US2011227394A1PendingUtilityA1
Method, Apparatus And Computer Program For Scheduling The Extraction Of A Resource And For Determining The Net Present Value Of An Extraction Schedule
Est. expiryJun 21, 2024(expired)· nominal 20-yr term from priority
Inventors:Merab Menabde
E21C 41/30G06Q 10/063G06Q 10/0631G06Q 10/06314G06Q 10/06315
39
PatentIndex Score
0
Cited by
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References
0
Claims
Abstract
The scheduling of extraction of a resource within a mine is disclosed which comprises obtaining drill hole grade data relating to the resource and creating a plurality of different block models, with each block in each model having a resource grade and each of the block models honouring the drill hole grade data. An extraction schedule is determined based on a cut-off grade policy to increase expected net present value of the mine. The cut-off grade policy can be a variable cut-off grade and the scheduling using the variable cut-off grade is converted from a non-linear representation to a linear representation and a mixed integer programming formulation is used for simultaneous optimisation of the extraction schedule and the cut-off grade.
Claims
exact text as granted — not AI-modified1 . A method of scheduling the extraction of a resource in a mine medium comprising the steps of:
obtaining drillhole grade data relating to the resource contained within the medium; creating a plurality of different block models of the mine with a processor, each formed of a plurality of blocks, each block in each model having a resource grade, each of the block models honouring the drillhole grade data; and generating a single schedule for extraction of blocks from the mine with the processor based on a cut-off grade policy to increase expected net present value having regard to all of the plurality of different block models.
2 . The method of claim 1 wherein the creation of the plurality of different block models is a conditional simulation technique which provides the generation of a plurality of equally probable block model realisations, all of which honour the data and first and second order statistics of the resource represented, respectively, by probability distribution function and variogram.
3 . The method of claim 1 wherein the number of blocks in each of the block models is reduced by aggregating blocks prior to the step of scheduling extraction.
4 . The method of claim 1 wherein the cut-off grade is a variable cut-off grade.
5 . The method of claim 1 wherein the plurality of block models are used to provide an average of resource grade and block valuations and the average of the resource grade and block valuations is used to produce the scheduled extraction of blocks and the cut-off grade value to increase expected net present value.
6 . The method of claim 1 wherein the scheduled extraction of blocks is optimised by considering the average valuation produced from the conditional simulations.
7 . The method of claim 1 wherein all of the conditional simulations are optimized simultaneously to produce a single extraction schedule with a variable cut-off grade policy, and wherein all the conditional simulations are valued using this schedule and the valuations are averaged to produce the expected net present value.
8 . The method of claim 1 wherein the extraction schedule determines whether resource and medium corresponding to each block is sent for processing, sent to waste or stockpiled for later processing.
9 . The method of claim 1 wherein the step of scheduling using a variable cut-off grade is converted from a non-linear representation to a linear representation, and a mixed integer programming formulation is used for simultaneous optimisation of the extraction schedule and the cut-off grade.
10 . The method of claim 1 , wherein the schedule is determined by the following:
Maximise
(
1
N
∑
n
=
1
N
∑
i
=
1
P
∑
j
=
1
G
∑
t
=
1
T
V
i
j
n
x
i
j
t
d
t
)
(
1
)
subject to the following constraints:
1
N
∑
n
=
1
N
∑
i
=
1
P
∑
j
-
1
G
R
i
n
x
ijt
≤
R
t
0
,
for
all
t
(
2
)
1
N
∑
n
=
1
N
∑
i
=
1
P
∑
j
=
1
G
Q
ij
n
x
ijt
≤
Q
t
0
,
for
all
t
(
3
)
y
i
,
t
-
1
≤
y
it
,
for
all
i
and
t
(
4
)
∑
τ
=
1
t
∑
j
=
1
G
x
ij
τ
≤
y
it
,
for
all
i
(
5
)
y
it
≤
∑
j
=
1
G
∑
t
=
1
T
x
kj
τ
,
for
all
i
,
t
and
k
⋐
S
i
(
6
)
∑
j
=
1
G
δ
jt
=
1
,
for
all
t
(
7
)
x
ijt
≤
δ
jt
,
for
all
i
,
j
,
and
t
(
8
)
wherein:
T is the number of scheduling periods;
N is the number of simulations;
P is the total number of panels;
G is the number of all possible cutoff grades;
R i n is the total rock in the panel i in simulations n.
Q ij n is the total ore in the panel i, simulation n, when mined with the COG j;
V ij n is the value of the panel i, simulation n, when mined and processed with the COG j;
R t 0 is the maximum mining capacity in period t;
Q t 0 is the maximum processing rate in period t;
S i is the set of panels that must be removed before starting the panel i;
d t is the time discount factor;
x ijt is the fraction of the panel i is extracted with the COG j in period t;
y it is a binary variable equal to 1 if the extraction of the panel i has started in periods 1 to t, and equal to 0 otherwise;
δ jt is a binary variable controlling the selection of the COG applied in period t.
11 . An apparatus for scheduling the extraction of a resource within a medium in a mine from which drillhole grade data relating to the resource contained within the medium has been obtained comprising:
a processor for creating a plurality of different block models of the mine, each formed of a plurality of blocks, each block in each model having a resource grade, each of the block models honouring the drillhole grade data; and a processor for generating a single schedule for extraction of blocks based on a cut-off grade policy to increase expected net present value having regard to all of the plurality of different block models.
12 . The apparatus of claim 11 wherein the processor is for creating conditional simulations which provides the generation of a plurality of equally probable block model realisations, all of which honour the data and first and second order statistics of the resource represented, respectively, by probability distribution function and variogram.
13 . The apparatus of claim 11 wherein the processor is also for reducing the number of blocks in each of the block models by aggregating blocks prior to the step of scheduling extraction.
14 . The apparatus of claim 11 wherein the cut-off grade is a variable cut-off grade.
15 . The apparatus of claim 11 wherein the processor is for providing an average of resource grade and block valuations from the plurality of models, and the average of the resource grade and block valuations is used to produce the scheduled extraction of blocks and the cut-off grade value to increase expected net present value.
16 . The apparatus of claim 11 wherein the processor is for scheduling extraction of blocks optimised by considering the average valuation produced from the conditional simulations.
17 . The apparatus of claim 11 wherein all of the conditional simulations are optimized simultaneously to produce a single extraction schedule with a variable cut-off grade policy, and wherein all the conditional simulations are valued using this schedule and the valuations are averaged to produce the expected net present value.
18 . The apparatus of claim 11 wherein the extraction schedule determines whether resource and medium corresponding to each block is sent for processing, sent to waste or stockpiled for later processing.
19 . The apparatus of claim 11 wherein the processor is for converting from a non-linear representation to a linear representation when a variable cut-off grade is used, and a mixed integer programming formulation is used for simultaneous optimisation of the extraction schedule and the cut-off grade.
20 . The apparatus of claim 11 , wherein the schedule is determined by the following:
Maximise
(
1
N
∑
n
=
1
N
∑
i
=
1
P
∑
j
=
1
G
∑
t
=
1
T
V
ij
n
x
ijt
d
t
)
(
1
)
subject to the following constraints:
1
N
∑
n
=
1
N
∑
i
=
1
P
∑
j
-
1
G
R
i
n
x
ijt
≤
R
t
0
,
for
all
t
(
2
)
1
N
∑
n
=
1
N
∑
i
=
1
P
∑
j
=
1
G
Q
ij
n
x
ijt
≤
Q
t
0
,
for
all
t
(
3
)
y
i
,
t
-
1
≤
y
it
,
for
all
i
and
t
(
4
)
∑
τ
=
1
t
∑
j
=
1
G
x
ij
τ
≤
y
it
,
for
all
i
(
5
)
y
it
≤
∑
j
=
1
G
∑
t
=
1
T
x
kj
τ
,
for
all
i
,
t
and
k
⋐
S
i
(
6
)
∑
j
=
1
G
δ
jt
=
1
,
for
all
t
(
7
)
x
ijt
≤
δ
jt
,
for
all
i
,
j
,
and
t
(
8
)
wherein;
T is the number of scheduling periods;
N is the number of simulations;
P is the total number of panels;
G is the number of all possible cutoff grades;
R i n is the total rock in the panel i in simulations n.
Q ij n is the total ore in the panel i, simulation n, when mined with the COG j;
V ij n is the value of the panel i, simulation n, when mined and processed with the COG j;
R t 0 is the maximum mining capacity in period t;
Q t 0 is the maximum processing rate in period t;
S i is the set of panels that must be removed before starting the panel i;
d t is the time discount factor;
x ijt is the fraction of the panel i is extracted with the COG j in period t;
y it is a binary variable equal to 1 if the extraction of the panel i has started in periods 1 to t, and equal to 0 otherwise;
δ jt is a binary variable controlling the selection of the COG applied in period t.
21 . A tangible computer readable medium comprising a computer program for scheduling the extraction of a resource within a medium in a mine comprising:
code which when executed by a processor receives drillhole grade data relating to the resource contained within the medium; code which when executed by a processor creates a plurality of different block models of the mine, each formed of a plurality of blocks, each block in each model having a resource grade, each of the block models honouring the drillhole grade data; and code which when executed by a processor generates a schedule for extraction of blocks from the mine based on a cut-off grade policy to increase expected net present value having regard to all of the plurality of different block models.
22 . The tangible computer readable medium of claim 21 wherein the code for creation of the plurality of different block models is code for performing a conditional simulation technique which provides the generation of a plurality of equally probable block model realisations, all of which honour the data and first and second order statistics of the resource represented, respectively, by probability distribution function and variogram.
23 . The tangible computer readable medium of claim 21 wherein the program further comprises code for reducing the number of blocks in each of the block models by aggregating blocks prior to the step of scheduling extraction.
24 . The tangible computer readable medium of claim 21 wherein the code for scheduling further comprises code for optimizing extraction by considering the average valuation produced from the conditional simulations.
25 . The tangible computer readable medium of claim 21 wherein the code for scheduling further comprises code for using all of the conditional simulations to optimize simultaneously to produce a single extraction schedule with a variable cut-off grade policy, and wherein all the conditional simulations are valued using this schedule and the valuations are averaged to produce the expected net present value.
26 . The tangible computer readable medium of claim 21 wherein the extraction schedule determines whether resource and medium corresponding to each block is sent for processing, sent to waste or stockpiled for later processing.
27 . The tangible computer readable medium of claim 21 wherein the code for scheduling using a variable cut-off grade converts from a non-linear representation to a linear representation, and further comprises a mixed integer programming formulation is used for simultaneous optimisation of the extraction schedule and the cut-off grade.
28 . The tangible computer readable medium of claim 21 , wherein the schedule is determined by the following:
Maximise
(
1
N
∑
n
=
1
N
∑
i
=
1
P
∑
j
=
1
G
∑
t
=
1
T
V
ij
n
x
ijt
d
t
)
(
1
)
subject to the following constraints:
1
N
∑
n
=
1
N
∑
i
=
1
P
∑
j
-
1
G
R
i
n
x
ijt
≤
R
t
0
,
for
all
t
(
2
)
1
N
∑
n
=
1
N
∑
i
=
1
P
∑
j
=
1
G
Q
ij
n
x
ijt
≤
Q
t
0
,
for
all
t
(
3
)
y
i
,
t
-
1
≤
y
it
,
for
all
i
and
t
(
4
)
∑
τ
=
1
t
∑
j
=
1
G
x
ij
τ
≤
y
it
,
for
all
i
(
5
)
y
it
≤
∑
j
=
1
G
∑
t
=
1
T
x
kj
τ
,
for
all
i
,
t
and
k
⋐
S
i
(
6
)
∑
j
=
1
G
δ
jt
=
1
,
for
all
t
(
7
)
x
ijt
≤
δ
jt
,
for
all
i
,
j
,
and
t
(
8
)
wherein;
T is the number of scheduling periods;
N is the number of simulations;
P is the total number of panels;
G is the number of all possible cutoff grades;
R i n is the total rock in the panel i in simulations n.
Q ij n is the total ore in the panel i, simulation n, when mined with the COG j;
V ij n is the value of the panel i, simulation n, when mined and processed with the COG j;
R t 0 is the maximum mining capacity in period t;
Q t 0 is the maximum processing rate in period t;
S i is the set of panels that must be removed before starting the panel i;
d t is the time discount factor;
x ijt is the fraction of the panel i is extracted with the COG j in period t;
y it is a binary variable equal to 1 if the extraction of the panel i has started in periods 1 to t, and equal to 0 otherwise;
δ jt is a binary variable controlling the selection of the COG applied in period t.
29 . A method of mining a mine comprising a resource within a medium, the method comprising:
removing medium and resource from the mine using an extraction schedule created by the following steps: obtaining drillhole grade data relating to the resource contained within the medium; creating with a processor a plurality of different block models of the mine, each formed of a plurality of blocks, each block in each model having a resource grade, each of the block models honouring the drillhole grade data; and scheduling with a processor, extraction of blocks based on a cut-off grade policy to increase expected net present value having regard to all of the plurality of different block models.
30 . The method of claim 29 wherein the creation of the plurality of different block models is a conditional simulation technique which provides the generation of a plurality of equally probable block model realisations, all of which honour the data and first and second order statistics of the resource represented, respectively, by probability distribution function and variogram.
31 . The method of claim 29 wherein the number of blocks in each of the block models is reduced by aggregating blocks prior to the step of scheduling extraction.
32 . The method of claim 29 wherein the plurality of block models are used to provide an average of resource grade and block valuations and the average of the resource grade and block valuations is used to produce the scheduled extraction of blocks and the cut-off grade value to increase expected net present value.
33 . The method of claim 29 wherein the scheduled extraction of blocks is optimised by considering the average valuation produced from the conditional simulations.
34 . The method of claim 29 wherein all of the conditional simulations are optimized simultaneously to produce a single extraction schedule with a variable cut-off grade policy, and wherein all the conditional simulations are valued using this schedule and the valuations are averaged to produce the expected net present value.
35 . The method of claim 29 wherein the extraction schedule determines whether resource and medium corresponding to each block is sent for processing, sent to waste or stockpiled for later processing.
36 . The method of claim 29 wherein the step of scheduling using a variable cut-off grade is converted from a non-linear representation to a linear representation, and a mixed integer programming formulation is used for simultaneous optimisation of the extraction schedule and the cut-off grade.
37 . The method of claim 29 , wherein the schedule is determined by the following:
Maximise
(
1
N
∑
n
=
1
N
∑
i
=
1
P
∑
j
=
1
G
∑
t
=
1
T
V
ij
n
x
ijt
d
t
)
(
1
)
subject to the following constraints:
1
N
∑
n
=
1
N
∑
i
=
1
P
∑
j
-
1
G
R
i
n
x
ijt
≤
R
t
0
,
for
all
t
(
2
)
1
N
∑
n
=
1
N
∑
i
=
1
P
∑
j
=
1
G
Q
ij
n
x
ijt
≤
Q
t
0
,
for
all
t
(
3
)
y
i
,
t
-
1
≤
y
it
,
for
all
i
and
t
(
4
)
∑
τ
=
1
t
∑
j
=
1
G
x
ij
τ
≤
y
it
,
for
all
i
(
5
)
y
it
≤
∑
j
=
1
G
∑
t
=
1
T
x
kj
τ
,
for
all
i
,
t
and
k
⋐
S
i
(
6
)
∑
j
=
1
G
δ
jt
=
1
,
for
all
t
(
7
)
x
ijt
≤
δ
jt
,
for
all
i
,
j
,
and
t
(
8
)
wherein;
T is the number of scheduling periods;
N is the number of simulations;
P is the total number of panels;
G is the number of all possible cutoff grades;
R i n is the total rock in the panel i in simulations n.
Q ij n is the total ore in the panel i, simulation n, when mined with the COG j;
V ij n is the value of the panel i, simulation n, when mined and processed with the COG j;
R t 0 is the maximum mining capacity in period t;
Q t 0 is the maximum processing rate in period t;
S i is the set of panels that must be removed before starting the panel i;
d t is the time discount factor;
x ijt is the fraction of the panel i is extracted with the COG j in period t;
y it is a binary variable equal to 1 if the extraction of the panel i has started in periods 1 to t, and equal to 0 otherwise;
δ jt is a binary variable controlling the selection of the COG applied in period t.
38 . A method of determining the net present value of a mine comprising a resource within a medium, the method comprising the steps of:
obtaining drillhole grade data relating to the resource contained within the medium; creating a plurality of different block models of the mine with a processor, each formed of a plurality of blocks, each block in each model having a resource grade, each of the block models honouring the drillhole grade data; and generating a schedule for extraction of blocks from the mine with a processor based on a cut-off grade policy to provide the net present value having regard to all of the plurality of different block models.
39 . The method of claim 38 wherein the creation of the plurality of different block models is a conditional simulation technique which provides the generation of a plurality of equally probable block model realisations, all of which honour the data and first and second order statistics of the resource represented, respectively, by probability distribution function and variogram.
40 . The method of claim 38 wherein the number of blocks in each of the block models is reduced by aggregating blocks prior to the step of scheduling extraction.
41 . The method of claim 38 wherein the cut-off grade is a variable cut-off grade.
42 . The method of claim 38 wherein the plurality of block models are used to provide an average of resource grade and block valuations and the average of the resource grade and block valuations is used to produce the scheduled extraction of blocks and the cut-off grade value to increase expected net present value.
43 . The method of claim 38 wherein the scheduled extraction of blocks is optimised by considering the average valuation produced from the conditional simulations.
44 . The method of claim 38 wherein all of the conditional simulations are optimized simultaneously to produce a single extraction schedule with a variable cut-off grade policy, and wherein all the conditional simulations are valued using this schedule and the valuations are averaged to produce the expected net present value.
45 . The method of claim 38 wherein the extraction schedule determines whether resource and medium corresponding to each block is sent for processing, sent to waste or stockpiled for later processing.
46 . The method of claim 38 wherein the step of scheduling using a variable cut-off grade is converted from a non-linear representation to a linear representation, and a mixed integer programming formulation is used for simultaneous optimisation of the extraction schedule and the cut-off grade.
47 . The method of claim 38 , wherein the schedule is determined by the following:
Maximise
(
1
N
∑
n
=
1
N
∑
i
=
1
P
∑
j
=
1
G
∑
t
=
1
T
V
ij
n
x
ijt
d
t
)
(
1
)
subject to the following constraints:
1
N
∑
n
=
1
N
∑
i
=
1
P
∑
j
-
1
G
R
i
n
x
ijt
≤
R
t
0
,
for
all
t
(
2
)
1
N
∑
n
=
1
N
∑
i
=
1
P
∑
j
=
1
G
Q
ij
n
x
ijt
≤
Q
t
0
,
for
all
t
(
3
)
y
i
,
t
-
1
≤
y
it
,
for
all
i
and
t
(
4
)
∑
τ
=
1
t
∑
j
=
1
G
x
ij
τ
≤
y
it
,
for
all
i
(
5
)
y
it
≤
∑
j
=
1
G
∑
t
=
1
T
x
kj
τ
,
for
all
i
,
t
and
k
⋐
S
i
(
6
)
∑
j
=
1
G
δ
jt
=
1
,
for
all
t
(
7
)
x
ijt
≤
δ
jt
,
for
all
i
,
j
,
and
t
(
8
)
wherein;
T is the number of scheduling periods;
N is the number of simulations;
P is the total number of panels;
G is the number of all possible cutoff grades;
R i n is the total rock in the panel i in simulations n.
Q ij n is the total ore in the panel i, simulation n, when mined with the COG j;
V ij n is the value of the panel i, simulation n, when mined and processed with the COG j;
R t 0 is the maximum mining capacity in period t;
Q t 0 is the maximum processing rate in period t;
S i is the set of panels that must be removed before starting the panel i;
d t is the time discount factor;
x ijt is the fraction of the panel i is extracted with the COG j in period t;
y it is a binary variable equal to 1 if the extraction of the panel i has started in periods 1 to t, and equal to 0 otherwise;
δ jt is a binary variable controlling the selection of the COG applied in period t.
48 . A method for scheduling the extraction of a resource contained within a medium in a mine, comprising the steps of:
obtaining drillhole grade data relating to the resource contained within the medium; creating a block model of the mine with a processor with each block in the model having a resource grade; generating a schedule for extraction of blocks from the mine with a processor based on a variable cut-off grade value to increase expected net present value, the variable cut-off grade being represented by a non-linear function; and converting the non-linear representation to a linear representation with the processor and using a mixed integer programming formulation for a simultaneous optimisation by the processor of an extraction sequence provided by the scheduled extraction and cut-off grade policy.
49 . The method of claim 48 wherein the non-linear function is converted to a plurality of linear values by producing a plurality of step values relating to the non-linear function, and selecting one of the values to provide the cut-off grade decision by the mixed integer programming function.
50 . The method of claim 48 wherein the block model is a deterministic model built by spatial interpretation of the data using a Kriging procedure.
51 . The method of claim 48 wherein the block model is a conditional simulation producing a plurality of different block models providing a number of equally probably realisations of block models honouring the drillhole data and first and second order statistics of the resource distributed in the medium represented, respectively, by probability distribution and variogram.
52 . The method of claim 48 wherein the number of blocks in each of the block models is reduced by aggregating blocks prior to the step of scheduling extraction.
53 . The method of claim 48 wherein the plurality of block models are used to provide an average of resource grade valuations and the average of the resource grade valuations is used to produce the scheduled extraction of blocks and the cut-off grade value to increase expected net present value.
54 . The method of claim 48 wherein the scheduled extraction of blocks is optimised by considering the average valuation produced from the conditional simulations.
55 . The method of claim 48 wherein all of the conditional simulations are optimised to produce a plurality of extraction schedules, each having valuations and the valuations are averaged to produce the net present value, and the extraction schedule comprises the extraction schedule which is closest to that giving the net present value.
56 . The method of claim 48 wherein the extraction schedule determines whether resource and medium corresponding to each block is sent for processing, sent to waste or stockpiled for later processing.
57 . The method of claim 48 , wherein the schedule is determined by the following:
Maximise
(
1
N
∑
n
=
1
N
∑
i
=
1
P
∑
j
=
1
G
∑
t
=
1
T
V
ij
n
x
ijt
d
t
)
(
1
)
subject to the following constraints:
1
N
∑
n
=
1
N
∑
i
=
1
P
∑
j
-
1
G
R
i
n
x
ijt
≤
R
t
0
,
for
all
t
(
2
)
1
N
∑
n
=
1
N
∑
i
=
1
P
∑
j
=
1
G
Q
ij
n
x
ijt
≤
Q
t
0
,
for
all
t
(
3
)
y
i
,
t
-
1
≤
y
it
,
for
all
i
and
t
(
4
)
∑
τ
=
1
t
∑
j
=
1
G
x
ij
τ
≤
y
it
,
for
all
i
(
5
)
y
it
≤
∑
j
=
1
G
∑
t
=
1
T
x
kj
τ
,
for
all
i
,
t
and
k
⋐
S
i
(
6
)
∑
j
=
1
G
δ
jt
=
1
,
for
all
t
(
7
)
x
ijt
≤
δ
jt
,
for
all
i
,
j
,
and
t
(
8
)
wherein;
T is the number of scheduling periods;
N is the number of simulations;
P is the total number of panels;
G is the number of all possible cutoff grades;
R i n is the total rock in the panel i in simulations n.
Q ij n is the total ore in the panel i, simulation n, when mined with the COG j;
V ij n is the value of the panel i, simulation n, when mined and processed with the COG j;
R t 0 is the maximum mining capacity in period t;
Q t 0 is the maximum processing rate in period t;
S i is the set of panels that must be removed before starting the panel i;
d t is the time discount factor;
x ijt is the fraction of the panel i is extracted with the COG j in period t;
y it is a binary variable equal to 1 if the extraction of the panel i has started in periods 1 to t, and equal to 0 otherwise;
δ jt is a binary variable controlling the selection of the COG applied in period t.
58 . An apparatus for scheduling the extraction of a resource contained within a medium in a mine, comprising:
a processor for receiving drillhole grade data relating to the resource contained within the medium; a processor for creating a block model with each block in the model having a resource grade; a processor for scheduling extraction of blocks from the mine based on a variable cut-off grade value to increase expected net present value, the variable cut-off grade being represented by a non-linear function; and a processor for converting the non-linear representation to a linear representation and using a mixed integer programming formulation for a simultaneous optimisation of an extraction sequence provided by the scheduled extraction and cut-off grade policy.
59 . The apparatus of claim 58 wherein the processor is for converting the non-linear function to a plurality of linear values by producing a plurality of step values relating to the non-linear function, and selecting one of the values to provide the cut-off grade decision by the mixed integer programming function.
60 . The apparatus of claim 58 wherein the block model is a deterministic model built by spatial interpretation of the data using a Kriging procedure.
61 . The apparatus of claim 58 wherein the block model is a conditional simulation producing a plurality of different block models providing a number of equally probably realisations of block models honouring the drillhole data and first and second order statistics of the resource distributed in the medium represented, respectively, by probability distribution and variogram.
62 . The apparatus of claim 58 wherein the number of blocks in each of the block models is reduced by aggregating blocks prior to the step of scheduling extraction.
63 . The apparatus of claim 58 wherein the processor is for averaging of resource grade valuations from the block models and the average of the resource grade valuations is used to produce the scheduled extraction of blocks and the cut-off grade value to increase expected net present value.
64 . The apparatus of claim 58 wherein the scheduled extraction of blocks is optimised by considering the average valuation produced from the conditional simulations.
65 . The apparatus of claim 58 wherein the processor is for optimizing all of the conditional simulations to produce a plurality of extraction schedules, each having valuations and the valuations are averaged to produce the net present value, and the extraction schedule comprises the extraction schedule which is closest to that giving the net present value.
66 . The apparatus of claim 58 wherein extraction schedule determines whether resource and medium corresponding to each block is sent for processing, sent to waste or stockpiled for later processing.
67 . The apparatus of claim 58 , wherein the schedule is determined by the following:
Maximise
(
1
N
∑
n
=
1
N
∑
i
=
1
P
∑
j
=
1
G
∑
t
=
1
T
V
ij
n
x
ijt
d
t
)
(
1
)
subject to the following constraints:
1
N
∑
n
=
1
N
∑
i
=
1
P
∑
j
-
1
G
R
i
n
x
ijt
≤
R
t
0
,
for
all
t
(
2
)
1
N
∑
n
=
1
N
∑
i
=
1
P
∑
j
=
1
G
Q
ij
n
x
ijt
≤
Q
t
0
,
for
all
t
(
3
)
y
i
,
t
-
1
≤
y
it
,
for
all
i
and
t
(
4
)
∑
τ
=
1
t
∑
j
=
1
G
x
ij
τ
≤
y
it
,
for
all
i
(
5
)
y
it
≤
∑
j
=
1
G
∑
t
=
1
T
x
kj
τ
,
for
all
i
,
t
and
k
⋐
S
i
(
6
)
∑
j
=
1
G
δ
jt
=
1
,
for
all
t
(
7
)
x
ijt
≤
δ
jt
,
for
all
i
,
j
,
and
t
(
8
)
wherein;
T is the number of scheduling periods;
N is the number of simulations;
P is the total number of panels;
G is the number of all possible cutoff grades;
R i n is the total rock in the panel i in simulations n.
Q ij n is the total ore in the panel i, simulation n, when mined with the COG j;
V ij n is the value of the panel i, simulation n, when mined and processed with the COG j;
R t 0 is the maximum mining capacity in period t;
Q t 0 is the maximum processing rate in period t;
S i is the set of panels that must be removed before starting the panel i;
d t is the time discount factor;
x ijt is the fraction of the panel i is extracted with the COG j in period t;
y it is a binary variable equal to 1 if the extraction of the panel i has started in periods 1 to t, and equal to 0 otherwise;
δ jt is a binary variable controlling the selection of the COG applied in period t.
68 . A tangible computer readable medium comprising a computer program for scheduling the extraction of a resource contained within a medium in a mine, comprising:
code which when executed by a processor receives drillhole grade data relating to the resource contained within the medium; code which when executed by a processor creates a block model with each block in the model having a resource grade; code which when executed by a processor schedules extraction of blocks based on a variable cut-off grade value to increase expected net present value, the variable cut-off grade being represented by a non-linear function; and code which when executed by a processor converts the non-linear representation to a linear representation and using a mixed integer programming formulation for a simultaneous optimisation of an extraction sequence provided by the scheduled extraction and cut-off grade policy.
69 . The tangible computer readable medium of claim 68 wherein the non-linear function is converted to a plurality of linear values by producing a plurality of step values relating to the non-linear function, and selecting one of the values to provide the cut-off grade decision by the mixed integer programming function.
70 . The tangible computer readable medium of claim 68 wherein the block model is a deterministic model built by spatial interpretation of the data using a Kriging procedure.
71 . The tangible computer readable medium of claim 68 wherein the block model is a conditional simulation producing a plurality of different block models providing a number of equally probably realisations of block models honouring the drillhole data and first and second order statistics of the resource distributed in the medium represented, respectively, by probability distribution and variogram.
72 . The tangible computer readable medium of claim 68 wherein the number of blocks in each of the block models is reduced by aggregating blocks prior to the step of scheduling extraction.
73 . The tangible computer readable medium of claim 68 wherein the plurality of block models are used to provide an average of resource grade valuations and the average of the resource grade valuations is used to produce the scheduled extraction of blocks and the cut-off grade value to increase expected net present value.
74 . The tangible computer readable medium of claim 68 wherein the scheduled extraction of blocks is optimised by considering the average valuation produced from the conditional simulations.
75 . The tangible computer readable medium of claim 68 wherein all of the conditional simulations are optimised to produce a plurality of extraction schedules, each having valuations and the valuations are averaged to produce the net present value, and the extraction schedule comprises the extraction schedule which is closest to that giving the net present value.
76 . The tangible computer readable medium of claim 68 wherein the extraction schedule determines whether resource and medium corresponding to each block is sent for processing, sent to waste or stockpiled for later processing.
77 . The tangible computer readable medium of claim 68 , wherein the schedule is determined by the following:
Maximise
(
1
N
∑
n
=
1
N
∑
i
=
1
P
∑
j
=
1
G
∑
t
=
1
T
V
ij
n
x
ijt
d
t
)
(
1
)
subject to the following constraints:
1
N
∑
n
=
1
N
∑
i
=
1
P
∑
j
-
1
G
R
i
n
x
ijt
≤
R
t
0
,
for
all
t
(
2
)
1
N
∑
n
=
1
N
∑
i
=
1
P
∑
j
=
1
G
Q
ij
n
x
ijt
≤
Q
t
0
,
for
all
t
(
3
)
y
i
,
t
-
1
≤
y
it
,
for
all
i
and
t
(
4
)
∑
τ
=
1
t
∑
j
=
1
G
x
ij
τ
≤
y
it
,
for
all
i
(
5
)
y
it
≤
∑
j
=
1
G
∑
t
=
1
T
x
kj
τ
,
for
all
i
,
t
and
k
⋐
S
i
(
6
)
∑
j
=
1
G
δ
jt
=
1
,
for
all
t
(
7
)
x
ijt
≤
δ
jt
,
for
all
i
,
j
,
and
t
(
8
)
wherein;
T is the number of scheduling periods;
N is the number of simulations;
P is the total number of panels;
G is the number of all possible cutoff grades;
R i n is the total rock in the panel i in simulations n.
Q ij n is the total ore in the panel i, simulation n, when mined with the COG j;
V ij n is the value of the panel i, simulation n, when mined and processed with the COG j;
R t 0 is the maximum mining capacity in period t;
Q t 0 is the maximum processing rate in period t;
S i is the set of panels that must be removed before starting the panel i;
d t is the time discount factor;
x ijt is the fraction of the panel i is extracted with the COG j in period t;
y it is a binary variable equal to 1 if the extraction of the panel i has started in periods 1 to t, and equal to 0 otherwise;
δ jt is a binary variable controlling the selection of the COG applied in period t.Join the waitlist — get patent alerts
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