approach for estimating a probable range of pit lake
TRANSCRIPT
Approach for Estimating a Probable Range of Pit Lake Concentrations for Mine Pits
with Sulfide Wall Rock
Sarah Doyle; Cory Conrad; Colleen Kelley; Steve Lange; Rick Frechette; Houston Kempton
August 14, 2014
Outline
• What is a probabilistic model? • Benefits using a probabilistic approach • Methods • Example presentation of results • Limitations and ongoing challenges • Summary
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Session 2: Tailings Technologies
• Scaling of lab data (temp, grain size, accelerated weathering) • Thermodynamic database: assume constants are correct and that
our system is in equilibrium • Assume complete mixing of ponds • Complex mechanisms (cyanide attenuation, sorption) are simplified.
Some are not modeled (coprecipitation)
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Limitations of Modeling
• We can model uncertainty using Monte Carlo methods (represent inputs as range of values to get range of outcomes)
• We can avoid scaling issues by using field testing results as much as possible
“All models are wrong, but some are useful” - George E.P. Box, statistician
What is a probabilistic model?
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0.00
0.10
0.20
0.30
1 2 3 4 5 6 7 8 9 10 11 12Pr
obab
ility
Den
sity
Total Iron [mg/L]
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0 1 2 3 4 5
Pro
babi
lity
Initial Percent Sulfide [%]
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0.05
0.10
0.15
70 71 72 73 74 75 76 77 78 79 80
Pro
babi
lity
Pan Evap Coefficient [%]
Pro
babi
lity
INPUTS OUTPUTS
Benefits of Probabilistic Models
• Probabilistic models may reduce comments on EISs by allowing parties to “agree to disagree”
• Why is this important? – Rosemont (AZ): >43,000 comments; – NorthMet (MN): >50,000 comments; – Average EIS contractor cost for the DOE in 2013:
$2.9 million* – Federal Agencies are moving towards requiring EIS’s
for changes to existing projects
5 *U.S. Government Accountability Office, National Environmental Policy Act: Little Information Exists on NEPA Analyses, April 2014
Benefits of Probabilistic Models
• Increases understanding of project risks – Helps with: closure
cost estimates, alternatives analyses, etc.
• Worst-case scenario isn’t always obvious for complex, dynamic systems
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Benefits of Probabilistic Models
7 Data from INAP Pit Lake Database
Modeling Methods
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MODELING
Conceptual Water Balance
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Evaporation
PIT LAKE
Direct Precipitation
Pit Wall Runoff
Groundwater
BEDROCK
Other Inflows
Conceptual Solute Balance
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Damaged Rock Zone
Other Inflows
Pit Wall Runoff
Groundwater Oxidation of Sulfide Wall Rock
Evapo-Concentration
Pit Wall Runoff
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𝑃𝑃𝑃 𝑊𝑊𝑊𝑊 𝑅𝑅𝑅𝑅𝑅𝑅 = 𝐶𝑅𝑅𝐶𝐶𝑅𝑃𝐶𝑊𝑃𝑃𝑅𝑅 ∗ 𝐹𝑊𝑅𝐹 𝑅𝑊𝑃𝐶 + 𝑆𝑅𝑊𝑅𝑃𝐶𝑆 𝑅𝐶𝑊𝐶𝑊𝑆𝐶𝑅 𝑅𝐶𝑅𝑓 𝑆𝑅𝑊𝑅𝑃𝑅𝐶 𝑂𝑂𝑃𝑅𝑊𝑃𝑃𝑅𝑅
Session 2: Tailings Technologies
• Data available for modeling depends on the phase of the project.
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Available Data
Static Tests • ABA • Whole rock • Leach Tests
(MWMP, SPLP)
• NAG
Kinetic Tests • Humidity
cells
Field Tests • Field barrels • Test plots
Field Data • Site samples
Better Modeling Data
Kinetic Program Comparing FB and HC
• Field barrel acidity greater and faster • Humidity cells have such high flush rates that they do not develop “hot spots”
or acidic micro-environments where the sulphide oxidizing bacteria thrive.
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3
4
5
6
7
8
9
10-Aug-10 7-Jan-11 6-Jun-11 3-Nov-11 1-Apr-12
Lab
pH
COL 28 - EBX2 (S=1.86%)
HC 1 EBX2 (S=2.04%)
COL 48 - IBX(S=2.10%)HC 2 IBX (S=2.16%)
COL 55-01 - DA(S=1.26%)HC 3 DA (S=1.26%)
COL 19 - H (S=1.12%)
Kinetic Program Comparing FB and HC
• Greater flush of alkalinity in the humidity cells than in the field barrel due to high water infiltration rates and volumes
0.001
0.01
0.1
1
10
100
10-Aug-10 7-Jan-11 6-Jun-11 3-Nov-11 1-Apr-12
Alka
linity
(mg/
kg/w
k) COL 28 - EBX2
(S=1.86%)HC 1 EBX2 (S=2.04%)
COL 48 - IBX(S=2.10%)HC 2 IBX (S=2.16%)
COL 55-01 - DA(S=1.26%)HC 3 DA (S=1.26%)
Kinetic Program Comparing FB and HC
• Greater sulphate release in the humidity cell tests than in field barrels due to lack of secondary mineral precipitation
0.1
1
10
100
1000
10-Aug-10 7-Jan-11 6-Jun-11 3-Nov-11 1-Apr-12
SO4
(mg/
kg/w
k)
COL 28 - EBX2 (S=1.86%)
HC 1 EBX2 (S=2.04%)
COL 48 - IBX (S=2.10%)
HC 2 IBX (S=2.16%)
COL 55-01 - DA (S=1.26%)
HC 3 DA (S=1.26%)
COL 19 - H (S=1.12%)
HC 4 H (S=1.12%)
Solute Release from Sulfide Oxidation
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• Rate of sulfate formed from oxidation of sulfide wall rock was estimated based on humidity cell data.
• Defined in model by
slope and y-intercept
Solute Release from Sulfide Oxidation
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• Solute assumed to be released from sulfide oxidation if statistically significant correlation exists between sulfate and the solute in NAG Leachate
• Solute release modeled as ratio to sulfate release.
Solute Release from Sulfide Oxidation
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• If solute is not correlated to sulfate concentrations in NAG leachate, then it is not released with sulfide oxidation in the model.
• Solute can still be released from pit wall runoff based on leach test results.
Damaged Rock Zone Properties
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DAMAGED ROCK
Parameter Min Max
Thickness1 3 m 15 m
Size Factor2 0.05 0.2
Percent in Contact with Runoff3
30% 90%
Sulfate Leach Rate From Humidity Cell Data
1Radian 1997; McClosky et al. 2003 2Malmstrom et al. 2000; Lopez et al. 1997 3Frostad et al. 2005; Hollings et al. 2001
Model Input
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• Inflow concentrations are defined by probability distribution, in this case normal distribution, defined by mean and standard error of the mean
3.1416
MW_Stderr
3.1416
MW_Mean MW_Conc
Solute Balance Simulations
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• GoldSim uses Monte Carlo method for propagating uncertainty in model inputs into uncertainties in model outputs.
• Simulations conducted several thousand times (realizations)
• For each realization, a different parameter value is selected from the input probability distribution.
0
1
2
3
4
5
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0 10 20 30 40 50 60 70 80 90 100(m
g/L)
Time (yr)
Fluoride
Percentiles Greatest Result 95% Percentile 75% Percentile 25% Percentile 5% Percentile Least Result Mean Median
Geochemical Controls
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• Bulk concentrations from the GoldSIM are analyzed for geochemical controls using PHREEQC:
• Atmospheric O2 and CO2 • Solubility controls for
probable mineral phases in pit lakes*
• Sorption to precipitated ferrihydrite
*Eary, L.E., Geochemical and equilibrium trends in mine pit lakes, Applied Geochemistry 14 (1999) 963-987
Summary Chemical Processes
Model Results
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Model Results
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Model Results
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Model Results
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Model Results
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Summary “ Doubt is not a pleasant condition, but certainty is absurd”
- Voltaire
• Focus should be on capturing the uncertainty • Oxidation of sulfide wall rock is a large source
of uncertainty and commonly represented inaccurately
• Allows parties to “agree to disagree”
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Ongoing Challenges
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• When you set a percentile, there won’t be any interest in lower percentiles.
• 90th Percentile precedent:
• Idaho Cobalt EIS • PolyMet EIS • Ecological Risk Assessment Guidance
References (1 of 2)
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Eary, L.E., 1999. Geochemical and equilibrium trends in mine pit lakes, Applied Geochemistry 14, pp. 963-987 Frostad, S., Klein, B., and Lawrence, R.W. 2005. Determining the weathering characteristics of a waste dump with field tests. Int. J. of Surface Mining, Reclamation and Environment, 19(2), pp. 132 – 143. Hollings, P., Hendry, M.J., Nicholson, R.V., and Kirkland, R.A. 2001. Quantification of oxygen consumption and sulphate release rates for waste rock piles using kinetic cells: Cluff Lake uranium mine, northern Saskatchewan, Canada. Applied Geochemistry, Vol. 16, pp. 1215-1230. International Network for Acid Prevention, Pit Lake Database <http://pitlakesdatabase.org/database/home.asp> Lopez, D.L., Smith, L. and Beckie, R. 1997. Modeling water flow in waste rock piles using kinematic wave theory. In Proceedings of the Fourth InternationalConference on Acid Rock Drainange, Vancouver, B.C. Canada, May 31 – June 6, 1997. Vol. 2, pp. 497 – 513.
References (2 of 2)
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Malmstrom, M., Destouni, G., Banwart, S., and Stromberg, B.H.E. 2000. Resolving the scale-dependence of mineral weathering rates. Env. Sci. and Technol, 34(7), pp. 1375 – 1378. McClosky, L., R. Wilmoth, J.LeFever, and D. Jordan, 2003. Evaluation of technologies to prevent acid mine drainage generation from open pit highwalls, In: Proceedings of the 6th International Conference on Acid Rock Drainage, Cairns, Australia, 14-17 July, 2003, pp. 541-547. Radian, 1997. Predicted water quality in the Betze-Screamer pit lake. Prepared for Barrick Goldstrike Mines, Inc., Elko, NV. Prepared by Radian International. U.S. Government Accountability Office, 2014. National Environmental Policy Act: Little Information Exists on NEPA Analyses, April 2014.