融药超细尾矿絮凝效率指标及多因素交互分析

盛明强; 关子依; 江传智; 饶家浪; 朱奇新; 刘静

doi:10.19509/j.cnki.dzkq.tb20250421

融药超细尾矿絮凝效率指标及多因素交互分析

doi: 10.19509/j.cnki.dzkq.tb20250421

南昌大学，南昌 330031
南昌工学院，南昌 330108

基金项目: 江西省教育厅科学技术研究项目（GJJ2402815）；国家自然科学基金项目（52169027）

详细信息

作者简介:
盛明强：E-mail：mqsheng@ncu.edu.cn

通讯作者:
E-mail：1140545998@qq.com

计量
- 文章访问数: 282
- PDF下载量: 24
- 被引次数: 0
出版历程
- 收稿日期: 2025-09-18
- 录用日期: 2025-12-02
- 修回日期: 2025-11-10
- 网络出版日期: 2025-12-22

Indicators and multi-factor interaction analysis of flocculation efficiency for ultra-fine tailings with flocculant addition

Nanchang University, Nanchang 330031, China
Nanchang Institute of Science and Technology, Nanchang 330108, China

More Information

Author Bio:
E-mail：mqsheng@ncu.edu.cn

Corresponding author: E-mail：1140545998@qq.com

摘要

摘要:
通过优化矿浆质量浓度、絮凝剂单耗和搅拌速率等参数，可提高絮凝沉降性能，解决超细尾矿在土工管袋充灌过程中存在的沉降速度慢、脱水效率低等问题，从而提高尾矿脱水后的力学强度和堆存稳定性。通过量筒沉降试验，引入“絮凝效率”作为核心评价指标，采用单因素分析法研究矿浆质量浓度、絮凝剂单耗和搅拌速率对絮凝效果的影响；基于Box-Behnken设计（BBD）进行3因素3水平响应曲面试验，利用Design-Expert软件建立絮凝效率与各因素之间的定量模型，分析多因素交互作用及其机理。单因素试验表明，矿浆质量浓度为4%～8%、絮凝剂单耗为0.5～1.5 mL、搅拌速率为300～500 r/min时絮凝效率较高；响应曲面设计优化模型的方差分析结果显示，各因素对絮凝效率的影响显著性依次为：$X_3^2 $>$X_1^2 $>$X_2 $>$X_1X_2 $>$X_1 $>$X_2^2 $>$X_3 $>$X_2X_3$（X₁，X₂，X₃）。最优参数组合为矿浆质量浓度5.45%、絮凝剂单耗0.5 mL、搅拌速率415 r/min，此时絮凝效率预测值为8.086 %/(mg·kg⁻¹)，实测值与预测误差小于5%。本研究建立的絮凝效率模型能有效预测超细尾矿絮凝沉降性能，响应曲面法可揭示多因素交互作用机制，优化参数组合显著提升絮凝效率，为土工管袋脱水工艺提供理论依据和技术支持。
- 超细尾矿 /
- 沉降性能 /
- 絮凝效率 /
- 响应面设计（BBD） /
- Design-Expert软件
Abstract:
Objective
This study aims to enhance flocculation-sedimentation performance by optimizing parameters such as slurry mass concentration, specific flocculant consumption, and agitation rate, thereby solving problems such as slow sedimentation speed and low dewatering efficiency during the filling of geotextile tubes with ultra-fine tailings. The ultimate goal is to improve the mechanical strength and storage stability of dewatered tailings.
Methods
Cylinder sedimentation tests were conducted, with "flocculation efficiency" introduced as the core evaluation indicator. Single-factor analysis was employed to investigate the effects of slurry mass concentration, specific flocculant consumption, and agitation rate on flocculation performance. A three-factor, three-level Box-Behnken design (BBD) was utilized for response surface methodology (RSM) experiments. Design-Expert software was applied to establish a quantitative model between flocculation efficiency and various factors to analyze multi-factor interactions and their underlying mechanisms.
Results
Single-factor tests indicated that higher flocculation efficiency was achieved within the following ranges: slurry mass concentration of 4%-8%, specific flocculant consumption of 0.5-1.5 mL, and agitation rate of 300-500 r/min. Analysis of variance of the response surface optimization model showed that the significance of factors affecting flocculation efficiency followed the order: square of agitation rate > square of slurry mass concentration > specific flocculant consumption > interaction between slurry mass concentration and specific flocculant consumption > slurry mass concentration > square of specific flocculant consumption > agitation rate > interaction between specific flocculant consumption and agitation rate > interaction between slurry mass concentration and agitation rate. The optimal parameter combination was identified as slurry mass concentration of 5.45%, specific flocculant consumption of 0.5 mL, and agitation rate of 415 r/min, yielding a predicted flocculation efficiency of 8.086 %/(mg·kg⁻¹). The error between measured and predicted values was less than 5%.
Conclusion
The established flocculation efficiency model can effectively predict the flocculation-sedimentation performance of ultra-fine tailings. The response surface methodology can reveal the mechanisms of multi-factor interactions, and the optimized parameter combination significantly enhances flocculation efficiency. This study provides a theoretical foundation and technical support for the dewatering process of geotextile tubes.
- ultra-fine tailings /
- sedimentation performance /
- flocculation efficiency /
- Box-Behnken design (BBD) /
- Design-Expert software

HTML全文

图 1 铝尾矿粒径分布

Figure 1. Particle size distribution of aluminum tailings

下载: 全尺寸图片幻灯片

图 2 絮凝效率与矿浆质量浓度(a)、絮凝剂单耗(b)、搅拌速率(c)的关系

Figure 2. Relationship between flocculation efficiency and slurry mass concentration (a), specific flocculant consumption (b), agitation rate (c)

下载: 全尺寸图片幻灯片

图 3 超细尾矿絮凝效率预测值与实际值关系

Figure 3. Relationship between predicted and actual flocculation efficiency of ultra-fine tailings

下载: 全尺寸图片幻灯片

图 4 超细尾矿絮凝效率残差分布

Figure 4. Residual distribution of flocculation efficiency for ultra-fine tailings

下载: 全尺寸图片幻灯片

图 5 矿浆质量浓度c、絮凝剂单耗p′和搅拌速率N交互作用影响分析

Figure 5. Interaction effect analysis of slurry mass concentration c, specific flocculant consumption p′ and agitation rate N

下载: 全尺寸图片幻灯片

表 1 不同矿浆质量浓度试验工况

Table 1. Test conditions at different slurry mass concentrations

类别	工况	矿浆质量浓度c/%	絮凝剂单耗p′/mL		搅拌速率 N/(r·min⁻¹)
类别	工况	矿浆质量浓度c/%	絮凝沉降	自然沉降	搅拌速率 N/(r·min⁻¹)
不同矿浆质量浓度	1#	4	1	0	400
	2#	6	1	0	400
	3#	8	1	0	400
	4#	10	1	0	400
	5#	12	1	0	400
	6#	14	1	0	400
不同絮凝剂单耗	1#	6	0.3	0	400
	2#	6	0.4	0	400
	3#	6	0.5	0	400
	4#	6	1	0	400
	5#	6	1.5	0	400
	6#	6	2	0	400
	7#	6	2.5	0	400
不同搅拌速率	1#	6	1	0	300
	2#	6	1	0	400
	3#	6	1	0	500
	4#	6	1	0	600

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表 2 响应面试验因素与水平

Table 2. Factors and levels for response surface experiments

试验因素	因素编码	因素水平
试验因素	因素编码	−1	0	1
矿浆质量浓度c/%	X₁	4	6	8
絮凝剂单耗p′/mL	X₂	0.5	1	1.5
搅拌速率N/(r·min⁻¹)	X₃	300	400	500

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表 3 试验方案与结果

Table 3. Experimental design and results

试验编号	试验类型	矿浆质量浓度c/%	絮凝剂单耗p′/mL	搅拌速率 N/(r·min⁻¹)	絮凝效率 E/(%/(mg·kg⁻¹))
1	因子点	4	1.0	300	3.169
2	因子点	8	1.0	500	3.225
3	因子点	4	1.5	400	4.795
4	中心点	6	1.0	400	7.067
5	中心点	6	1.0	400	6.975
6	因子点	4	1.0	500	3.622
7	因子点	8	1.0	300	2.419
8	因子点	6	1.5	300	4.567
9	中心点	6	1.0	400	6.975
10	因子点	8	0.5	400	4.949
11	因子点	6	0.5	300	5.511
12	因子点	4	0.5	400	7.001
13	中心点	6	1.0	400	7.067
14	因子点	6	1.5	500	4.231
15	因子点	8	1.5	400	5.249
16	因子点	6	0.5	500	6.576
17	中心点	6	1.0	400	6.455

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表 4 超细尾矿絮凝沉降絮凝效率模型方差分析结果

Table 4. ANOVA results of flocculation efficiency model for ultra-fine tailings sedimentation

来源	平方和	自由度	均方差	F值	P值
模型	39.21	9	4.36	54.04	<0.0001
X₁	0.9419	1	0.9419	11.68	0.0112
X₂	3.37	1	3.37	41.84	0.0003
X₃	0.4940	1	0.4940	6.13	0.0425
X₁X₂	1.57	1	1.57	19.47	0.0031
X₁X₃	0.0312	1	0.0312	0.3864	0.5539
X₂X₃	0.4907	1	0.4907	6.09	0.0430
X₁²	13.06	1	13.06	161.93	<0.0001
X₂²	0.5205	1	0.5205	6.46	0.0386
X₃²	17.49	1	17.49	216.94	<0.0001
残差	0.5644	7	0.0806	—	—
失拟	0.2996	3	0.0999	1.51	0.3410
纯误差	0.2647	4	0.0662	—	—
总离差	39.78	16		—	—
预测R²=0.9858；调整R²=0.9676；变异系数CV=5.37%；信噪比=21.5296

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表 5 超细尾矿沉降絮凝效率优化模型方差分析结果

Table 5. ANOVA results of optimized flocculation efficiency model for ultra-fine tailings flocculation-sedimentation

来源	平方和	自由度	均方差	F值	P值
模型	39.18	9	4.90	65.79	<0.0001
X₁	0.9419	1	0.9419	12.65	0.0074
X₂	3.37	1	3.37	45.32	0.0001
X₃	0.4940	1	0.4940	6.64	0.0328
X₁X₂	1.57	1	1.57	21.09	0.0018
X₂X₃	0.4907	1	0.4907	6.59	0.0333
X₁²	13.06	1	13.06	175.39	<0.0001
X₂²	0.5205	1	0.5205	6.99	0.0295
X₃²	17.49	1	17.49	234.96	<0.0001
残差	0.5955	8	0.0744	—	—
失拟	0.3308	3	0.0827	1.25	0.3410
纯误差	0.2647	4	0.0662	—	—
总离差	39.78	16		—	—
预测R²=0.9858；调整R²=0.9701；变异系数CV=5.16%；信噪比=23.1735

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表 6 絮凝效率预测值与实测值

Table 6. Predicted and measured flocculation efficiency

数据类型	矿浆质量浓度c/%	絮凝剂单耗p′/mL	搅拌速率 N(r·min⁻¹)	絮凝效率 E/(%/mg·kg⁻¹)
预测值	5.45	0.5	415	8.086
实测值1	5.45	0.5	415	7.998
实测值2	5.45	0.5	415	8.100
实测值3	5.45	0.5	415	8.128

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