05 Strategic Mine Planning 1

8/11/2019 05 Strategic Mine Planning 1

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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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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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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.


7/39Copyright 1983-2005 Datamine Corporate Limited 7

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



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.



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.



The results of the correlation analysis indicate that all key-variables are strongly correlated, except P (in POVER and inPSFT).


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



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.


SiO2

Control



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.



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



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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Area Constraints

Pit Road



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Area Constraints

Pit Road



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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.


Area Constraints

Pit Road



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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.


Area Constraints

Pit Road



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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).


Area Constraints

Pit Road



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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.


Area Constraints

Pit Road



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Area Constraints

Pit Road



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Area Constraints

Pit Road



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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.

05 Strategic Mine Planning 1

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