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Metal Removal from Synthetic Mine Tailing Leachates using Fixed-Bed Ion-Exchange Columns

Authors:

Abstract

The generation and disposal of solid wastes from the mining industry has the potential to cause significant, enduring environmental damage. Atmospheric weathering of waste stockpiles leads to the formation of metal-rich leachates, which have been shown to adversely affect ecological communities, and can act as barriers towards meeting legislative water quality targets. The removal of problematic metals from tailings prior to disposal may pose an opportunity to prevent this damage. This research presents results from dynamic column experiments aimed at assessing the performance of several commercial ion-exchange resins for metal removal from simulated tailing leachates. Artificial mixed-metal solutions were prepared to simulate the conditions expected following a sulphuric acid leach of mine tailings. Metal breakthrough from each column was modelled to gain an insight to the selectivity and capacity of each tested resin, allowing identification of the most-suitable resins to take forward to process optimisation and scale-up.
Copper Cobalt Africa, incorporating the 9th Southern African Base Metals Conference
Livingstone, Zambia, 1012 July 2018
Southern African Institute of Mining and Metallurgy 353
Metal Removal from Synthetic Mine Tailing Leachates
using Fixed-Bed Ion-Exchange Columns
Alex L. Riley*, Sarah E. Pepper, Ella L. Sexton and Mark D. Ogden
Separations and Nuclear Chemical Engineering Research (SNUCER) Group,
Department of Chemical and Biological Engineering, University of Sheffield, United Kingdom
*Corresponding author: ariley4@sheffield.ac.uk
The generation and disposal of solid wastes from the mining industry has the potential to
cause significant, enduring environmental damage. Atmospheric weathering of waste
stockpiles leads to the formation of metal-rich leachates, which have been shown to
adversely affect ecological communities, and can act as barriers towards meeting
legislative water quality targets. The removal of problematic metals from tailings prior to
disposal may pose an opportunity to prevent this damage. This research presents results
from dynamic column experiments aimed at assessing the performance of several
commercial ion-exchange resins for metal removal from simulated tailing leachates.
Artificial mixed-metal solutions were prepared to simulate the conditions expected
following a sulphuric acid leach of mine tailings. Metal breakthrough from each column
was modelled to gain an insight to the selectivity and capacity of each tested resin,
allowing identification of the most-suitable resins to take forward to process optimisation
and scale-up.
INTRODUCTION
The process of mining, while essential for global mineral resource supply chains, is inherently
associated with the potential risk for environmental damage; particularly towards surface waters and
aquifers. In the United Kingdom, it has been conservatively estimated that 6% of surface water bodies
nationally are adversely affected by mine waters (Mayes et al., 2009), predominantly from abandoned
mine workings. Following mine abandonment, the management options available are dependent on
several factors, such as the morphology of the mine network and the local hydrogeology (Westermann
et al., 2017). Given that most metal mines in the United Kingdom were abandoned following their
peak production in the 18th and 19th centuries (Crane et al., 2016), pumping capabilities are rarely
available, and groundwater resurgence leads to uncontrolled discharge of metalliferous waters in
many cases (Banks et al., 2017). The effects of such releases include the damage of downstream
ecological communities, adverse effects on chemical water quality, and potential degradation of public
supply aquifers where groundwaters are contaminated (Armitage et al., 2007; Byrne et al., 2013;
Neymeyer et al., 2007).
The generation of metal-rich waters, both during active mine working and following abandonment, is
related to the interaction of previously unexposed mineral surfaces with resurging groundwater, as
described below for pyrite, FeS2 (Kruse et al., 2013; Younger et al., 2002). The pyritic oxidation process
occurs in four stages: oxidation and dissolution of exposed pyrite (Equation 1); conversion of ferrous
iron to ferric iron (Equation 2); hydrolysis of ferric iron (Equation 3); and further pyritic oxidation by
354
aqueous ferric iron (Equation 4). Mineral oxidation reactions may also be accelerated in the presence
of certain lithotrophic bacteria (Johnson and Hallberg, 2003).
2 FeS2 s+ 7 O2 aq + 2 H2O l 2 Fe (aq)
2+ + 4 SO4 (aq)
2- + 4 H (aq)
+ [1]
2 Fe (aq)
2+ + 0.5 O2 (aq) + 2 H (aq)
+ Fe (aq)
3+ + H2Ol [2]
2 Fe3+ + 6 H2Ol 2 FeOH3 s+ 6 H+(aq) [3]
14 Fe3+ + FeS2 (s) + 8 H2O (l) →2 SO4 (aq)
2- + 15 Feaq
2+ + 16 H(aq)
+ [4]
It is the products of the above reactions that give mine waters their physicochemical characteristics;
namely, a low pH, high sulphate concentration, and propensity for rapid precipitation of secondary
minerals (e.g., iron hydroxides (Equation 3)). The acid conditions within underground mine systems
promote the dissolution of other metal-bearing mineral deposits in the host rock (Wolkersdorfer,
2008), leading to elevated concentrations of metals such as copper, manganese, nickel, and zinc in
mine waters. Additionally, where the pH of waters is below pH 4, the reaction of clay minerals can
result in increased aluminium concentrations within effluent waters (Banks et al., 1997). Given the
variation of geology within mines from different geographical areas, the elemental composition of
mine waters also varies from region to region (Mayes et al., 2010).
In addition to the flooding of disused mine systems, mine tailings can act as a source of metalliferous
leachates. Mine tailings are the solid waste byproducts of mining and are commonly disposed of in
custom-built tailing facilities. In instances where tailings are heaped and open to the atmosphere, the
percolation of rainwater through the heap can instigate weathering reactions similar to those
previously described, resulting in leachate formation. Given the adverse effects of mine waste
leachates on the environment, the removal of contaminant metals is a priority; both from waters being
discharged from mine adits and from tailing heap leachates. Furthermore, should the metal
contaminants be leached and recovered from the solid mine tailings themselves in a controlled
manner prior to dumping, the risk of environmental damage from mine wastes could be reduced.
The research presented here aims to investigate the suitability of a range of commercially available
ion-exchange resins to remove the problematic metals encountered in mine tailing leachates. Building
upon previous work by the authors, this paper presents the results of dynamic column experiments
from mixed-metal solutions, with the eventual aim of creating a multi-column ion-exchange system
for the treatment of aqueous mine wastes.
EXPERIMENTAL
Resin and Solution Preparation
The ion-exchange resins were either kindly donated by Purolite Ltd. (S930, S950, S957) or purchased
from Sigma-Aldrich (M4195). Key specifications of each resin used are provided in Table I, as given by
manufacturer specification sheets. Before column packing, resins were preconditioned to their
hydrogen form by batch contact with excess 1 M sulphuric acid for 24 h on an orbital shaker.
Preconditioned resins were washed multiple times with deionised water to ensure complete removal
of residual sulphuric acid.
A solution was prepared by dissolving sulphate salts of Al3+, Co2+, Cu2+, Fe3+, Mn2+, Ni2+ and Zn3+,
chosen to represent the metals more commonly associated with mine wastes. The solution was
acidified to pH 1.43 (identified in previous experiments to be appropriate for efficient metal removal
by the studied resins) using dilute sulphuric acid. Concentrations used for experiments were
artificially high (200 mg/L per metal) to reduce experimental run time, and all metal concentrations
were equal to limit any effects related to concentration difference between metal species.
355
Table I: Key characteristics of ion-exchange resins used (N/A = data not available from supplier, PS-DVB =
polystyrene-divinylbenzene); capacities converted to eq/L from manufacturer specification sheets.
Resin
Acronym
Functionality
Capacity
(eq/L)
Polymer
matrix
Moisture
(%)
Particle
size (µm)
Dowex
M4195
M4195
Bis-picolylamine
1.1-1.3
PS-DVB
40-60
297-841
Puromet
MTS9300
S930
Iminodiacetic acid
1.6
PS-DVB
52-60
425-1000
Puromet
MTS9500
S950
Aminophosphonic acid
1.3
PS-DVB
60-68
N/A
Puromet
MTS9570
S957
Phosphonic/sulphonic
acid
0.64
PS-DVB
55-70
N/A
Column Setup
Small-scale columns (Sorbtech flash cartridge, 5 mL) were packed with each ion-exchange resin and
capped at either end with Teflon frits, resulting in a bed volume of 5 mL wet-settled resin per column.
During packing, intermittent agitation of the resin promoted a homogenous distribution of particle
size throughout the bed. A reverse-flow setup was employed to ensure efficient mass transfer between
the solution and resin, whereby the solutions were introduced at the bottom of the column and
pumped upwards against gravity. A Heidolph ‘Hei-Flow Value 01’ peristaltic pump with ‘SP Quick’
pump head was used to pass solutions through the column at a constant rate (10 BV/h), with effluents
collected using a BioRad Model 2110 fraction collector. Initial solutions and treated samples were
diluted using a 1% nitric acid solution prior to metals analysis by inductively coupled plasma optical
emission spectroscopy (ICPOES).
Breakthrough Modelling
Three commonly applied breakthrough models were used to analyse the breakthrough curves
generated by each column experiment; the Modified Dose Response model (Equations 5 and 6), the
BohartAdams model (Equation 7) and the Thomas model (Equation 8) (Albadarin et al., 2012;
Hamdaoui, 2009). Symbol definitions are provided in the Nomenclature section.


[5]

[6]
 [7]

 [8]
RESULTS
Weak Base Resin M4195
The low retention of Al3+, Mn2+ and Fe3+ ions within the M4195-packed column resulted in complete
breakthrough (Ct/Co > 1) after contact with 30 mL of solution, equating to six bed volumes (Figure 1).
Suppressed breakthrough of Zn2+ and Co2+ indicated a stronger affinity of these metals towards the
bis-picolylamine functional groups of M4195. The concentration ratio of most metals exceeded unity
shortly after reaching complete breakthrough; an indication that metals had switched from being
loaded to being displaced from the resin. The displacement of metals is indicative of the selectivity of
each resin, with ions of lower affinity being displaced by those with higher affinity towards the
functional group a principal often encountered in instances where multiple metal species compete
for sorbent sites (Escudero et al., 2013). A high selectivity towards Ni2+ ions by M4195 was observed
356
(Figure 1) and, given that Cu2+ concentration in effluent solutions remained below limits of detection,
it can be assumed that this resin has the highest selectivity towards copper over the other metals.
Figure 1: Breakthrough curves of metals after passing through M4195 column at pH 1.43. (Al3+ = +, Co2+= □,
Fe3+ = ♦, Mn2+ = ×, Ni2+ = ▲, Zn2+= ○. Note: Cu2+ concentrations remained below limits of detection. Dotted
lines represent model fitting (marked by an asterisk in Table II).
Modelling of breakthrough curves confirmed that lower operating capacities (Qo) were associated
with metals that showed faster breakthrough (Table II). It is important to note that the common
breakthrough models are designed for single-species solutions, where Ct/Co cannot exceed 1. It is for
this reason that the modelled breakthrough curves in the presented figures are not able to exceed this
value. Despite this, the breakthrough models used here showed reasonably good fits for most metals
(Table II). Low R2 values for Cu2+ modelling are symptomatic of the limited variation in data points,
where maximum concentration ratio reached only 0.02 (data not presented in Figure 1).
Table II: Selected model parameters from breakthrough analysis of M4195.
Asterisk (*) indicates that the model fit is displayed in Figure 1.
Modified dose response
BohartAdams
Thomas
a
b
Qo
R2
Ka
W
R2
Kt
Qo
R2
Al3+
35.78
19.3
1.84
0.997
0.45
4.42
0.997*
0.4
1.84
0.997
Co2+
4.63
47.04
3.99
0.971
0.02
9.97
0.985*
0.02
4.59
0.982
Fe3+
11.29
20.51
1.64
0.989
0.14
3.94
0.992*
0.14
1.64
0.992
Mn2+
45.39
19.43
1.54
0.993
0.51
3.64
0.996*
0.51
1.52
0.996
Ni2+
5.76
208.24
16.96
0.964
0.01
41.42
0.978*
0.01
17.28
0.978
Zn2+
9.66
32.19
2.63
0.955
0.08
6.34
0.964*
0.08
2.65
0.964
Weak Acid Resins S930, S950
During loading of the S930-packed column, the only metal to reach complete breakthrough was Zn2+,
which exceeded Ct/Co after 32 mL (6.4 BV) of solution had been pumped through (Figure 2).
357
Adsorption of other metals in solution (Al3+, Co2+, Fe3+, Mn2+ and Ni2+) exhibited the typical
breakthrough curve shape, but concentration ratios remained below 1. This suggests multiple species
were bound to the resin throughout the experiment, although there was a slight upward trend in
concentration ratio, which is likely a result of the steadily increasing Cu2+ loading (Figure 2). It is
expected that the metals with lower affinity for the iminodiacetic acid functionality, e.g., Zn2+, Co2+,
Al3+, Mn2+ (Riley et al., 2018) would be displaced first as Cu2+ consumes resin capacity. The extraction
of Fe3+ by S930 at low pH has been reported previously (e.g., Amphlett et al., 2018, Riley et al., 2018),
lending explanation to the slightly suppressed breakthrough when compared to other metals studied
(with exception of Cu2+).
Figure 2: Breakthrough curves of metals after passing through S930 column at pH 1.43. (Al3+ = +, Co2+= □,
Cu2+ = ◊, Fe3+ = ♦, Mn2+ = ×, Ni2+ = , Zn2+= ○. Dotted lines represent model fitting
(marked by an asterisk in Table III).
Breakthrough modelling indicated that S930 had a notably higher operating capacity towards Cu2+
over the other metals (Table III). The low R2 values indicate that the available models were not able to
satisfactorily describe metal adsorption for Fe3+, Mn3+ and Al3+; a result of the presence of competing
ions.
Table III: Selected model parameters from breakthrough analysis of S930.
Asterisk (*) indicates that the model fit is displayed in Figure 2.
Modified dose response
BohartAdams
Thomas
a
b
Qo
R2
Ka
W
R2
Kt
Qo
R2
Al3+
1.9
12.45
1.31
0.884
0.07
3.18
0.855
0.08
1.48
0.855*
Co2+
2.42
15.02
1.43
0.938*
0.06
3.26
0.897
0.06
1.51
0.897
Cu2+
2.11
397.82
34.89
0.997*
0.004
55.31
0.969
0.005
25.8
0.969
Fe3+
0.81
13.28
1.15
0.630*
0.004
8.43
0.289
0.005
0
0.284
Mn2+
3.74
11.32
1.00
0.841*
0.12
2.18
0.828
0.13
1.02
0.828
Ni2+
2.26
15.64
1.44
0.951*
0.05
3.29
0.897
0.06
1.52
0.897
Zn2+
4.65
15.09
1.40
0.992*
0.08
3.1
0.991
0.08
1.45
0.991
358
Metal adsorption to the aminophosphonic acid functional groups of S950 appeared to favour the
trivalent transition metals. The order that metals reached complete breakthrough (Ct/Co = 1) from the
column was Ni2+ > Co2+ > Zn2+ > Mn2+ > Cu2+, while Al3+ and Fe3+ did not exceed a Ct/Co value
greater than 0.8. Beyond 100 mL (20 BV) solution throughput, all divalent metal species had begun to
be displaced from the column (Figure 3) by Al3+ and Fe3+ ions. This order of selectivity matches closely
the extraction profiles observed under static conditions (Riley et al., 2018). The modelling of dynamic
adsorption to S950 further highlighted the effectiveness of trivalent metal removal, with the Thomas
model operating capacity for Al3+ being an order of magnitude higher than that for the other metal
species. Interestingly, Fe3+ had an operating capacity similar to other metal ions (Table IV), potentially
suggesting that if the loading cycle was continued, Al3+ would be the dominant metal species
remaining in the column.
Figure 3: Breakthrough curves of metals after passing through S950 column at pH 1.43. (Al3+ = +, Co2+= □,
Cu2+ = ◊, Fe3+ = ♦, Mn2+ = ×, Ni2+ = ▲, Zn2+= ○. Dotted lines represent model fitting
(marked by an asterisk in Table IV).
Table IV: Selected model parameters from breakthrough analysis of S950.
Asterisk (*) indicates that the model fit is displayed in Figure 3.
Modified dose response
BohartAdams
Thomas
a
b
Qo
R2
Ka
W
R2
Kt
Qo
R2
Al3+
1.07
55.61
8.86
0.959
0.005
34.94
0.983
0.005
12.42
0.983*
Co2+
3.33
11.62
1.67
0.914
0.06
5.11
0.936
0.06
2.08
0.936*
Cu2+
2.21
26.62
3.53
0.972
0.02
11.98
0.991
0.02
4.66
0.991*
Fe3+
0.94
24.81
3.26
0.905*
0.01
19.45
0.632
0.01
4.31
0.632
Mn2+
2.84
22.61
3.01
0.935
0.03
9.53
0.964
0.03
3.82
0.995*
Ni2+
3.7
10.13
1.41
0.832
0.08
4.2
0.853
0.08
1.72
0.853*
Zn2+
2.94
16.09
2.26
0.851
0.03
7.18
0.880
0.03
2.86
0.880*
359
Strong Acid Resin S957
A relatively high volume of solution was passed through the S957-packed column before any metals
were detected in effluent solutions (approximately 60 mL or 12 BV). During this initial period, it was
assumed that all metal species were being adsorbed to the phosphonic-/sulphonic-acid functional
groups. The breakthrough pattern of metals from the column can be described as three distinct groups
(Figure 4). Ni2+, Co2+, and Zn2+ are first to reach complete breakthrough, with Ni2+ and Co2+ beginning
to be displaced from the column after 100 mL (20 BV) throughput (Figure 4). This is due to increased
loading of Cu2+, Fe3+ and Mn2+, which are also expected to eventually be displaced by Al3+.
Concentration ratios of Al3+ did not exceed 0.09 throughout the experiment, with modelling results
suggesting a very high operating capacity for aluminium by S957 (Table V). Model fit R2 values were
generally reasonable (> 0.95), except for aluminium (R2 = 0.846), which is most likely due to the low
breakthrough concentration ratio.
Figure 4: Breakthrough curves of metals after passing through S957 column at pH 1.43. (Al3+ = +, Co2+= □,
Cu2+ = ◊, Fe3+ = ♦, Mn2+ = ×, Ni2+ = ▲, Zn2+= ○. Dotted lines represent model fitting
(marked by an asterisk in Table IV).
Table V: Selected model parameters from breakthrough analysis of S957.
Asterisk (*) indicates that the model fit is displayed in Figure 4.
Modified dose response
BohartAdams
Thomas
a
b
Qo
R2
Ka
W
R2
Kt
Qo
R2
Al3+
2.46
255.08
32.40
0.846*
0.01
32.02
0.795
0.01
21.60
0.795
Co2+
14.54
91.58
10.37
0.988
0.04
15.41
0.991
0.03
10.4
0.991*
Cu2+
13.28
101.57
10.67
0.962
0.03
15.86
0.964
0.03
10.71
0.964*
Fe3+
7.92
109.56
11.67
0.999*
0.02
17.12
0.995
0.02
11.56
0.995
Mn2+
7.58
111.89
11.80
0.997*
0.02
17.25
0.992
0.02
11.65
0.992
Ni2+
17.64
88.96
9.66
0.969
0.05
14.35
0.972
0.04
9.69
0.972*
Zn2+
13.01
92.71
10.11
0.995
0.04
15.02
0.997
0.03
10.14
0.997*
360
DISCUSSION
M4195 exhibited low retention of Al3+, Fe3+ and Mn2+ under column operation (Figure 1). The
tendency for low adsorption of these metals by M4195 at the studied pH has previously been
described in the literature (Riley et al., 2018), indicating similar behaviour between batch and column
operation. Low Fe3+ retention by M4195 was reported by Canner et al. (2017), along with intermediate
Co2+ uptake, which was also observed in Figure 1. The high Cu2+ selectivity and operating capacity
strongly suggests that this resin would be suitable for treating mine tailings, as copper is commonly
encountered in such wastes.
Concentration ratio of effluents from the S930 column increased rapidly, yet multiple metal species
remained associated with the iminodiacetic-acid functionality throughout the experimental period
(Figure 2). The presence of a tertiary nitrogen atom and two carboxyl groups causes the high Cu2+
extraction preference by S930 (Marhol and Cheng, 1974). The uptake of multiple species on S930
suggests that this resin could be used to remove multiple metals from tailings leachates, particularly if
the Cu2+ was removed by M4195 prior to treatment with S930, as this would leave more capacity
available for the adsorption of other divalent metals.
The chelating weak-acid resin, S950, revealed a distinct affinity towards trivalent ionic species; in this
case, Al3+ and Fe3+. The favourability towards trivalent metal extraction was also observed under static
experimental conditions (Riley et al., 2018), particularly under higher proton concentration. It is
proposed that by placing S950 towards the back-end of a coupled-column treatment system, where
proton exchange from previous columns would decrease solution pH, selective removal of trivalent
metal species could be maximised. The dual-functionality resin, S957, also had a higher selectivity
towards Al3+, with a higher operating capacity for Al3+ than S950 (Table IV and V). The seemingly
higher total capacity of S957, indicated by the delayed metal breakthrough in Figure 4 (also in Canner
et al., 2017), could be used to suggest that S957 would be more suitable for ‘scrubbing’ of residual
metal species from the end of the treatment system. However, more research on the thermodynamics
and kinetics of metal separations by S957 is required to better understand its behaviour.
CONCLUSIONS
A series of dynamic loading experiments using an artificial mine waste leach solution were carried out
for several commercially available ion-exchange resins. The aim of the work was to assess the
suitability of each adsorbent for its potential application in a coupled-column treatment process
designed to remove problematic metals from aqueous mine wastes; either from ‘naturally’ occurring
mine waters or from a sulphuric acid leach of mine tailings. The preliminary results presented here
suggest that the contaminated solutions should first be passed through a column of M4195 to remove
Cu2+ (and likely Ni2+), followed by S950 to adsorb the trivalent metal species (Fe3+, Al3+), and finally
S930 to remove residual divalent metals. The high capacity of S957 suggests that this could be used as
a final ‘scrubbing’ column. The results of further experiments combining ion-exchange columns of
different functionality are being awaited currently, which will help to improve and further
understand the efficiency of this process prior to scale-up.
ACKNOWLEDGEMENTS
The authors would like to acknowledge the Separations and Nuclear Chemical Engineering Research
(SNUCER) Group at the University of Sheffield, whose members have all assisted with this work in
some capacity. Dr Will Mayes and Bob Knight from the University of Hull, in addition to the MIDAS
facility, University of Sheffield, are thanked greatly for facilitating access to ICP-OES analytical
equipment, without which this work would not have been possible.
361
NOMENCLATURE
Ct = analyte concentration at time, t; mg/L
Co = analyte concentration at t = 0; mg/L
Ft = cumulative flow-through at time, t; L
a, b = modified dose response model constants
Qo = maximum operational column capacity; mg/g
m = mass of resin used; g
Ka = BohartAdams adsorption rate constant
t = time; h
W = BohartAdams column adsorption capacity; mg/g
F = volumetric flow rate; L/h
Kt = Thomas model constant
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