Solidification of Red Mud Based on Geopolymerization and Nanomaterial Composite Optimization

doi:10.16552/j.cnki.issn1001-1625.2025.1026

Abstract

Abstract:

The resource utilization of red mud has long been limited by its high pH value characteristic. A red mud solidification technology based on synergistic reactions of solid wastes and optimized compounding of nanomaterials was proposed by the reverse utilization of the strongly alkaline environment of red mud， the geopolymerization potential of solid wastes such as fly ash and mineral waste residue， and the filling-dispersion function of nanomaterials. Orthogonal mechanical experiments were designed to obtain the optimized mix proportions for the red mud solidification， aided by the variance and range analyses. Scanning electron microscopy and X-ray diffraction analyses were conducted on typical ratio samples to reveal the solidification mechanism of red mud， aided by micro-pore structure analysis. The results indicate that the optimal mass ratio for red mud solidification is m（metakaolin）∶m（fly ash）∶m（mineral waste residue）∶m（calcium chloride）∶m（sodium silicate）∶m（nano-SiO₂）∶m（nano-Al₂O₃）∶m（carbon nanotube）=20.00%∶25.81%∶12.90%∶7.74%∶7.74%∶23.46%∶1.16%∶1.16%. The products of geopolymerization and hydration-hydrolysis reactions are both detected in all the samples. Dissolution rate and degree of chemical reaction completion of silicon-aluminum substances are improved， and the elongated pores larger than 1.0 μm are better filled， through the silicon-aluminum ratio regulation by the nanomaterial adjustments， to optimize the microstructure of the solidified red mud and provide strength assurance for it. This technology achieves a high red mud incorporation rate of 80% （mass fraction）， providing a new strategy for the resource utilization of bulk solid wastes.

Key words: red mud, solid waste co-solidification, geopolymerization, nanostructure optimization, orthogonal test, silicon-aluminum ratio regulation

CLC Number:

X705

TIAN Shumei, LUO Haowen, WANG Hongxing, RUAN Junhao, ZHAO Tiantian, ZHANG Xiaoyi, WU Shangwei. Solidification of Red Mud Based on Geopolymerization and Nanomaterial Composite Optimization[J]. BULLETIN OF THE CHINESE CERAMIC SOCIETY, 2026, 45(5): 1693-1708.

Figures/Tables 22

Table 1 Basic physical and mechanical indicators of RM

Category of characteristic	Characteristic name	Scope of indicator
Physical characteristic	Particle diameter/mm	0.088~0.25
	Specific gravity/（g·cm^-3）	2.7~2.9
	Bulk density/（g·cm^-3）	0.8~1.0
	Dry density/（g·cm^-3）	1.0~1.5
	Wet density/（g·cm^-3）	1.5~2.0
	Liquid limit/%	40~80
	Plastic limit/%	20~40
	Index of plasticity	20~60
	Melting point/℃	1 200~1 250
Mechanical property	Osmotic coefficient/（cm·s^-1）	1×10^-7~1×10^-9
	Coefficient of compressibility a_1-2/MPa^-1	0.2~0.5
	Internal friction angle φ	15°~30°
	Cohesive strength c/kPa	10~50
	Natural state bearing capacity/kPa	50~100

Table 2 Main chemical composition of raw materials

Material	Mass fraction/%
Material	SiO₂	Al₂O₃	Fe₂O₃	TiO₂	CaO	MgO	K₂O	Na₂O	SO₃	Other
MK	55.06	43.02	0.76	0.24	0.17	0.06	0.55	0.06	—	—
FA	45.10	24.20	3.90	—	1.60	2.10	—	—	1.60	—
MWR	34.36	16.89	0.36	—	38.13	6.23	0.41	0.24	1.08	1.08
RM	20.92	26.26	5.43	3.17	18.31	—	—	6.36	—	19.55

Table 3 Technical parameters of nanomaterials

Parameter	Nano-SiO₂	Nano-Al₂O₃	CNT
Average grain diameter/nm	10~20	20	10~20
Purity/%	99.80	99.99	99.50
Specific area/（m²·g^-1）	240	120~150	225±25
Bulk density/（g·cm^-3）	0.06~0.14	0.05~0.50	0.01~0.10
Crystal form	Amorphism	Gamma	Honeycomb lattice
Appearance	White powder	White powder	Black powder
pH value	3.5~7.0	6.0~8.0	6.5~7.5
Fiber length/µm			5~15

Fig.1 Specimen preparation and testing process

Table 4 Curing material value range and value level

Material	Functioning characteristic	Value range/%	Orthogonal test value level （mass fraction）/%
RM	Main object to solidify	80（fixed value）
MK	Provide SiO₂ and Al₂O₃to ensure the lower limit of intensity	Curing agent 20 （fixed value）
FA	Supplement SiO₂ and Al₂O₃， commonly used auxiliary curing materials	5~20^{［7⁃8，18］}	5	7.5	10	15	20
MWR	Functionally similar to FA， but rich in CaO	0~20^［7，18］	0	5	10	15	20
CC	Rich in calcium material， regulate alkalinity， improve curing effect	0~15^{［15，18］}	0	3	6	10	15
SS	Form a complex alkali activator with sodium hydroxide in RM to improve dissolution rate of active ingredients	0~15^［15］	0	3	6	10	15
Nanophase material	Optimize nano-porous structure and improve performance	1~10^［7，15］	1	3	5	7	10

Table 5 Range analysis method

Variable	Representation	Implication
$δ 1$	$δ 1 = I 1 - Y$	Difference between average of test results at each value level and average of all test results
$δ 2$	$δ 2 = I 2 - Y$
$δ 3$	$δ 3 = I 3 - Y$
...	…….	Other levels of value
$T$	$M a x δ 1, δ 2, … …, δ n - M i n (δ 1, δ 2, … …, δ n)$	Range

Table 5 Range analysis method

Variable	Representation	Implication
$δ 1$	$δ 1 = I 1 - Y$	Difference between average of test results at each value level and average of all test results
$δ 2$	$δ 2 = I 2 - Y$
$δ 3$	$δ 3 = I 3 - Y$
...	…….	Other levels of value
$T$	$M a x δ 1, δ 2, … …, δ n - M i n (δ 1, δ 2, … …, δ n)$	Range

Table 6 Variance analysis method

Method	Abbreviation	Total	Factor	Error
Variance	SS'	$S S T = ∑ k = 1 n y k - Y 2$	$S S j = ∑ k = 1 m' I k - Y 2$	$S S E = S S T - ∑ j = 1 p S S j$
Degree of freedom	DOF	$f T = n m' - 1$	$f j = m' - 1$	$f E = f T - ∑ j = 1 p f j$
Mean square departure	MSD		$S j = S S j / f j$	$S E = S S E / f E$
F value	F		$F j = S j / S E$

Table 6 Variance analysis method

Method	Abbreviation	Total	Factor	Error
Variance	SS'	$S S T = ∑ k = 1 n y k - Y 2$	$S S j = ∑ k = 1 m' I k - Y 2$	$S S E = S S T - ∑ j = 1 p S S j$
Degree of freedom	DOF	$f T = n m' - 1$	$f j = m' - 1$	$f E = f T - ∑ j = 1 p f j$
Mean square departure	MSD		$S j = S S j / f j$	$S E = S S E / f E$
F value	F		$F j = S j / S E$

Fig.2 Typical stress-strain curves for SRM

Fig.3 Orthogonal test results of SWC-SRM

Fig.4 Cluster mean results of SWC-SRM

Table 7 Variance analysis of SWC-SRM

Material	Sum of deviations squared	Degree of freedom	F value	Sensitivity sequencing
FA	0.253	4	5.606	5
MWR	0.391	4	8.663	4
CC	0.409	4	9.066	3
SS	0.539	4	11.931	1
Nano-SiO₂	0.488	4	10.811	2

Table 8 Range analysis of SWC-SRM

Index	Level	FA	MWR	CC	SS	Nano-SiO₂
K value	1	2.80	2.80	2.83	2.89	2.66
	2	2.79	2.92	2.95	2.89	2.56
	3	2.93	2.54	2.83	2.82	2.86
	4	2.70	2.79	2.64	2.50	2.83
	5	2.63	2.81	2.60	2.75	2.95
Best level		3	2	2	2	5
Extreme deviations R		0.30	0.38	0.34	0.40	0.38
Sensitivity ranges from large to small		SS， nano-SiO₂， MWR， CC， FA

Fig.5 Orthogonal test results of NC-SRM

Fig.6 Cluster mean results of NC-SRM

Table 9 Variance analysis of NC-SRM

Material	Sum of deviations squared	Degree of freedom	F value	Ranking of impact
Nano-SiO₂	4.652	4	1.163	1
Nano-Al₂O₃	1.286	4	0.322	3
CNT	2.686	4	0.672	2

Table 10 Range analysis of NC-SRM

Index	Level	Nano-SiO₂	Nano-Al₂O₃	CNT
K value	1	3.453	4.213	4.513
	2	3.467	3.527	4.113
	3	4.260	3.987	3.740
	4	4.027	4.020	3.773
	5	4.540	4.000	3.607
Best level		5	1	1
Extreme deviations R		1.087	0.687	0.907
Sensitivity ranges from large to small		Nano-SiO₂， CNT， nano-Al₂O₃

Fig.7 SEM images of typical SRM

Fig.8 SEM images of microscopic pore structure of typical SRM

Fig.9 Microscopic pore statistics based on MPA

Fig.10 Microscopic porosity statistics based on MDMP

Table 11 Pore structure parameters of SRM

Sample	Total pore area/μm²	Average pore area/μm²	Porosity/%	Average pore perimeter/μm	Mean pore size/μm
N7	33.39	0.242	9.873	2.60	0.874
N8	20.83	0.495	6.169	3.23	1.041
N24	11.37	0.162	3.332	1.83	0.679

Fig.11 XRD patterns of SRM

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