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SME Annual MeetingMarch 1-3, 1999, Denver, Colorado

Preprint 99-36

NEVADA PLANNING AT NEWMONT GOLD COMPANY

S. Hoerger,

Newmont Gold Co.

Denver, CO

L. Hoffman,

Newmont Gold Co.

Denver, CO

F. SeymourNewmont Gold Co.

Denver, CO

ABSTRACT

Newmont has a large integrated operation in Northern Nevadawith numerous mines, stockpiles, and processing facilities.There are over ninety defined metallurgical ore types and over

sixty defined gold recovery process options.

To take better advantage of the available synergies, a completeNevada wide mine plan and process model along with a mixed

Integer and Linear Programming optimization tool has beendeveloped and implemented. This system is maintained andoperated by a Nevada Planning Group. Standard in-houseplanning software tools and methods have been implemented atall mine sites. While increasing the effectiveness of planning in

Nevada, these changes have allowed a significant reduction inoverall Mine Engineering staff.

HISTORY

In late 1995, Newmont recognized the need for an expandedplanning function to help optimize the simultaneous mining ofmultiple pits and processing of ores through multiple plants

along the Carlin Trend. Dr. Kadri Dagdelen, Assistant Professorof Mine Engineering from the Colorado School of Mines, andMr. Edgar Urbaez, Graduate Student, were hired to help developa Mixed Integer and Linear Programming solution. By the end

of the summer in 1996, the mathematical formulation had beendeveloped and small scale tests were successfully run using aspreadsheet based linear program solver from LINDO.

However, difficulties were encountered scaling up theproblem. To remedy this, a MicroSoft Access Database wasdesigned with over 100 tables to hold the deposit model,production and sequencing constraints, and financial

information. Also, Dr. Thomas Knowles, Professor ofOperations Research from the Illinois Institute of Technology

was hired to help tune the problem formulation and take fulladvantage of the LINDO system. By the Spring of 1997 fullscale tests for Carlin had been run.

On May 5, 1997, Newmont merged with Santa Fe PacificGold and the process of integrating the combined Nevada

Operations began. The potential efficiency gains from this tool

were greatly increased. However, the larger Nevada wide modelpresented more scaling complications. Furthermore, there weredifferent planning methods and tools as well as inconsistent costinformation between the sites. A review of the Nevada Planning

functions was undertaken. Mine planning methods and toolswere standardized. A Nevada planning group was formed andwas given the responsibility for examining Nevada widescenarios and maintaining a consistent cost database. By the

Spring of 1998 the cost and constraint data had been cleaned upand production runs on the optimizer were underway.

NEWMONT OBJECTIVE

Newmonts objective with Nevada Operations is to mine,route, and process material so as to make the most efficient useof capital equipment over the life of the mines. This amounts tomanaging Nevada Operations for cash flow and translates into

making decisions that maximize the Net Present Value. Miningdecisions include the timing of open pit layback andunderground stope development, capital expenditures, andmining rates. Material routing decisions are based on cost vs.

recovery tradeoffs and are subject to capacity and blendingconstraints. Processing decisions include the timing of plantstartup and shutdown, capital expenditures, ore processing ratesand other operating parameters.

The tool has been developed to assist the Nevada planningorganization with making these types of decisions.

THE MODEL

A key challenge is to keep the mathematical model to a

solvable size and maintain a realistic representation of theproblem. The following sections describe the ObjectiveFunction, Variables, Cost Coefficients, Constraints, and Integer

Programming.

Objective Function

For a given scenario, the objective is to select the flows ofmaterials from Mine Sources to Plant Processes, Mine Sources

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to Stockpiles, and Stockpiles to Plant Processes that maximizeNPV while meeting production targets and constraints. This isset up as a linear combination of the material flow variables andis shown at the end of the Cost Coefficients section.

Variables

The LP variables all represent material tonnage flows and can

be divided into the following three classes X, Y, and Z.

X = Mine Source to Process Plant tons by time periodY = Mine Source to Stockpile tons by time period

Z = Stockpile to Process Plant tons by time period

Schematically this is represented by time period as follows:

Mine Sources (X) Process Plants

(Y) (Z)

Stockpiles To set up the Linear Program problem formulation, thesevariables are indexed into six dimensions. The name,description, and approximate size of each dimension for

Newmonts Nevada operations follow:

Dimension Size Description

Source 50 independent pit, layback,

or UG mineSequence 5 subdivision of source into

sequence (annual plans)Increment 20 subdivision of sequence into

homogeneous units (Samemetallurgical properties andprocessing costs)

Destination 60 metallurgical process including

waste dumpsPeriod 20 time period (usually years)Stockpile 8 stockpile area

For a full rectangular dense model, the total number ofvariables would be 50*5*20*60*20*8 = 48,000,000. It is notpractical to solve an LP formulation of this size. Therefore, theAccess database is used to trim unlikely combinations until the

problem size is on the order of 100,000 variables. Examples ofvariables that are trimmed include mining the last minesequences in the early time periods and routing barren material

to a roaster or autoclave process.

Cost Coefficients

The cost coefficients have been divided into components that

represent only two of the variable dimensions at a time. Thiskeeps cost data entry and management to a reasonable level.The cost model uses the following five components:

INPIT. The cost of mining material and bringing it to daylight.This is a function of Source and Sequence only.

EXPIT. The cost of hauling material from pit daylight toprocessing plant or dump. This is a function of Source andProcess only.

PROCESS. The cost of processing the material as well asmetallurgical recovery of the gold. This is a function of Processand material Increment only.

STOCKPILE. The cost of hauling material from daylight to

stockpile area. This is a function of Source and Stockpile Areaonly.

RECLAIM. The cost of rehandling stockpile material andhauling it to the processing plant. This is a function of Stockpile

Area and Process only.

Schematically these cost coefficient components can be

represented as follows:

INPIT EXPIT PROCESS

STOCKPILE RECLAIM

Finally we add the PRICE and the time value of money

DISCOUNT factors which are a function of the time Period.With these coefficients defined, it is possible to calculate the NetPresent Value as the following linear function of the variablesX, Y, and Z:

NPV = X * MineToProcessCoefficient +

Y * MineToStockpileCoefficient +Z * StockpileToProcessCoefficient

Where:

MineToProcessCoefficient = ( - INPIT - EXPIT PROCESS +

(GRADE * RECOVERY * PRICE) ) * DISCOUNT

MineToStockpileCoefficient = ( - INPIT - STOCKPILE) *DISCOUNT

StockpileToProcessCoefficient = ( - RECLAIM PROCESS +

(GRADE * RECOVERY * PRICE) ) * DISCOUNT

This represents the Linear Program Objective Function

Constraints

Constraints are posed in the traditional LP formulation as alinear function of the variables X, Y, and Z. These minimum

and maximum constraints include:

Mining rates Processing rates Metallurgical blending limits (CO3/SS ratio etc.) Cash flow generation rates Gold production generation ratesInteger Programming

Discrete events such as plant startup or plant shutdown are

represented as Integer Programming components of the model.Integer variables are used with fixed costs as coefficients in the

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objective function and with maximum processing capacities ascoefficients in the constraint equations.

Mine sequencing constraints (an earlier sequence must be

completely mined out before the next sequence can beginmining) are also represented as Integer Programmingcomponents of the model.

More than a few Integer Programming variables quickly lead

to prohibitive execution times. Therefore, they must be usedsparingly. Accordingly, in many cases, the timing of plantstartup and shutdown as well as mining rates are fixed for a

given scenario. Depending on the scenario, the fixed costs maybe treated as rigidly fixed and excluded from the analysis or maybe apportioned across the facilities nominal tonnage rate and

included in a variable cost. Integer variables and fixed/variablecost handling are managed through the Access Database.

THE OPTIMIZER IN PRACTICE

A multidisciplinary Nevada Planning Group including

representatives from Management, Metallurgy, Mine Planning,and Accounting are responsible for the operation of theoptimization tool and for the interpretation and presentation ofits results. The seven step process of running a scenario and the

approximate time involved is as follows:

Gather information. Determine the scenario to be run andgather the necessary mine plans, costs, and constraints. This

involves a lot of communications with Site Management,Mining Engineering, Plant Metallurgists, and Accounting. Thetime required for this step ranges from a few minutes for a minorvariation on an existing scenario to several days for asignificantly new scenario. Depending on the scenarios being

analyzed, some costs may be treated as variable costs or fixedcosts.

Enter information. The data must be entered into the Access

database. Mine plan information is loaded electronically fromgrade tonnage results files. Cost and constraint information is

entered manually through forms built in Access. Validation thatthe data has been correctly entered is critical. This step can take

from a few minutes for a minor variation of an existing scenarioto several days for a significantly new scenario. The completeAccess Database file for one Newmont Nevada scenario is about70 megabytes.

Formulate LP problem. A macro is run in Access that createsan ASCII file with the LP problem formulation for LINDO.This includes the compact but complete LP formulation with all

defined variables, coefficients, constraints, and the objectivefunction. The macro takes about one hour to run.

Solve LP Problem. A typical problem contains about 100,000

variables, 400,000 coefficients, 20,000 constraints, and takesabout 3 hours to solve on a 400MHz Pentium II processor PCwith 128 Megabytes of RAM. LINDO lists the solution in

another ASCII file.

Report Results. The solution is imported back into the Accessdatabase and a macro is run to report out results. The reports listresults by sources, stockpiles, and destinations with grades,

recoveries, and costs. The macro completes this task in about anhour and a half.

Verify Results. The results are reviewed and interpreted for

reasonableness. For example, the mathematically optimum

solution may reroute material clear across Nevada because of aone penny per ton savings. In practice this may not be the bestchoice and parameters may need to be adjusted accordingly.When an unexpected flow of material is indicated, the constraint

and economic cause is tracked down and verified. This is a timeconsuming process that can take days. However, it is essentialto have confidence in the results prior to making anyrecommendations or taking any action. Often one set of results

will lead to another set of runs.

Communicate Results. Once promising scenarios have been

identified, results will be transferred to a spreadsheet to performdetailed non-linear accounting computations and to produce

summary reports and graphs. These results will form the basisfor management decisions and requests for more what-ifanalyses. Throughout the planning process, the Nevada Planning

Group will be communicating with site mine and processingengineers as key constraints are identified.

INTEGRATION OF NEVADA PLANNING

To take full advantage of the synergies available as a result ofthe merger between Newmont and Santa Fe, it was clear that anintegration of the entire planning process was required. Thus,the review of the Mine Planning functions throughout Nevada

was undertaken. The optimizer as it has come to be known isonly one component of the solution and the planningorganization has been realigned in order to take full advantageof this tool.

Nevada Planning Group

As a result of this review, the Nevada Planning Group wasformed. It is comprised of representatives from NevadaManagement, Metallurgy, Mine Engineering, and Accounting.

This multi disciplinary team is responsible for assembling costinformation from all of Nevada and insuring that it is consistent.There is a lot of interaction between this group andManagement, Site Long Term Planning Engineers, Site Chief

Engineers, Site Plant Superintendents, and Site Accounting fromthe three sites, Carlin, Twin Creeks, and Lone Tree.

Schematically, the interaction is as follows:

Carlin Twin Creeks

Management Management

Mine Engineering Mine EngineeringPlant Metallurgy Plant MetallurgyAccounting Accounting

Nevada

Planning

Group

Lone Tree Complex Management Mine Engineering

Plant Metallurgy Accounting

Advantages of Centralized Planning Cost Database

For the results of the optimizer to be valid, the development ofa centralized planning cost database was essential. The

traditional cost data available from accounting is historical only

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and often needs interpretation and modification when used forplanning purposes. Also, it was essential to have a consistentplanning approach to the treatment of overhead and fixed costsbetween all of the sites.

This consistent centralized cost data is now available to siteMining Engineers for site mine planning. This has eliminated aduplicated independent effort at each site resulting in

Engineering manpower savings.

Benefits of Integrated Planning

With efficient planning, many alternate scenarios are rapidlyanalyzed in limited detail, and only the most viable ones areselected for complete analysis. This approach has allowed

Newmont in Nevada to look for innovative cash generatingmining solutions with limited mine engineering resources.

An emphasis has been placed on consistent methodologies,training, and extensive communications between the Nevada

Planning Group and the mine site groups. The same integratedin-house modeling, mine planning, and ore control software isnow used at all of the Nevada Sites. The net result is that theoverall Mine Engineering staff has been reduced by over ten

percent while improving the value of the mine planningsolutions produced.

BENEFITS OF THE OPTIMIZER

The interaction between all of the mines and plants is complexto the point that it is difficult to grasp many of the opportunitiesthat are present. The optimizer tool allows a systematicapproach to examining options. It has led to a number of

decisions that have significantly increased overall profitability inNevada. It has also eliminated seemingly promising scenariosthat were in fact unattractive.

One of the earliest synergies identified during Newmont /Santa Fe merger planning was to treat high-grade Deep Star ore

originally destined for the Carlin roaster at the Twin Creeksautoclave. The optimizer was used to confirm the benefits.

Given Deep Stars high grade, the increase in recovery fortreatment at the Twin Creeks autoclave compared to treatment atthe Carlin Trend roaster more than offset the highertransportation cost of shipping the ore over 100 miles. In

addition, the ores high carbonate content improves the blendingat the autoclave, allowing more high-sulfide higher gradematerial to be processed sooner. Switching the Deep Startonnage from the roaster to the autoclave postpones lower grade

tons at the autoclave and accelerates lower grade tons at theroaster. These deferred autoclave tons are less profitable thanthe accelerated roaster tons, further enhancing the benefit of thisswitch. Since the merger, additional Carlin Trend ores have

been identified which can benefit from the Twin Creeks andLone Tree processing facilities.

The optimizer has been used to prioritize mine development.As new mines and laybacks are contemplated, they are analyzed

to see if their grades are sufficiently high to compete for limitedprocessing capacity in existing facilities. If the new mine canprovide more profitable ores, the mines providing the displaced

lower profit ores become candidates for deferral.

The optimizer has also been used to plan facility shutdownsand/or capacity reductions as the amount of oxide material

mined on the Carlin Trend decreases. The optimizer works

through the combinations of cutoff grades, tonnages, processingcost differences, recovery differences and fixed costs to suggestan optimum. These decisions are being finalized as the paper isbeing written.

CONCLUSION

By developing a NPV maximizing mixed Linear and Integer

Programming tool and incorporating it into an integrated

planning process in Nevada, Newmont has been better able totake advantage of the available synergies from the sites in

Northern Nevada. This has led to increased profitability inNevada as a whole which is particularly important in this periodof low gold prices.