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Open Access 23-03-2023 | Original Paper

Characterization of an Iron Ore Tailing Sample and the Evaluation of Its Representativeness

Authors: Jessé Joabe Vieira Carneiro, Eduardo Antonio Gomes Marques, António Joaquim Pereira Viana da Fonseca, Roberto Lopes Ferraz, Ângelo Henrique Cruz Oliveira

Published in: Geotechnical and Geological Engineering | Issue 5/2023

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Abstract

The massive annual tonnage of iron ore tailings, plus the more demanding environmental policies after the catastrophic collapses of Fundão Dam in 2015 and Feijão Dam I in 2019, have forced Brazilian mining companies to search for more sustainable and safer alternatives for tailings disposal. The Brazilian mining industry has been studying the dry stacking of filtered tailings. Most companies seek the development of great dry stacks, over 200 m high, to absorb the whole tailings generation. As tailings assume a structural role, it is vital to characterize the dry stacks and understand their behaviour. This study aimed to characterize index properties, evaluate the effect of compaction on the hydraulic behaviour, and evaluate field variability of iron ore tailings from a mine in Quadrilátero Ferrífero, Brazil. Also, a database of iron ore tailings properties from Quadrilátero Ferrífero was collected and used to evaluate the results. Specimens were compacted to 93%, 95%, 97%, and 100% of Proctor’s maximum dry unit weight, and the hydraulic conductivity was defined for several confining pressures (100, 200, 400, 800, 1600 and 1900 kPa) for each degree of compaction. Index properties of the studied tailing were similar to other iron ore tailings from the Quadrilátero Ferrífero, hydraulic conductivity was proportional to the void ratio, and the field tests indicated some variability of both material properties and degree of compaction of the stacked material. The database presented in this study supplies the mining industry with a reference point for future projects.

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Avg.

Average value

ca.

Circa

C–H

Constant-head

Cons.

Consolidation

CPTu

Piezocone penetration test

C _u

Coefficient of uniformity

C _c

Coefficient of curvature

Dam

D ₅₀

Mean grain size

Degree of compaction

Void ratio

e _max

Maximum void ratio

e _min

Minimum void ratio

F–H

Falling head

F.P.

Flow pump

Fut.

Future storage

F.W.P.

Flexible wall permeameter

G.P.

Guelph permeameter

Gh.

Goethite

G _s

Specific gravity

Hist.

Historical data

Hm.

Hematite

Hydraulic conductivity

Liquid limit

L.S.

Large Strain

Mine

Max.

Maximum value

Mg.

Magnetite

Min.

Minimum value

Non plastic

Ovr.

Overflow

Plasticity index

Plastic limit

Pl.

Plastic

Quadrilátero Ferrífero

Remoulded

R.W.P.

Rigid wall pemeameter

Saturation

S _p

Specific surface

Stdev.

Standard deviation

Strd.

Stored tailings

TSF

Tailings storage facility

t ₅₀

Measured time for 50% dissipation

Undisturbed

Un.

Unidentified

Und.

Underflow

Water content

w _field

Water content of embankment

w _dev.

Water content deviation

w _opt

Optimum water content

Bulk unit weight

γ _d

Dry unit weight

γ _d _,field

Dry unit weight of embankment.

γ _d _,max

Maximum dry unit weight

σ' _v

Effective vertical stress

1 Introduction

Tailings represent an environmental liability to the mining industry and must be appropriately deposited in a tailings storage facility (TSF). The massive tonnage of iron ore production in Brazil requires large TSFs and, after the catastrophic collapses of Fundão Dam in 2015 (the biggest environmental disaster in the country) and Feijão Dam I in 2019 (which led to hundreds of deaths), both by flow liquefaction (Morgenstern et al. 2016; Robertson et al. 2019; CIMNE 2021), mining companies in Brazil have been searching for more sustainable and safer alternatives for tailings storage based on a dry iron ore processing production. Vale S.A., responsible for 71,69% of all iron ore commercial production in Brazil in 2021 (ANM 2023), has invested about R$ 66 billion in the development of technologies and facilities for dry iron ore processing production in the last decade (Vale 2020). Dry stacking of dewatered tailings is one of the technics that have been studied and gradually implemented by major mining companies in Brazil.

Dry stacking is a stable way of storing tailings in an unsaturated and dense solid state. Even though other dewatering technics have been studied (Fourie et al. 2007; Fourie and Jones 2010; Lees 2016; Sahi et al. 2019; Zhang et al. 2022; Shafaei et al. 2022), tailings dewatering is usually done in a large capacity vacuum or pressure belt filters, so they are also called filtered tailings (Davies & Rice 2001). When tailings are dewatered, there is a decrease in the water content and an increase in the effective solids content that implies higher effective stresses, consequently, higher internal resistance in the material (Davies et al. 2010). However, the Brazilian mining companies intend to develop great dry stacks (over 200 m high) to absorb tailings generation, and before building enormous dry stacks, it is vital to understand the intrinsic characteristics and the tailings behaviour under high confining stresses (Lupo and Hall 2011; Davies 2011; Hore and Luppnow 2014; Crystal et al. 2018; Furnell et al. 2022).

This study aimed to 1) understand the intrinsic properties of tailings that will be stored in a dry stack in the Quadrilátero Ferrífero (Minas Gerais, Brazil), 2) understand the material’s hydraulic behaviour in different states when changing the degree of compaction and confining pressures, and 3) verify the material’s variability in the field after quality–control tests. Despite the singularities of each company’s mineral beneficiation processes, similarities among tailings from different facilities in the Quadrilátero Ferrífero (QF) are expected because the mines are in the same geological formation. Therefore, a database of the tailings characteristics of iron ore mines in the QF has been collected and used to analyse the tailings studied in this research. Comparing the properties of different tailings generated in the QF helps identify patterns and ranges of variation, which can be used to analyse iron ore tailings from the same formation and with similar physical characteristics.

2 Materials and Methods

Disturbed samples of iron ore tailings were collected in airtight plastic bags directly from the pressure belt filter before being deposited and compacted in the TSF in a mining complex located in the QF, where the total tailings are currently being press filtered and deposited in the TSF. The experimental program described in this chapter was developed in the Laboratory of Soil Mechanics of the Federal University of Viçosa (LMS-UFV) (Viçosa, Brazil) and the Geotechnical Laboratory of the Faculty of Engineering of the University of Porto (LabGeo-FEUP) (Porto, Portugal). Additionally, a trial embankment was built by the mining company, in which field compaction quality-control tests were performed and made available for the research.

2.1 Sample Preparation

Tailing samples were dried at room temperature (~ 22 ℃), loosened, sieved, homogenized, and stored in airtight plastic bags. Before each test, samples were loosened, sieved, and homogenized again to avoid lumps.

2.2 Index Tests

Index tests were performed to determine the Atterberg limits, maximum and minimum void ratios, grain size distribution, and specific gravity of the iron ore tailings. Samples were prepared according to the Brazilian Association of Technical Standards (ABNT) Brazilian Regulatory Standard (NBR) 6457:2016.

2.2.1 Grain Size Distribution

Grain size analyses were conducted according to the International Standards Organization (ISO) 17,892–4:2016 and ABNT NBR 7181:2016. Twenty-four analyses were performed at LMS-UFV: one with the homogenized sample, and twenty-three with the material resulting from the hydraulic conductivity tests. Three analyses were performed at LabGeo-FEUP: two with the homogenized samples and one with the material resulting from the standard Proctor test.

The grain size analyses were performed to evaluate the uniformity of the samples sent to both laboratories. Additionally, it was evaluated the evolution or not of the grain size distribution due to the static (material resulting from the hydraulic conduction tests) and dynamic compaction (material resulting from the standard Proctor test).

2.2.2 Specific Gravity

Three tests were performed to define the specific gravity of the iron ore tailing samples: one test at LMS-UFV and two at LabGeo-FEUP to verify the similarity of the samples sent to both laboratories. The tests followed the ABNT NBR 6458:2016 and ISO 17892–3:2015 methods, respectively.

2.2.3 Atterberg Limits

Tests to determine plasticity and liquid limits were performed at LMS-UFV in conformity with the ABNT NBR 7180:2016 and NBR 6459:2016, respectively.

2.2.4 Maximum and Minimum Void Ratios

At LMS-UFV, the minimum and maximum void ratios were determined following test method A.1 of ABNT NBR 16,843:2020 and test method A of ABNT NBR 16,840:2020, respectively.

2.3 Proctor Compaction Test

Samples tested in the compaction tests were prepared according to the ABNT NBR 6457:2016 method. Standard Proctor tests were performed in both laboratories. At the LMS-UFV it was adopted the equipment and procedures according to the ABNT NBR 7182:2016: small rammer with 2,5 ± 0,01 kg and 50,0 ± 0,5 mm of diameter; small mould with an inside diameter of 100,0 ± 0,1 mm and height of 127,3 ± 0,3 mm; drop height of 305 ± 2 mm; 26 blows per layer and three layers. At LabGeo-FEUP it was adopted the equipment and procedures according to the National Laboratory for Civil Engineering (LNEC) E 197:1966: small rammer with 2,49 kg and 50 mm of diameter; small mould with an inside diameter of 102 mm and height of 117 mm; drop height of 305 mm; 25 blows per layer and three layers.

2.4 Field Test

To evaluate the quality of field compaction, the mining company determined the dry unit weight (γ_d) and water content (w) of 218 samples collected in different areas of a trial embankment. Each lift of the trial embankment was 0.5 m tick. Two samples were collected in some of the checked points to evaluate the compaction homogeneity of the layer: one on the top and another at the base of the compacted layer. Bulk unit weight (γ) was determined by the Drive-Cylinder Method, following the ABNT NBR 9813:2016; water content was determined by oven drying the sample at 110 °C ± 5 °C following the Brazilian National Highway Department (DNER) test method (ME) 213:1994.

The γ_d and w values of each sample were compared to the maximum dry unit weight (γ_d,max) and optimum water content (w_opt) defined after standard Proctor tests performed every 1000 m³ of compacted tailings. A total of thirty-three standard Proctor tests were performed by the mining company technical staff, and the results were made available for interpretation. The standard Proctor tests were performed according to the ABNT NBR 7182:201 (with similar equipment and procedures as presented in subsection 2.3).

2.5 Hydraulic Conductivity Test

The hydraulic conductivity tests were performed in a flexible wall permeameter adapted from the triaxial system following the ASM D5084:2016, Method A (constant head). It used triaxial-type cells with all the components. Sixteen hydraulic conductivity tests were performed in the different confining stresses with gradients equal to 4, 8 and 12, and no significant difference was observed in the results. Therefore, the other eighteen tests were realized after saturation and consolidation with a constant hydraulic gradient equal to 8 until at least four values of constant hydraulic conductivity were obtained. The data acquisition was done after every 20 s with electronic pressure transducers.

Four tests (one for each degree of compaction) were conducted after the saturation stage, at 10 kPa of effective confining pressure, and thirty tests were performed after the isotropic consolidation at different effective confining stresses (100 kPa, 200 kPa, 400 kPa, 800 kPa, 1600 kPa, and 1900 kPa): eight specimens with the degree of compaction, DC = 93%; eight with DC = 95%; eight with DC = 97% and six with DC = 100%.

At LMS-UFV it was adopted specimens circa (ca.) 50 mm in diameter × 100 mm long. At LabGeo-FEUP it was used sixteen specimens ca. 72 mm in diameter × 144 mm long, and five specimens ca. 60 cm in diameter × 120 mm long. The specimens were prepared through static compaction using a split mould and a press. At LMS-UFV, samples were compacted in four layers. For each layer, it was determined the exact mass of tailings to compensate for the effect of over-compaction. After the compaction of each layer, the material was scarified to avoid smooth planar surfaces between the compacted layers and improve the adherence between them. Specimens tested at LabGeo-FEUP followed the same preparation procedures, but they were compacted into six layers.

3 Iron Ore Tailings from the Quadrilátero Ferrífero, Brazil

Based on a comprehensive literature collection of data, it was possible to provide an overview of the index (physical), compaction and permeability characteristics of the QF iron ore tailings.

3.1 Grain Size Distribution

The grain size distribution of the tailings is affected by different factors, such as the mineral composition, the efficiency of the processing, and the disposal methods (Vick 1990). Robertson et al. (2019) subdivided the grading curves of Feijão Dam I into coarse (maximum fines content = 50%), fine (50% to 96% fines), and slime (~ 100% fines) tailings. Besides grain size distribution, the main difference among materials was the material’s response to CPTu loading. According to the authors, coarse tailings presented a drained behaviour during CPTu; fine tailings presented an undrained behaviour with rapid pore pressure dissipation (measured time for 50% dissipation, t₅₀ < 400 s), and slimes presented an undrained behaviour with longer slow pore pressure dissipation (t₅₀ > 1000 s). Oathes et al. (2022) used the same delineation of materials to model the Feijão Dam I failure.

A gradual change in the mechanical and hydraulic behaviour of the tailings with the fines content increase is expected. However, the approach presented by Robertson et al. (2019) allows us to search for tendencies in the tailings’ behaviour based on their index properties. Thus, in Table 1, the limits determined by Robertson et al. (2019) were adopted to classify the grain size distributions collected in the literature (Fig. 1), and the subsequent index properties are given in Sects. 3.2 to 3.6

Table 1

Statistic of the grain size distribution curves and percentage of grain sizes of the QF iron ore tailings

Tailing	Statistics	D₅₀ (mm)	C_U	C_c	% Fines < 0.075 mm	Percent ABNT—NBR 6502:1995
						Clay < 0.002 mm	Silt 0.002–0.06 mm	Sand
						Clay < 0.002 mm	Silt 0.002–0.06 mm	Fine 0.06–0.2 mm	Medium 0.2–0,6 mm	Coarse 0.6–2.0 mm	Gravel > 2.0 mm
All (72)	Avg	0.069	23.0	4.6	62.2	7.8	45.0	35.8	8.6	1.7	1.0
	Stdev	0.053	38.1	7.8	24.2	8.5	21.4	18.3	10.2	3.5	4.4
	Max	0.290	233	31.7	100	41	85.5	72	45	16	32
	Min	0.004	2.2	0.1	10	0	3	0	0	0	0
Coarse (25)	Avg	0.119	24.6	4.1	35.9	3.6	23.3	49.9	17.3	4.0	1.9
	Stdev	0.057	53.3	8.6	11	4.3	10.2	12.6	11.1	5.0	6.4
	Max	0.290	233	30.8	50	13.5	38.5	72	45	16	32
	Min	0.074	2.2	0.4	10	0	3	18.5	3	0	0
Fine (40)	Avg	0.048	22.9	5.6	72.2	7.3	53.9	33.0	4.7	0.5	0.7
	Stdev	0.021	29.1	7.9	14.2	6.0	15.5	13.0	5.9	1.3	2.9
	Max	0.120	106	31.7	100	19.5	82	55	23	5	18
	Min	0.009	2.3	0.1	50	0	23	10	0	0	0
Slime (7)	Avg	0.006	17.9	1.3	99	25.9	72.2	1.9	0.0	0.0	0.0
	Stdev	0.002	14.2	0.3	1.2	9.9	8.9	2.2	0.0	0.0	0.0
	Max	0.011	46.4	1.6	100	41	85.5	5.5	0	0	0
	Min	0.004	6.7	0.7	97	10	59	0	0	0	0

D₅₀ mean grain size, C_u coefficient of uniformity, C_c coefficient of curvature, Avg average, Stdev Standard deviation, Max maximum value, Min minimum value

From 72 grading curves, 25 were coarse tailings, and 40 were fine tailings. Based on the high values of the coefficient of uniformity (C_u) and the coefficient of curvature (C_c), coarse and fine tailings would be classified as well-graded or gap-graded soils, respectively. However, most grade curves presented in Fig. 1 are reasonably vertical, followed by a tail of finer particles that confers the high values of C_u and C_c. The same characteristic was identified by Li et al. (2018) for gold tailings. According to the authors, these materials are relatively poorly graded instead of well graded.

Therefore, nearly all coarse and fine tailings presented poorly graded curves composed mainly of silt and fine sands. The average grain size distribution curve of the coarse tailings indicates a silty sand material with 49.9% of fine sand and 23.3% of silt, while the average grain size distribution curve of the fine tailings indicates a sandy silt material with 53.9% of silt and 33.0% of fine sand. The slimes showed more well-graded curves composed mainly of silt size material (average = 72.2%) and clay size material (average = 25.9%). Nonetheless, the standard deviation values of all categories indicate considerable variability of the tailings’ grading curves.

Most tailings grade curves are between the lower boundary of the fine tailings samples presented by Robertson et al. (2019) and the upper boundary of the samples tested by Silva (2010). Figure 2 presents the contours in which the grain size distribution curves of slimes (in light pink), and fine and coarse tailings (in navy blue) from the Quadrilátero Ferrífero are most likely to be. In white, it is also presented the average curve that separates the fine tailings from the coarse tailings.

It is worth mentioning that Fundão Dam and Feijão Dam I collapsed through flow liquefaction. As the defined contours are inside the grain size distribution range initially presented by Robertson et al. (2019) for Feijão Dam I, all the tailings of the Quadrilátero Ferrífero located inside these contours may be susceptible to liquefaction. Yet based on Robertson et al. (2019), the tailings below the average curve (with more than 50% of fines) are prone to have undrained behaviour under saturated conditions. The grain size distribution alone is not enough to dictate if the material is susceptible to liquefaction or not, but it may be used as a preliminary reference to identify potentially liquefiable tailings, as presented by Ishihara et al. (1980) for cyclic liquefaction susceptibility.

3.2 Specific Gravity

The tailings’ specific gravity (G_s) depends on the specific gravity and concentration of each constituent mineral. As iron minerals have high specific gravity, the higher their contents, the higher the specific gravity of the tailings. Table 2 shows the high variability of iron mineral contents among samples. Some tailings are composed essentially of quartz, such as QF—AM01 (Pires et al. 2019), and others are composed mainly of iron minerals, such as Feijão D. I—Coarse Tailings (Robertson et al. 2019). The hematite, magnetite, and goethite grades also vary from each sample, and the hematite concentration is usually the highest. Iron content is inversely proportional to the efficiency of iron ore processing. Old tailings deposits (e.g., Feijão Dam I) tend to have higher iron content than tailings generated nowadays (e.g., Fernandinho mine future storage). Moreover, slime samples tend to present higher grades of iron minerals than other tailings.

Table 2

Mineral and elemental composition of the QF iron ore tailings

Tailing	Hematite (%)	Magnetite (%)	Goethite (%)	Fe (%)	Reference
Morro Agudo M. (Crs.)	–	–	–	23	Presotti (2002)
Monjolo Dump (Crs.)	–	–	–	20.9 (16.5–26.6)	Santos (2004)
Germano D. (Crs.)	–	–	–	14.2	Pereira (2005)
Germano Pit (F.)	–	–	–	19.9 (17.6–22.2)
Itabiruçu D. (Crs.)	–	–	–	20.1 (19.2–21.0)
Forquilha III D. (F.)	–	–	–	34.5 (33.7–35.2)
Forquilha II D. (Crs.)	–	–	–	53.96
Campo Grande D. (Crs.)	–	–	–	42.45
Córrego do Doutor D. (Crs.)	–	–	–	21.4
Córrego do Doutor D. (F.)				23.0
Germano D. (Crs.)	< 20	–	< 5	13.1	Tolentino (2010)
Fundão D. (Un.)	–	–	–	17.54 (10,71–32,1)	Rezende (2013)
Fundão D. (Slm.)	42.9	0	30.9	–	Morgenstern et al. (2016)
Fernandinho M. Fut. (Crs.)	–	–	–	14.6	Carvalho (2017)
Fernandinho M. Fut. (F.)				26.9
Pontal D. Arm 1 (Un.)	45.3 (38.2–56.0)	0.4 (0.04–0.9)	4.4 (2.9–7.7)	34.2 (29.9–38.8)	Ferreira (2018)
Pontal D. Beach (Un.)	–	–	–	28.0 (21.4–35.3)
Serra Azul M. (Crs)	–	–	–	30.3	Dauce et al. (2019)
QF AM01 (Crs.)	14.5	–	0.9	11.4	Pires et al. (2019)
QF AM02 (F.)	42.0	–	2.0	27.4
QF AM03 (F.)	28.9	–	12.6	25.0
QF AM04 (F.)	46.7	–	7.1	32.8
QF AM05 (Crs.)	18.2	–	4.0	12.7
QF AM06 (F.)	34.0	–	2.8	23.9
QF AM07 (F.)	39.5	–	4.6	26.8
Feijão D. I Hist. (Un.)	77.1	8.6	7.1	51.5 (38.5–66.6)	Robertson et al. (2019)
Feijão D. I (Crs.)	87.3 (86.8–87.7)	7.1 (6.5–7.6)	3.2 (3–3.4)	–
Feijão D. I (F.)	48.0 (43.1–54.1)	1.5 (1.3–1.9)	15.0 (10.2–20.7)	–
Feijão D. I (Slm.)	47.3 (44.4–50.1)	0.4 (0.4–0.4)	33.0 (32.0–34.0)	–
Fundão D. (Slm.)	–	–	–	51.4	Lima & Abreu (2020)

Fe iron content, M mine. D dam, Slm slime, Fut future storage, QF Quadrilátero Ferrífero, Hist historical data, Crs coarse, F fine, Un Unidentified

Table 3 shows that the average specific gravity of all samples, coarse, fine tailings, and slimes do not diverge much: 3.66 g/cm³ considering all samples, 3.63 g/cm³ for coarse tailings, 3.59 g/cm³ for fine tailings, and 3.97 g/cm³ for slimes. However, they present significant standard deviation, and comparing Table 2 with Table 4, it is identified that tailings with higher iron content have higher specific gravity and tailings with lower iron content have lower specific gravity, as expected.

Table 3

Statistical parameters of the QF iron ore tailing index properties: mineral and elemental compositions, specific gravity, void ratios, maximum dry unit weight, optimum water content, and Atterberg limits

Samples	Stat	Hm. (%)	Mg. (%)	Gh. (%)	Fe (%)	Gs (g/cm³)	e_min	e_max	w_opt (%)	γ_d,max (kN/m³)	Atterberg limits of plastic samples
											LL	PL	PI
All	Avg	42.3	3.0	9.5	27	3.66	0.64	1.21	13.4	20.3	28.7	17.5	11.2
	Stdev	20.9	3.2	11.3	12.3	0.61	0.10	0.22	3.7	2.0	12.8	5.9	8.1
	Max	87.7	8.6	34.0	66.6	5.22	0.91	1.77	21.2	25.6	60.0	32.4	31.0
	Min	14.5	0.0	0.9	10.7	2.38	0.45	0.87	5.3	17.4	15.0	9.3	3.0
Coarse tailings	Avg	35.0	7.1	3.3	23.2	3.63	0.63	1.14	12.6	20.0	31.7	14.7	17.0
	Stdev	38.2	0.6	1.4	11.4	0.73	0.10	0.20	3.5	2.2	15.7	5.1	10.7
	Max	87.7	7.6	5.0	54.0	5.22	0.85	1.63	19.9	25.6	52.0	21.0	31.0
	Min	14.5	6.5	0.9	11.4	2.38	0.45	0.87	5.3	17.5	21.0	11.0	10.0
Fine tailings	Avg	39.9	1.5	7.4	26.7	3.59	0.67	1.31	13.4	20.2	27.6	17.6	9.9
	Stdev	8.0	0.3	6.5	5.7	0.50	0.08	0.22	4.0	1.8	13.6	5.9	8.0
	Max	54.1	1.9	20.7	35.2	5.14	0.91	1.77	21.2	22.8	60.0	32.4	31.0
	Min	28.9	1.3	2.0	17.6	2.66	0.54	1.02	6.2	17.4	15.2	9.3	3.0
Slimes	Avg	45.1	0.2	32	51.4	3.96	–	–	16.5	22.2	31.4	18.0	13.4
	Stdev	3.2	0.2	1.3	–	0.21	–	–	0.3	0.5	12.8	7.3	7.3
	Max	50.1	0.4	34	–	4.32	–	–	16.7	22.6	53.0	31.0	26.0
	Min	42.9	0	30.9	–	3.61	–	–	16.3	21.9	15.0	11.0	4.0

Stat statistics, Hm hematite, Mg magnetite, Gh goethite, Fe iron content, Gs specific gravity, e_min and e_max minimum and maximum void ratios, w_opt optimum water content. γ_d,max maximum dry unit weight, LL liquid limit, PL plastic limit, PI plasticity index, Avg average, Stdev Standard deviation, Max maximum value, Min minimum value

Table 4

Specific gravity, maximum and minimum void ratios of the QF iron ore tailings

Tailing	G_s (g/cm³)	e_min	e_max	Reference
Monjolo dump (Crs.)	3.38 (2.89–4.41)	0.65 (0.58–0.75)	0.95 (0.89–1.05)	Presotti (2002)
Monjolo dump (Crs.)	3.08 (2.95–3.19)	0.57 (0.51–0.65)	0.95 (0.88–0.99)	Santos (2004)
Germano pit (F.)	3.33 (3.27–3.40)	0.62	1.29 (1.26–1.33)	Pereira (2005)
Itabiruçu D. (Crs.)	3.37 (3.34–3.41)	0.67	1.38 (1.37–1.39)
Forquilha III D. (F.)	3.88 (3.81–3.95)	0.61 (0.57–0.64)	1.68 (1.59–1.77)
Forquilha II D. (Crs.)	4.80	0.74	1.63
Campo grande D. (Crs.)	4.15	0.74	1.25
Córrego do doutor D. (Crs.)	3.37	0.60	1.37
Córrego do doutor D. (F.)	3.40	0.61	1.25
Forquilha III D. (Crs.)	3.96 (3.42–4.52)	0.54 (0.45–0.59)	1.11 (1.02–1.21)	Dornas (2008)
Forquilha III D. (F.)	3.56 (3.27–3.99)	0,54	1.09
Pontal D. (Crs.)	3.32	0.57 (0.50–0.63)	1.04	Alves (2009)
Pontal D. (F.)	3.41	0.91	1.70
Feijão D. I (Crs.)	4.54 (3.80–5.11)	0.75 (0.57–0.80)	1.34 (1.14–1.48)	Silva (2010)
Feijão D. I (F.)	4.38 (3.80–4.72)	0.74	1.30
Feijão D. I (Slm.)	4.00	–	–
Fundão D. (Crs.)	2.90 (2.77–3.17)	0.55 (0.53–0.57)	1.04	Rezende (2013)
Fundão D. (F.)	3.08 (2.94–3.08)	–	–
Itatiaiuçu D. (F.)	3.70 (3.44–3.84)	–	–	Silva (2014)
Germano D., B3 (Crs.)	2.91	–	–	Ferreira (2016)
Germano D., B3 (Slm.)	3.83	–	–
Pau branco M. (Slm.)	3.90	–	–	Gomes et al. (2016)
Fundão D. (Crs.)	3.00 (2.38–4.34)	0.53 (0.47–0.58)	0.87	Morgenstern et al. (2016)
Fundão D. (Slm.)	3.90 (3.69–3.99)	–	–
SD-02 Stack (Crs.)	2.85 (2.55–3.32)	–	–	Carvalho (2017)
SD-02 Stack (F.)	3.61 (3.31–4.10)	–	–
Fernandinho M. Fut. (Crs.)	3.23 (3.21–3.27)	0.64 (0.63–0.67)	1.06 (1.05–1.08)
Fernandinho M. Fut. (F.)	3.00 (2.98–3.02)	0.71 (0.69–0.72)	1.04 (1.02–1.05)
Pontal D., Arm I (Crs.)	3.43 (3.23–3.82)	0.63 (0.55–0.85)	1.13 (0.97–1.45)	Ferreira (2018)
Pontal D., Arm I (F.)	3.64 (3.23–3.99)	0.65 (0.60–0.70)	1.21 (1.17–1.26)
Pontal D., Beach (Crs.)	3.40 (3.06–3.68)	0.49 (0.46–0.52)	0.97 (0.90–1.04)
Pontal D., Beach (F.)	3.30 (3.14–3.61)	–	–
Forquilha I D. (F.)	3.52 (2.74–4.29)	0.67 (0.63–0.70)	1.27 (1.20–1.35)	Miranda (2018)
Serra Azul Mine (Crs.)	3.71 (3.54–3.84)	0.74 (0.71–0.77)	1.05 (1.01–1.09)	Mohallem (2018)
Serra Azul Mine (F.)	3.56 (3.41–3.72)
Fundão D. (Slm.)	4.17	–	–	Lima & Abreu (2020)
Feijão D. I Hist. (Crs.)	4.60 (2.71–5.22)	–	–	Robertson et al. (2019)
Feijão D. I Hist. (F.)	4.21 (2.66–5.14)	–	–
Feijão D. I (Crs.)	4.89 (4.64–4.99)	–	–
Feijão D. I (F.)	3.89 (3.87–3.90)	–	–
Feijão D. I (Slm.)	3.97 (3.61–4.32)	–	–

G_s specific gravity, e_min and e_max minimum and maximum void ratios, M mine, D dam, Crs coarse, Slm slime, Fut future storage, Hist historical data, F fine

3.3 Atterberg Limits

Most of the analysed samples of the QF iron ore tailings, presented in Table 5, were non-plastic (69%). The number of plastic samples tends to increase in the finer tailings. Evaluating the type of tailings, 94% of the coarse tailings were non-plastic, 77% of the fine tailings were non-plastic, and 100% of the slimes were plastic. However, it is noteworthy that the average values of the plastic samples (Table 3) indicated low plasticity, even for slimes. Based on the Casagrande plasticity chart, high-plastic materials (LL > 50%) were identified only by Ferreira (2018), Miranda (2018), and Robertson et al. (2019).

Table 5

Atterberg limits of the QF iron ore tailings

Tailing	Number of samples			Atterberg limits plastic samples
	NP	PL	LL	PL	PI	Reference
Feijão D. I (Un.)	10	6	24 (18.2–33.3)	17.9 (13.8–23.5)	6.1 (4.3–9.8)	Silva (2010)
Itatiaiuçu D. (F.)	3	0	–	–	–	Silva (2014)
Germano D., B3 (Crs.)	1	0	–	–	–	Ferreira (2016)
Germano D., B3 (Slm.)	0	1	24.1	11.2	12.9
Fundão D. (Crs.)	1	0	–	–	–	Morgenstern et al. (2016)
Fundão D. (Slm.)	0	3	27.1 (26.0–28.4)	17.4 (16.0–19.0)	9.7 (7.0–11.1)
SD-02 stack (Crs.)	3	0	–	–	–	Carvalho (2017)
SD-02 stack (F.)	12	2	16.7 (15.2–18.2)	10.9 (9.3–12.4)	5.9 (5.8–5.9)
Fernandinho M. Fut. (Crs.)	3	0	–	–	–
Fernandinho M. Fut. (F.)	3	0	–	–	–
Pontal D., Arm I (Crs.)	7	0	–	–	–	Ferreira (2018)
Pontal D., Arm I (F.)	8	0	–	–	–
Portal D., Beach (Crs.)	5	3	31.7 (21.0–52.0)	14.7 (11.0–21.0)	17.0 (10.0–31.0)
Portal D., Beach (F.)	1	3	46.3(37.0–60.0)	22.3 (21.0–24.0)	24.0 (13.0–31.0)
Forquilha I D. (F.)	2	2	39.1 (24.5–53.7)	25.2 (17.9–32.4)	14.0 (6.6–21.3)	Miranda (2018)
Serra Azul M. (Crs.)	2	0	–	–	–	Mohallem (2018)
Serra Azul M. (F.)	0	2	20.0 (18.1–21.9)	13.6 (12.3–14.9)	6.4 (5.7–7.0)
Feijão D. I Hist. (Crs.)	15	0	–	–	–	Robertson et al. (2019)
Feijão D. I Hist. (F.)	25	5	22.2 (18.0–30.0)	16.8 (14.0–23.0)	5.2 (4.0–7.0)
Feijão D. I (Crs.)	12	0	–	–	–
Feijão D. I (F.)	0	2	21.0 (19.0–22.0)	17.0 (16.0–18.0)	4.0 (3.0–4.0)
Feijão D. I (Slm.)	0	15	43.0 (15.0–53.0)	25.4 (11.0–31.0)	17.7 (4.0–26.0)

NP non plastic, Pl plastic. LL liquid limit, PL plastic limit, PI plasticity index, D dam, Un Unidentified, Crs coarse, Slm slime, M mine, Strd Stored tailings, Fut future storage, Hist historical data, F fine

3.4 Maximum and Minimum Void Ratios

The maximum and minimum void ratios (limiting void ratios) are affected by several factors, such as the grain size distribution, fines content, mean grain size, particles shape, and intrinsic characteristics of the fine and coarse fractions (Cubrinovski and Ishihara 1999, 2002; Carraro and Prezzi 2008). The liming void ratios reflect not only the physical properties, but also the characteristics of soil behaviour, such as compressibility, contractiveness, and flow potential (Cubrinovski and Ishihara 2000, 2002). Non-plastic soils tend to pass from a coarse-dominated structure to a fines-dominated structure when increasing the fines content. Until the transition zone, around 30% to 40% of fines content, e_max and e_min tend to vary at the same rate. However, for soils with fines content above 40%, e_max tends to increase at a faster rate than e_min, resulting in more compressible soils (Lade et al. 1998; Cubrinovski and Ishihara 2002; Cubrinovski et al. 2010; Mijic et al. 2021).

Different authors in the literature have determined the maximum and the minimum void ratios of tailings with fines content up to 40% to extend the use of relative density to tailings (Vick 1990). Torres-Cruz & Santamarina (2020) expand the determination of e_max and e_min to characterize the behaviour of non-plastic silts. According to the authors, determining the limiting void ratios may assist the elaboration of sampling programs. However, Carraro & Prezzi (2008) alert that e_max and e_min, specially e_max, are affected by the method adopted to determine them, and the real e_max of natural loose deposits and tailings dams’ reservoirs may not be assessed by the conventional standard methods.

Table 4 presents the maximum and minimum void ratios of the QF iron ore tailings found in the literature. As expected, the coarse tailings show average e_min and e_max slightly lower than the fine tailings (Table 3). The maximum void ratio is two times more dispersed than the minimum void ratio for both fine and coarse tailings. High values of e_max were found for coarse and fine tailings, even though the increase of e_max should be proportional to the fines content increase. The variability of e_max may result from the physical properties of the tailings and the method adopted by each author to define it.

3.5 Maximum Dry Unit Weight and Optimum Water Content

Most w_opt. values of the tailings generated in the Quadrilátero Ferrífero found in the literature and presented in Table 6 are between 10 and 17%. A small increase in the average w_opt. is observed with the fines content increase: 12.6% for coarse tailings, 13.4% for fine tailings, and 16.5% for slimes (Table 3). Most of γ_d,max is between 17.7 kN/m³ and 21.7 kN/m³, the average values found for coarse (γ_d,max = 20,0 kN/m³) and fines (γ_d,max = 20,2 kN/m³) tailings do not show significant change. The slimes show a higher average value (γ_d,max = 22.2 kN/m³), which is consistent with the tendency of slime tailings to present higher iron ore content (Vick 1990). Notice that the maximum dry unit weight is directly proportional to the specific gravity of the material. Therefore, it is expected to have higher values of γ_d,max for the samples with higher G_s—as for Forquilha III Dam samples (Dornas 2008)—and lower values of γ_d,max for samples with lower G_s—as for Germano Dam, Bay 3, coarse tailings samples (Ferreira 2016).

Table 6

Maximum dry unit weight and optimum water content of the QF iron ore tailings

Tailing	w_opt (%)	γ_d,max (kN/m³)	Reference
Forquilha III D. (Crs.)	11.0 (9.3–12.1)	23.13 (20.47–25.56)	Dornas (2008)
Feijão D. I (Crs.)	18.2 (16.1–19.9)	22.22 (20.68–23.27)	Silva (2010)
Feijão D. I (F.)	17.9 (16.1–19.7)	22.01 (21.50–22.51)
Brucutu M. flotation + magnetic C. (Crs.)	10.8	18.95	Silva et al. (2013)
Timbopeba M. flotation (Crs.)	11.3	18.75
Conceição M. flotation (Crs.)	13.8	17.75
Vargem Grande M. flotation (F.)	14.6	17.39
Pico M. Flotation (F.)	12.5	18.50
Fabrica M. underflow (F.)	13.3	17.65
Itatiaiuçu D. (F.)	12.4 (11.9–12.9)	21.83 (21.58–22.07)	Silva (2014)
Germano D., B3 (Crs.)	14.1	17.50	Ferreira (2016)
Germano D., B3 (Slm.)	16.3	21.90
Fundão D. (Crs.) modified proctor*	13.1	17.84	Morgenstern et al. (2016)
Fundão D. (Slm.)	16.7	22.56
SD-02 stack (F.)	7.2 (6.2–9.2)	19.21 (17.71–20.79)	Carvalho (2017)
Fernandinho M. future tailings (F.)	21.2	19.57
Pontal D. Arm I (Crs.)	11.2 (10.3–12.4)	21.18 (18.56–22.56)	Ferreira (2018)
Pontal D. Arm I (F.)	10.2 (9.0–11.4)	21.91 (20.75–22.81)
Portal D. Beach (Crs.)	10.4 (5.3–14.7)	20.77 (19.91–22.17)
Portal D. Beach (F.)	12.3	22.17
Serra Azul M. (F.)	12.8	21.66	Mohallem (2018)

^*Value not used to calculate the averages on Table 3. w_opt optimum water content, γ_d,max maximum dry unit weight, D dam, C concentration, M mine, Crs coarse, F fine, Slm slime

3.6 Hydraulic Conductivity

The hydraulic conductivity (k) of tailings is hard to generalize because it is affected by several characteristics, such as fines content, plasticity, confining stresses, void ratio, stratigraphy, material structure, sampling, and testing procedures. According to Vick (1990), hydraulic conductivity can vary from 10^–10 m/s in slimes to 10^–4 m/s in coarse tailings. Moreover, hydraulic conductivity tends to reduce up to five times the initial value by reducing the void ratio, while this reduction can be about ten times in slimes.

Chapuis & Aubertin (2003) studied the applicability of the Kozeny-Carman (KC) equation to mine tailings. According to the authors, the KC equation may be used to predict the k-value of tailings if it accounts for the particle shape effect, as in Eq. 1. However, the KC equation does not estimate k-values of intact tailings samples due to their increased stratigraphy and high anisotropy.

$$\log \frac{{\text{k}}}{{1{\text{m}}/{\text{s}}}} = 1,46 \cdot \left[ {0,5 + \frac{{{\text{e}}^{3} }}{{G_{{\text{s}}}^{2} S_{{\text{p}}}^{2} \left( {1 + e} \right)}}} \right] + 1,99$$

(1)

where, k is the hydraulic conductivity, e void ratio, G_s specific gravity, S_p specific surface.

Table 7 presents average values of the hydraulic conductivity obtained by different authors of tailings generated in facilities located in the Quadrilátero Ferrífero. The range of variation of the hydraulic conductivity is very similar to the one presented by Vick (1990): 1.15 × 10⁻¹⁰ m/s (Morgenstern et al. 2016) to 1.45 × 10⁻⁴ m/s (Santos 2004). However, the lowest k-value was obtained in an oedometer test in coarse tailings with e = 0.70, which does not match the expected values for such material. This value is even lower than the values obtained for slimes of the same TSF, analysed by the same authors.

Table 7

Average hydraulic conductivity of the QF iron ore tailings

Tailing	Test type	σ'_v (kPa)	e	k (m/s)	Reference
Monjolo Dump R. (Crs.)	R.W.P. C-H	–	0.67–0.62	4.77 × 10⁻⁵	Santos (2004)
		–	0.77–0.70	8.94 × 10⁻⁵
		–	0.86–0.81	9.45 × 10⁻⁵
		–	0.98–0.91	6.00 × 10⁻⁵
Monjolo Dump U. (Crs.)	F.P. C-H	–	0.86–0.79	8.86 × 10⁻⁵
Monjolo Dump In Situ (Crs.)	Borehole C-H	–	0.86–0.79	1.45 × 10⁻⁴
	G.P. 2 Height	–	0.86–0.79	1.17 × 10⁻⁴
	G.P. 2 + Height	–	0.86–0.79	7.16 × 10⁻⁵
	G.P. 1 Height	–	0.86–0.79	8.94 × 10⁻⁵
Forquilha III D. R. (Un.)	Oedometer	–	< 0.70	4.58 × 10⁻⁷	Dornas (2008)
Pontal D. Und. R. (Crs.)	F.P. C-H	–	0.86–0.80	1.04 × 10⁻⁵	Alves (2009)
Pontal D. CB3 R. (Crs.)		–	0.84–0.75	1.50 × 10⁻⁶
Pontal D. Ovr. R. (F.)		–	1.10–0.80	3.35 × 10⁻⁸
Fundão D. U. (Crs.)	R.W.P.C-H	–	–	3.72 × 10⁻⁶	Rezende (2013)
	Oedometer	12.5–3200	0.76–0.45	2.84 × 10⁻⁶
		25–3200	1.33–0.79	2.31 × 10⁻⁶
		100–3200	0.65–0.52	3.54 × 10⁻⁶
	F.W.P. C-H	75–600	–	6.88 × 10⁻⁶
Fundão D. R. (Crs.)	Oedometer	6.3–100	0.86–0.82	1.32 × 10⁻⁸	Morgenstern et al. (2016)
		25–300	0.82–0.79	1.68 × 10⁻⁹
		50–600	0.79–0.77	4.35 × 10⁻¹⁰
		50–3200	0.77–0.70	1.15 × 10⁻¹⁰
Fundão D. R. (Slm.)	Oedometer	2–1024	1.05–0.73	2.49 × 10⁻⁹
	L.S.C	0.4–1000	2.61–0.83	2.26 × 10⁻⁸
Fernandinho M. Fut. R. (Crs.)	R.W.P. F–H	0–800	e₀ = 0.80	7.45 × 10⁻⁷	Carvalho (2017)
	Oedometer	200–800	0.71–0.66	2.10 × 10⁻⁶
Fernandinho M. Fut. R. (F.)	R.W.P. F–H	0–800	e₀ = 0.82	4.68 × 10⁻⁷
	Oedometer	100–400	0.78–0.75	1.55 × 10⁻⁸
Pontal D. Beach R. (Crs.)	F.W.P. C-H	–	1.25	3.47 × 10⁻⁵	Ferreira (2018)
		–	0.66–0.56	5.08 × 10⁻⁸
Pontal D. Beach R. (F.)	F.W.P. C-H	–	1.53–1.39	5.36 × 10⁻⁵
		–	0.60	7.85 × 10⁻⁸
Forquilha I D. R. (Crs.)	F.W.P. F–H	–	0.84–0.65	1.82 × 10⁻⁶	Miranda (2018)
Forquilha I D. U. (F.)		–	1.01–0.88	6.55 × 10⁻⁹
Serra Azul M. R. (Crs.)	F.W.P. F–H	–	0.87–0.84	1.25 × 10⁻⁶	Mohallem (2018)
Serra Azul M. R. (F.)		–	0.81	4.20 × 10⁻⁸
Feijão D. I Berm Fill (Crs.)	G.P. 1 Height	–	0.86–0.70	3.55 × 10⁻⁵	Robertson et al. (2019)
Feijão D. I (F.)		–	1.33–0.95	6.95 × 10⁻⁶
Feijão D. I (Slm.)		–	1.62–1.15	1.08 × 10⁻⁶

σ'v effective vertical stress, e void ratio, k hydraulic conductivity, R remolded, R.W.P rigid wall permeameter, C-H constant head, U. undisturbed, F.P. flow pump, G.P Guelph permeameter, D dam, Ovr Overflow, Und underflow, F.W.P. flexible wall permeameter, Crs coarse, Un Unidentified, F fine, Slm slime, L.S.C. large strain consolidation, M mine, Fut future storage, F–H falling head

The void ratios of most samples shown in Table 7 were between 0.60 and 0.90. For this range, taking the average values of each author and excluding the sample analysed by Morgenstern et al. (2016), considered as an outlier, the average value of the hydraulic conductivity of the tailings is 1.92 × 10⁻⁵ m/s_, with a standard deviation of 3.36 × 10⁻⁵ m/s. In the tests where k was calculated to different confining stresses, there was a progressive reduction of its value with the increasing stresses and void ratio reduction. Overall, coarse tailings presented a variation of one-half to five times the initial value, following the usual reduction presented by Vick (1990). One exception was the sample analysed by Morgenstern et al. (2016), which reduced a hundred times the initial value. The fine tailings showed a variation of three times the initial value. The slimes studied by Morgenstern et al. (2016) reduced three times the initial value in the oedometer test; in the large strain consolidation test, the hydraulic conductivity at the final void ratio was sixty times lower than the initial value. The hydraulic conductivity is considerably affected by the test used to measure it. Furthermore, comparing the hydraulic conductivities of fine and coarse tailings measured by the same author and the same type of test, Table 7 shows that, on average, fine tailings tend to have hydraulic conductivity ten to one hundred times lower than coarse tailings.

4 Results and Discussions

4.1 Grain Size Distribution

As synthesized in Table 8, the grain size distributions presented 83% to 94% of fines passing the nº 200 sieve, from which the majority are silt size particles. The samples UFV and FEUP—Sample A (Fig. 3) showed high similarity. Nonetheless, FEUP—Sample B showed some divergence on the central portion of the curve. The FEUP—Sample B, likewise the FEUP—Sample A—Proctor, exhibited higher content of particles ranging from 0.02 mm to 0.04 mm than the other samples.

Table 8

Main characteristics of the grain size distribution curves

Sample	D₅₀ (mm)	C_U	C_c	Percent fines < 0.075 mm
FEUP—A	0.044	48.6	8.2	85.2
FEUP—A—Proctor	0.020	24.2	7.0	84.7
FEUP—B	0.026	15.4	7.2	83.0
UFV	0.037	28.8	5.6	89.6
UFV—Post-H.C. test	0.035 (0.034–.037)	34.9 (27.4–45.5)	6.1 (4.9–8.5)	91.7 (87.7–94.0)

D₅₀ mean grain size, C_u coefficient of uniformity, C_c coefficient of curvature, Post-H.C post hydraulic conductivity test

When comparing FEUP—Sample A with FEUP—Sample A—Proctor the fines content increases in the central portion of the curve, indicating some particle breakage due to dynamic compaction. On the other hand, UFV—Post-hydraulic conductivity test samples did not show a significant difference from the UFV sample, indicating that samples compacted statically (with a press) did not significantly evolve.

Additionally, the tailings studied in this research are within the limits defined in 0 for the QF iron ore tailings (Fig. 4). The grain size curves of the studied material are very similar to the upper limit of the samples of fine tailings of Feijão Dam I tested by Robertson et al. (2019). The values of C_u, and C_c are in the one standard deviation range of the QF fine tailings (Table 9) indicating that curves have similar shapes. The high values of C_u and C_c are a consequence of the tail of fines shown by the curves, likewise the other tailings from the Quadrilátero Ferrífero. Therefore, the material could also be classified as relatively poorly graded instead of well-graded.

Table 9

Statistics of standard Proctor and field tests performed by the mining company

Statistics	γ_d,max (kN/m³)	w_opt (%)	γ_d,field (kN/m³)	w_field (%)	DC (%)	w_dev. (%)
Avg	21.2	11.8	21.0	13.3	99.4	1.50
Stdev	0.90	0.80	1.09	1.90	4.30	1.90
Max	22.9	14.0	23.5	21.4	115	10.2
Min	18.6	10.3	17.8	9.40	87.3	− 2.90

γ_d,max maximum dry unit weight, w_opt optimum water content. γ_d,field dry unit weight of embankment. w_field water content of embankment. DC degree of compaction, w_dev. water content deviation, Avg average, Stdev Standard deviation, Max maximum value, Min minimum value

4.2 Specific Gravity

Specific gravity was determined on three samples: UFV (G_s = 3.218 g/cm³), FEUP—Sample A (G_s = 3.184 g/cm³), and FEUP—Sample B (G_s = 3.213 g/cm³). These values were consistent with each other and the literature, with an average of 3.205 g/cm³. The specific gravity of the analysed tailings is considerably close to the average value of QF fine tailings (G_s = 3.59 g/cm³) presented in subsection 3.2 when compared to the range of variation (2.66–5.14). Moreover, this value is consistent with the samples in Table 4 with low iron content.

4.3 Atterberg Limits

As with most iron ore tailings displayed in Table 5, the plasticity and liquid limit tests indicated that the studied tailings are non-plastic. Following the Unified Soil Classification System (USCS), explained in the ASTM D2487:2017, the material is classified as silt or silt with sand, depending on the grain size distribution curve.

4.4 Maximum and Minimum Void Ratios

With an average water content of 0.66%, the sample dried at room temperature presented e_min = 0.60 and e_max = 1.18, and respective γ_d,max = 19.70 kN/m³ and γ_d,min = 14.46 kN/m³. The maximum dry unit weight is lower than γ_d,max = 20.71 kN/m³ and γ_d,max = 21.05 kN/m³ obtained after the standard Proctor tests presented in 0, but still compatible with them. The slight variation of γ_d,max is partially explained by the fines content increase identified after the standard Proctor test, indicating some particle breakage (see 0), which results in the rearrangement of the material. The broken particles tend to fill the voids resulting in higher dry unit weights. The e_min and e_max are a little bit lower than the average values found in Table 3 for the fine tailings of the Quadrilátero Ferrífero (e_min = 0.67 and e_max = 1.31), but they are into the one standard deviation range of the sets.

4.5 Maximum Dry Unit Weight and Optimum Water Content

The dry unit weight-water content curves defined after the standard Proctor tests performed at LMS-UFV and LabGeo-FEUP are plotted in Fig. 4. In both curves, the optimum water content was w_opt = 11.7%, and the maximum dry unit weight presented a small variation (γ_d,max)_FEUP = 20.71 kN/m³ and (γ_d,max)_UFV = 21.05 kN/m³. The divergence of the values of dry unit weight found at UFV and FEUP may be associated with the variability of the samples or the intensity of particle breakage in each test. Compared to the parameters presented in Table 3, the values of w_opt. and γ_d,max are into the one standard deviation range of the QF fine tailings, which means that the values are considerably close to the average values obtained in the literature (w_opt = 13.4% and γ_d,max = 20.2 kN/m³).

According to Crystal et al. (2018), materials with S higher than 85% are likely to behave as saturated in a dry stack. However, with degrees of compaction higher than 90% of standard Proctor, no samples (FEUP and UFV) presented degrees of saturation (S) above this reference. Even though the material does not reach S = 85%, it is close to reaching saturation, and water infiltration must be prevented at all costs when compacting wet of optimum. Additionally, when stacking the tailings, if the deposited tailings are compressible, as they are gradually loaded, the material will tend to consolidate, extruding the air existing in the internal voids, reducing the void ratio, and increasing saturation (Lupo and Hall 2011).

4.6 Field Tests

The standard Proctor tests performed in the field exhibited average w_opt and γ_d,max similar to those found in this research (Table 9). The one standard deviation range indicated a high concentration of values between γ_d,max = 20.33 kN/m³ and γ_d,max = 22.13 kN/m³, and w_opt between 11% and 12.6%. However, the maximum and minimum values diverge significantly from the average. Two samples have even shown pairs of values (w_opt, γ_d,max) above the zero air voids curve (S = 100%), just as the other 42 pairs of values determined in the field tests (Fig. 4). These results suggest considerable variability in the characteristics of the tailings in some portions of the trial embankment.

Taking the FEUP and UFV curves as references, the average water content of the field samples in Table 9, w_field = 13.3%, is equivalent to DC between 95 and 97%. The average dry unit weight of the field samples, γ_d,field = 21.0 kN/m³ is equivalent to DC = 98.9%, adopting the average γ_d,max = 21.2 kN/m³ as reference. Moreover, 82% of the field samples indicate compaction wet of optimum. Of the 218 field tests, 78 show a degree of compaction higher than 100% of standard Proctor and, from these 78 tests, 64 show water content above the optimum. There is no significant divergence of the values of γ_d and w when comparing the sampling position—top or bottom of the layer—in Fig. 4. It suggests that the same degree of compaction was achieved at the top and the bottom of the layers indicating that the compaction energy was adequate.

The samples on the top of the chart (Fig. 4) have pairs of dry unit weight and water content higher than the maximum dry unit weight and optimum water content of the standard Proctors of reference. A homogeneous material could never achieve a state of DC > 100% compacted wet of optimum, even if compacted with higher energy. These results firmly indicate variability of the material in the field, and a unique sample is not enough to define the behaviour of the dry stack. The macro-scale behaviour of the tailings is strongly dependent on the micro-scale interparticle contact, which is affected by the morphology (shape, size and surface roughness and fabric) and elastic characteristics of the tailings’ particles (Sandeep and Senetakis 2019; Nardelli and Coop 2019; Ren et al. 2021). Therefore, the variability of the tailings composition directly affects the mechanical behaviour of a dry stack.

The variability of the tailings requires not only the constant characterization of the material but also the adjustment of the specifications for stacking it. This dynamic may not be easily implemented in large-scale production, so the impact of the tailings’ variability in the dry stack behaviour should be minimized. Stabilization of mining tailings with different binding agents has been studied (Barati et al. 2020; Bruschi et al. 2021; Pereira dos Santos et al. 2022; Servi et al. 2022) and should become an attractive alternative to overcome the impact of tailings variability in large scale dry stacks.

4.7 Hydraulic Conductivity

The void ratios of the specimens varied from 0.64 to 0.47 under different degrees of compaction and confining stresses. Figure 5 shows that permeability decreases with the reduction of the void ratio. The specimens compacted with higher void ratios—lower degrees of compaction—showed higher hydraulic conductivities, except those tested at 10 kPa of confining stress, which presented almost the same permeability.

The KC equation curve in Fig. 5 was determined adopting the average specific gravity (G_s = 3.205 g/m³) and the specific surface (S_p = 402.44 m²/kg) determined from the average grain size distribution curve of the UFV—Post-hydraulic conductivity test samples displayed in 0 following the method of Chapuis & Légaré (1992). The trend defined by the KC equation (Eq. 1) is not adherent to the results of the tests. The measured hydraulic conductivities and reduction rate are higher than those determined by the KC equation. It indicates that the studied tailings, in the studied range of void ratios, are more sensitive to void ratio reduction than the materials used to formulate the KC equation.

Comparing the highest (4.10 × 10⁻⁷ m/s) and the lowest (6.23 × 10⁻⁸ m/s) values, the factor of reduction of the hydraulic conductivity with decreasing void ratio is about 6.6 times. This rate is higher than the maximum expected reduction factor stated by Vick (1990) for coarse tailings (5 times) and lower than the maximum reduction factor for slimes (10 times). Thus, the value is consistent with the material, since it is classified as fine tailing composed predominantly of silt size particles.

After constant-head tests in flexible wall permeameter, Ferreira (2018) found k = 7.85 × 10⁻⁸ for a sample of non-plastic fine tailings with e = 0.60 (Table 7). Compared to the studied tailings, the hydraulic conductivity is 2.3 times lower than the value defined by the lower boundary in Fig. 5 (k = 1.83 × 10⁻⁷) and 5.8 times lower than the value defined by the average trend line (k = 4.58 × 10⁻⁷) for the same void ratio. The value determined by Ferreira (2018) is 3.4 times higher than the value predicted by the KC equation (k = 2.31 × 10⁻⁸), so it is closer to the lower boundary defined in Fig. 5.

5 Conclusions

The studied tailings exhibited characteristics consistent with some other iron ore tailings of the Quadrilátero Ferrífero. The grain size distribution curves of the samples compacted statically did not show significant divergence from the initial sample. Nonetheless, the dynamically compacted specimen showed a fines content increase in the central portion of the curve. It indicates the evolution of the grain size distribution due to dynamic compaction, but not static. The standard Proctor tests done for quality control of the trial embankment showed that the field samples converged to average values of optimum water content and maximum dry unit weight close to those found at laboratory tests. However, some samples showed considerable variation. Variability of both material properties and degree of compaction was identified in the trial embankment. Therefore, mapping the tailings’ variations is essential to stack the material in an adequate initial state. The main variations of the material must be characterized, and the proper degree of compaction and optimum water content must be specified for each of them. The higher the degree of compaction, the lower the hydraulic conductivity. The hydraulic conductivity determined after the constant head tests were proportional to the void ratio of each specimen. For the studied range of void ratios, k varied from 6.23 × 10⁻⁸ m/s to 4.10 × 10⁻⁷ m/s, a reduction factor of approximately 6,6 times. The k measured was higher than that predicted by the KC equation, and the material showed to be more sensitive to the void ratio variation for the studied range.

The data presented in section 3 is a literature review of results found by different authors for different iron ore tailings of the Quadrilátero Ferrífero. The data survey had the objective of compilating the physical properties and trying to identify trends. The properties showed variability, but samples with similar index properties were also identified. Some properties, such as maximum and minimum void ratios and hydraulic conductivity, are more sensitive to the methodology adopted for their measurement and showed higher variability. All properties presented must be carefully analysed together, and variability must be accounted for when using them as references. For a 200 m high dry stack with an average bulk unit weight of 23,5 kN/m³ (based on w_opt. and γ_d,max defined in subsection 4.5), the confining stresses in the base of the stack exceed the maximum confining stress of 1.900 kPa adopted in the hydraulic conductivity tests. Higher confining stresses could not have been adopted due to the stress limitation of the equipment. As further research, it is proposed to evaluate the void ratio and hydraulic conductivity reduction with higher confining stresses. Additionally, it is proposed to evaluate the deposition, layering, and microstructure formation in TSFs; the impact of the confining stresses, and variability of degree of compaction in the mechanical behaviour of the tailings; the impact of stabilization techniques in the mechanical and hydraulic behaviour of the tailings, and the numerical modelling of the dry stacking with adequate constitutive models.

Acknowledgements

The authors are very grateful to the Federal University of Viçosa, the Faculty of Engineering of the University of Porto and the CONSTRUCT – Institute of R&D in Structures and Construction, Portugal, and the mining company for the technical support to the development of this study.

Declarations

Conflict of interest

The authors have no conflicts of interest to declare. All co-authors have observed and affirmed the contents of the paper and there is no financial interest to report.

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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Alves ARC (2009) Disposição compartilhada de rejeito e estéril gerados no processo de extração de minério de ferro. University of Brasília (in Portuguese), Thesis

ANM (2023) Brazilian mineral yearbook—main metallic commodities. Brasília, Brazil

Barati S, Piltan & , Shourijeh T, et al (2020) Stabilization of iron ore tailings with cement and bentonite: a case study on Golgohar mine. Bull Eng Geol Env 79:4151–4166. https://doi.org/10.1007/s10064-020-01843-6/PublishedCrossRef

Bruschi GJ, dos Santos CP, de Araújo MT et al (2021) Green stabilization of bauxite tailings: mechanical study on alkali-activated materials. J Mater Civil Eng. https://doi.org/10.1061/(ASCE)CrossRef

Carraro JAH, Prezzi M (2008) A new slurry-based method of preparation of specimens of sand containing fines. Geotech Test J 31:

Carvalho WDS de (2017) Sistema de disposição compartilhada de estéreis e rejeitos desaguados da mina de Fernandinho. Federal University of Ouro Preto (in Portuguese), Thesis

Chapuis RP, Légaré P-P (1992) A Simple method for determining the surface area of fine aggregates and fillers in bituminous mixtures. In: Meininger RC (ed) Effects of Aggregates and Mineral Fillers on Asphalt Mixture Performance. ASTM STP 1147, Philadelphia, pp 177–186

Chapuis RP, Aubertin M (2003) On the use of the Kozeny-Carman equation to predict the hydraulic conductivity of soils. Can Geotech J 40:616–628. https://doi.org/10.1139/t03-013CrossRef

CIMNE (2021) Computational analyses of Dam I failure at the Corrego de Feijao mine in Brumadinho Final Report. Barcelona

Crystal C, Hore C, Ezama I (2018) Filter-pressed dry stacking: design considerations based on practical experience. In: tailings and Mine Waste 2018. Keystone, pp 209–219

Cubrinovski M, Ishihara K (1999) Empirical correlation between SPT N-value and relative density for sandy soils. Soil Found 39:61–71. https://doi.org/10.3208/sandf.39.5_61CrossRef

Cubrinovski M, Ishihara K (2000) Flow potential of sandy soils with different grain compositions. Soils Found 40:103–119. https://doi.org/10.3208/sandf.40.4_103CrossRef

Cubrinovski M, Ishihara K (2002) Maximum and minimum void ratio characteristics of sands. Soil Found 42:65–78. https://doi.org/10.3208/sandf.42.6_65CrossRef

Cubrinovski M, Rees S, Bowman E (2010) Effects of non-plastic fines on liquefaction resistance of sandy soils. Geotech Geolog Earthquake Eng 17:125–144. https://doi.org/10.1007/978-90-481-9544-2_6CrossRef

da PresottiS, E (2002) Influência do teor de ferro nos parâmetros de resistência de um rejeito de minério de ferro. Federal University of Ouro Preto (in Portuguese), Thesis

da Silva WP (2010) Estudo do potencial de liquefação estática de uma barragem de rejeito alteada para montante aplicando a metodologia de Olson (2001). Federal University of Ouro Preto (in Portuguese), Thesis

Dauce PD, de Castro GB, Lima MMF, Lima RMF (2019) Characterisation and magnetic concentration of an iron ore tailings. J Market Res 8:1052–1059. https://doi.org/10.1016/j.jmrt.2018.07.015CrossRef

Davies MP, Rice S (2001) An alternative to conventional tailing management—“dry stack.” In: tailings and Mine Waste 2001, 1st edn. CRC Press, p 10

Davies M, Lupo J, Martin T, et al (2010) Dewatered tailings practice—trends and observations. In: tailings and Mine Waste 2010, 1st edn. CRC Press, London, p 10

Davies M (2011) Filtered dry stacked tailings—the fundamentals. In: tailings and Mine Waste 2011. Vancouver

de Silva JP, S, (2014) Evaluation of the flow scheme influence in the geotechnical behavior of a tailing dam heightened through the upstream method. University of São Paulo (in Portuguese), Thesis

Dornas ALL (2008) Análise do comportamento geotécnico da barragem Forquilha III para a geometria atual e para alteamentos futuros pelo método de montante. Federal University of Ouro Preto (in Portuguese), Thesis

dos Santos CP, Bruschi GJ, Mattos JRG, Consoli NC (2022) Stabilization of gold mining tailings with alkali-activated carbide lime and sugarcane bagasse ash. Trans Geotech. https://doi.org/10.1016/j.trgeo.2021.100704

Ferreira DS (2016) Análise do comportamento geotécnico do aterro experimental executado sobre um depósito de rejeitos finos. Federal University of Ouro Preto (in Portuguese), Thesis

Ferreira DB (2018) Liquefação de rejeitos de minério de ferro - estudo de caso: sistema pontal em Itabira/MG. Federal University of Ouro Preto (in Portuguese), Thesis

Fourie AB, Jones CJFP (2010) Improved estimates of power consumption during dewatering of mine tailings using electrokinetic geosynthetics (EKGs). Geotext Geomembr 28:181–190. https://doi.org/10.1016/j.geotexmem.2009.10.007CrossRef

Fourie AB, Johns DG, Jones CJFP (2007) Dewatering of mine tailings using eletrokinetic geosynthetics. Can Geotech J 44:160–172. https://doi.org/10.1139/T06-112CrossRef

Furnell E, Bilaniuk K, Goldbaum M et al (2022) Dewatered and stacked mine tailings: a review. ACS ES and T Engineering 2:728–745CrossRef

Gomes RB, de Tomi G, Assis PS (2016) Iron ore tailings dry stacking in Pau Branco mine, Brazil. J Market Res 5:339–344. https://doi.org/10.1016/j.jmrt.2016.03.008CrossRef

Hore C, Luppnow D (2014) Karara Iron Ore TSF-design considerations for a unique large scale dry stack facility. In: tailings and Mine Waste 2014. Keystone, p 11

Ishihara K, Troncoso J, Kawase Y, Takahashi Y (1980) Cyclic strength characteristics of tailings materials. Soil Found. https://doi.org/10.3208/sandf1972.20.4_127CrossRef

Lade P, v., Liggio CD, Yamamuro JA, (1998) Effects of Non-Plastic Fines on Minimum and Maximum Void Ratios of Sand. Geotech Test J 21:336–347. https://doi.org/10.1520/gtj11373jCrossRef

Lees A (2016) Geotechnical finite element analysis. ICE Publishing

Li W, Coop MR, Senetakis K, Schnaid F (2018) The mechanics of a silt-sized gold tailing. Eng Geol 241:97–108. https://doi.org/10.1016/j.enggeo.2018.05.014CrossRef

Lima RMF, Abreu FDPVF (2020) Characterization and concentration by selective flocculation/magnetic separation of iron ore slimes from a dam of Quadrilátero Ferrífero—Brazil. J Market Res 9:2021–2027. https://doi.org/10.1016/j.jmrt.2019.12.034CrossRef

Lupo J, Hall J (2011) Dry stack tailings—Design considerations. Tailings and Mine Waste’10—Proceedings of the 14th International Conference on Tailings and Mine Waste 327–334. https://doi.org/10.1201/b10569-40

Mijic Z, Bray JD, Riemer MF et al (2021) Test method for minimum and maximum densities of small quantities of soil. Soils Found 61:533–540. https://doi.org/10.1016/j.sandf.2020.12.003CrossRef

Miranda TM (2018) Análise do potencial de liquefação de um rejeito de minério de ferro por meio de ensaios de laboratório e de campo. Federal University of Ouro Preto (in Portuguese), Thesis

Mohallem SDS (2018) Análise de sistema de co-disposição dos rejeitos de minério de ferro gerados na mina de Serra Azul - Itatiaiuçu/MG. Federal University of Ouro Preto (in Portuguese), Thesis

Morgenstern NR, Vick SG, Viotti CB, Watts BD (2016) Report on the immediate causes of the failure of the Fundão Dam

Nardelli V, Coop MR (2019) The experimental contact behaviour of natural sands: normal and tangential loading. Geotechnique 69:672–686. https://doi.org/10.1680/jgeot.17.P.167CrossRef

Oathes TJ, Asce SM, Boulanger RW, Asce F (2022) Nonlinear Viscoplastic Modeling of the Feijão Dam 1 Failure

Pereira EL (2005) Estudo do potencial de liquefação de rejeitos de minério de ferro sob carregamento estático. Federal University of Ouro Preto (in Portuguese), Thesis

Pires KDS, Mendes JJ, Figueiredo VC et al (2019) Mineralogical characterization of iron ore tailings from the quadrilatero ferrifero, Brazil, by eletronic quantitative mineralogy. Mater Res 22:1–7. https://doi.org/10.1590/1980-5373-MR-2019-0194CrossRef

Ren J, Li S, He H, Senetakis K (2021) The tribological behavior of iron tailing sand grain contacts in dry, water and biopolymer immersed states. Granul Matter. https://doi.org/10.1007/s10035-020-01068-0CrossRef

Rezende VA (2013) Estudo do comportamento de barragem de rejeito arenosos alteada por montante. Federal University of Ouro Preto (in Portuguese), Thesis

Robertson PK, de Melo L, Williams DJ, Wilson GW (2019) Report of the expert panel on the technical causes of the failure of Feijão dam I

Sahi A, el Mahboub K, Belem T et al (2019) Dewatering of mine tailings slurries using superabsorbent polymers (SAPs) reclaimed from industrial reject of baby diapers: a preliminary study. Minerals. https://doi.org/10.3390/min9120785CrossRef

Sandeep CS, Senetakis K (2019) Influence of morphology on the micro-mechanical behavior of soil grain contacts. Geomech Geophys Geo-Energy Geo-Resour 5:103–119. https://doi.org/10.1007/s40948-018-0094-6CrossRef

Santos AG (2004) Influência do teor de ferro na condutividade hidráulica saturada de um rejeito de minério de ferro. Federal University of Ouro Preto (in Portuguese), Thesis

Servi S, Lotero A, Silva JPS et al (2022) Mechanical response of filtered and compacted iron ore tailings with different cementing agents: focus on tailings-binder mixtures disposal by stacking. Constr Build Mater. https://doi.org/10.1016/j.conbuildmat.2022.128770CrossRef

Shafaei F, Doulati Ardejani F, Bahroudi A et al (2022) Mechanical-electrical dewatering (EDW) of mine tailings: influence of voltage level on water recovery and moisture reduction. Miner Eng. https://doi.org/10.1016/j.mineng.2021.107303CrossRef

Silva JP de S, Mendes M, Milonas J, et al (2013) Geotechnical parameters of iron ore tailings from the Quadrilátero Ferrífero after different treatments and ore processing. Proceedings of the 16th International Seminar on Paste and Thickened Tailings 261–271. https://doi.org/10.36487/ACG_rep/1363_20_Silva

Tolentino MVC (2010) Estudo da viabilidade técnica do aproveitamento do resíduo arenoso da mineração do itabirito. Federal University of Minas Gerais (in Portuguese), Thesis

Torres-Cruz LA, Santamarina JC (2020) The critical state line of nonplastic tailings. Can Geotech J 57:1508–1517. https://doi.org/10.1139/cgj-2019-0019CrossRef

Vale (2020) Repair and development. Dry iron ore processing. Dry processing at Vale. http://www.vale.com/brasil/EN/aboutvale/reports/atualizacoes_brumadinho/Pages/dry-processing.aspx. Accessed 1 Dec 2020

Vick SG (1990) Planning, design, and analysis of tailings dams, 2nd edn. BiTech Publishers Ltd., Vancouver

Zhang C, Ma C, Xiong J, Jiang Q (2022) Tailings dam geotechnical stability improvement due to flocculants treated fine tailings dewatering. Geotech Geol Eng. https://doi.org/10.1007/s10706-022-02300-9CrossRef

Title: Characterization of an Iron Ore Tailing Sample and the Evaluation of Its Representativeness
Authors: Jessé Joabe Vieira Carneiro
Eduardo Antonio Gomes Marques
António Joaquim Pereira Viana da Fonseca
Roberto Lopes Ferraz
Ângelo Henrique Cruz Oliveira
Publication date: 23-03-2023
Publisher: Springer International Publishing
Published in: Geotechnical and Geological Engineering / Issue 5/2023
Print ISSN: 0960-3182
Electronic ISSN: 1573-1529
DOI: https://doi.org/10.1007/s10706-023-02430-8

Springer Professional

Abstract

Publisher's Note

1 Introduction

2 Materials and Methods

2.1 Sample Preparation

2.2 Index Tests

2.2.1 Grain Size Distribution

2.2.2 Specific Gravity

2.2.3 Atterberg Limits

2.2.4 Maximum and Minimum Void Ratios

2.3 Proctor Compaction Test

2.4 Field Test

2.5 Hydraulic Conductivity Test

3 Iron Ore Tailings from the Quadrilátero Ferrífero, Brazil

3.1 Grain Size Distribution

3.2 Specific Gravity

3.3 Atterberg Limits

3.4 Maximum and Minimum Void Ratios

3.5 Maximum Dry Unit Weight and Optimum Water Content

3.6 Hydraulic Conductivity

4 Results and Discussions

4.1 Grain Size Distribution

4.2 Specific Gravity

4.3 Atterberg Limits

4.4 Maximum and Minimum Void Ratios

4.5 Maximum Dry Unit Weight and Optimum Water Content

4.6 Field Tests

4.7 Hydraulic Conductivity

5 Conclusions

Acknowledgements

Declarations

Conflict of interest

Publisher's Note

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