Process Operating Costs with Applications in Mine Planning and Risk Analysis Doug Halbe1 and T.J. Smolik2

ABSTRACT This paper discusses techniques for estimating treatment plant operating costs, including identification of high-impact cost areas and expected key cost variations year-by-year. Application of this information to cost development in the mine block model is presented, followed by a discussion of the risk uncertainty and cost-impact sensitivity in operating cost estimates. An example is carried from the initial base cost estimate and year-by-year annual process cost variations through the use of Excel spreadsheets and Monte Carlo simulation. INTRODUCTION The question that must be raised when preparing an estimate of a process operating cost is this: What will the cost estimate be used for and what level of accuracy is required in its development? It is also necessary to realize that operating costs will change during the term of an operation's life. The estimator should identify the key parameters that drive these changes. This text will start with development of various types of process cost estimates, outline procedures for developing these costs, and then outline some methods of examining the impacts of major variable changes over the term of the projected mine’s operational life.

For this paper the battery limits for mill operation will commence with the crusher feed pocket and end with facilities for loading of the final product for transportation off-site. Costs for transportation, smelting, and refining of product are excluded, but are interrelated with other mill costs and should be included in any economic study. Project mining and administration costs, as well as taxes, amortization, depletion, depreciation, and related items will not be included. Costs are expressed in non-inflated US dollars, and most units, including tonnes, (t) are metric.

The format for cost estimation used in this paper is very similar to that used by most plants for budgeting of on-going operations.

Most of the costs for a new milling operation can be estimated quite accurately, given a good base of test data and sufficient effort to obtain accurate wage and price information. Thus, an experienced metallurgist should be able to estimate operating costs using detailed quantitative data and appropriate geological/metallurgical information to within about 10% of final observed values

Estimation of operating cost can be required for a number of uses, and we will discuss each of these separately in this paper:

• Project evaluation effort with little process basic information • Advanced studies including feasibility study applications • Ore reserve optimization with process costs used in block models • Process cost projections, risk areas and economic sensitivities

This paper will use the terminology used by most engineering companies working in the

mining area (from Pincock Allen & Holt, 1998):

1 Doug Halbe Consultant P.C., Salt Lake City, Utah 2 TJS Enterprises LLC, Blaine, Washington

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• Conceptual study: Based on sufficient drilling to define a resource, but flowsheet development and cost estimation are often based on limited testwork and engineering design.

• Prefeasibility study: Based on a higher level of testwork and engineering; and typically of the order of +/- 30% accuracy.

• Feasibility study. Sufficient detail and accuracy to be used for a positive “go” decision and financing purposes, with an accuracy of +/- 20% or better.

EARLY-STAGE ESTIMATES: ORDER OF MAGNITUDE COSTS In many cases it is necessary to make a very rough preliminary estimate of both capital and operating costs with very little information available. This is the case where exploration geologists have identified an interesting target and need enough information to decide whether further expenditure is warranted. Frequently the only information available will be an estimate of the likely maximum tonnage and a very preliminary head grade, based on the results of a few drill holes. There will be no test data, nor, at this point, any reason to carry out any testwork to develop a flowsheet, reagent quantities, or recoveries. This calls for what is truly referred to as a “back-of-the-envelope” estimate. Total time expended on looking at some core and making a cost estimate might be a few hours; or, as more information becomes available and a more careful estimate is warranted, a few days. The initial decision required here by the exploration department is “Yes, this looks, from a metallurgical point of view, like the economics might be good; so further drilling appears warranted,” or “No, the deposit it too small, and it’s located in Alaska 500 km from the nearest road, and this is not sphalerite, it’s high-iron marmatite.”

Many companies maintain a database of information on costs of their operation and of available cost information of other operations. This information is typically plotted on a graph of total mill operating cost versus milling capacity in tonnes per day. Figure 1 is such a curve, showing the total milling costs plotted versus tonnes per day of mill capacity. The data includes flotation, cyanidation and those using both mills, and is taken directly from the Canadian Mining Journal 2001 Mining Sourcebook. The shape of the curve is typical, and the relative scatter around the fitted curve is surprisingly small.

Curves of this sort can give a very rough estimate of milling costs, but no more, since large variations in reagent costs, ore hardness, power and labor costs and most other factors can exist between similar deposits.

Another, more accurate, method frequently used for preliminary estimates is the use of an estimator’s guide. Western Mine Engineering (WME) provides, on a subscription basis, such a guide that includes cost models for various types of mining and milling operations, for both capital and operating costs. Mill operating costs include both cyanidation and flotation plants, with a number of throughput capacities ranging from 100 to 80,000 tonnes per day (tpd).

An advantage of this system is that, as more information becomes available it is possible to improve the accuracy of the cost. A significant amount of detail is provided with each set of costs in the WME guide, so that it is possible to intelligently adjust these costs to better fit an individual prospect. Separate costs are given for each model for supplies and materials, labor, administration, and sundry items. These are accompanied by detailed manning charts, and costs for electricity, grinding and wear steel, reagents, and other items. Adjustment of power costs, for example, from the WME base of $0.072/kWh to the expected project power costs is a simple arithmetic calculation. Thus it is possible to periodically improve the accuracy of the estimate as the prospect is developed, but before the stage at which a more detailed estimate is requried or warranted.

Frequently throughout this paper we will refer to the WME Mining Cost Service. This is not intended as an advertisement for WME. It is simply that we have found this an excellent source of useful information for cost estimating for mining projects.

Another good source of information on costs of actual operating mines is World Mine Cost Data Exchange (www.minecost.com). This is a database of shared information from mining analysts that gives reasonably detailed cost information for a large number of working mines.

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y = 863.34x-0.5514

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Figure 1 Total Milling Costs – Canadian Mills: Flotation and/or Cyanidation (CMJ 2001) ADVANCED STUDIES: PREFEASIBILITY AND FEASIBILITY STUDIES As a new project continues to demonstrate positive economics, it reaches a point where a preliminary or prefeasibility study should be carried out; and this is frequently done internally by a mining company. The initial prefeasibility study not only provides the first systematic estimate of capital and operating costs, but also serves to highlight any project areas that have been overlooked, or areas where data is lacking or weak.

The basic information needed for the development of prefeasibility level process operating costs is as follows:

• Metallurgical balance (typically for the life-of-mine average grade) • Life of mine production plan • Lab and/or pilot plant operating data and results • Bond ball mill work indices, SAG mill indices, abrasion index data • Flowsheet with material balances for each unit operation • Basic design criteria • Equipment list (key equipment or detailed, depending on the level of the estimate)

with motor sizes • Water balance • Unit costs for labor and consumables (including fuel and energy) Operating costs are generated initially for average or typical life-of-mine conditions.

Development of more accurate costs for a feasibility study varies only in the amount of supporting information and detail required. For a feasibility study the key cost drivers need to be evaluated and adjusted year by year, as discussed later in this paper. For this discussion we will discuss the development of basic, preliminary operating costs in each of the major cost areas of milling, and include in each section some comments and suggestions for further improving the accuracy.

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Salaries and Wages The cost of labor (supervision, operating, maintenance) can range from as high as 40% of total milling costs for a small, 1,000 tonne per day plant to perhaps 10% in a large, 50,000 tpd concentrator. Thus, at least for preliminary studies, the accuracy with which this number needs to be estimated will vary with the plant size. However, an experienced metallurgist armed with a flowsheet and design criteria should be able to prepare an organization chart and manning schedule. A less experienced metallurgist might wish to use the manning tables from the WME cost engineering notebook for reference (and these can provide a useful check even for an experienced operator).

For operating companies, wage and salary costs are available from existing company operation costs, with information on similar operations frequently available from a database in the personnel department. If the new project is located in an area with other mines, or similar industrial operations, wage rates can usually be obtained from this source. State and country governmental agencies may be able to provide typical wage information. Several private services provide such information. A convenient source for detailed mining information is the WME Mining Cost Service, which provides very detailed wage and benefit costs for a large number of mines, by state and province in the United States and Canada, both union and nonunion.

Wage and salary information is conveniently presented in a form such as that in Table 1. This lists each position, the number of people required in each position, the wage rate or salary, and annual cost. Points to consider:

• Fringes or payroll burdens must be included, and are usually included in the wage and

salary information. For North American operations these range from 35-40% of the base wage/salary. It is important to ensure that you know exactly what is included and excluded from the burden.

• Provide additional people to cover for vacations, illness, dumped shifts, and training. • Estimate the amount of overtime needed. • The level of supervision and manning required will vary significantly by country. • Contract labor may be used, and should be inducded in this section. The data in this and subsequent cost tables has been developed for a hypothetical 5,000 tpd

(1,825,000 tonnes per annum) carbon-in-pulp (CIP) plant. Power and Utilities Power, like labor, is one of the major costs of milling operations. Power as a percentage of total operating costs in the Canadian Mining Journal data plotted earlier ranged from 5 to 32%, with the majority of the values falling between 15 and 30%.

It is possible to estimate the power requirements and resulting operating costs for a plant with a reasonable degree of accuracy if sufficient design information is available. Table 2 shows an abridged typical spreadsheet for estimating power costs. This requires an equipment list with associated motors. The largest consumers of power will be the grinding mills, and the power for these mills can be calculated reasonably well with Bond ball mill and SAG mill work indices. For a preliminary estimate this list will probably include only the major items of equipment. Some provision must be made for the additional minor equipment, typically by adding an allowance to the overall load list. It is also necessary to estimate the hours per day of operation of each motor, and the expected power draw. A fully-loaded and correctly engineered ball mill will draw close to its designated power rating for 24 hours per day. A sump pump might operate only one hour per day, at half or less of its motor power.

The totals from the motor list information (excluding standby's) can be used to calculate total kilowatt hour demand, kilowatt-hour usage per day and per tonne of mill feed.

For feasibility studies, installed kilowatts, loads, and all related power consumption information will be available from studies by the electrical engineering group.

Power rates can be determined by inquiry with the local company supplying power. If sufficient power is not already available at the mill site, then a power line will have to be run. If this is done by a power company, the cost may be included by them in their determination of rates.

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Table 1 Wages and salaries

$/hr $/yr Total Category Job title No. (Note 1) (Note 2) $/yrSalaried Mill superintendent 1 100,000 100,000

General foreman 1 80,000 80,000 Shift foreman 4 70,000 280,000 Mill maint. engineer 1 90,000 90,000 Electrical engineer 1 90,000 90,000 Maint. foreman 1 80,000 80,000 Maintenance planner 1 50,000 50,000 Metallurgist 2 70,000 140,000 Met. technician 1 50,000 50,000 Gold room foreman 1 75,000 75,000 Chief assayer 1 60,000 60,000 Instrument technician 1 50,000 50,000 Clerk 1 40,000 40,000 Total salaried 17 1,185,000

Hourly- opns Control room op'tor 4 26.00 237,952 Crusher operator 2 21.00 96,096 Grinding operator 4 21.00 192,192 Cyanidation operator 4 21.00 192,192 Gold room crew 2 26.00 118,976 Day crew 2 20.00 91,520 Assayers 2 24.00 109,824 Samplers 2 20.00 91,520 Laborers 2 18.00 82,368 Total op'ns - hourly 24 1,212,640

Hourly-maint. Mechanics 5 24.00 274,560 Mechanics' helpers 3 20.00 137,280 Electricians 2 26.00 118,976 Electricians' helpers 1 22.00 50,336 Total maint.- hourly 11 581,152

Total employees 52 Total $/yr 2,978,792Total $/t (on 1,825,000 tpa) 1.63Notes: 1. Includes a burden of 40%. For conversion to $/yr, multiply by 2080 (40 hrs/wk x 52 weeks/yr) and by 110% to allow for 10% overtime. 2. For salaried personnel, includes a burden of 38%.

If it is necessary to generate power on site, suppliers of power generation equipment can provide data for estimating operating costs. For preliminary estimates the WME estimating book contains power cost data by state and province in the United States and Canada. Both energy and demand changes need to be calculated. Demand charges can be quite large and their influence becomes more significant where daily hours are low for high kilowatt equipment, or where the plant itself does not operate on a continuous basis. Total kW (excluding stand-bys) is shown on the equipment list – power cost table (Table 2). Once the monthly demand charge is determined, it can be calculated back to an equivalent energy charge and added to the energy charge, so a single rate can then be applied. If the step of listing equipment kW in a power cost table is not taken, the energy charge should at least be "rounded up" a bit to allow for the demand charge. DO NOT assume, just because a power rate is available from some reference source, that the power company will be able to supply power to your operation.

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Table 2 Power cost (abridged)

Total Operating Usage kW-hr Area Item No.(1) kW - ea. kW Hours/day factor per day Crushing Jaw crusher 1 112 112 14 0.5 783

Feeder 1 7 7 14 0.7 73 Conveyor 1 15 15 14 0.7 146 Dust collector 1 7 7 14 0.7 73

Grinding Feeder 1 7 7 24 0.7 125 Conveyor 1 15 15 24 0.7 251 SAG mill 1 2243 2243 24 0.92 49,525 Fresh water pump 1 15 15 12 0.7 125 Misc. other --- --- --- --- --- 378 Subtotal - grinding 4103 90,594 Subtotal - other 528 13,506 Total kW and kWh/day 4631 104,101 Grinding, $/year 2,314,683 Other, $/year 345,087 Total, $/year ($0.07/kWh) 2,659,770 Grinding, $/t 1.27 Other, $/t 0.19 Total $/t ($0.07/kWh) 1.46 Total kWh/t (5000 tpd) 20.8

Notes: (1) Excludes stand-bys

Heating of buildings can be a significant cost in northern US and Canadian operations, and nonexistent in the southern US. Calculation of this cost is usually outside the scope of work expected of metallurgists, but does need to be determined. (A paper by Barrat, et al., 1975, is old, but does provide some information that could be used by a metallurgist pressed to make some preliminary calculations.)

Water is a significant concern for an operation. As with power, DO NOT assume that you will automatically find water if you drill for it, or that you can use the water from a nearby source just because it is there. It is usually a very good idea to determine a water source (including quality) and to estimate both usage and cost even for preliminary cost studies to ensure that this critical requirement is kept at a high profile. Other than that, there are no general rules here. It is necessary to develop a project water balance to determine the water consumption for the project (by season, wet years and dry years). Typically, water will be supplied from a well field some distance from the plant site. The pumping equipment motors should be included in the motor list, and necessary manpower for maintenance and periodic checking should be included in the manpower list.

Other utility costs are typically low: fuel for the mill vehicles, propane for melting furnaces for a gold room, and similar costs. These can usually be included in a small percentage added for miscellaneous utility costs, at least for preliminary estimates. Reagents, Wear Steel and Supplies The reagents necessary for mill operation are determined from lab and/or pilot plant testwork. Total reagent cost, for the mills listed in the Canadian Mining Journal 2001 Mining Sourcebook range from 5 to 40% of total milling costs, indicating that these costs are highly project specific. There is no substitute for basic testwork..

For flotation, the quantities used in laboratory tests are generally close to those expected in mill operation. Some estimators add 10 or 20% to bench-scale lab flotation and cyanidation reagent consumption, and/or decrease the amount somewhat if a large percentage of mill water is recirculated. Cyanide consumption in production heaps, however, can be 25-30% of that determined in lab columns (unless cyanicides are present). (Albert 2001; McClelland 2001)

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Flocculant quantities can be determined by lab thickening tests, and can provide a reasonable estimate of usage. When these tests are run by suppliers, the test report will usually provide the supplier’s best estimate of consumption.

For the base case a consumption for each reagent is estimated. As discussed later in this paper, this may vary from feed type to feed type (particularly in cyanidation plants), and this variation must be allowed for in accurate cost estimates, either periodically as the ore changes, or in the ore reserve mining block model. Pilot plants are not typically run just to determine reagent consumptions. However, if the cost of one or more key reagents is a major percentage of the total milling cost, a pilot plant run with recycle of process water, and using more than one ore type, may be required in order to determine costs to the required level of accuracy. Lock cycle flotation tests can also be used to improve the accuracy of reagent use estimates. Reagent prices are best determined, even for preliminary estimates, by direct inquiry to suppliers, from actual costs of other plants in the area, or from the company’s corporate purchasing department, particularly where the company purchases on a corporate-wide basis. Where this level of accuracy is not required the WME Mining Cost Service lists the cost range of a large number of “Chemicals”. The Chemical Marketing Reporter, found in the periodicals section of most good engineering libraries, lists the current prices for many less-commonly-used chemicals. Listed reagent prices can vary significantly from year to year and location to location; prices for truck-load quantities or bulk reagent are usually significantly lower than those for small lots; and freight to plant site may or may not be included in the listed price. Thus listings of prices at best provide only preliminary estimating data.

Grinding media and wear steel are significant milling costs, and must be estimated. Steel costs as a percentage of total milling costs can vary significantly, but might typically be 10%. The estimating accuracy in wear steel costs, unfortunately, leaves something to be desired. For a modest fee many test labs will determine the Bond Abrasion Index (AI) for a sample (or better, several samples) of mill feed. This is a purely empirical test, and is done dry. However, metal usage depends not only on ore characteristics, as measured by this test, but on pulp chemistry, steel quality and operating practice. (The last is particularly important in SAG mills.) Empirical formulas developed by Fred Bond using the AI are available for predicting wear for crusher liners, and for wet and dry grinding mill liners and grinding media, in pounds of metal per kilowatt hour. These formulas are listed in the SME Mineral Processing Handbook, Section 30, under Abrasiveness. (Note, however, that these formulas date back to 1963 and significant improvements in metal quality have been made since that time. Unpublished data indicates that for current high quality metallurgical steel these calculated values could be reduced as much as 50 percent.)

A good procedure is to conduct AI tests to determine how the sample evaluated compares with others. With AI information it is possible to review operating data from other plants with similar conditions and AI’s, and make a reasonable estimate of expected wear. Generally the lab performing the tests will have a database of this sort of information. Engineers at the test lab or consulting engineers with extensive experience in grinding circuits can be very useful here.

If pulp chemistry is expected to present particular problems (e.g. high-chloride process water) this method may significantly underestimate the metal loss.

A useful reference in this area is the chapter “Comminution Energy Usage and Material Wear” (Charles and Gallagher) in Design and Installation of Comminution Circuits (Mular and Jergensen, 1982)

Freight, delivered to the plant site, and taxes must be included with supply costs, unless handled in a separate overall project account.

Maintenance supplies include such things as pump impellers, steel and liners for chutes, pipes and values, and replacements parts for equipment, but exclude wear steel. This is a difficult cost to estimate. The cost of maintenance supplies in a typical flotation or cyanidation plant ranges from five to ten percent of total direct operating costs. Thus less accuracy in this estimate isless acceptable.

Information on similar-sized plants in similar service provides a good estimate, but this information may not be available. A number of rules of thumb for estimating maintenance supply costs are available. The Australasian Institute of Mining and Metallurgy published in 1993 the Cost Estimation Handbook for the Australian Mining Industry, a 400-page book (Nokes and Lanz

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1993) that contains detailed information and procedures on cost estimation. The authors of the section on Beneficiation Operating Cost suggest that “Five per cent per annum [as a percentage of the purchase cost of equipment] is a reasonable figure but could be higher if the ore is known to be abrasive or the process environment is aggressive. Normal range is 3% to 10%.” This is generally consistent with other rules of thumb. It is worth noting (particularly to nonmetallurgists) that, unlike mining equipment, it is usually assumed that milling equipment is not replaced over the life of the project (i.e. capital provision) but is treated as an ongoing wear item.

The category of “operating supplies” is frequently listed as a line item in milling cost budgets and estimates, and usually is about 10 to 15% of the maintenance supplies, and thus is a small number.

Where warranted, it is possible to obtain from manufacturers typical wear information for most pieces of equipment and thus build up an estimate of repair part cost. Engineering companies generally maintain an in-house database of maintenance supplies costs, frequently by unit process area, and this may provide good information for similar plants.

Reagents and wear steel, with individual consumption and cost, are conveniently summarized in a table such as that shown as Table 3. Tailings and Effluent Treatment, and Environmental Costs Construction of the initial tailing disposal facility is a capital cost. Pumping of tailings to the facility, returning process water to the plant, routine monitoring of the pipelines and dam, and pump and pipeline maintenance are routine mill costs, and should be included as part of the sections above. Continuous raising of the dam using tailings or mine waste or by a contract construction firm is usually treated as an operating cost. Frequently a lift is added to the dam every several years as a separate construction project. This can be handled as a sustaining capital cost (such as the replacement of mine trucks when they wear out) or can be estimated and included as an accrued monthly milling cost. Regular monitoring of the disposal facility by outside consultants should be provided for as noted below, under consultants.

Effluent treatment (for example, destruction of cyanide before pumping to a tailing impoundment) is usually handled as one of the mill’s unit operations. Power, manpower, and maintenance supplies are provided along with other mill costs. Reagent costs are projected from test data, much as flotation or cyanidation costs. Table 3 Reagents and wear steel

% of % of total Usage $ Cost per $/year reagent & dir. mill

Category Item kg/t feed kg.. $/t (000's) steel cost costs Reagents Sodium cyanide 0.88 1.98 1.74 3,175 59 25

Lime 1.59 0.22 0.35 638 12 5 Activated carbon 0.04 2.01 0.08 147 3 1 Flocculant 0.01 4.29 0.04 71 1 1 Caustic soda 0.05 0.33 0.02 27 1 0 Hydrochloric acid 0.03 0.22 0.01 11 0 0 Misc fluxes 0.01 18 0 0

Wear liners Jaw crusher 0.003 3.30 0.01 19 0 0 SAG mill 0.052 2.22 0.12 212 4 2 Ball mill 0.049 2.22 0.11 199 4 2

Grinding balls SAG mill 0.43 0.55 0.24 433 8 3 Ball mill 0.51 0.51 0.26 470 9 4

Total 2.97 5,420 100 42

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In some cases an environmental department is included under the milling department. Staffing, analytical requirements (whether in-house or external), reclamation costs, use of outside firms, and supplies can be significant. It is recommended that these be estimated separately from the mill operating budget since their scope, a service group, is project-wide. For final summation this can be included in milling costs, if appropriate. Other Costs The above costs constitute the majority of normal plant operating costs. However, there are a number of other costs that may need to be included and do at least need to be considered:

• Assaying (If this is done at the mine site the staff costs are typically included in the mill manpower listing, but supplies and utilities need to be allowed.)

• Charges from other departments • Consultants • Contract services • Mobile equipment fuel and maintenance • Occupational health and safety costs (All or part of these costs may be included in

milling costs, or may be included in the indirect costs/project overheads.) • Refinery staff and supplies for a gold room • Royalties on patented processes • Safety supplies • Security • Training (Mill training staff would be included in the mill manpower chart, but outside

training and/or training supplies need to be considered.)

Indirect costs are not included in the scope of this paper. These include such items as project administrative and general costs, corporate office costs, insurance (including fire, casualty and liability), sales, taxes (except sales tax), depreciation, depletion, amortization, townsite operating costs, overall infrastructure maintenance and similar costs. However, it is necessary to ensure that all of these costs are covered somewhere in the feasibility study. Total Mill Direct Operating Costs Total mill costs can be summarized in one summary table, such as that shown in Table 4, which should also include all of the appropriate miscellaneous costs noted above. Uncertainties, Contingencies and Accuracy Contingencies need to be discussed. A contingency is included in a capital cost estimate to provide for "...unforeseeable elements of cost within the defined project scope; particularly important where previous experience relating estimates and actual costs has shown that unforeseeable events which will increase costs are likely to occur." (Gentry and O'Neil 1984)

Generally speaking, we do not expect “major unforeseen events” in an operating cost estimate. However, there are uncertainties, and these can be handled in several ways.

The price of diesel fuel, or power, for example, could vary widely over the life of a mine project. The Bureau of Labor Statistics petroleum products index varied from 105.9 in 1981 down to 53.9 in 1988, and back up to 101 in mid 2001. Obviously the effect of a doubling of a major cost category like fuel or power could greatly affect project economics. Significant swings,particularly in these two major cost areas, are likely to occur. Uncertainty can be quantified statistically with a Monte Carlo procedure, or it can be estimated by calculating project cost changes over a range of estimated fuel or power costs.

A factor that can have a significant beneficial effect is the gradual reduction in costs with increasing process knowledge and experience, and or increased use of automation. Also worth noting: with new or novel processes, the uncertainty in the cost estimate may be significantly greater.

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Table 4 Total mill direct operating costs % of

total oper.

Item Source/calc. Subtotals $/year $/t costsSalaries and wages From salaries and wages 2,978,792 1.63 22

(manning) table Power From power cost table (motor list) Grinding 2,314,683 1.27 17 Other 345,087 0.19 3Other utilities and fuels Water Included in above categories 0 0.00 0 Natural gas/propane 4200 Btu/t C stripped, regenerated 37,000 0.02 0 Fuel oil 0 0.00 0 Diesel fuel and gasoline Included with vehicles 0 0.00 0Reagents and wear steel From reagents and wear steel table Sodium cyanide 3,175,000 1.74 24 Other reagents 912,000 0.50 7 Wear steel, media 1,333,000 0.73 10Maintenance supplies & mtl. 5% of equip. purchase price 931,000 0.51 7

($18.62 milion) Op. supplies, oil & lube, misc. 15% of maint. supplies & material 139,650 0.08 1Assaying 40 mill samples/day X $6/sample) 87,600 0.05 1Contract charges Allowance: outside labs, contract 100,000 0.05 1

work, consultants Mobile equipment 2 p'kup trucks @2190 hrs/yr @$10/hr 43,800

1 Bobcat @+B82190 hrs/yr @$7.50/hr 16,425 1 Ft.-end loader @1100 hrs/yr @$50/hr 55,000 1 Forklift @1460 hrs/yr @$15/hr 21,900 137,125 0.08 1

Tailings treatment (at $0.35/tonne of mill feed) 638,750 0.35 5Tailings dam accrual ($1,100,000 at end of three years) 366,667 0.20 3Gold room costs Included in items above 0 $0.00 0Total direct mill oper. costs 13,496,361 $7.40 100

Inflation cannot be predicted, but can be expected to occur. An estimate of inflation can be included in the final project cash flow projection by the accounting department, along with taxes, depletion, depreciation, and related costs. A simplifying assumption, typically built into initial cost projections, is that overall cost escalation will be offset by escalation in the price of the product. While workable for initial projections, this assumption has been vastly off-base for more than a decade (see www.westernmine.com, Feb. 17, 2000 News Release). Parameters of Feasibility Studies: The basic data in the prefeasibility study is determined in greater detail and requires more project-specific information for development of a bankable feasibility study. Examples of the additional information needed for the feasibility study might be:

• More detailed flowsheets and water/material balances • Pilot plant campaign results • Additional geological/metallurgical characterization and laboratory replicated tests • Additional sample acquisition and testing and more detailed grinding work index testing

(completed in conjunction with the projected mine production ore delivery schedules) • Competitive supplier quotes on consumables and power supplies • Detailed projections of annualized process costs, including ore grades, metal recoveries

and geological characterization • Product quality determinations and concentrate parameters • Tailing storage operating costs and possible final reclamation costs • Clarification of other significant issues or process risks

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• Development of operating statistical data quantifying the cost estimate accuracy and significant risk parameters.

PROCESS OPERATING COSTS FOR ORE RESERVE ESTIMATES One of the main reasons for developing operating costs is to predict net values of metal revenue and operating costs for units of ore. Mining optimization techniques create economic values for blocks of ore that are appropriately sized to match the analytical database and distinguishable volumes (blocks) of ore. Once the geological resource has been defined by grade, geological ore type, and block size, a mine reserve optimization technique can be used to quantify ore reserves. The optimization will require the use of all direct and some indirect operating costs associated with the development, breakage, mining, processing and disposal of the associated waste products. A profit matrix is used to define the net value of each block of ore. A simplified diagram from a paper by Baird and Satchwell (2001) on the application of economic parameters to pit optimization is reproduced below to illustrate the block value profit, or net revenue, that can be used in ore reserve optimization.

3-D Model

Calculate: Value @ Recovered Metal times Metal Price

Calculate: Direct Costs Mining + Process* + G&A *Process Costs are the subject of this document

Value – Direct Costs – Royalty – Penalties – Treatment/Refining = Net Revenue

The optimization procedure is commonly done with a Lerchs-Grossmann optimization

program for open pit mining and utilizes current dollars. The processing cost, including tailing disposal and treatment, is one component of the direct unit costs associated with a project. The unit values for mining and general and administration (G&A) costs must also be quantified for an ore reserve calculation.. We will evaluate only the process costs and associated metallurgical parameters in this text.

The process unit cost and the recovered metal projections must be defined by the metallurgist. While one base value is commonly developed for a major geologic type of ore, using this value of cost or revenue for all blocks of ore is not appropriate for the quantification of ore reserves. In addition to mineral grade values, the relationships of ore type and process costs will play a key role in establishing valid ore reserves and mine plans. These parameters will be interrelated and must be defined by the metallurgist for the mine optimizer. This section of text will discuss some of these relationships and provide examples of the type of calculations that can be developed from a process cost estimate. Metallurgical Parameters Net block revenues will be determined by the projected ore grade, metallurgical recovery, and metal price assumptions used for each ore block. This means that the product shipping and treatment cost will have to be defined and either included in the unit processing cost value for each block of ore in the optimization or calculated separately as indicated in the section above. The economic cost variations observed in the blocks may well depend upon such things as variable concentrate grades or mineral recoveries determined by geological characteristics of different ore blocks. Some of the metallurgical relationships that should be quantified from the metallurgical test program are as follows:

1. Ore grade relationships to metal recovery and concentrate grade 2. Grinding work indices and their relationship to geology in the mine

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3. Other mineral components that can impact final concentrate grades 4. Mineral components that adversely impact processing costs, such as copper minerals that

affect sodium cyanide consumption and reagent cost in gold mills. 5. Any other major costs unique to the particular ore type.

The optimizing ore reserve procedure will require that the unit operating cost established as a

base for a major component of the ore body can be modified by mathematical relationships established from the metallurgical test program. The key economic components must be identified and evaluated in the metallurgical testing program so these relationships can be quantified. The processing costs in the block model will be the sum of the non-varying costs plus each of those costs that has been chosen to be varied according to block parameters. For the examples below the processing costs are made up of the following:

Processing cost = (power cost for grinding + cyanide cost + other non-variable costs)

Use of Grinding Circuit Indices to Determine Operating Costs: The early metallurgical testing program should identify the main geological types and evaluate them, not only with respect to metallurgical responses and reagent consumptions but also with regard to grinding power, media and liner consumption. Grinding costs always comprise a significant portion of processing costs.

The Bond indices for ball mill grinding are often used to quantify throughput and grind product size, and Bond abrasion indices can be used for calculating grinding circuit metal costs. Some mines use “modified” grinding index relationships to improve the day-to-day performance of a plant through operational optimizations. MinnovEX (Kosick, Dobby, and Bennett 2001; Bennett, Dobby, and Kosick 2001; Dobby, Bennett and Kosic 2001) have developed a SAG Power Index (SPI) test for sizing SAG mills and generating operating costs for this portion of the grinding circuit. Combining the “operating” SPI information for SAG mills with the “operating work index” derived from a laboratory work index for ball mills generates the information needed for their grinding circuit operating costs. MinnovEX uses this procedure in Comminution Economic Evaluation Tools called CEET or CEET 2. After plant evaluations, including some testing, and characterizing of ore samples with SPI and their laboratory ball mill work index measurement, MinnovEX use an optimization procedure to assist new mines in specifying their grinding circuit equipment sizes or to help operating mines increase their production from existing equipment.

In a similar manner, with laboratory pendulum tests and using a data base of information and/or a grinding circuit analyses, the JKSimMet procedure (Kruttschnitt Centre undated) can be used to optimize the grinding/throughput relationships in operating plants as well as develop operating cost projections for grinding circuits. Semi-autogenous grinding work indices can also be generated from MacPherson Tests (McKen, Rabe, and Mosher 2001) and this work index can be used to size mills and power train units for autogenous and semi-autogenous grinding mills. Most laboratory and pilot scale generated grinding work indices are converted to “operating” indices for appropriate use. The operating indices for grinding power and metal consumption reflect plant equipment characteristics, improvements in controls and technology, corrections for mill feed and product sizing, and significant improvements in grinding liner design and liner and media metallurgy over the last few years.

Based on the operating work indices and knowing the power input, average unit cost of electrical power, and metal liners/grinding media consumptions, relationships can be developed and used in the block model for revisions in the grinding circuit portion of the process operating cost. A common assumption is that plant capital equipment is generally specified and installed to process the ores with the higher work index, unless the hard ore is only a minor component of the ore body. Once these grinding circuit values are determined in an appropriate testing program, the information is available to quantify their impacts on process unit operating costs. If the grinding indices of the ore change significantly from the original design projections used to size the plant equipment, then the plant throughput can increase/decrease when lower/higher work index ore enters the mill. Combining the grinding circuit information with other data on consumables,

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utilities, labor and metallurgical parameters will develop the process information needed for the modified unit process cost predictions. Example 1. Base unit operating cost with a SPI value of 10 kWh/t and a Bond Work Index of 16 kWh/t. If some mineralized blocks have an SPI of 8 kWh/t, and a Bond Work Index of 12 kWh/t, the typical unit cost equation for the ore optimization can be developed from the test data. Grinding power cost (assuming the same mill feed and product sizes) would change as the ratio the work indices of the new ore block to the work indices of the of base ore block.

Power cost for grinding = Base power cost for grinding [(8 + 12)/(10 + 16)]

Whatever indices are used for these calculations, it is important to use consistent procedures for the grinding work and abrasion indices determinations and cost projections. These unit cost relationships must be developed by the process person and provided to the mine planning engineer for the mining ore reserve calculations. Impact of Adverse Ore Constituents Another example of a cost modifier that should be developed by the process cost estimator is the impact of adverse constituents in some ores that significantly impact ore processing cost. This adverse constituent could impact actual processing costs or impact the smelting and refining revenues. One such example might be the impact of soluble copper mineral components in some ores during the cyanidation of gold/silver ores. Example 2. Assuming that the database has been developed in the ore body sampling and metallurgical testing program, a relationship given to the ore optimizer could be based on a base value of 0.06% soluble copper and laboratory testing with various mine samples:

Cyanide cost = Base cyanide cost [(% Sol Cu/0.06)] x (sol. Cu- CN use function) Revenue Changes Per Unit of Ore Processed In a similar manner to the above calculations, the revenues projected from individual mineralized blocks must be projected by the process metallurgist and ore reserve optimizer. Two important revenue areas are (1) metal recoveries and (2) quality of concentrates produced on-site. Example 3. Calculations for Ore Grade and Impact on Recovery. Within similar ore types, it is imperative that the metallurgical test program develop information on product recovery versus ore grade. If there are many significantly different ore types in the ore body, then enough grade variation must be tested to develop the recovery/ore grade mathematical relationship for each ore type. This data will then be used by the ore optimizer to determine recovered product multiplied by the assumed metal price, to indicate the revenue portion of the block model. The metallurgical test program data set will usually establish the mathematical relationship by regression equations.

Value/ore block = tonnes in block x ore grade (% or g/t) x recovery-grade function x US$/unit of product

Calculations on the Quality of Concentrate. Shipping costs, concentrate treatment charges, and smelter/refinery returns will be affected by concentrate grades and impurity levels. These factors will have to be projected from the laboratory and pilot plant data generated from samples taken from the ore body. Concentrate grade relationships, or impurity levels, should be defined by mathematical relationships on the mineralized block’s contents of metals, impurities or other diluents (such as; pyrite, graphite or sericitic clays). The economic affects will be project specific and with a properly defined resource database, can then be used in the optimization program directly to project the revenue portions of each block in the mine’s block model.

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SENSITIVITIES AND RISKS ASSOCIATED WITH PROCESS OPERATING COSTS Following the basic development of the process operating costs shown in Table 4 of this text, the metallurgist should indicate the sensitivity of the basic assumptions in the cost projections to uncertainty in the world’s economic markets. One of the uncertainties will be the price projections for the mine’s products. However, that feature will be left to the economic planners for the mine. Other sensitivities important to the overall financial analyses are projections of ore quality, product returns, and all direct and indirect costs for the mine’s operation. Some of the uncertainties that should be addressed when determining process operating costs are as follows:

1. Price forecasts for consumables 2. Price forecasts for energy 3. Variations in mine reserve blocks related to grinding energy or reagent consumptions. 4. Confidence in ore grade and quality, mill throughput and metal recoveries (These may be

different with simple process circuits or complex and integrated circuits). 5. Others, as defined for the particular process application

The process data set for the mine’s life is usually developed on an annual basis. This data set will include the annual mill throughput, head grades, metal recoveries and the projected direct operating costs, including tailing disposal and environmental costs. These costs are to be expressed in current dollars (no escalation) and any projections of consumable prices or energy price changes also expressed in the same current dollar terms. This data set will become the process contribution and together with all of the other mining, G&A, and product treatment costs will become the basis for the mine financial analyses that will be used for determining project feasibility or economic returns. The average process cost, per tonne or per unit of metal produced, for the mine’s future operation will be derived from this data set. Table 5 indicates a typical annual cost with some process information indicated on a projected eight-year mine life. We will use the information in this table for sensitivity analyses and further determinations of risk impacts on unit costs. Conventional Sensitivity Analysis One of the most common methods for evaluation of different events on a unit cost of production is to simply vary each individual factor by a certain amount. The variation amount will be determined from earlier test data and available consumable cost information, and then the impact on the unit process cost calculated. For example, it might be determined that a particular reagent consumption could vary as much as 30% higher than used in the basic determination and this may represent 25% of the estimated unit process cost. Therefore, the impact of this increased reagent consumption would be an effect of 7.5% increase in the estimated unit operating cost. Figure 2 illustrates a common method of presenting the effect of such changes in process variables over a range of values. In this example, using data from Table 5, increasing either the head grade or the throughput significantly reduces the per ounce cost. Reducing energy or cyanide costs even as much as 15% has a much smaller effect. Cost Analyses using Risk Probabilities and Monte Carlo Analyses A well-known probabilistic method can also be used to determine process operating cost sensitivities and risk events happening simultaneously. The Monte Carlo technique can address risk probabilities of events, their simultaneous occurrence and identify which events impact operating costs significantly. The technique also permits examination and correlation of related events. Costing analyses using this type of statistical procedure have been used for many years by large industrial firms to predict cost profiles and future economics. Although there are some options available for Monte Carlo simulation programming using Microsoft’s Excel software, we will use Decisioneering’s product called Crystal Ball® (Decisioneering, 2001) for examples in this text. Crystal Ball® is a commercial add-in to Excel. This analysis can be easily adapted to our earlier unit process cost estimates. The final results will generate a statistical output that allows the impact of uncertain events to be quantified and understood.

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1 0 0

1 1 0

1 2 0

1 3 0

1 4 0

1 5 0

1 6 0

+ 1 5 % + 1 0 % + 5 % 0 -5 % -1 0 % -1 5 %P a ra m e t e r - % c h a n g e

$/oz

. pro

duce

dH e a d g ra d eT h ro u g h p u tE n e rg y c o s t sN a C N c o s ts

Figure 2 Sensitivity Chart – Year 7 Table 5: Eight-year process operating costs and Metallurgical Factors Parameters Year 1 Year 2 Year 3 Year 4 Year 5 Year 6 Year 7 Year 8 Throughput - tpa (000) 1825 2000 1722 1722 1722 1610 1610 1610 Grind energy - kWh/t 16.26 16.26 18.89 18.89 18.89 20.20 20.20 20.20 Head grade - oz/t 3.1 3.4 3.4 2.8 2.8 2.5 2.5 2.5 % recovery 90.00 92.00 92.00 88.00 88.00 85.00 85.00 85.00 % Cu (acid soluble) 0.06 0.06 0.07 0.07 0.08 0.08 0.09 0.09 Work Index – SAG 11.0 11.0 13.0 13.0 13.0 14.0 14.0 14.0 Work Index – ball mill 14.0 14.0 16.0 16.0 16.0 17.0 17.0 17.0Mill op. costs - $/t Salaries and wages 1.63 1.49 1.73 1.73 1.73 1.85 1.85 1.85Power Grinding 1.27 1.16 1.34 1.34 1.34 1.44 1.44 1.44 Other 0.19 0.17 0.20 0.20 0.20 0.21 0.21 0.21Other utilities and fuels 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02Reagents and wear steel Sodium cyanide 1.74 1.74 2.04 2.04 2.33 2.33 2.63 2.63 Other reagents 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 Media and wear steel 0.73 0.67 0.77 0.77 0.77 0.83 0.83 0.83Maintenance supplies & mtl. 0.51 0.47 0.54 0.54 0.54 0.58 0.58 0.58Op. supplies, oil & lube, misc. 0.08 0.07 0.08 0.08 0.08 0.09 0.09 0.09Assaying 0.05 0.04 0.05 0.05 0.05 0.05 0.05 0.05Contract charges 0.05 0.05 0.06 0.06 0.06 0.06 0.06 0.06Mobile equipment 0.08 0.07 0.08 0.08 0.08 0.09 0.09 0.09Tailings treatment 0.35 0.35 0.35 0.35 0.35 0.35 0.35 0.35

Tailings dam accrual 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.20Total direct mill op. costs 7.40 6.99 7.97 7.97 8.26 8.60 8.90 8.90Gold prod'n - oz/yr 164,250 202,400 174,266 136,382 136,382 109,480 109,480 109,480Mill cost - $/oz 82.17 69.09 77.32 100.60 104.35 126.53 130.89 130.89

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Example of Risk Analyses with Crystal Ball®. The initial basis of the evaluation will start with the annualized process costs developed from the earlier mill cost information reproduced in Table 5. The resulting processing costs can be evaluated on the basis of either; (1) cost per tonne of ore processed or (2) cost per unit of metal product generated by the mine. The cost per unit of metal produced will bring the uncertainty factors of projected ore grades, metal recoveries and, perhaps, concentrate quality into the risk matrix for examination. This is generally the better unit cost to examine due to the interrelationship of these parameters to other processing unit costs and the direct relationship to the product and the mine’s revenues. The method used is up to the investigator, but we will illustrate an example that addresses the impact of a number of risk elements on both average unit costs.

The initial step is to establish a matrix of cost impact (risk) events, their projected uncertainty ranges and the probability of each range’s occurrence. It must be noted that significant events should be chosen for this evaluation and not all events. The matrix can be based on variabilities observed in laboratory investigations, pilot plant studies or metallurgical/geological sampling campaigns. Process risks involved with complex or simple plants can be addressed in the matrix as can projections from specialists on future commodity or energy prices. For the example in this text, a matrix of events and probabilities were chosen with the assumptions shown below in Table 6.

The assumptions of probabilities and ranges were run with Crystal Ball® 2000 software (1000 simulation runs for each year). With the input of the above risk matrix and the simulation runs with Crystal Ball® software, the operating unit cost information can be compared on an annual basis with the original unit cost estimates shown in Table 5. Summary information that can be analyzed from the simulation run is as follows, and is shown in Table 7: - Median or mean of the simulation results for each year’s data - The probability (say 90%) that the unit costs will be less than a certain maximum value - The probability (say 90% ) that the unit costs will be greater than a certain minimum

value - Sensitivity charts indicating the variables with the highest impact on costs Figure 3 shows annual operating cost trends, in $/ounce produced, of the simulation data reproduced in Table 7. This trend plot indicates the 80% and 20% probability ranges, in which 80%, or 20%, of the predicted operating cost values occur. These operating cost ranges and the variability noted in the annual data are more useful in mining economic analyses than a single, average operating cost projection. For reference purposes, the average process cost of gold produced in this Monte Carlo simulation was $114.40/oz with $4.70/oz standard deviation and with 80% of the values occurring between $108.63/oz and $120.58/oz. Any on-going analyses should use the annualized cost information rather than the average cost projection for economic predictions. Figures 4 and 5 are the sensitivity charts for information derived from Year 7 Monte Carlo runs. These charts indicate the relative importance of key variables by indicating their relative contribution (0% to 100%) of the variance noted in the simulation. Figure 4 indicates that the major influence of the unit production cost per tonne of ore processed in year 7 is grinding work index and its correlated impact on plant throughput (combined variance contribution of 74%), with the effect of acid soluble copper content changes in the ore contributing 26% to the variance. Interestingly, the ranking of variables on unit cost per ounce of gold produced in Year 7, Figure 5, is (1) ore grade/metal recovery (83% variance contribution), (2) work index/throughput (13%) and (3) acid soluble content of the ore (4%). The unit costs, per ounce of gold produced, show considerably more variability on an annual basis than the costs per tonne of ore processed.

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Table 6: Statistical matrix of cost impact (risk) events 1. Mine—Geological Ore Characterization and Grinding Energy: Varied annually depending upon measured Work Indices* A normal probability distribution with a mean of 16.26 kWh/t and a standard deviation of 0.50 kWh/t used for years 1 and 2 with similar distributions around higher, grinding energy values in succeeding years. * correlated on a 1:1 negative basis with process throughput 2. Metal Recovery Predictions: Customized probability distribution correlated 1:1 with ore grade - 10% probability (continuous range) that Metal Recovery is between 80% and 85% - 25% probability (continuous range) that Metal Recovery is between 85% and 88% - 40% probability (continuous range) that Metal Recovery is between 88% and 90% - 15% probability (continuous range) that Metal Recovery is between 90% and 92% - 10% probability (continuous range) that Metal Recovery is between 92% and 94% 3. Ore Grade: Correlated 1:1 with metal recovery A normal probability distribution, with a mean of 3.1 g/tonne and standard deviation of 0.31 g/t Au for year 1 with similar distributions around other projected ore grade values for succeeding years. 4. Annual Plant Throughput: Customized probability distribution varied annually and correlated 1:1 with grinding energy requirement - 30% probability (continuous range that the plant will process between 1,500,000 and

1,750,000 tonnes/year (year 1) - 50% probability (continuous range) that the plant will process between 1,750,000 and

1,850,000 tonnes/yr (year 1) - 20% probability (continuous range) that the plant will process between 1,850,000 and

2,000,000 tonnes/yr (year 1) * Other probability distributions used for succeeding years 5. Acid Soluble Copper Values in Ore: Correlated directly with sodium cyanide reagent consumption and annual costs A normal probability distribution with an acid soluble copper content mean of 0.06% and a standard deviation of .01% with similar distributions around higher values in succeeding years Table 7 Comparison of Crystal Ball® results with original $/tonne and $/oz unit cost Operating results: Parameter Year 1 Year 2 Year 3 Year 4 Year 5 Year 6 Year 7 Year 8 Base from Table 5 $/t 7.40 6.99 7.97 7.97 8.26 8.60 8.90 8.90 Simulation median 7.51 7.28 7.96 7.98 8.26 8.43 8.77 8.73 Simulation mean - $/t 7.56 7.33 7.98 7.99 8.28 8.47 8.78 8.77 Simulation std. dev-$/t 0.37 0.53 0.41 0.40 0.41 0.40 0.42 0.43 Simulation 10% < $/t 7.14 6.68 7.45 7.46 7.73 7.97 8.23 8.25 Simulation 10%> $/t 8.09 8.10 8.52 8.51 8.83 9.03 9.34 9.36 Base from Table 5 $/oz 82.17 69.09 77.32 100.60 104.35 126.53 130.89 130.89 Simulation median 85.31 74.93 81.77 100.17 103.70 119.77 123.53 122.89 Simulation mean - $/oz 86.12 75.80 82.41 101.00 104.79 121.20 124.70 124.74 Simulation std. dev-$/oz 8.33 7.98 7.65 10.10 10.66 13.53 13.39 13.88 Simulation 10%< $/oz 76.01 66.30 72.75 88.29 91.71 105.02 108.55 108.31 Simulation 10%> $/oz 96.86 86.35 92.45 114.01 119.21 139.56 143.15 142.79

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Gold Operating Costs - $/oz

$60.00

$80.00

$100.00

$120.00

$140.00

$160.00

Year 1

Year 2

Year 3

Year 4

Year 5

Year 6

Year 7

Year 8

80%20%Median

Figure 3 Year-by-year operating costs in dollar/ounce produced

After the information has been analyzed on the above annual operating costs and risk assumptions, further refinement and other risk matrix simulations can be made. Rather than varying throughput with ore hardness, an alternative risk matrix might be developed whereby the throughput is maintained by allowing the ore grind circuit products to increase in particle size with ores of higher work index. This requires process information quantifying the recovery loss in values with larger ore particle sizes; information that is often readily available from the metallurgical testing programs. In fact, this simulation would be quite valuable for mine management to determine which operating practice should be used for ores of varying ore grade and hardness. The Monte Carlo simulation technique can help analyze operating costs by indicating the parameters where risk or uncertainty is higher and where changes produce the greatest economic effect. Process evaluators should analyze the background metallurgical information carefully and use the variabilities derived from replications of different orebody samples and associated metallurgical testing to establish risk probabilities and process variable ranges (Smolik, 2000). Other probability assessments and risk impacts may also apply in the analysis as the plant operators gain knowledge (positive impact) and the plant becomes older (negative factor). These possibilities, and their associated probabilities, should be examined when the risk matrices are developed. Addressing risks in statistical terms through Monte Carlo simulations is quite useful when a cost estimator views the overall economic situation and prepares an estimate for determining a mine's viability. Financial advisors can contribute to the evaluation by providing commodity and energy pricing, in current dollars, and perhaps inflationary or currency rate projections, for the cost estimator's use in a risk matrix. The value of this economic cost analysis really depends upon (1) defining the key parameters, (2) using unbiased probability predictions and (3) using realistic cost impact ranges. There is a degree of subjectivity in the analysis and its effective use requires knowledge, experience and good judgment from the estimators and metallurgists.

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Sensitivity Char tTarge t Fore cas t: Ye ar 7 - Ope rating Cos t

-100.0% -50.0% 0.0% 50.0% 100.0%

Y ear 7: Throughput

Y ear 7: Work Index

Y ear 7: A c id Soluble Copper

Y ear 7: Gold Recovery

Y ear 7: Head Grade

Figure 4 Sensitivity - dollars/tonne milled

Sensitivity ChartTarget Forecast: Year 7 - Gold Op Cost

-100.0% -50.0% 0.0% 50.0% 100.0%

Year 7: Head Grade

Year 7: Gold Recovery

Year 7: Throughput

Year 7: Work Index

Year 7: Acid Soluble Copper

Figure 5 Sensitivity - dollars/ounce produced

ACKNOWLEDGEMENTS Our thanks to Eric Spiller (Washington Group) and John Mosher (Hazen Research) for discussions and information on media and liner wear and abrasion testing. Particular thanks to the following who reviewed the draft text and provided valuable comments: Larry Goldman, Decisioneering, Inc. Roger Sawyer, Kennecott Minerals Brian Johnston, Fluor Daniel, Inc. Otto Schumacher, Western Mine Engineering Ken Major, Hatch & Associates Phil Walker, Newmont Mining Peter MacPhail, Homestake Mining Co.

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