287
For each model generated, it is applied a transfer function to convert resources into mineral reserves and build the mine plan. From these several plans, it is possible to obtain the
probability of a point of the deposit having a certain value and the associated error in the
prediction. That is, it is possible to calculate the risks of a particular block does not contain
the amount of metal planned.
The simulation algorithms applied to the study of mineral deposits were developed some
decades ago and since then several works, not limited only to mining, have been elaborated
allowing the improvement of the techniques and the emergence of new methods. These studies present case studies in which the uncertainty associated with the prediction of a geological model and the risks that this uncertainty can bring to a mineral enterprise (Peroni, 2002;
Souza, 2007; Diedrich, 2012).
Although the simulation methods allow quantification of uncertainty in the resource estimation, they still have limited application in the mineral industry.
Among the reasons for this low use, three deserve to be highlighted:
i. Amount of computational time and skilled labor for carrying out the task.
ii. Lack of technical knowledge among mine planners, regarding the use of simulation
techniques.
iii. Mining companies culture, which is still based on deterministic models.
Some studies of underground mine stochastic planning have been developed in recent
years, as an example Mello (2015), but, in general, limited to the simulation of the metal
content.
In this study, the parameters that map uncertainty regarding the amount of metal contained
in a mineral deposit will be evaluated through the construction of probabilistic models.
These models will then be submitted to a transfer function and multiple plans will be created in order to determine the risks associated with the production plan established for the
mine.
2 METHODOLOGY
This study aims at developing a methodology to minimize the risk associated with the quantity of metal produced by a mining company and, therefore, to guarantee the stability in
producing metal.
The profit (FB) is calculated by the difference between revenue and extraction cost of each
stope (Clark; White, 1976).
FB = Revenue − Costs
(1)
Revenue is the arithmetic product of the sale price of the recoverable metal present in the
stope and the respective price:
Revenue = V × d × t × R × P
(2)
where:
V: stope volume
d: rock density
t: metal grade
R: plant global recovery
P: sale price.
The purpose of this study is to evaluate the risks contained in the first three elements
of equation (2), that is, the elements that can affect the quantity of metal contained in the
forecast.
The sequence of the work to evaluate the geological risk in the underground mine planning
is shown in Figure 1.
For each model generated, it is applied a transfer function to convert resources into mineral reserves and build the mine plan. From these several plans, it is possible to obtain the
probability of a point of the deposit having a certain value and the associated error in the
prediction. That is, it is possible to calculate the risks of a particular block does not contain
the amount of metal planned.
The simulation algorithms applied to the study of mineral deposits were developed some
decades ago and since then several works, not limited only to mining, have been elaborated
allowing the improvement of the techniques and the emergence of new methods. These studies present case studies in which the uncertainty associated with the prediction of a geological model and the risks that this uncertainty can bring to a mineral enterprise (Peroni, 2002;
Souza, 2007; Diedrich, 2012).
Although the simulation methods allow quantification of uncertainty in the resource estimation, they still have limited application in the mineral industry.
Among the reasons for this low use, three deserve to be highlighted:
i. Amount of computational time and skilled labor for carrying out the task.
ii. Lack of technical knowledge among mine planners, regarding the use of simulation
techniques.
iii. Mining companies culture, which is still based on deterministic models.
Some studies of underground mine stochastic planning have been developed in recent
years, as an example Mello (2015), but, in general, limited to the simulation of the metal
content.
In this study, the parameters that map uncertainty regarding the amount of metal contained
in a mineral deposit will be evaluated through the construction of probabilistic models.
These models will then be submitted to a transfer function and multiple plans will be created in order to determine the risks associated with the production plan established for the
mine.
2 METHODOLOGY
This study aims at developing a methodology to minimize the risk associated with the quantity of metal produced by a mining company and, therefore, to guarantee the stability in
producing metal.
The profit (FB) is calculated by the difference between revenue and extraction cost of each
stope (Clark; White, 1976).
FB = Revenue − Costs
(1)
Revenue is the arithmetic product of the sale price of the recoverable metal present in the
stope and the respective price:
Revenue = V × d × t × R × P
(2)
where:
V: stope volume
d: rock density
t: metal grade
R: plant global recovery
P: sale price.
The purpose of this study is to evaluate the risks contained in the first three elements
of equation (2), that is, the elements that can affect the quantity of metal contained in the
forecast.
The sequence of the work to evaluate the geological risk in the underground mine planning
is shown in Figure 1.