复合改良剂对石墨尾矿物理性状的影响

易富; 杨磊; 杜常博; 程传旺

doi:10.12153/j.issn.1674-991X.20230898

复合改良剂对石墨尾矿物理性状的影响

doi: 10.12153/j.issn.1674-991X.20230898

易富^{1, 2,},
杨磊^1, ,,
杜常博¹,
程传旺¹

1.
辽宁工程技术大学土木工程学院
2.
北京京能地质工程有限公司

基金项目: 黑龙江省揭榜挂帅项目（2023ZXJ05A02）

详细信息

作者简介:
易富（1978—），男，教授，主要研究方向为环境岩土与道路工程，yifu9716@163.com

通讯作者:
杨磊（1999—），男，硕士研究生，主要研究方向为环境岩土，17356538577@163.com

中图分类号: X53
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- 文章访问数: 129
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- 被引次数: 0
出版历程
- 收稿日期: 2023-12-18
- 录用日期: 2024-03-01
- 修回日期: 2024-01-07

Effect of composite modifier on the physical properties of graphite tailings

YI Fu^{1, 2
,},
YANG Lei^{1
, ,},
DU Changbo¹,
CHENG Chuanwang¹

1.
College of Civil Engineering, Liaoning Technical University
2.
Beijing Jingneng Geological Engineering Co., Ltd.

摘要

摘要:
为了解决石墨尾矿复垦过程中物理性状较差的问题，研制出一种能够改善其透气性、保水性及结构性的复合改良剂。首先，通过室内试验分别探究玉米秸秆、牛粪和风化煤3种材料对石墨尾矿物理性状的影响以及施用量范围；其次，采用响应面法（RSM）设计试验，得到复合改良剂（玉米秸秆+牛粪+风化煤）的最优配比；最后，分别从透气性、保水性和结构性上分析改良后尾矿的物理性能，并利用扫描电镜（SEM）技术探讨复合改良剂对其微观结构的影响及作用。结果表明：施用单一改良材料时，玉米秸秆对石墨尾矿透气性影响效果最为明显，牛粪对其保水性提升最为显著，风化煤对其大团聚体含量的影响效果最强。复合改良剂的最优配比为玉米秸秆4.10%、牛粪10.49%、风化煤2.94%，此时石墨尾矿的容重、田间持水量和大团聚体含量分别为1.1 g/cm³、61.71%和84.51%；对比改良前石墨尾矿总孔隙率和水稳性大团聚体含量分别提升了17.42%和14.48%，持水能力也得到明显改善；SEM分析显示改良后石墨尾矿表面出现明显胶结物，颗粒之间由角-面接触和边-面接触转变为直接点接触，大量颗粒凝聚在一起形成团聚结构，从微观尺度验证了复合改良剂对石墨尾矿物理性状的影响。该复合改良剂对石墨尾矿物理性状改善作用显著，对于实现石墨尾矿复垦利用具有可行性。
- 石墨尾矿 /
- 尾矿复垦 /
- 固废利用 /
- 复合改良 /
- 微观结构
Abstract:
In order to solve the problem of poor physical properties of graphite tailings in the process of reclamation, a composite modifier which can improve its air permeability, water retention and structure was developed. Firstly, the effects of corn straw, cow dung and weathered coal on the physical properties of graphite tailings and the range of application amount were investigated through laboratory tests. Secondly, the response surface method (RSM) was used to design the experiment, and the optimal ratio of the composite modifier (corn straw+cow dung+weathered coal) was obtained. Finally, the physical properties of the modified tailings were analyzed in terms of air permeability, water retention and structure, and the effects of the composite modifier on the microstructure were discussed by scanning electron microscopy (SEM).The results showed that when a single modified material was applied, corn straw had the most obvious effect on the permeability of graphite tailings, cow dung had the most significant effect on its water retention, and weathered coal had the strongest effect on its large aggregate content. The optimum ratio of the compound improvers was 4.10% corn straw, 10.49% cow dung and 2.94% weathered coal. At this time, the bulk density, maximum water holding capacity and large aggregate content of graphite tailings were 1.1 g/cm³, 61.71% and 84.51%, respectively. Compared with the graphite tailings before improvement, the total porosity and water-stable macroaggregate content of graphite tailings increased by 17.42% and 14.48%, respectively, and the water holding capacity was also significantly improved. SEM analysis showed that there were obvious cements on the surface of graphite tailings after improvement, and the particles changed from angle-surface contact and edge-surface contact to direct point contact. A large number of particles agglomerated together to form an agglomerated structure. The effect of composite modifier on the physical properties of graphite tailings was verified from the microscopic scale. The results show that the composite modifier has a significant effect on improving the physical properties of graphite tailings, and it is feasible to realize the soil utilization of graphite tailings.
- graphite tailings /
- tailings reclamation /
- solid waste utilization /
- compound modifier /
- microstructure

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图 1 石墨尾矿XRD图谱

Figure 1. XRD pattern of graphite tailings

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图 2 石墨尾矿粒径分布曲线

Figure 2. Particle size distribution curve of graphite tailings

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图 3 改良剂对石墨尾矿容重的影响

Figure 3. Effect of modifier on bulk density of graphite tailings

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图 4 改良剂对石墨尾矿田间持水量影响

Figure 4. Effect of improver on field water holding capacity of graphite tailings

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图 5 改良剂对石墨尾矿大团聚体含量影响

Figure 5. Effect of modifiers on the content of large aggregates of graphite tailings

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图 6 实测值与预测值对比分析

Figure 6. Comparison of measured value and predicted value

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图 7 石墨尾矿容重三维响应曲面图

Figure 7. Three-dimensional response surface diagram of graphite tailings bulk density

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图 8 石墨尾矿田间持水量三维响应曲面图

Figure 8. Three-dimensional response surface diagram of maximum water holding capacity of graphite tailings

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图 9 石墨尾矿大团聚体含量三维响应曲面图

Figure 9. Three-dimensional response surface diagram of large aggregate content in graphite tailings

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图 10 石墨尾矿总孔隙率对比

Figure 10. Comparison diagram of total porosity of graphite tailings

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图 11 石墨尾矿持水能力对比

Figure 11. Comparison of water holding capacity of graphite tailings

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图 12 石墨尾矿水稳性大团聚体含量对比

Figure 12. Graphite tailings water-stable macro-aggregates comparison diagram

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图 13 石墨尾矿改良前微观结构

Figure 13. Microstructure of graphite tailings before improvement

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图 14 石墨尾矿改良后微观结构

Figure 14. Microstructure diagram of graphite tailings after improvement

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图 15 复合改良剂作用机制

Figure 15. Mechanism of action of compound modifier

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表 1 石墨尾矿基本理化性质

Table 1. Basic physical and chemical properties of graphite tailings

pH	有机质含量/%	总氮含量/%	总磷含量/%	速效氮含量/(mg/kg)	速效磷含量/(mg/kg)	速效钾含量/(mg/kg)
8.21	1.05	0.314	0.042	376.20	4.14	56.50

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表 2 试验材料基本理化性质

Table 2. Basic physical and chemical properties of test materials

材料	pH	C/N	有机碳含量/(g/kg)	全氮含量/(g/kg)	全磷含量/(g/kg)	全钾含量/(g/kg)
玉米秸秆	7.06	64	486.14	0.670	0.141	1.482
牛粪	8.74	18	291.77	2.598	1.150	0.987
风化煤	6.04	80	562.50	4.400	0.240	1.910

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表 3 单一改良试验设计

Table 3. Single improved experimental design

试验组	秸秆施用量/%	牛粪施用量/%	风化煤施用量/%
1	0	0	0
2	1	0	0
	0	1	0
	0	0	1
3	2	0	0
	0	5	0
	0	0	2
4	4	0	0
	0	10	0
	0	0	3
5	6	0	0
	0	15	0
	0	0	4

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表 4 响应面因素设计与水平编码

Table 4. Response surface factor design and horizontal coding

水平编码	秸秆施用量（x₁）/%	牛粪施用量（x₂）/%	风化煤施用量（x₃）/%
低(−1)	3	9	1
中心(0)	4	10	2
高(1)	5	11	3

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表 5 响应面试验设计与测试结果

Table 5. Response surface experimental design and test results

序号	秸秆施用量/%	牛粪施用量/%	风化煤施用量/%	容重/ （g/cm³）	田间持水量/%	大团聚体含量/%
1	3	9	2	1.23	57.7	78.65
2	5	9	2	1.08	56.4	81.92
3	3	11	2	1.22	59.6	80.58
4	5	11	2	1.07	58.8	83.71
5	3	10	1	1.25	58.6	76.88
6	5	10	1	1.09	57.9	79.81
7	3	10	3	1.21	59.7	82.79
8	5	10	3	1.06	58.8	85.83
9	4	9	1	1.20	56.9	78.99
10	4	11	1	1.16	59.2	79.40
11	4	9	3	1.10	57.9	83.49
12	4	11	3	1.08	61.2	85.21
13	4	10	2	1.13	62.5	81.26
14	4	10	2	1.14	62.1	80.84
15	4	10	2	1.12	62.3	81.40
16	4	10	2	1.13	62.1	81.09
17	4	10	2	1.15	61.9	81.31

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表 6 响应面回归模型方差分析

Table 6. Analysis of variance of response surface regression model

响应值	P	失拟项	R²	$R_{\mathrm{Adj}}^2 $	$R_{\mathrm{Pred}}^2 $	AP	C_V/%
Y₁	<0.000 1	0.183 9	0.939 0	0.924 9	0.886 6	26.71	1.45
Y₂	<0.000 1	0.213 1	0.991 2	0.979 9	0.905 1	26.74	0.48
Y₃	<0.000 1	0.077 1	0.974 5	0.968 6	0.950 6	43.06	0.51

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