AMD Prediction and Implications for Mine Water Quality_TonyJong
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1. AMD Prediction & Implications for
Mine Water Quality
Dr Tony Jong
2. AMD and Mine Water Quality Dr Tony Jong
2
Introduction
Coal is deposited within reducing environments that have potential to
produce sulfides (most commonly pyrite (FeS2)).
Waste generated through mining in the form of overburden and coal
processing (rejects and tailings) can expose these sulfides to air and
water, resulting in their oxidation.
Oxidation of pyrite can be represented by the following overall
reaction:
As acid (H2SO4) water migrates through a site (e.g. tailings or waste
rock), it further reacts with other minerals and may dissolve a range of
metals and salts.
3. AMD and Mine Water Quality Dr Tony Jong
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Introduction
Potentially lead to the generation of acid and metalliferous drainage
(AMD).
AMD can be:
- Acidic drainage characterised by low pH and elevated dissolved
metals.
- Neutral drainage acid completely neutralised by dissolution of
common carbonate minerals (such as calcite, dolomite and
magnesite), leading to precipitation and thus removal of metals such
as Al, Cu and Pb.
- Near-neutral but metalliferous elevated in metals that are soluble
at neutral pH conditions such as Zn, As, Ni and Cd.
- Saline drainage no dissolved metal residues remain, but contain
high (sulfate) salinity.
4. AMD and Mine Water Quality Dr Tony Jong
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AMD Prediction
A number of procedures have been developed to assess the acid
generating characteristics of mine waste materials.
Overall acid generating potential of a sample is mainly evaluated by its
Acid Base Account (ABA) and the Net Acid Generation (NAG) test.
These are static test procedures (i.e. single measurement in time).
The ABA is a theoretical balance between a samples maximum
capacity to generate acid (MPA) relative to its acid neutralising
capacity (ANC).
The NAG test represents a direct measurement of the net amount of
acid generated by the sample.
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AMD Prediction
The Net Acid Production Potential (NAPP) and the ANC/MPA ratio are
two measures of the ABA.
- NAPP > 0 positive net acid producing potential (PAF)
- NAPP 0 non-acid forming (NAF) or potentially acid consuming
(AC).
Total sulfur is commonly used to calculate MPA, but chromium
reducible sulfur (CRS) provides a better estimate of pyritic sulfur and
thus NAPP.
The ANC/MPA ratio provides an indication of the relative margin of
safety (or risk) within a material to generate acid. Typically this is
between 1.5 and 3.
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AMD Prediction
The ANC/MPA ratio provides an indication of the relative margin of
safety (or risk) within a material to generate acid. Typically, this ratio is
2.
NAPP and NAG values provide an indication of the potential for acid
generation from a sample; however, additional test work is required to
predict the potential for metalliferous or saline drainage.
Typically done by conducting leachability tests (1:5 deionised water to
solid extractions).
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Geochemical Classification
NAG test commonly used in conjunction with the NAPP to reduce the
risk of misclassifying the acid-generating potential of the mineral waste
sample.
Comparison between NAPP and NAG results will help identify
uncertainties that require follow up.
Typical geochemical classification scheme based on NAPP and NAG
results:
Geochemical Classification
NAPP
(kg H2SO4/t NAGpH
Potentially Acid Forming (PAF) >10a
<4.5
Potentially Acid Forming Low Capacity (PAF-LC) 0 to 10a
<4.5
Non-Acid Forming (NAF) -100 to <0 4.5
Acid Consuming (AC) <-100 4.5
Uncertain (UC)b
>0 4.5
<0 <4.5
a
Site-specific but typically in the range 5 to 20 kg H2
SO4
/t.
b
Further testing required to confirm material classification.
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Geochemical Classification
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Potential Acid Risk
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Metals and Salinity
Composite Number
S1 S2 S3 S4 S5 S6 S7 S8 S8
No. of Sub-samples 5 4 2 6 2 3 2 4 2
Parameters
Livestock
Drinking
Watera
Siltstone
#
Claystone
#
Carbonaceous
Claystone
#
Sandstone
#
Sandstone/siltstone
Conglomerate
Siltstone/
Claystone/
Sandstone
Shale
Mudstone
Ca 1000b
1.4 1.0 2.7 1.3 <1 <1 <1 <1 <1
Mg 2000c
1.0 1.0 1.2 1.0 <1 <1 <1 <1 <1
SO4
2-
1000d
137 75 79 75 11.6 22.4 23.3 4.6 10.2
Al 5 0.75 0.18 0.16 0.50 0.04 0.04 0.04 0.08 0.11
As 0.5 to 5e
0.096 0.026 0.009 0.090 0.006 0.005 0.089 0.003 0.004
B 5 0.10 0.03 0.01 0.06 <0.1 <0.1 <0.1 <0.1 <0.1
Cd 0.01 0.0003 0.0001 0.0001 0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001
Cr 1 0.001 0.001 0.001 0.001 <0.001 <0.001 <0.001 <0.001 <0.001
Co 1 0.001 0.001 0.001 0.001 <0.001 <0.001 <0.001 <0.001 <0.001
Cu 0.4 to 5f
0.001 0.001 0.001 0.001 <0.001 <0.001 <0.001 <0.001 <0.001
Pb 0.1 0.001 0.001 0.001 0.001 <0.001 <0.001 <0.001 <0.001 <0.001
Hg 0.002 0.0003 0.0001 0.0001 0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001
Mo 0.15 0.043 0.015 0.014 0.029 0.002 0.010 0.010 0.002 0.009
Ni 1 0.001 0.001 0.001 0.001 <0.001 <0.001 <0.001 <0.001 <0.001
Se 0.02 0.02 0.01 0.01 0.01 <0.01 <0.01 <0.01 <0.01 <0.01
U 0.2 0.001 0.001 0.001 0.001 <0.001 <0.001 <0.001 <0.001 <0.001
Zn 20 0.005 0.003 0.003 0.005 <0.005 <0.005 <0.005 <0.005 <0.005
All values in mg/L. #
Mean value; where values were less than the limit of reporting (LOR), the LOR value was used for calculation purposes. a
ANZECC &
ARMCANZ (2000). b
Stock should tolerate concentration if calcium is the dominant cation and dietary phosphorus levels are adequate. c
Insufficient
information is available to set trigger value; however, concentrations up to 2000 mg/L have been found to have no adverse effects on cattle. d
No adverse
effects to stock are expected if the concentration does not exceed 1000 mg/L. e
May be tolerate if not provided as a food additive and natural levels in the
diet are low. f
Dependent on livestock species.
11. AMD and Mine Water Quality Dr Tony Jong
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Water Quality Prediction
Water quality prediction based on geochemical modelling:
- The Geochemist's Workbench (Bethke et al., 2008)
- PHREEQC (USGS, 2011).
Modelling conducted by calculating chemical equilibrium distributions
and typically does not take into account kinetic reaction rates.
For GWB, general thermodynamic database can model solutions:
- Up to 3 molal (~96,000 袖S/cm).
- Temperatures from 0 to 300 C.
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Water Quality Prediction
Analyte
ANZECC
(2000)1
Wet
Season
Water
Conceptual Mixtures
20%
Model 1
Water
40%
Model 1
Water
60%
Model 1
water
80% Model
1 water
80%
Creek
60%
Creek
40%
Creek
20% Creek
pH
6.0 to
8.0a
5.72 5.99 6.09 6.14 6.17
Al 27b
18 0.18 0.19 0.21 0.23
Cd 0.06 0.1 0.42 0.75 1.08 1.40
Co ID 0.39 1.49 2.60 3.71 4.82
Cr(VI) 0.01 1 0.01 0.01 0.01 0.02
Cu 1.0 2 1.97 1.98 1.98 1.98
As 1.8c
1 0.02 0.04 0.05 0.07
Mn 1200 0.65 136 271 406 541
Ni 8 2.36 6.41 10.5 14.6 18.6
Pb 1.0 0.65 0.91 1.18 1.44 1.71
Se 5 1 0.01 0.01 0.01 0.01
Zn 2.4 60 258 456 655 854
Results for equilibrated drainage
water mixed with receiving Creek
water
Cd, Co, Pb and Zn
concentrations exceed the
ANZECC (2000) trigger values
for 99% species protection.
All values in 亮g/L unless otherwise stated. 1
For 99% species protection.
a
Lowland rivers ( 150 m altitude); b
pH > 6.5; b
Limit for total Cr; c
Sum of
As(III) and As(V); ID = Insufficient data to derive a reliable trigger value.
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Water Quality Prediction
Mineralisation/precipitation as
AMD is treated with hydrated
lime (i.e. increasing pH).
Ag, As, Sb, Se, U and Sn are not
removed by simple precipitation
with hydrated lime.
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Conceptual AMD Risk Map
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Conclusion
The ABA and NAG test provides a useful screening of AMD potential,
and can delineate areas that need more detailed investigation.
Static tests provides a snap-shot of the mine waste materials potential
to cause or alleviate AMD.
Leachability tests combined with geochemical modelling can provide
an initial indication of the potential water quality impacts and/or AMD
treatment efficiency.