05 strategic mine planning 1
TRANSCRIPT
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Copyright Datamine Corporate Limited1
Strategic Mine Planning: Iron ore Case
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Copyright 1983-2005 Datamine Corporate Limited 2
Hotteling
Mining Economics
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Copyright 1983-2005 Datamine Corporate Limited 3
Iron Ore Multimine Problem
http://www.tradekey.com/kb-Iron-Ore-Fines/
WANTED:Iron ore 58% fines Commodity: IRON ORE FINES ORIGIN: INDIA SPECIFICATIONS: Fe 58%rejection -57% Al2O3 3% rejection -2% SiO2 6%, 57 US$/ton
http://go4worldbusiness.com/show/members/latest/inqdetails.asp?objid=130298http://go4worldbusiness.com/show/members/latest/inqdetails.asp?objid=130298 -
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Copyright 1983-2005 Datamine Corporate Limited 4
Current Iron Ore Solution
Single Cut-Off gradeFix by Pit, Fix in Time
Individual Pit ShellsLerch Grossman
Individual Independent
Pushback Shells
Multimine OptimizationLP using Xpac, Mine Max, Whittle MM, Comet
Max Global NPV???
Blending Targets just in the last process
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NPV Multimine Strength-weakness for the Iron Ore Project
Multimine Current Solution: Constrained to main mine site
with small satellites ore bodies. Input Pit sequence impact
results, giving preference topushbacks of the first mine,after the second the third.
To find the optimal pit
sequences is necessary to playwith the pit permutations.
1 2 3
1 23
12 3
12 3
1 23123
71!850,478,588,567,862,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000
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Datamine SolutionULTIMATE PITLG LARGE PITMR SMALL PIT
LP MULTIMINE PITTARGETS
NPVSBLENDING WITH
TARGETS
PUSHBACKGENERATOR
GT-CURVE
PUSH/BENCH
GT-CURVEPIT/BENCH
LP MULTIMINESCHEDULETARGETS
SCHEDULER
STOCK PILE
The ultimate pit will generate a pit limit. With large pits, LG will provide sufficient reserves for 40 years with a short processing time.With small pits, Maximising Resources will provide a maximum limit.
The grade tonnage curves by bench and by pit will generate a globalreserves inventory. Studio will be used to create the grade-tonnagecurves which will become the input for JC-LP al gorithm.
The LP algorithm will determine the optimal mix, suggesting tonnage, periods, average grades by pit and by period. The algorithm targetswill be maximized for NPV given the product specification as a set ofconstraints. As a sub-product for every constraint, t he LP algorithmwill provide shadow prices plus reduced cost for every decisionvariable. The
A Blended Sequence will be created using the blend targets defined by pit and by period (from the LP algorithm).
Pushbacks will be generated using the Blending Sequence plusoperational constraints: sinking rate, minimum width, contiguity andmaterial tonnage.
Using the Pushbacks as i nput, grade-tonnage curves by bench and by pushback will be generated. These curves will be a representation ofthe operational reserve inventory.
Using the Pushback grade-tonnage curves the LP algorithm willgenerate more operational optimization results. The results willinclude targets by pushback and by year.
Using the LP targets, which list tonnages, blends, pushbacks and
periods, a final schedule will be generated using the Scheduler in NPVS. This will be used in future designing stages, includingstockpile optimization.
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A proof-of-concept study at pilot scale to generate a multi-pit,long-term production plan for CVRD Carajs which maximizesthe Net Present Value of CVRD's operation , while:
meeting the specification (product quality) targets;
respecting the available infrastructure facilities; adhering to the maximum amount of waste removal;
meeting the total material movement targets; and
feasible according to CVRD's operating constraints.
Industrial Application 1: CVRD Pilot Project
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Grade stabilization in the two key-products (SFCJ and PFCJ)considering unavoidable increase in SiO2 over time:
CVRD Terms of Reference
The acronym within brackets below each grade refers to thespecific variable name as defined and used by CVRD.
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Solution Methodology
Multimine GlobalOptmization
Grade-Tonnagecurves by Pit,
Bench &Interval
ControlVariable
Results Table
Multi-correlation Analysis
Blended SequenceOptimization
Individual PitSchedules
Check OperationalConstraints
Sensitivity Analysis
Shadow Pricesand ReducedCosts Tables
Correlation analysis using Pearson's andEuclidian Distance models to determine theextent of the correlation between key variables
and to define the control variable(s) for the pilotproject.
Schedule optimization and parameterization todetermine grade-tonnage curves by bench, byincremental pit and by key-variable interval,generating a global reserves inventory for all the
pits.
Definition of the optimal mix/blend of ore andaverage grades by pit and by period. Theresulting blended sequence is maximized forNPV in a iterative fashion, according to productspecification and operational constraints.
Economic and operational analysis of themultimine scheduling results. Identification ofcritical constraints and parameters. Shadowprices and reduced costs analysis on key-variables and constraints to guide the operationaldecision-making process.
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The results of the correlation analysis indicate that all key-variables are strongly correlated, except P (in POVER and inPSFT).
Multi-correlation Analysis
FEOVER SIOVER ALOVER MNOVER POVER FESFT SISFT ALSFT MNSFT PSFT
FEOVER 1.0 0.7 0.6 0.4 0.4 1.0 0.8 0.5 0.3 0.4SIOVER 0.7 1.0 0.6 0.3 0.4 0.7 0.9 0.5 0.2 0.4
ALOVER 0.6 0.6 1.0 0.3 0.7 0.6 0.5 0.9 0.3 0.7MNOVER 0.4 0.3 0.3 1.0 0.1 0.4 0.5 0.4 0.9 0.1POVER 0.4 0.4 0.7 0.1 1.0 0.4 0.2 0.6 0.1 0.9FESFT 1.0 0.7 0.6 0.4 0.4 1.0 0 .8 0.5 0.3 0.4SISFT 0.8 0.9 0.5 0.5 0.2 0.8 1.0 0.5 0.4 0.2
ALSFT 0.5 0.5 0.9 0.4 0.6 0.5 0.5 1.0 0.4 0.6MNSFT 0.3 0.2 0.3 0.9 0.1 0.3 0.4 0.4 1.0 0.1PSFT 0.4 0.4 0.7 0.1 0.9 0.4 0.2 0.6 0.1 1.0
0-0.4%0.4%-0.5%0.5%-0.7%0.7%-1%
**. Correlation is s ignificant at the 0.01 level (2-tailed).
Correlations
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Euclidian distance model analysis suggest a similar behaviorbetween SiO2, Al2O3 and Mn. This means that gradestabilization of SiO2 will also control both Al2O3 and Mn.
P has not been used as a control variable, but it has beenmeasured and recorded in all scheduling runs.
Multi-correlation Analysis
SiO2
Control
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Global Optimization and Blended ScheduleN5 N4E
N4W
Year 1Year 2Year 3
Year 10
Global optimization and blended scheduling results byincremental pit, by bench, by period and by key-variableinterval, generating an optimal schedule and the globalmineable reserves inventory for all the pits.
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Scheduling Results
The table illustrates the scheduling results obtained, in thisinstance for SiO2 in the ore for Pit N5, Benches 5, 6 and 7.
Similar results were compiled for all benches of pits N4E, N4Wand N5, for all key-variables.
BENCH CUT PERIOD1 PERIOD2 PERIOD3 PERIOD4 PERIOD5 PERIOD6 PERIOD7 PERIOD8 PERIOD9 PERIOD10
SIO2%
PIT-N5 5 0.03 0 0 0 0 0 0 0 0 0 0
PIT-N5 5 0.50 0 0 0 0 0 0 0 0 0 0PIT-N5 5 1.00 0 0 0 0 0 0 174000 0 0 0
PIT-N5 5 1.20 0 0 0 0 0 0 296250 0 0 0
PIT-N5 5 2.00 0 0 0 0 0 0 0 0 0 0
PIT-N5 5 3.00 0 0 0 0 0 0 0 2589211 0 0
PIT-N5 5 100.00 0 0 0 0 0 0 0 0 0 0
PIT-N5 6 0.03 0 0 0 0 0 0 0 0 0 0
PIT-N5 6 0.50 0 0 0 0 0 0 0 0 0 0
PIT-N5 6 1.00 963023.438 0 0 0 0 0 0 0 0 0
PIT-N5 6 1.20 0 0 0 0 0 0 1585453.13 0 0 0
PIT-N5 6 2.00 0 2870335.16 4345745.84 0 0 0 0 0 0 0
PIT-N5 6 3.00 0 0 0 0 0 0 4898994 0 0 0PIT-N5 6 100.00 0 0 0 0 0 0 0 0 0 0
PIT-N5 7 0.03 0 0 0 0 0 0 0 0 0 0
PIT-N5 7 0.50 0 0 0 0 0 0 0 0 0 0
PIT-N5 7 1.00 1328039.13 0 0 0 0 0 0 0 0 0
PIT-N5 7 1.20 0 0 1774921.88 0 0 0 0 0 0 0
PIT-N5 7 2.00 0 0 0 13980915 0 0 0 0 0 0
PIT-N5 7 3.00 0 0 0 0 0 0 0 5817006 0 0
PIT-N5 7 100.00 0 0 0 0 0 0 0 0 0 0
TOTAL ORE
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Scheduling Results: Surface Area Restrictions
The diagram on the left shows the initial surface topography in the N4E, N4Wand N5 pits, matching the 2010 pit limits established by CVRD (diagram on theright). The pit road and area constraints provided by CVRD (to be mined out onlyafter 2010) are also marked on the diagram.
Initial Topo Active Area Final Pit Constraints (2010)
Area Constraints
Pit Road
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Scheduling Results: Scheduled Pit Surfaces for 2006
The diagram on the left shows the scheduled pit surfaces for 2006, that respectboth the area restrictions and the pit road.
Pit Surfaces for 2006
Area Constraints
Pit Road
Final Pit Constraints (2010)
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Scheduling Results: Scheduled Pit Surfaces for 2007
The diagram on the left shows the scheduled pit surfaces for 2007, that respectboth the area restrictions and the pit road.
Pit Surfaces for 2007
Area Constraints
Pit Road
Final Pit Constraints (2010)
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Scheduling Results: Scheduled Pit Surfaces for 2008
The diagram on the left shows the scheduled pit surfaces for 2008, that respectboth the area restrictions and the pit road.
Pit Surfaces for 2008
Area Constraints
Pit Road
Final Pit Constraints (2010)
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Scheduling Results: Scheduled Pit Surfaces for 2009
The diagram on the left shows the scheduled pit surfaces for 2009. The positionof the pit road has been clearly preserved eventhough extensive mining is takingplace near its location.
Pit Surfaces for 2009
Area Constraints
Pit Road
Final Pit Constraints (2010)
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Scheduling Results: Scheduled Pit Surfaces for 2010
The diagram on the left shows the scheduled pit surfaces for 2010. The positionof the pit road has been clearly preserved eventhough extensive mining is takingplace near its location.
Pit Surfaces for 2010
Area Constraints
Pit Road
Final Pit Constraints (2010)
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Scheduling Results: Scheduled Pit Surfaces for 2011
The diagram on the left shows the scheduled pit surfaces for 2011. The positionof the pit road has been preserved for one additional year (the constraint wasthat the pit road had to be preserved at least until 2010).
Pit Surfaces for 2011
Area Constraints
Pit Road
Final Pit Constraints (2010)
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Scheduling Results: Scheduled Pit Surfaces for 2012
The diagram on the left shows the scheduled pit surfaces for 2012. The 2010 pitroad and area constraints do not apply any longer so mining is taking placewithin the limits of all three mines.
Pit Surfaces for 2012
Area Constraints
Pit Road
Final Pit Constraints (2010)
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Scheduling Results: Scheduled Pit Surfaces for 2013
The diagram on the left shows the scheduled pit surfaces for 2013. The 2010 pitroad and area constraints do not apply any longer so mining is taking placewithin the limits of all three mines.
Pit Surfaces for 2013
Area Constraints
Pit Road
Final Pit Constraints (2010)
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Scheduling Results: Scheduled Pit Surfaces for 2014
The diagram on the left shows the scheduled pit surfaces for 2014. The 2010 pitroad and area constraints do not apply any longer so mining is taking placewithin the limits of all three mines.
Pit Surfaces for 2014
Area Constraints
Pit Road
Final Pit Constraints (2010)
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Scheduling Results: Ore and Waste Totals
The chart shows the yearly totals for ore and waste, as well asthe variation of the stripping ration between 2006 (period 1)and 2016 (period 10). The expected results were achievedand a more detailed consultancy project is recommended toimprove waste removal variations during and after period 6.
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Scheduling Results: Ore Production by Pit
The chart shows the contribution of each pit (N4W, N4E, N5)to the total ore production from 2006 (period 1) to 2015 (period10).
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
PERIOD1 PERIOD2 PERIOD3 PERIOD4 PERIOD5 PERIOD6 PERIOD7 PERIOD8 PERIOD9 PERIOD1
N4W
N4E
N5
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Scheduling Results: Key-Variable Grade Variation
The table shows the scheduling results for the grade variationof the key-variables. The results match CVRD's stabilizationtargets for the two key-products (SFCJ and PFCJ).
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Scheduling Results: Economic Analysis
The table shows the economic analysis of the scheduled
production. It considers the following parameters provided byCVRD: Sale contract of 0.55 US$/Ton unit Fe Ore cost of 1.15 US$/Ton ore Processing cost of 8.8 US$/Ton Waste removal cost of 1.15 US$/Ton Interest rate of 10%.
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Sensitivity Analysis: Shadow Prices
Allowable Allowable Shadow
Variable Name Increase Decrease Price
TON TON US$/TONPER1 SIOVER 563.8 3435.9 64.6PER2 SIOVER 1897.1 1258.0 13.2PER3 SIOVER 461.8 649.1 10.3PER4 SIOVER 468.8 565.1 38.6PER5 SIOVER 3276.2 4206.6 42.1PER6 SIOVER 6077.3 7020.6 47.2PER7 SIOVER 8343.1 5664.9 47.8PER8 SIOVER 5330.8 2575.7 32.7PER9 SIOVER 10576.7 1545.2 0.7
PER10 SIOVER 4237.8 6938.5 44.5PER1 ALOVER 4329.0 1037.7 -70.7PER2 ALOVER 5198.9 2011.0 -46.2PER3 ALOVER 922.9 590.8 -48.4PER4 ALOVER 1585.2 1494.0 -58.4PER5 ALOVER 5164.4 4022.3 -65.6PER6 ALOVER 3533.1 4891.1 -71.8PER7 ALOVER 16856.9 3714.0 -63.8PER8 ALOVER 5330.5 6443.2 -65.3PER1 MNSFT 191.3 1248.3 -56.8
PER2 MNSFT 341.1 601.3 -33.6PER3 MNSFT 5760.9 2801.5 -44.4PER4 MNSFT 1349.5 1968.6 -77.5PER5 MNSFT 5021.5 3910.9 -78.1PER6 MNSFT 4497.3 6298.6 -50.9
Shadow prices havebeen calculated foreach key-variable ineach period.
This indicates theallowances in mass foreach key-variablewithout deviating fromthe optimal solution.
It also providesmanagement with ameasure of the
economic impact ofrelaxing constraints atdifferent schedulingperiods.
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Sensitivity Analysis: Reduced Costs
The table shows theresults of the reducedcosts analysis for SiO2in Pit N5.
Reduced costs can beused as a decision-making tool formeasuring the impactof eventual deviationsfrom the optimumschedule allowingmanagement tochoose which cut-offinterval has lesseconomic impact in aparticular schedulingperiod.
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Industrial Case 2: MBR Project
HEM SIB RSIB CG IBLI MIAN Total
0.92 5.93 2.39 2.16 2.71 0.62 14.73 2.93 12.63 4.48 1.94 3.47 1.55 27.01 3.18 13.13 4.66 1.30 3.07 0.77 26.11 3.70 12.28 4.36 1.35 2.87 0.45 25.01 3.33 13.49 3.91 1.15 2.90 0.51 25.29 2.12 13.50 3.87 0.76 3.85 0.40 24.51
19.26 3.83 6.77 29.85 19.26 3.83 6.77 29.85 19.26 3.83 6.77 29.85
19.26 3.83 6.77 29.85 20.18 4.01 5.96 30.16 20.18 4.01 5.96 30.16 20.18 4.01 5.96 30.16 20.18 4.01 5.96 30.16 20.18 4.01 5.96 30.16 20.36 3.98 6.04 30.38 20.36 3.98 6.04 30.38 20.36 3.98 6.04 30.38 20.36 3.98 6.04 30.38 20.36 3.98 6.04 30.38 20.36 3.98 6.04 30.38 20.36 3.98 6.04 30.38 20.36 3.98 6.04 30.38
16.18 411.79 90.88 8.65 124.08 4.31
TOTAL
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Mine Sequencing - Traditional Method with NPVS
"helicopter mining";
Mining takes place simultaneously along the entire the pit area:
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Mine Sequencing - Solution Methodology for MBR
Ultimate Pit withBlending Algorithm
(Initial Run)
Refine Schedule with Adjustment Factor
(% Final Pit)
Year > 1 ?or
Targets T ?
GeneratePushbacks
CreateSchedule
CheckTargetsT
Area Variableset
A1+A1+A3
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PERIOD MASS
0
1
2
3
4
5
6
7
HEM SIB RSIB CG IBLI MIANVARIABLE
M T O
MBR
NPV
0
510
15
20
25
30
35
40
45
50
mbr npv
waste
ore
SAP GALIN
50.891 45.913
(*) 3Mt on Pilha Itabiritos
MBR Mine Sequence: Results for Year 1
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MBR Mine Sequence: Results for Year 3
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NPVS4 enhancements for Iron Ore
Blending Targets by Period
OES output, Mining Sequence in Block Model
Better Pushback Design.
l k d
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Conclusion, Remarks and Future Opportunities
The Multimine solution provides optimal scheduling results plus valuableinformation for management decision.
The current Datamine capability is offered through consulting serviceswhich apply the Multimine solution methodology.
As a future development this methodology will be incorporated into thesoftware products in the Datamine Solution Footprint.
The approach can be applied to other opportunities in the Market
1. Oil Sands2. Base Metals
F O i i
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Future Opportunities
Oil Sands Canada, 98 BiUS$ Investment
Not using NPV, using Real Options Currently there is no mining scheduler software supporting RO Opportunity to be the first supplier of Consulting and Software Implementation
using RO.
Operational Improvement in SouthAmerica Codelcos Drill to Mill expansion requirements. Phelps Dodge SAG Mill optimization. Pelambres Increasing Work Index Problem. Antamina, Yanacocha, Goldfields, SPCC outsorcing requeriments in Mine
Planning.