Preparation of LDHs-CRMA composite modified asphalt by response surface methodology and its characterization

doi:10.11949/0438-1157.20190856

Abstract

Abstract:

Mg-Al layer double hydroxides (Mg-Al LDHs) modified asphalt was prepared by melt blending with Karamay asphalt and waste crumb rubber (CR), which has UV aging resistance. The physical properties including softening point, penetration index (PI), ductility were tested, and the three indicators were “normalized”into overall desirability by Hassan s mathematical method. The Box-Behnken mathematical relation model between overall desirability and affecting factors was established, and the technology of preparing composing LDHs/CRMA was optimized. The optimum preparation conditions were: shear temperature 173℃, shear time 89 min, and shear rate 3500 r/min. The aging resistance of composite modified asphalt was evaluated by UV aging simulation test. The FT-IR and SEM revealed that the introduction of LDHs can reduce the production of oxygen-containing functional groups during asphalt aging, and the anti-UV aging performance was improved.

Key words: preparation, modified asphalt, mathematical modeling, response surface methodology, optimization, anti-UV aging

摘要：

采用熔融共混法，以克拉玛依90^#沥青为原料，Mg-Al水滑石(LDHs)与废橡胶粉(CR)作为改性剂，制备了具有抗紫外老化性能的水滑石/废胶粉复合改性沥青(LDHs/CRMA)，并对其软化点、针入度指数(PI)、延度进行了测试。通过Hassan数学方法将三个指标“归一化”得到总评“归一值”，采用响应面分析法建立总评“归一值”与各因素之间的Box-Behnken数学模型，得到了LDHs/CRMA的最优制备工艺条件为：剪切温度173℃、剪切时间89 min、剪切速率3500 r/min。通过紫外老化模拟实验对复合改性沥青的抗老化性能进行了评价，结果表明，引入水滑石可以减少沥青老化过程中含氧官能团的产生，抗紫外老化性能得到了明显提高。

关键词: 制备, 改性沥青, 数学模拟, 响应面法, 优化, 抗紫外老化

CLC Number:

TU 535
U 414

Yuying LU, Liming YU, Jiake YANG, Wusong ZENG, Jiangyin LU. Preparation of LDHs-CRMA composite modified asphalt by response surface methodology and its characterization[J]. CIESC Journal, 2020, 71(3): 1362-1369.

鲁玉莹, 余黎明, 杨加可, 曾武松, 陆江银. 响应面法优化制备LDHs-CRMA复合改性沥青及其表征[J]. 化工学报, 2020, 71(3): 1362-1369.

Figures/Tables 11

References 36

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Factor	Symbol	Level
Factor	Symbol	-1	0	1
shear temperature/℃	A	165	175	185
shear time /min	B	60	90	120
shear rate/(r/min)	C	3500	5000	6500

Factor	Symbol	Level
Factor	Symbol	-1	0	1
shear temperature/℃	A	165	175	185
shear time /min	B	60	90	120
shear rate/(r/min)	C	3500	5000	6500

实验号	A	B	C	针入度(100 g，5 s)/0.1 mm			PI	软化点/℃	5℃延度/mm	OD
实验号	A	B	C	5℃	15℃	25℃	PI	软化点/℃	5℃延度/mm	OD
1	185	60	5000	6.1	22.1	62.2	-1.479	47.1	231	0
2	175	90	5000	11.0	27.7	69.2	0.008	50.0	221	0.4924
3	165	90	6500	11.1	18.4	45.8	1.816	49.1	212	0.5509
4	175	120	3500	12.6	22.9	46.8	2.461	49.8	221	0.6463
5	175	90	5000	10.9	26.0	63.6	0.292	50.2	222	0.5352
6	185	90	6500	11.6	27.2	73.8	-0.030	48.1	240	0.3611
7	175	90	5000	11.4	23.4	67.0	0.263	49.9	210	0.4951
8 9	165 185	120 120	5000 5000	13.9 8.20	25.0 16.6	73.3 41.8	0.695 0.826	49.4 53.4	285 119	0.6033 0.2959
10	185	90	3500	6.80	20.7	54.8	-0.813	50.0	208	0.3611
11	175	60	3500	11.3	22.4	60.3	0.646	51.4	261	0.7018
12	175	120	6500	9.20	17.0	48.2	0.722	52.6	112	0
13	175	90	5000	13.2	18.8	69.3	0.712	49.1	220	0.4935
14	165	90	3500	12.2	21.0	71.8	0.258	50.9	259	0.6269
15	165	60	5000	7.30	31.1	71.3	-1.365	50.3	243	0.2298
16	175	60	6500	7.80	29.1	70.2	-1.139	49.0	223	0.2633
17	175	90	5000	12.6	20.7	73.6	0.286	50.4	210	0.5253

实验号	A	B	C	针入度(100 g，5 s)/0.1 mm			PI	软化点/℃	5℃延度/mm	OD
实验号	A	B	C	5℃	15℃	25℃	PI	软化点/℃	5℃延度/mm	OD
1	185	60	5000	6.1	22.1	62.2	-1.479	47.1	231	0
2	175	90	5000	11.0	27.7	69.2	0.008	50.0	221	0.4924
3	165	90	6500	11.1	18.4	45.8	1.816	49.1	212	0.5509
4	175	120	3500	12.6	22.9	46.8	2.461	49.8	221	0.6463
5	175	90	5000	10.9	26.0	63.6	0.292	50.2	222	0.5352
6	185	90	6500	11.6	27.2	73.8	-0.030	48.1	240	0.3611
7	175	90	5000	11.4	23.4	67.0	0.263	49.9	210	0.4951
8 9	165 185	120 120	5000 5000	13.9 8.20	25.0 16.6	73.3 41.8	0.695 0.826	49.4 53.4	285 119	0.6033 0.2959
10	185	90	3500	6.80	20.7	54.8	-0.813	50.0	208	0.3611
11	175	60	3500	11.3	22.4	60.3	0.646	51.4	261	0.7018
12	175	120	6500	9.20	17.0	48.2	0.722	52.6	112	0
13	175	90	5000	13.2	18.8	69.3	0.712	49.1	220	0.4935
14	165	90	3500	12.2	21.0	71.8	0.258	50.9	259	0.6269
15	165	60	5000	7.30	31.1	71.3	-1.365	50.3	243	0.2298
16	175	60	6500	7.80	29.1	70.2	-1.139	49.0	223	0.2633
17	175	90	5000	12.6	20.7	73.6	0.286	50.4	210	0.5253

Source	Sum of squares	df	Mean square	F	P	Significance
model	0.70	11	0.063	126.93	<0.0001	significant
A	0.12	1	0.12	246.37	<0.0001
B	0.025	1	0.025	50.78	0.0008
C	0.29	1	0.29	588.39	<0.0001
AB	1.506×10^-3	1	1.506×10^-3	3.01	0.1432
AC	1.440×10^-3	1	1.440×10^-3	2.88	0.1504
BC	0.011	1	0.011	21.61	0.0056
A²	0.025	1	0.025	49.89	0.0009
B²	0.094	1	0.094	187.31	<0.0001
C²	8.012×10^-3	1	8.012×10^-3	16.02	0.0103
A²B	0.12	1	0.12	244.11	<0.0001
A²C	0.13	1	0.13	254.46	<0.0001
residual	2.500×10^-3	5	5.000×10^-4
lack of fit	8.315×10^-4	1	8.315×10^-4	1.99	0.2308	not significant
pure error	1.669×10^-3	4	4.171×10^-4
cor total R²=0.9964	0.70 CV=5.29%	16