Simplified cost models for prefeasibility mineral evaluations T.W. Camm

Abstract - In this US Bureau of Mines repor t , mine and mill cost models are presented to make estimates of the cost t o deve lop mineral deposi ts in the desert region of the southwest United States . Regression analysis was used t o generate capital and operat ing cost equa- tions for each model based or1 dai ly production capac - i ty . These models are used for Potential Supply Analy- sis ( P S A ) studies b y the Bureau, which analyzes the economic benefits of minerals in a region. Introduction

The US Bureau of Mines conducts studies of the economic impacts of regulations on federal lands. These studies are part of the Bureau Potential Supply Analysis (PSA) program. To meet the needs of these studies, a methodology was developed to estimate operating and capital costs for a min- eral deposit given its tonnage, grade and depth.

The format for the cost models in this study was developed at the Bureau's Western Field Operations Center (WFOC), Spokane, WA, for studies of known and undiscovered re- sources on Federal lands. These cost models are described in a Bureau publication by Camm (1991).

Description

To provide engineering analysis for PSA studies. mine and mill cost models were produced to make estimates of the cost to develop mineral deposits in the desert region of the southwest United States. Regression analysis was used to generate capital and operating cost equations for each model based on daily production capacity (Camm, 1992). These models are used for PSA studies by the Bureau to analyze the economic benefits of minerals in a region. An example of using cost models for PSA studies is a recent report by the Bureau demonstrating the economic impacts of minerals in an area in southern California by Wetzel et al., (1992).

Typically, deposit models for regional studies provide tonnage, grade and depth variables. A new approach to cost modeling was developed to provide useful input to the economic evaluation of study areas based on these param- eters (Camm and Smith, 1991). The modeling approach used in this simplified methodology is particularly suited for making quick cost estimates where specific design param- eters are unavailable. Users in the Bureau's Resource Evalu- ation and Policy Analysis divisions, professionals outside the Bureau performing similar evaluations, and those who need

a quick cost estimate for a mineral deposit will find the approach of this simplified method particularly useful.

The key benefits of the method are:

that it demands less engineering background of the user, makes it easier to apply escalation factors, is versatile in applications to a variety of deposit types, occupies significantly less space on a computer than

alternative systems and uses only limited design parameters to develop a cost

estimate.

The cost derived using these models should be considered a prefeasibility estimate.

Models were developed using a variety of sources to provide the most accurate representation of costs available. Costs for several operations at varying tonnages were esti- mated. Wherever feasible, the capacity scenario was based on actual operations. Site information available included flowsheets, kquipment lists and manning charts. This infor- mation was augmented by data from cost handbooks and references and by the Bureau Cost Estimating System (CES) (US Bureau of Mines, 1987).

Additionally, cost models developed at WFOC for previ- ous studies were adapted for certain cases where feasible. Cost models were developed for each of the mine and mineral processing types listed in Table 1. Cost models for access roads and powerlines were also included.

~ o l l o w i n ~ determination of representative daily capaci- ties for each model and gathering of pertinent data, capital and operating costs were generated for each capacity. These costs were summarized in the following categories: labor, equipment, steel, lumber, fuel, lube, explosives, tires, con- struction materials, reagents and electricity. In addition, a separate category for sales tax was included. A total cost equation was also included for each model for users who do not require a cost breakdown into each of the individual categories, but only an overall cost estimate. Table 1 lists the models developed.

Regression analysis was used to generate capital and

T.W. Carnrn, member SME, is a mining engineer with the US Bureau of Mines Western Field Operations Center, Spokane, WA. SME Preprint 93-85, SME Annual Meeting, Feb. 15-1 8,1993, Reno, NV. Manuscript Feb. 19, 1993. Discussion of this peer-reviewed and approved paper is invited and must be submitted, in duplicate, prior to September 30, 1994.

MINING ENGINEERING JUNE 1994 559

Table 1 - List of cost models 1 Infrastructure

Access roads Powerlines

Open Pit Mine Models Small Large

Underground Mine Models Depth factors Block caving Cut-and-fill Room-and-pillar Shrinkage stope

Mill Models Tailings pond Autoclave-carbon-in-leach

(CIL)-electrowinning CIP-electrowinning CIL-electrowinning Carbon-in-pulp (CIP)-

electrowinning Countercurrent decantation

(CCD)-Merrill Crowe Float-roast-leach Flotation, one product

sublevel longhole Flotation, two broducts Vertical crater retreat Flotation, three products

Gravity Heap leach Solvent extraction-

electrowinning

operating cost equations for each model in the form shown in Table 2. Equations for each category listed above that were appropriate for each model were calculated in this form. Adjustment factors were also developed for variations in haulage distance for the open pit models and for variations in depth of mining for the underground models.

Each model includes a brief discussion and a summary table of cost equations as shown in Table 2. For each underground model, a schematic is also provided to illustrate the mine method. Figure 1 showsatypicalschematic. Alsoincluded are simplified flowsheet$ for each mill model, as illustrated in Fig. 2.

Cost curves summarizing the total cost equations for capital and operating costs for each model were developed. These are illustrated in Figs. 3-5. The corresponding total cost equations are summarized in Table 3. Table 4 provides the total cost equations for depth factors for underground mine models, access roads, power lines and tailings ponds.

Table 3 - Minelmill total cost equations

Capital Operating Cost model cost. $ cost. $

Open Pit Mine Models Small open pit 1 60,000(X)0.515 71 .O(X)-O 414 Large open pit 2,67O(X)O 917 5.14(X)-0.148

Underground Mining Models Block caving 64,80o(X)O.~~~ 48.4(X)-O 'I7 Cut-and-fill 1 ,250,000(X)0-461 279.9(X)-0.294 Room-and-pillar 97,6OO(X)O 644 35.5(X)-O.l 71 Shrinkage stope 1 79,000(X)0 620 74.9(X)-0.160 Sublevel lonahole 1 1 5 , 0 0 O ( X ) ~ . ~ ~ ~ 41 .9(X)-0.181 Vertical crater retreat

Mill Models Autoclave-CIL-EW CIL-EW CIP-EW CCD-MC Float-roast-leach Flotation, one product Flotation, two product Flotation, three product Gravity Heap leach Solvent extraction

Metric ton t = st x 0.907 184; nla = not applicable X = capacity in short tons per day

Table 2 - CIP mill model (capacity range 1-20,000 stpd)

Category

Labor Equipment Steel Lube Construction material Electricity Reagents Sales tax Total

Capital cost. $

Operating cost, $

484(X)-O 641 2 1 .6(X)-0.463 0.993(X)O O 11.4(X)-0.463

nla 26.8(X)-O 365

2.75(X)O O 0.409(X)-0.057 105(X)4303

Metric ton t = st x 0.907 184; nla = not applicable X = capacity of mill in short tons per day mill feed

v Fig. 1 - Cut-and-fill schematic.

Ore Crushing Grinding Thickener Dvuilar

Und.dlm

CIP tanks

Tailings

I Fig. 2 - CIP mill flowsheet. 560 JUNE 1994 MINING ENGINEERING

W A C I T Y . .Vd m a d OPEN PIT CAPITAL COSTS

W~ITY. .w nulril OPEN PIT OPERATING COSTS

Fig . 3 - O p e n pit capi ta l a n d operat ing costs ( a v e r a g e 1 9 8 9 dollars).

rm 1.m 1o.m 1m.m CAPACIN, rw

UNDERGROUND MINING CAPITAL COSTS

1m I .OW 1o.m 1 m.m CAPACITY. Wd

UNDERGROUND MINING OPERATING COSTS

Fig. 4 - U n d e r g r o u n d mining capi ta l a n d operat ing costs ( a v e r a g e 1 9 8 9 dollars).

Depth factor costs should be added to the cost equations for the underground mining model being evaluated.

Example

To demonstrate the individual cost models, the CIP- electrowinning model provides an example of how each model is presented. The discussion describes the design used for the model, followed by sample calculations using the equations from Table 2.

This model is designed for evaluating oxide gold deposits. The CIP-electrowinning process is most often used for pro- cessing oxide gold ores with little or no byproducts. The cost equations are valid for ore tonnage capacities of 907 t/d to 1.8 kt/d (1000 to 20,000 stpd). For this model, a grade of 0.3 g/ t (0.1 oz/st) Au was assumed, with a recovery of 89%.

Mine-run ore is initially crushed with a jaw, then a cone crusher. Crushed ore is then ground in arodmill and sent through cyclones. The oversize is sent to a ball mill, while the undersize is sent to a thickener. The ovefflow from the thickener is sent to

a series of carbon adsorptioncolumns, while the underflow goes through a series of agitated leach tanks.

After leaching, the slurry is fed to the CIP circuit, which consists of a series of tanks with high efficiency agitators. Carbon is moved countercurrent to the slurry, which moves by gravity from the first to the last tank. Barren slurry from the last tank of the CIP circuit is sent to the tailings pond. The loaded carbon from the CIP circuit and the carbon columns is sent to the stripping tanks. Pregnant strip solution is sent to the electrowinning circuit. Electrowinning cells are used to plate gold onto steel wool cathodes. Loaded cathodes are removed, treated with dilute sulfuric acid and sent to the refining furnace, where a dorC is produced for shipment. Stripped carbon is regenerated in a kiln and returned to the circuit.

Figure 2 illustrates a simplified flowsheet for a CIP mill flowsheet. Costs are summarized in Table 2.

The following calculations use the equations from Table 2 for a CIP mill, using a feed rate of X = 6.7 kt/d (7429 stpd).

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C A P A C N , sVd mlll lsed MILLING CAPITAL COSTS

CAPACITY, rVd mill bed MILLING OPERATING COSTS

Fig. 5 - Milling capital and operating costs (average 1989 dollars). Capital cost estimate

L a b o r = 1 1 4,800(7,429)0.527 = 12,586,999 E q u i p m e n t = 1 4 5 , 6 0 0 ( 7 , 4 2 9 ) 0 . ~ ~ ~ = 19 ,596 ,255 S t e e l = 4 2 , 6 0 0 ( 7 , 4 2 9 ) ~ . ~ ~ ~ = 4,712,603 Cons t ruc t ion mate r ia l = 5 5 , 8 0 0 ( 7 , 4 2 9 ) ~ . ~ ~ ~ = 7,055,851 S a l e s t a x = 1 4,600(7,429)0,545 = 1,879,360 T o t a l f r o m a b o v e ca tegor ies = 45 ,831 ,066

If an evaluator does not require the cost breakdown provided using the above equations, the total cost can be calculated using the total cost equation:

T o t a l = 3 7 2 , 0 0 0 ( 7 , 4 2 9 ) ~ . ~ ~ ~ = 45 ,797 ,876 (Comparing totals using individual cost categories vs.

total cost equation: 45,831,066145,797,876 = 1.001, 0.1 % difference due to rounding in regression equations.)

Operating cost estitnate

L a b o r = 484(7,429)-0.641 = 1.60

I Table 4 - Depth factor, infrastructure and tailings equations / Cost model Cost equation

Underground mining depth factor Capital cost, $ +371 + 1 80(D)(X)0.404 Operating cost, $/st +2,343/(X) + 0.440(D)/(X)

+ 0.001 63(D) Access road capital cost, $/mi

40 ft wide 76,00O(R) 60 ft wide 1 12,00O(R) 80 ft wide 148,00O(R)

Powerline capital cost, $/mi 20-ft pole height 298,20O(P) 30-ft pole height 304,40O(P) 40-11 pole height 310,40O(P)

Tailings pond capital cost Tailings pond, $ + $/acre 146,000 + 1,783(A) Dam, $/linear ft 161 (L) Liner', $/acre 5(L) + 35,79O(A) Liners are required only in certain states

Meter = ft x 0.3048; m2 = acre x 4046.856: km = mi x 1.609 344; metric ton = st x 0.907 184. X = Capacity of mine, st/d; D = depth of shaft to bottom of ore body, ft. R = Length of road to construct, mi P = Length of powerline to construct, mi A = Area of tailings pond, acres L = Length of impoundment dam to construct around tailings pond, ft

E q u i p m e n t = 2 1 .6(7,429)-0.463 = 0 . 3 5 S t e e l = 0.993(7,429)0.0 = 0 .99 L u b e = 1 1 .4(7,429)-0.463 = 0.1 8 Elect r ic i ty = 26.8(7,429)-0.365 = 1 . 0 4 R e a g e n t s = 2.75(7,429)O.O = 2.75 S a l e s t a x = 0.409(7,429)-0.057 = 0.25 To ta l f r o m a b o v e ca tegor ies = $7.1 6 ls t o r e (mi l l feed)

Using the total cost equation only: T o t a l = 105(7,429)-0.303 = $7.05/st o r e (mi l l feed)

(7.1617.05 = 1.016, 1.6% difference due to rounding in regression equations.)

Summary

Cost models have been developed for two open pit mod- els. six underground mine models and 1 1 mill models. Ad- ditional models are available for estimating costs of access roads, powerlines and tailings ponds. This report provides an introduction to cost models developed for Bureau Potential Supply studies. An expanded discussion and full set of models are found in Bureau IC 9298 by Carnrn (1991). +

References

Camm, T.W and Smith, M.. 1991, "A review of cost estimating methods for prefeasibility type stud~es." Proceedings of the Second Canadian Conference on ComputerApplications in theMinerallndustry. Vol. 2, R Poulin, R.C.T. Pakalnis. and A.L. Mular, eds., Univ. Brit~sh Columbia, Vancouver, BC, Sept. 15-18, pp. 563-571 Camm, T.W.. 1991. 8mplifiedCostModels forprefeasibility MineralEvaluat~ons. BuMines lC 9298. 35 pp. Camm, T.W.. 1992. "The development of cost models using regression analysis," Preprint no 92-48, SME Annual Meeting. 4 pp. US Bureau of Mines, 1987, Bureau of Mines Cost Estimating System Handbook, Part 1 Of 2 parts, Surface and Underground Mining, BuMines IC 9142, 631 pp. US Bureau of Mines, 1987, Bureau of Mines Cost Estrmafing System Handbook, Part 2 of Pparts, Mineral Processing, BuMines lC 9143, 565 pp. Wetzei. N.. et al.. 1992. Economlc Analysis of the Minerals Potential of the East Mojave Nahonal Scenic Area. California, BuMines OFR 56-92, 79 pp

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