Estimation of the potential production rate

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Article written by Sci '14: Travis Dominski, Brett Kolankowski, Andrew Marck, Michal Pasternak, Steve Shamba

Production rate and mine life, play a large role in determining the project economics. A higher production rate typically allows for lower operating costs, while the subsequent shorter mine life maximizes the Net Present Value of ore extraction. However, a higher production rate requires a greater capital cost, as larger equipment and infrastructure is required. Estimation of production rate is a problem that has been looked at by many scholars. The most well-known scholar to look at the problem was H. K. Taylor who developed the empirical Taylor’s Rule, a rule of thumb that is commonly taught to Mining Engineering students. While the most popular, Taylor’s Rule is not the only method that can be used when estimating production rate. Taylor’s Rule only takes into account tonnage, while other methods use the grade of the ore and financial factors.

Taylor’s Rule

Taylor's Rule of Thumb for Mine Life

H. K. Taylor a mining engineering working with Placer Development Limited, proposed "Taylor's Law" at mine valuation and feasibility study seminar in Spokane, Washington in 1976[1]. This rule was then published in 1977. Taylor realized the need for such a rule as the existing "supposedly optimum mining rate have long been estimated by elementary economic theory, usually by present-value methods, but it has been observed that many such exercises show a bias towards high rates of working that are unachievable or undesirable in practice."[1] The previous methods had lead to inexperienced companies proposing mines with wildly unrealistic rates. Taylor based the emperical rule off of nearly 30 mining projects, mostly young mines. Taylor's rule was tested in 1984 by McSpadden and Schaap, who checked it against 45 open-pit copper deposits. McSpadden and Schaap found the rule needed to be tweaked slightly, however this finding was due to the specivity of their mine types compared to Taylor's wide range of mine types.

Equation for Mine Life and Production Rate

The empirical equation for mine life that Taylor developed is:

The equation can be used to find the production rate by

Assuming a mine operating 350 days a year, Taylor's rule gives the equation

Taylor's rule was originally developed for metric tonnes, but it can be applied to short tons. As the difference between metric tonnes and short tons is lessened by the 4th root, the resulting error in mine life would only be +2.5%.


Taylor identified several senarios where his rule of thumb does not work well:

  • Old mines far into the stages of operation (can work faster)
  • Unusually large deposits (>200Mt as suggested mining rate would be unobtainable)
  • Very deep, flat ore bodies (production limited by hoisting limits of shaft)
  • Steeply dipping tabular or massive deposits that are worked in steps towards great depths (limited to rate of deepening of the working)
  • Erratically mineralized multi-vein systems (production rate limited to discovery rate)

USBM/USGS Modifications

Modifications of Taylor's Rule

Taylor's rule has been modified and tweaked by the United States Bureau of Mines (USBM) and its successor the United States Geological Survey (USGS) to a large and more modern set of data.[2] All the modicications of Taylor's rule use the same general relationship, and just revise the variables.

D.A. Singer, W.D. Menzie and K.R. Long revised Taylor's rule in 1998, based on a data set of 41 open pit gold-silver mines[3]. Their model found that appropriate rates should for open pit gold-silver mines should be significantly higher than Taylor's Rule suggests. Their resulting equation was:

D.A. Singer, W.D. Menzie and K.R. Long also adapted Taylor's rule for underground massive sulfide deposits in 2000.[4] In this modification it clear that the Taylor's rule overestimates the underground mining rate as it was calibrated to open pit mines. The resulting equation for underground massive sulfide deposits is:

Long and Singer further studies Taylor's rule in 2001 and calibrated it to 45 open pit copper mines.[5] The open pit copper model proved to have a curve halfway between the 1998 gold-silver curve and Taylor's Rule. Since the mines are the same type as those used for Taylor's rule, it can be seen that the realistic production rate has increased in the decades since Taylor's rule was first developed.

The latest study on Taylor's rule was completed by Long in 2009.[6] Long's study is the most extensive of all studies looking at the relationship between Capacity and Reserve. The study looked at 342 open pit and 197 underground mines located in the Americas and Australia. Long found that there was a significant difference between the production rate of underground vs. open pit and block caving. The equation found for underground deposits was found to be:

The equation for open pit and block caving deposits was found to be:

Long's 2009 study also found that introducing the variables grade and capital cost played a factor in estimating production rate, however expected tonnage was the primary factor. Long did generate equations involving grade and capital cost for open pit, however the inputs for these equations were not clarified.


Many scoping studies use the original Taylor's Rule as a starting point for production rate regardless of the type of mine. It is clear from the USBM/USGS modifications of Taylor's Rule that a better estimate is possible adding the variable of mine type as well as expected tonnage. The table below acts as a guide to selecting an appropriate version of Taylor's rule. The general equation for Taylor's rule is:

Mine Type a b Source # Mines
Unknown 0.0143 0.75 Taylor (1986) ~30
Open Pit - Gold/Silver 0.416 0.5874 Singer, Menzie, Long (1998) 41
Open Pit - Copper 0.0236 0.74 Singer, Long (2001) 45
Open Pit/Block Caving - Other 0.123 0.649 Long (2009) 342
Underground - Massive Sulfide 0.0248 0.704 Singer, Menzie, Long (2000) 28
Underground - Other 0.297 0.562 Long (2009) 197

Other Methods

Wells (1978)

In 1978, H.M. Wells published the paper “Optimization of mining engineering design in mineral valuation”, which proposed maximizing the present value ratio (PVR) in order to find the optimal production rate.[7] PVR was the ratio of PVOUT (the present value of positive cash flows) to PVIN (the present value of negative cash flows). A PVR greater than 1 represented a profitable production rate while a PVR less than 1 was an unprofitable production rate. The optimal production rate is the rate that causes the PVR to be at its maximum.

Lizotte and Elbrond (1982)

Y. Lizotte and J. Elbrond researched optimization of production rates in 1982. They approached the problem using open-ended dynamic programming and created a model for the problem. However they concluded that there were vast difference between their model and realistic mining.[7]

Cavender (1992)

B. Cavender took a theoretical approach to determining appropriate mine life looking at the finance side of mining. He developed three techniques for finding the mine life that optimized the NPV of the project. Cavender looked at cash flow, stochastic risk modeling, and option pricing. Since the model deals with a hypothetical mining and does not include realistic mining constraints, it has little application to real mine design.

Smith (1997)

L.D. Smith in 1997 found that estimating a mines production rate was better determined by a range than a specific point. Smith's paper proposed that an appropriate range of production rates with a upper limit as the rate that resulted in the highest NPV. The lower limit of this range was found to be the rate that best repaid capital costs.

Abdel Sabour (2002)

Effects of various parameters on optimal production rate (Abdel Sabour)[7]

Using a mathematical model, S.A. Abdel Sabour looked at the effect of various physical, economic and financial factors on the optimal production rate.[7] For the physical factors it was found that the optimal production rate increases with both the tonnage and grade of the deposit. A higher gold price resulted in a higher production rate. The production rate also depended on the expected growth rate of gold prices, with a higher growth rate resulting in a lower production rate. If mining cost growth rate is expected to be high, the optimal production rate is should also be high to avoid higher mining costs in later years. The final factor considered was the cost of capital (discount rate). It was found that for a low (~5%) and high(~35%) cost of capital, the production rate should be low, however between these points of cost and capital, the production rate should be higher.

Abdel Sabour's definition of the optimal production rate is the rate that generates the highest NPV. To obtain the optimal rate this microeconomic theory was applied. Since these optimal production rates look at economic theory rather than engineering design constrains, his work is not useful on it's own to estimate production rate. It can however can be used to tweak the results of other production rate estimates, such as Taylor's rule.


Taylor's rule is the best way get a preliminary estimate of the production rate and the mine life during mine design. This is due to its simplicity of calculation since it involves only one variable, as well as the real world applicability of the rule since it is built upon real world data. Modifications by the USBM/USGS should be considered when using Taylor's rule, as they tweak it to better suit the type of mine.


  1. 1.0 1.1 Taylor, H.K., 1986, Rates of working mines; a simple rule of thumb: Transactions of the Institution of Mining and Metallurgy, v. 95, section A, p. 203-204.
  2. Cite error: Invalid <ref> tag; no text was provided for refs named num1
  3. Singer, D.A., Menzie, W.D., and Long, K.R., (1998). A simplified economic filter for open-pit gold-silver mining in the United States: U. S. Geological Survey Open-File Report 98-207, 10 p., accessed February 02, 2014, at
  4. Singer, D.A., Menzie, W.D., and Long, K.R., (2000), A simplified economic filter for underground mining of massive sulfide deposits. accessed February 02, 2014, at
  5. Long, K.R., and D.A. Singer, (2001), A Simplified Economic Filter for Open-Pit Mining and Heap-Leach., accessed February 02, 2014, at
  6. Long, K.R., (2009), A Test and Re-Estimation of Taylor's Empirical Capacity–Reserve Relationship. accessed February 02, 2014, at
  7. 7.0 7.1 7.2 7.3 Abdel Sabour, S. A. "Mine size optimization using marginal analysis." Resources Policy 28, no. 3 (2002): 145-151.