PITSTRAT@copyright 1998,99,2000,01,02 L.F. Scheepers L.S.L.P.S.
OPEN PIT OPTIMISATION MODEL
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OPEN PIT OPTIMISATION MODEL : “PITSTRAT” @copyright 1998 L.F. Scheepers of LSLPS.
TABLE OF CONTENTS
Page
BACKGROUND
1
DESCRIPTION
2
TIME PERIODS
5
STOCKPILING
7
MULTIPLE MINERALS BLENDING
7
TECHNICAL PARAMETERS
8
USER FRIENDLINESS
9
APPLICATION POTENTIAL
10
PAPER DELIVERED BY KUMBA RESOURCES SISHEN IRON ORE MINE
13
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OPEN PIT OPTIMISATION MODEL : “PITSTRAT” @copyright 1998 L.F. Scheepers of LSLPS.
BACKGROUND
The mining of mineral deposits in such a manner that at depletion, the maximum
possible profit is realised, has been an unsolved problem since man's discovery of the
usable elements buried beneath the earth's surface.
Since World War Two, the depletion of the most accessible of the world's high grade
reserves took place, forcing the mining industry into working with declining grade material.
As a result, the sequence of extraction has now become more important; and, in many
cases, has become a problem, the solution of which is vital to the existence of a profitable
operation.
During the past six decades, mathematicians, geostatisticians and mining engineers
have been actively applying their minds to finding elegant and comprehensive mathematical
models that would not only solve the open pit mining problem, but also provide optimal
answers in terms of maximum profitability over the life of open pit projects. The technique
that immediately comes to mind in seeking for an optimal answer, is mathematical
programming, specifically LP and MIP.
As far back as May 1964, Helmut Lerchs and Ingo Grossmann realised this and
reported that "a mathematical model taking into account all possible alternatives
simultaneously would provide optimal answers in terms of maximum profitability, however, it
would be of formidable size and its formulation/solution would be beyond the means of
present know-how".
Since the 1960's meteoric developments took place in the Computing Sciences and
today, mathematical models, constituting tens of thousands of variables and/or integer
variables in hundreds of thousands of constraints, are successfully solved within a
finite time period (couple of hours) on desktop computers and workstations.
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OPEN PIT OPTIMISATION MODEL : “PITSTRAT” @copyright 1998 L.F. Scheepers of LSLPS.
DESCRIPTION
The PITSTRAT optimisation software allows the planner to determine the optimal
"utility" (*) plan of the total system: "mining via plant to markets" in one optimisation run.
PITSTRAT indicates:
 which blocks from
 which pits are to be
 blended in which proportions in
 which time periods and routed to
 which plants in order to meet multiple mineral specifications so as to optimise
"utility" (*).
(*) Optimisation can mean maximisation or minimisation. Utility can be defined as cash flow
(in today’s money), N.P.V. (net present value being the discounted difference between
escalated revenue and escalated costs) or costs. Generally, optimisation of utility can mean
maximisation of “profits” or minimisation of “costs”.
Where-as the Lerchs Grossmann algorithm, as solved by the Mintec/Whittle software,
generates the mined out final pit shells as it will be at the end of the life of mine, PITSTRAT
generates the "how to get to the optimal pit over time” scenario. From year to year, as
time goes by, product specifications and sales volumes, exchange rates, selling prices and
technology can change and PITSTRAT is used to optimally plan for the exploitation of the
mentioned changes. The Mintec/Whittle software and PITSTRAT are complementary
systems.
The objective function of PITSTRAT is the maximisation of accounting contribution before
tax. Contribution is defined as the difference between revenue ex Fe-units sold and the
variable cost of producing those units – mining costs, waste removal costs, haulage costs,
handling costs, plant costs, loading and road/rail transport costs up to the point of sale. By
incorporating fixed costs (a user function), the objective function can be changed to
maximise cash flow before tax. In both cases, by entering the required factors, the
objective can be changed to maximise the N.P.V. of the above functions.
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OPEN PIT OPTIMISATION MODEL : “PITSTRAT” @copyright 1998 L.F. Scheepers of LSLPS.
The PITSTRAT constraints constitute the following:
 the open pit mining geometry
 hauling capacity between pits and plants
 plant capacity
 tons mined per pit capacity
 tons waste generated per pit capacity
 upper and lower limits on strip ratios
 markets
 sales volumes
 selling prices (with or without exchange rate)
 chemical specifications
The model will only “mine” while the contribution (utility) of the total system
remains positive. Each block is techno-economically evaluated in the full context of ALL
CONSTRAINTS in the model, including an element of cross-subsidy, e.g. a given layer/bench
of blocks may be unprofitable to mine, however, a block or layer/bench below it may be of
sufficiently high grade to economically warrant the mining of the unprofitable layer/bench or
block in order to reach and extract the high grade layer/bench or block. It should be clear
that the model does not use conventional cut-off grade policies to decide whether to mine a
block or not. It is far more complex than that, e.g. if mining costs increase with depth, the
model will e.g. cease mining at that layer where the contribution of the TOTAL system
becomes zero or negative. (This in itself can be an indication to initiate underground mining).
PITSTRAT output consists of a life of mine plan, split up into time periods to indicate to
the planner when to progress which pit(s) by how much. (Remember, PITSTRAT will identify
the most economical). In the case of the planner force feeding the model with unrealistic
constraints, e.g. specifying a chemical analysis or a strip ratio which is impossible to achieve,
the model will indicate so by generating an infeasible output report. If a plan is optimal, it
means that there is mathematically no better contribution (cash flow) plan possible than
the optimal plan, i.e. under the given constraints. Should one want to direct the planning into
different areas than what was indicated by the model in the optimal plan, e.g. due to limited
infrastructure or difficult terrain, then the involved blocks can be excluded from the input data,
and a second optimisation will then indicate the next best plan under the new set of
constraints. The difference in objective value tells the planner the worth of the first plan above
the second plan and thus whether it is economically justifiable to inject capital to procure
sufficient infrastructure and/or upgrade the terrain – depending the case in question.
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OPEN PIT OPTIMISATION MODEL : “PITSTRAT” @copyright 1998 L.F. Scheepers of LSLPS.
PITSTRAT output consists of a tabular display of blocks to be mined within the time
periods, with sub-totals, weighted averages and grand totals on tons and grades.
IMPORTANT NOTE: Computing considerations: large scale LP and MIP
applications place a high demand on computing resources and therefore, the user has to
empirically find the balance between output details and model execution times. The deciding
variables are the number of blocks and the number of time periods : the greater the number
of blocks, the less time periods can be covered by the optimisation in a pre-specified
execution time and vice versa, i.e. for a given hardware configuration.
Number of time periods. In order to comply with the avoidance of sub-optimisation
principles, it is required to generate models consisting of at least two time periods,
irrespective of the number of participating blocks. The first period can be regarded as the
short term plan and the second period as the long term or the balance of life of mine plan. A
three period model can be used to set up a short term plan, a medium term plan and a
long term plan, all in one matrix. (The implication if this is that the model “knows” the
sacrifices that will be incurred in later periods due to “high grade gains” in earlier
periods, and vice versa. PITSTRAT finds the optimal comprise plan). E.g., three time
periods can be set up to cover a quarter (three months) as the short term plan, a two year
plan as the medium term plan and the balance of life of mine as the long term plan. The
number of time periods and their lengths are under user control.
The pit geometry is not under user control, except by manipulating the block
dimensions within the geological modelling software package in use at the site, prior to an
optimisation run. Thus in order to do an optimisation run with different sloped pit(s), the
dimensions of the blocks must first be changed in order to represent the new slope.
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OPEN PIT OPTIMISATION MODEL : “PITSTRAT” @copyright 1998 L.F. Scheepers of LSLPS.
The geometrical rule is: to mine a given block in bench "k", the nine blocks
directly above the given block, i.e. in bench "k-1" must first be mined.
Bench "k-2" [top]
Bench "k-1" [middle]
Bench "k" [below]
Note : At this time fractions of blocks cannot be mined. Either a block is mined in its
totality, or not at all.
TIME PERIODS
Long term planning (or life of mine plan or strategic mine plan) defines the numbers,
sizes and shapes of the pits within an exploitable mineral resource as at the end of the life. It
serves as an aid in the evaluation of the economic ore body. This analysis is essential in the
planning for surface facilities such as treatment plant, waste dump, haulage fleet etc.
PITSTRAT can be used to find the optimal size of the operation. The infra-structure and utilities can be planned for
accordingly.
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OPEN PIT OPTIMISATION MODEL : “PITSTRAT” @copyright 1998 L.F. Scheepers of LSLPS.
Medium term planning ensures that the product requirements can be met from the
determined pit stages.
PITSTRAT generates plans to specifically ensure a continuous “on-specification” product, blended from the pits, if feasible .
Short term planning sequences the blocks within the pits into depletion schedules
taking into account the blending of the blocks in order to ensure that product specifications
are met.
By re-blocking the mining area which PITSTRAT allocated to the short term planning period, into smaller practical mine-able
(blasting) blocks, PITSTRAT will ensure that the short term plan’s sequencing and product specifications are met.
Operational planning or production planning is concerned with the present
operating state of the mine (drilling and blasting locations, blasting, floor inventory,
stockpiling, loading, hauling, crushing and blending to plant, despatch logistics) within the
confines of the most recent short term plan, the latter within the confines of the most recent
medium term plan, the latter within the confines of the most recent long term plan.
Over and above using PITSTRAT for the long, medium and short term planning requirements, it is foreseen and indeed
possible to use PITSTRAT (with or without modifications, depending on the complexity of the practical requirements) for
operational planning (scheduling) as well. This can include, among others, the scheduling of the blocks in the blast, the
loading of the blasted blocks, floor inventory, stockpiling, hauling and blending and delivery to the plant.
In general, it is the task of Mine Management via the planning function to keep these
plans in synchronisation and to ensure the optimality of, and between, the long, medium,
short and operational plans.
In general, by using LP and MIP models, the required optimality is achieved and maintained. Specifically, by generating a
three time period PITSTRAT model, the optimality of the long, medium and short term planning is guaranteed,
within one matrix / optimisation. Given this, the operational planning becomes a scheduling task.
Although input/output type schedulers are available on the shelf, It is best to apply mathematical optimisation techniques to
the scheduling (operational planning) as well, with, of course sufficient measures in place to avoid sub-optimisation.
Typical run-time scenario: a three period model can be used to set up a short term plan,
a medium term plan and a long term plan. Data volumes, PC hardware configuration and the
level of detail required in the plans, determine the number of time periods to be included. A
very likely planning scenario is:
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OPEN PIT OPTIMISATION MODEL : “PITSTRAT” @copyright 1998 L.F. Scheepers of LSLPS.

using the Mintec/Whittle final shell (with grouped blocks in order to reduce the number of
blocks) generate a three period plan : short term (a month or a quarter), medium term
(one two or two years) and the balance of life of mine.

In the second optimisation run, exclude the blocks that are present in the short term plan
of run one in order to produce a second short term, medium and balance of life of mine
plan.

Repeat until the end of the life of mine has been reached. (The process can be
automated).
The sequence of short term plans obtained during this process constitutes the detailed
life of mine plan. This approach to planning gives the planner the flexibility to plan the
mining activities using economic and technological forecasts by simply inserting in the
input data, per time period, the anticipated exchange rates, selling prices and volumes per
mineral types, costing and capacity figures and the stripping ratios. This way mine and/or
plant expansion studies under different economic scenarios can be conducted and included
in the sensitivity analysis.
STOCKPILING
In PITSTRAT it is possible to route extracted blocks to a stockpile for later processing,
rather than to the plant directly. By running the model iteratively (step-wise), the model can
be used to schedule the accumulation of blocks to stockpiles and the subsequent “blending
in” thereof during later periods. The fact that it is costing money to stockpile, will cause
PITSTRAT to keep stockpile levels at a minimum. Building up of stocks is penalised in the
objective function with an “interest” loss coefficient. Waste rock is stockpiled separately.
MULTIPLE MINERALS BLENDING
PITSTRAT is designed to handle multiple minerals within the blocks and upper and
lower limits can be placed on the final product specifications per mineral type. The
progression of the pits will then be scheduled in order to meet the final specification limits. If
the final lower limit product specifications cannot be met with the given blocks’ chemical
analyses, the model will indicate an infeasibility. By incorporating the stockpiling option,
blending flexibility increased with a smaller probability of “off-spec” product being produced.
The input chemical analyses of the blocks is assumed to be beneficiated values.
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OPEN PIT OPTIMISATION MODEL : “PITSTRAT” @copyright 1998 L.F. Scheepers of LSLPS.
TECHNICAL PARAMETERS
The technical parameters below reflect the limitations of the software (mathematical
formulation and the coding). The practical execution times of real world models are at this
stage limited by the speed of the hardware. Planners must therefore take care to generate
models that will solve on the PC hardware in use. The model design is generic in that as
many pits, blocks, time periods etc. can be generated by the matrix generator code as the
planner requires. The hardware may not cope. The optimisation time balances between
model size and the available PC hardware are gained with experience and no prior
installation hard and fast rules exist.

Number of pits : up to 100 under user control.

Number of time periods : up to 100 under user control.

Number of benches : up to 100 under user control.

Ore qualities : generalised up to 8 types under user control.

Pit dimensions : maximum number of blocks in X direction : 10000
maximum number of blocks in Y direction : 10000

The mining slope is a function of the block dimensions. The slope cannot vary within a pit.
Re-block using geological modelling package to generate different block dimensions in
order to change the mining slope.

Mining parameters and constraints :the planner can define minimum and maximum values for the following variables (per
each of the time periods):-
Chemical analysis : Up to 8 types of planner's choice. More can be accommodated, but with
model changes. The types below can be changed.








% Fe
% SiO2
%P
% Al2O3
% K2O
% MgO
%S
% CaO
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OPEN PIT OPTIMISATION MODEL : “PITSTRAT” @copyright 1998 L.F. Scheepers of LSLPS.
Plant: upper and lower limits on:
 mining capacity in tons ore per time period
 plant throughput in tons ore per time period
 waste removal in tons per time period
Sales / Markets : (point value forecasts):
 Selling price F.O.R. mine in $/t Fe (or $/t ore if required).
 Exchange rate
 Sales volume (tons per time period)
Fixed Cost :
 The fixed cost for the total operation in $ per time period.
Three-D Geological Block Model Input:
The model requires as geological inventory, the blocks from the final Mintec/Whittle shell.
Block chemical analysis to be in terms of beneficiated (final product) values.
Per block, the following is required:














X, Y and Z identifiers
Ore tons
Waste tons
Fe grade (mass %)
SiO2 grade (mass %
P (mass %)
Al2O3 (mass %)
K2O (mass %)
MgO (mass %)
S (mass %)
CaO (mass %) [up to 8 chemical analysis species]
the variable or cash cost of mining per ton of ore (depending on the objective function)
plant variable or cash cost per ton of ore throughput
haulage cost per ton of ore [can be increased by bench to take into account depth of
mining and distance of block from plant]
 waste removal cost per ton of waste [can be increased by bench to take into account
depth of mining and distance of block from plant]
Per pit, the following is required:
 maximum extraction per time period
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OPEN PIT OPTIMISATION MODEL : “PITSTRAT” @copyright 1998 L.F. Scheepers of LSLPS.
USER FRIENDLINESS
LP and MIP models in general require operators/planners that know the business
well, rather than computer experts. Furthermore, it often happens that a line manager gets
assigned the task of running the optimization model based planning systems on a part time
basis. Experience has shown that more often than not, the planning gets neglected this way,
as the pressures of the day to day line management demands, limit the time available to
properly design input scenarios and as importantly, to interpret the optimal solutions/plans
in detail before implementation, as well as to solve infeasible solutions. These activities are
extremely important and it is again stressed that the planner/user must know the business
and business alternatives well.
PITSTRAT is user friendly in that EXCEL is used as the input data vehicle.
Alternatively, an ASCII file can be used.
APPLICATION POTENTIAL
Optimize strategic planning. "Force feed" the existing strategic plan (based on input/output
techniques rather than on methods of optimization) into the model and record the "profit". Repeat the
run, but, relax the tight constraints of the existing plan within practical upper and lower limits. See
whether the model mine the same pits. Compare this "profit" to the existing plan's "profit". Observe the
bottle-necks and variables with high "marginal profits" and study the sensitivities of these
variables and their effect on profitability. Grades, costs and volumes can be altered. Answer
Management's techno-economic / mining questions scientifically.
The model is equipped to do selling price "break-even" studies for multiple products.
The model is able to indicate whether changes in existing markets and/or new markets are:


profitable (and quantify it)
able to fit in with shifts in the market place
affecting the pit deployment and in what way
With the aid of the model, the planners can conduct expansion studies. By relaxing an existing
bottleneck, e.g. haulage fleet capacity, the model will calculate the new fleet capacity and the resulting
profitability. The difference between the expanded fleet profitability run and the existing profitability,
off-set against the capital to expand the fleet, will indicate the pay-back period of such an expansion.
Similar studies for plant expansions or beneficiation changes (sinter, briquettes, pellets) can be
conducted.
The model can be used to conduct market contraction studies in order to indicate where to cut
back to harm "profit" least.
Model can be used to conduct rationalization studies. E.g. the evaluation of acquiring further
ore bodies and how to optimally blend these in with existing operations. It will determine the optimal
number of pits, their location and exploitation levels in conjunction with the present operations. It can
be used for setting up the annual production budgets.
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OPEN PIT OPTIMISATION MODEL : “PITSTRAT” @copyright 1998 L.F. Scheepers of LSLPS.
PITSTRAT can be used for contingency planning exercises. In the case of shut downs,
strikes etc., what is the second best plan ?
The Mintec/Whittle software calculates the final shell, PITSTRAT tells the planner how to get
there year by year under constraints of fluctuating sales volumes, prices, exchange rates, costs,
capacities and technologies for multiple minerals, multiple pits, multiple plants and multiple time
periods.
13
The South African Institute of Mining and Metallurgy (S.A.I.M.M.)
Colloquium : "Open Pit and Surface Mining"
29 August 2001.
Long Term Scheduling at Sishen Iron Ore Mine using Linear Programming Techniques.
Prepared by: D J Steynfaard
Head Mine Planning
Sishen Iron Ore Mine
Kumba Resources
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Long Term Scheduling at Sishen Iron Ore Mine using Linear Programming Techniques.
Prepared by: D J Steynfaard
Head Mine Planning
Sishen Iron Ore Mine
1 Introduction
Sishen Iron Ore Mine is a conventional truck and shovel operation and consists of a single large pit subdivided
into more than ten small, medium and large pits at varying depths. A bench height of 12.5 m is used with a road
width of 30 m and a ramp gradient of 8%. The primary equipment fleet currently consists of:
- 45 x 190 t Haulpak 730E diesel electric trucks.
- 8 x P&H 2300 XPB electric rope shovels.
- 2 x Demag H485 electric hydraulic shovels.
- 9 x Bucyrus Erie 49R electric rotary drills.
- 3 x Caterpillar 994 front end loaders.
The ore body is complex due to folding and sinkhole structures and strikes along a length of 12 km in the NorthSouth direction and dips to the West at about 15 degrees. The ore body consists mainly of massive and
laminated ore with about 15% conglomeratic ore. The ore body varies in thickness up to 100m with an average
of about 30m. The total ROM production is treated in a heavy media separation plant due to dilution with waste
rock on the contact zones and layers of shale included within the ore body.
Sishen uses Whittle 4X as a pit optimising tool in order to provide an optimum final pit layout with the resultant
mineable reserves. Currently about two-thirds of the in-situ reserves are economically mineable and included in
the final pit layout.
2 Long Term Scheduling
Sishen holds long-term (5-year) contracts with its clients and is known to be a reliable supplier of ore. In addition
to costs, consistent ore qualities are the main drivers of pit scheduling. The main ore qualities to be controlled
are the iron content, as well as the potassium and phosphorous impurities.
The long-term schedule generated with the Whittle nested pits provides a good theoretical guideline as to the
optimal pit deployment strategy based on economics. At Sishen however it cannot be closely followed due to the
following limitations:
- From an equipment capacity point of view the schedule does not take into account limitations due to
pit room and for example will require the total loading fleet’s capacity to be delivered from a small pit
with loading space for only one or two shovels.
- The schedule only considers economics based on cost and income and results in high variations in
ore quality from one year to the next. This may result in the mine losing market share in one of its
important market segments.
The mine has decided during 1997 to implement a linear programming based scheduling system in which these
constraints can be addressed. After investigating the market to see what was available, it was found that most
major mining software companies was still in development stage as far as LP scheduling was concerned. It was
decided to implement a scheduling system along with a SA firm “Large Scale Linear Programming Solutions”
which already had a number of similar implementations on mining sites in SA. This firm provided the interface
(code in OMNI) to the LP package H/XPRESS12, which is the backbone of the scheduling system. OMNI and
the LP package was provided by Haverly Inc. and Dash. and is widely used in the mining and other industries.
This package is capable of using multiple processors through a network, assisting in solving large LP matrices
and cutting down on solution time. Matrices of up to 30 000 – 50 000 integer variables in as many as 500 000
constraints have been solved overnight. The system is known as “PITSTRAT”.
15
3 Data Preparation
The data preparation is done in the same manner as for Whittle, using only the portion of the geological model
within the final pit limits through the Whittle interface of the general mining package. Block sizes are determined
by the working space required by mining equipment. At Sishen block sizes of 50m in the East-West direction
and 100m in the North-South direction are used in order to provide space for temporary ramps between active
mining faces, which have not yet reached final pit boundaries. The LP works on the principle that if a block is to
be mined, the nine blocks above it must also be mined. Since only blocks inside the final pit layout are utilized,
the schedule results in steep slopes at final pit boundaries with accessible berms at the active mining faces as
shown in figure 1.
Figure 1
Final Pit Boundaries
Active Mining Faces
Mining Berms
Geological Model
Weighted average ore qualities, mining and plant costs and ore and waste tonnages per block are accumulated
in separate lists for each pit that can be mined independently. In total about 30 000 blocks are used at the start
of the scheduling run. Equipment capacity constraints are provided by subdividing the total mine model into pits,
each with a minimum and maximum tonnage to be mined for the next scheduling period. The maximum tonnage
is determined by the number of shovels that will fit into the pit, each requiring two blasting blocks with a face
length of 200m each. Although this standard may be increased, it will result in a lower utilization of the
equipment as a result of frequent blasting and a lot of time spent on waiting and travelling between blasts. The
minimum tonnage per pit can be used in order to force the LP to mine out a specific pit prematurely for example
during an investigation into the potential of backfilling of waste rock from adjacent pits. After each scheduling
run, normally one-year periods during which the LP has found the optimum combination of blocks meeting the
constraints, the following actions are taken:
-
Remove the blocks mined in the previous period from the model.
Subdivide the model into more pit areas, if required.
16
-
Measure active face lengths where benches have not reached final pit limits. A maximum standard of 10
Mt/a per shovel and 400m active face length per shovel is used at Sishen.
Adjust required limits for the next period in terms of ore tonnages, qualities and production capacities
per pit area.
Start the optimization run for the next period.
The following is an extract from the LP model used:
*----------------------------------------------------------------TABLE PER PERIOD DETAILS
PE1
IFE
65.62
Minimum % Fe
XFE
65.82
Maximum % Fe
IPH
0.052
Minimum % P
XPH
0.054
Maximum % P
IK2
0.172
Minimum % K2O
XK2
0.192
Maximum % K2O
IPL
0
XPL
56842105
Maximum Product Tons
XMN
9E+09
Maximum Total Tons
IWS
0
XWS
9E+09
*----------------------------------------------------------------TABLE PITC CAPACITIES PER PIT PER PERIOD
1
XP1 240000000
IP1 0
XP2 40000000
IP2 0
XP3 80000000
IP3 0
XP4 60000000
IP4 0
*----------------------------------------------------------------DATA
TABLE PITNO1 NORTH PIT DETAILS
BLOCK
ZXXYY
PTON
WTON
FEGR
K2GR
PHGR
PCST
50183
0.0
152.2
0.000 0.0000 0.00000
0.00
50184
0.0
19853.0
0.000 0.0000 0.00000
0.00
50185
0.0
37103.6
0.000 0.0000 0.00000
0.00
50186
0.0
33798.3
0.000 0.0000 0.00000
0.00
50187
0.0
11441.5
0.000 0.0000 0.00000
0.00
50280
0.0
28913.0
0.000 0.0000 0.00000
0.00
50281
0.0
79862.4
0.000 0.0000 0.00000
0.00
74028
53503.1
73795.7 65.563 0.0880 0.03685
13.09
74029 141104.8
60064.7 64.984 0.0928 0.03308
13.09
74030 144537.0
49877.7 64.589 0.1053 0.02261
13.09
74031
20987.7 151441.2 65.078 0.1045 0.02497
13.09
74032
0.0
43718.6
0.000 0.0000 0.00000
0.00
.
.
.
WCST
3.92
3.92
3.92
3.92
3.92
3.83
3.83
4.49
4.40
4.57
4.37
4.20
17
4 Scheduling Results
After completing the scheduling run over the life of the mine, the cash flows from the schedule can be compared
to another schedule with different parameters, therefore quantifying the difference in parameters used, such as
mining at better qualities than the average of the reserves for a number of years.
The LP schedule mines in each period the minimum waste rock required exposing the ore mined for that period.
Since prestripping is required to expose the ore on a continuous basis, the additional waste rock prestripped
during a period is in practice done where the LP schedule has mined waste in the next period. Figure 2 shows
the stripping curves for Sishen as follows:
- The maximum stripping curve is the cumulative ore and waste mined if mining is done bench by bench
from the top to the bottom.
- The minimum stripping curve is the cumulative ore and waste mined if mining is done according to the
LP schedule without any exposed ore reserves and prestripping.
- The planned stripping curve is the cumulative ore and waste mined if mining is done according to the
approved stripping strategy, ensuring the required ore exposure and future profitability of
the mine without high risks.
3000
2800
Figure 2
Stripping Curves as on 01-07-2001
Cumulative Mt Waste Rock
2600
2400
2200
2000
1800
Maximum Stripping
1600
2016
1400
Minimum Stripping
1200
1000
Planned Stripping
800
2010
600
400
2005
200
0
0
100
200
300 400 500 600 700
Cumulative Mt Mine-Line Ore
800
900 1000
18
Figure 3 A three-dimensional view of scheduled blocks for a two-year face advance for a portion of the pit.
5 Conclusion
In conclusion it can be said that the LP schedule follows the same pattern in pit deployment as the Whittle
schedule, with the difference that low cost pits are usually mined at a lower rate and high cost pits mined sooner
due to ore quality and pit room constraints. It further highlights the possibilities in terms of future ore quality
trends and is a handy tool in order to evaluate and quantify different pit deployment strategies.
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