600	6	60	20	462.58	465.64
28	500	12	60	20	527.47	521.49
29	500	12	60	20	520.36	521.49

Table 3 Standard deviation and R2 for the regression model equation

Std. Dev.	Mean	C.V./%	PRESS	R²	Adj R²	Pred R²	Adeq precision
7.81	486.29	1.61	4529.62	0.96	0.91	0.77	17.20

Table 4 ANOVA statistics for the fitted model and parameters in the model

Source	Sum of squares	Degree of freedom	Mean square	F	Prob>F
Model	18880.62	14	1348.62	22.11	<0.0001
A	2093.10	1	2093.10	34.31	<0.0001
B	4.56	1	4.56	0.075	0.7885
C	642.89	1	642.89	10.54	0.0059
D	39.05	1	39.05	0.64	0.4370
AB	5.15	1	5.15	0.084	0.7757
AC	974.59	1	974.59	15.98	0.0013
AD	20.59	1	20.59	0.34	0.5705
BC	527.28	1	527.28	8.64	0.0108
BD	82.93	1	82.93	1.36	0.2631
CD	1407.71	1	1407.71	23.08	0.0003
A²	10819.05	1	10819.05	177.34	< 0.0001
B²	81.88	1	81.88	1.34	0.2660
C²	2506.94	1	2506.94	41.09	< 0.0001
D²	2865.11	1	2865.11	46.96	< 0.0001
Residual	854.08	14	61.01
Lack of fit	761.20	10	76.12	3.28	0.1319
Pure error	92.89	4	23.22
Cor total	19734.70	28

Fig.1 (a) Normal plot of residuals and (b) residuals versus the predicted values

Fig.2 (a) Pareto chart of percentage effect of each term and (b) percentage of contribution

Fig.3 3D plot and contour plots for adsorption capacity of iodine of CASAC with respect to the factors

Fig.4 SEM-EDS micrographs of (a) CAS and (b) CASAC

Fig.5 (a) Nitrogen adsorption-desorption isotherms and (b) pore size distribution of CASAC

Table 5 Structural information of CASAC

Sample

S_BET/(m²/g)

S_Micro/(m²/g)

V_Total/

(cm³/g)

Fig.6 FT-IR spectra of CAS and CASAC

Fig.7 (a) Effect of dosage of CASAC and (b) initial pH value on removal rate

Fig.8 (a) The fitted curves of adsorption kinetics of GA on CASAC and (b) intra-particle diffusion models for GA on CASAC

Table 6 Kinetic parameter of GA adsorption on CASAC

Sample	Pseudo first-order model		Pseudo second-order model		Elovich model
Sample	k₁/min^-1	R²	k₂/[g/(mg·min)]	R²	$α$ /[mg/(g·min)]	$β /$ (g/mg)	R²
CASAC	0.127	0.777	0.005	0.966	3243.449	0.277	0.867

Table 6 Kinetic parameter of GA adsorption on CASAC

Sample	Pseudo first-order model		Pseudo second-order model		Elovich model
Sample	k₁/min^-1	R²	k₂/[g/(mg·min)]	R²	$α$ /[mg/(g·min)]	$β /$ (g/mg)	R²
CASAC	0.127	0.777	0.005	0.966	3243.449	0.277	0.867

Table 7 Parameters of intra-particle diffusion model for GA adsorption on CASAC

Sample	Intra-particle diffusion
Sample	$k p 1 /$ [mg/(g·min^1/2)]	$C 1$	R²	$k p 2$ /[mg/(g·min^1/2)]	$C 2$	R²	$k p 3$ /[mg/(g·min^1/2)]	$C 3$	R²
CASAC	3.825	17.973	0.997	0.490	38.856	0.998	0.057	45.171	0.988

Table 7 Parameters of intra-particle diffusion model for GA adsorption on CASAC

Sample	Intra-particle diffusion
Sample	$k p 1 /$ [mg/(g·min^1/2)]	$C 1$	R²	$k p 2$ /[mg/(g·min^1/2)]	$C 2$	R²	$k p 3$ /[mg/(g·min^1/2)]	$C 3$	R²
CASAC	3.825	17.973	0.997	0.490	38.856	0.998	0.057	45.171	0.988

Fig.9 Adsorption isotherms of GA adsorption by CASAS

Table 8 Parameters of the adsorption isotherm model for GA on CASAC

Sample	Temperature/K	Langmuir model			Freundlich model
Sample	Temperature/K	K₁	q_m/(mg/g)	R²	K₂	1/n	R²
CASAC	298	0.207	107.819	0.955	29.895	0.251	0.967
	308	0.202	115.487	0.961	31.164	0.259	0.967
	318	0.242	117.591	0.961	34.192	0.247	0.965

Table 9 Adsorption thermodynamic parameters

Sample	T/K	ΔG/(kJ/mol)	ΔH/(kJ/mol)	ΔS/[J/(mol·K)]
CASAC	298	-10.37	9.78	67.62
	308	-11.05
	318	-11.73

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