S4_14_30_Eduardo_da_Silva.pdf

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MARKET ANALYSIS, MINE PLANNING ANDCOST ESTIMATION OF QUARRIES IN

SOUTHERN BRAZILEduardo Schimitt da Silva and Rodrigo de Lemos Peroni



• Objectives

• Location

• Market Analysis

• Cost Estimation

• Mine Planning

• Conclusions

Presentation Structure



• Verify and monitor aggregates industry evolution and

consolidation in local market.

• Establish cost models based on Stebbins and Leinart.

• Plan future operations to analyze economic

feasibility.

Objectives



Location



• National Mining Plan 2030 :

5.6% per year until 2022;

4.6% per year from 2023 to 2030.

Market analysis

Table - The aggregates production forecast – 2015/2022/2030

Aggregate Un. 2008 Rate 2015 Rate 2022 Rate 2030

Gravel Mt 217 5.6% 318 5.6% 465 4.6% 667



• Limiting radius 30 km:

Geographic and market features.

Transportation cost is a major factor restricting product

distribution.

Market analysis

Table - Basalt aggregates production records (t)

City 2007 2008 2009 2010 2011 2012

Santa Cruz do Sul 47,988 47,379 143,978 137,006 296,758 193,265

Candelária 11,418 253 1,980 3,600 3,968 0

Venâncio Aires 0 6,102 8,045 3,581 4,284 4,106

Vera Cruz 32,112 56,761 69,275 75,626 148,508 111,230

Total 91,714 111,197 223,279 219,816 453,519 308,601



• Historical production based in 2012 – 308,000 tonnes.

• Price is the most important variable.

Affects critical decisions on mine planning Directly related with:

• The optimal production rate;

• Final pit size;

• Expected degree of operational flexibility.• The most difficult variable to predict.

Different forecasts to future pricing, high possibility of error.

Market analysis

Table - Regional production of aggregates forecastAggregate Un. 2012 2015 Rate 2022 Rate 2030 Rate

Gravel t 308,000 363,000 5,6% 532,000 5,6% 762,000 4,6%



• Three scenarios are presented considering price

variation.

Considering Brazilian economic stability;

Observing inflation rate over the last years.

• Adjustment factor established per year:

5.00% - base case;

10.00% - optimistic scenario; 2.50% - pessimistic scenario.

Market analysis



Market analysis

Price Gravel 01 Year Value

(R$/t)

Year Value

(R$/t)

Year Value

(R$/t)

Year Value

(R$/t)

Base case price

forecast

2012 25.77 2015 29.83 2022 41.98 2030 62.02

Optimistic priceforecast

2012 25.77 2015 34.30 2022 66.84 2030 143.28

Pessimistic price

forecast

2012 25.77 2015 27.75 2022 32.99 2030 40.19

Table - Expected price variation within the period 2012/2015/2022/2030



• Stebbins and Leinart’s cost models.

• Problem: In case evaluators have limited information

about the deposit, they can use:

Models to determine the order-of-magnitude for the

estimates. Models to provide information about mine general costs.

Cost models for comparative approaches - compilation of

cost estimates x parameters based on estimates.

Exchange rate 1 US$ = 1.87 R$

Cost analysis



• Models are theoretical, not representative of any

existing mine.• Costs associated with each model account for all:

– labour,

–

supply and material, – operating expenses,

– equipment,

– costs of supervision, administration and management.

• Models made through Sherpa Cost EstimatingSoftware for surface mines (Stebbins 2009).

Cost analysis



Cost analysis

Operating costs R$(2012)/t ore

Supplies/materials 8.45

Labour 9.72

Equip. operation 3.35

Administration 4.42

Sundry items 2.59

Total operating costs 28.53

Table - Operating costs for 1000 t/day ore and 1000 t/day waste



• Operational costs as the models used are calculated

based on 1,000 tons per day of ore production and

1,000 ton per day of waste.

• Operation analyzed is characterized by very low

waste removal.• Based on this information and the variation of cost

models for different stripping ratios, it is estimated

that the cost range would be about 30% cost

reduction.

Cost analysis



• All costs need to be rendered in a structure suitable

for cash flow modeling in one or more optimization

systems.

• Three scenarios considering cost variation per year:

5.00% for a base case cost forecast; 10.00% for the pessimistic cost forecast scenario;

2.50% for an optimistic cost forecast scenario.

Cost analysis



Cost analysis

Cost Gravel 01 Year Value

(R$/t)

Year Value

(R$/t)

Year Value

(R$/t)

Year Value

(R$/t)

Base case cost

forecast

2012 19.95 2015 23.09 2022 32.50 2030 48.01

Pessimistic cost

forecast

2012 19.95 2015 26.55 2022 51.75 2030 110.92

Optimistic cost

forecast

2012 19.95 2015 21.48 2022 25.54 2030 31.12

Table - Expected cost variation within the period 2012/2015/2022/2030



• Estimated “ore” resources for study area is

approximately 5.2 Mt.

• Operational daily production is about 1,400 tons.

• The expected total production required in the region

analysed until the year 2030 is about 9.8 milliontons.

• Mine life expectation is 13.7 years.

Planning



• The financial analysis was developed based on the

following assumptions:

Income Tax (IT) 30%, discount rate (i) 10%, Current Price

(2012) R$ 25.77/t, Actual costs (2012) R$ 19.95/t.

Planning

Base Case Optimistic Pessimistic

Year Flow

2013 1,254,629 1,621,563 917,566

2015 1,542,487 2,934,134 274,2802022 3,178,295 13,847,153 -6,288,550

2025 2,926,947 16,392,678 -8,894,229

VPL 15,118,103 49,792,868 -15,769,081

Table – Base case cash flow with an annual price and cost average variation of 5%



• Generate cash flow and financially evaluate the

project.

• Provide essential data for survival and sustainable

development of mining aggregates projects.

• The expected production becomes necessary tosearch for new areas or increase reserves.

• Monitor the sector’s growth. The only way to keep

on business is understanding the strategy necessary

to provide market needs, and keeping control of

costs and management.

Conclusion



Acknowledgements

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