Ensuring Feasible Life of Mine Plans for Active...

Ensuring Feasible Life of Mine Plans for Active Operations

Dr. Martin L. Smith MineSmith, Pty Ltd Brisbane, Australia

[email protected]

Synopsis

• Mine Planning – what is it?

• The mine planning professional – who does it?

• The dominant role of the General Mine Planning (GMP) package

• A comparison of approaches to decision making between the minerals industry and all others

• Why existing GMP-based planning solutions yield operationally infeasible solutions

• Examples of how intelligent application of OR technology yields operationally optimal solutions

• LOBOS – a math programming platform for the minerals industry

3/21/2013 UFMG - Engenharia Minas, Belo Horizonte 2

In the midst of powerful technologies, we are failing to produce viable business plans through ignorance of the science of decision making and dependence on inadequate tools.

Mine Planning – what is it?

Essentially, mine planning is about what to mine and process, when to do it and how. It is decision making backed by a variety of engineering and earth science disciplines. Common planning decisions include: • Reserves vs. Resources • Cutoffs (material destination) • Mining methods • Mining and processing capacities and rates • Production resources • Access • Sequencing and scheduling of development and production

Greenfield projects from Brownfield (active) operations should be treated separately, as the focus of planning and level of complexity and risk is quite different, with the first placing emphasis on financial evaluation and the latter on production.


Planning Integrates Design


Mine Planners must see the forest from the trees

Constraints

Targets

Production targets drive the design process

Design constraints limit what can be accomplished in planning


The mine planner seeks to maximise stakeholder value

Indigenous Groups

Government Financial Institutions

Land owners

Share holders

While honouring the constraints determined by the deposit and engineering limitations

Access Rates

The larger world: Stakeholder Objectives

The mine: Limits on Production

Re

serv

es


The mine planning professional – who does it?

We know about geotechnical, ventilation and maintenance engineers, hydrogeologists, metallurgists and geologists. At least you have to get a degree in these professions, often a masters or higher. What about the mine planning engineer? What sort of degree and training does he receive…? What is his professional tool-set? What are the foundations of mine planning? In this industry design oriented engineers are thrust into decision making in a complex, multi-faceted and uncertain environment. For this they are unprepared and poorly equipped.


Current State of Mine Planning

• Mine planning has not evolved from an academic foundation as have design-based engineering disciplines

• It has relatively recent origins in industrial practice and software applications

• General Mine Planning software (GMPs) has transformed mine planning but for many defines what is possible and what is not

• Mining engineers are rarely trained in the foundations of planning. Instead the focus is on using GMPs to support standard mining operations

• Some specialised mine planning software is available, but these are often black boxes to engineers with limited if any training in the underlying technology

• This GMP-based software is inadequate to the task and oriented to greenfield project evaluation – not to the complexities of active operations

• In conclusion, while design engineers have a foundation in the basic sciences and are prepared for independent problem solving, mine planners are limited to GMPs!


GMP Constituent Technologies Database management:

•Drillhole assay, lithology & survey data

•Surveys of topography, face position & engineering structures

•Map data as points and strings

•Triangulated surfaces and volumes

•Geologic (block) models

•Engineering geometries Point and String Handling:

•Standard CAD functionality

•Mine design specific functionality

•Conversion of multiple strings into volumes

Surface and Volume modeling:

•Triangulation of points and strings

•Areas and volumes of intersction

•Block model interrogation Data Analysis & Geostats:

•Standard statistics

•Variography

•Inverse distance and Kriging

•Conditional Simulation

Scheduling:

•Bench sequencing

•Definitions of constraints

•Definition of scheduling units

•Optimisation?

•Schedule reporting

Pit Optimisation:

•Lerchs-Grossman algorithm

•Definition of slopes

•Economic modelling

•Selection of pushbacks

•Reserve reporting


Decision sciences, modelling and Operations Research

A mine planner without fundaments

Simulation: Evaluation of productivity, capacity and

feasibility

Geostatistics: Estimation,

categorisation and ore/waste

discrimination

Math Programming: Optimisation of mine plans. Options, risk and sensitivity analysis.

Financial modelling: Conversion schedules of cost and revenue into measures of value.

GMPs

Schedule Optimisation Conditional Simulation

Fin

anci

al R

isk

Ge

olo

gic

Ris

k


Alternative Approaches to Business Improvement

Currently in the Mining Industry

Buy expensive “optimization” software wo fully understanding the problem.

Train (and retain?) a mining engineer to run the application to a presupposed problem.

If the software can´t solve the problem stop (and hire a specialist consultant?) or live with an incomplete (infeasible?) solution.

But in the general industry

Hire a specialist in OR possibly with no prior experience the application.

Integrate the specialist into the planning team (train him up).

Within the team, the specialist defines problem and: • Selects most appropriate

solution technology • Or creates in-house solution • To optimize operations

Specialist becomes a driver of business improvement. … and repeat.


Why existing GMP-based planning solutions yield operationally infeasible solutions

• Mine specific solutions do not account for interaction of business units

• Predetermined levels of production and time granularity do not allow for operational planning

• Inability to add constraints for site specific problems

• No integration of production with haulage and dump construction

• No consideration of processing and environmental constraints

• Do not account for uncertainty

• Simplistic representation of the cutoff problem

• Black Box approach to problem solving – no flexibility

• Lack of OR awareness in Minerals industry – “Tail wagging the dog”


Mine specific solutions do not account for interaction of business units

Include multiple business streams, mines, plants, processes and products all competing for resources. Determine how much of each product to produce to optimize the overall business along with the activity levels of all mines and plants.

Multiple products: sell concentrate now or cathode later? Multiple processes: ore, concentrate, anode, cathode with by-products.

Is it more profitable to run ore from a different business unit through the concentrator?


Predetermined levels of production and time granularity do not allow for operational planning

Sto

pin

g U

nit

Sto

pe

Min

ing

Blo

ck

Pa

nel o

r A

rea

Granularity relates to the level of detail used in the planning process. Inappropriate granularity in the size of the scheduling unit and duration of the planning period are a major deficiency in commercial mine planning tools. Mismatched levels of granularity result in strategic planning targets that are operationally infeasible.


Inability to add constraints for site specific problems The general application, black box nature of GMP optimisation solutions does not allow for project specific decisions, constraints and objectives. Active operations are not necessarily NPV driven. Issues such as concentrate grades and penalties, plant and fleet utilisation and environmental constraints are more common drivers of production.

Total mining costs published from the mine context

Multiple products – metal and acid

System variables are deducted from the net benefit for equipment purchases and rehandle as is total waste cost, published from the mine context. The final net benefit is selected as the goal.


No integration of production with haulage and dump construction

Dump construction broken into lifts and cells with associated haulage productivity.

Bench attributes include shovel productivities and haulage rates for each material’s destination.

Pit specific variable and constraints used to track consumption of fleet hours for pit specific and global constraints on shovel and haulage fleet activity.


Optimised Production Fleets

Loader fleet size tracked as a function of activity levels in benches.

Loader fleet hour capacity utilisation tracked over time.

Haulage requirements based on production requirements by stage-bench, cycle time, tray capacity and equipment availability/utilization estimates.

Shovel productivity based on bench geometry. Capacity/availability and minimum shovel working space.


No consideration of processing and environmental constraints

Mill Throughput

Fill 2900

Pnyang SS 2900

Darai LS 2700

Monz PRY 3200

Monzodiorite 2600

Endoskarn 3000

Skarn 2700

PY Skarn 3100

Oxide Skarn 3700

Mill feed capacity is a function of throughput rates – not simply a tonnage limit. Geometallurgy must be accounted for.

Need to consider environmental limits on waste acidity, sulphur and metal in tails or contaminants in concentrates. 3/21/2013 UFMG - Engenharia Minas, Belo Horizonte 18

Do not account for uncertainty

• Planning is being conducted deterministically without consideration of the stochastic behaviour of many inputs (price, exchange rate, cost, capacity, grade, recovery, etc.).

• Robust mine planning requires greater flexibility in the toolbox used by planning engineers: – Conditional Simulation for geologic uncertainty

– Discrete Simulation for production systems

– Options analysis within an exact optimisation framework

– Sensitivity analysis using Linear Programming

– Probabilistic financial modelling with Monte Carlo simulation

• Robust mine planning cannot be limited to flexing a few external sources of risk and requires a systematic and intensive analysis of interacting sources of uncertainty. No single commercial platform is available with this capability, but math programming provides a framework for integrated risk assessment.


Weighted average grade estimates result in poor reconciliation

Simulation 1 Simulation 2 Simulation 3 Simulation 4 Kriged

Running multiple optimised schedules across a range of simulations provides a check on mill feed distributions, avoiding the errors resulting from planning using a smoothed estimator.


Discrete Production Simulation

3/21/2013 UFMG - Engenharia Minas, Belo Horizonte

Focus is production system analysis over a relatively short time frame. How do production fleets of various configurations interact in a static mine plan? Can use simulation to optimise elements of the production system, but not optimisation in the mathematical sense.

Extremely useful complement to deterministic schedule optimisation. Simulation provides realistic scheduling inputs such as equipment utilisation and productivity and production system capacities. Also valuable as a means of testing the feasibility of a production schedule at the level of operational planning.

21

Simplistic representation of the cutoff problem

• Cutoff varies both spatially and temporally as a function of many factors which are often site specific: – The geometallurgy of the deposit which is spatially variable

– Variations in costs (operating and capital) over time (also by mining area or method)

– Mining capacity which varies as a function of the working conditions and equipment used and which can also vary over time according to projected needs

– Milling capacity which can experience episodic changes

– As well as many additional constraints on production (environmental, materials handling, development rates, etc.) which limit the capacity to process specific ores at a given point in the schedule.

• Consequentially, the optimal cutoff is not a single value. Rather it is an outcome of an optimal production policy in which the ore delivered to the mill satisfies all constraints on production while delivering maximum value (as defined by the objective function).

• A complete cutoff policy is derived from an optimised production schedule.


Analyze the distribution of cutoff bins by destination for each mining area based on the objective function (here NSR/t). When material at the same value goes to multiple destinations, evaluate the constraints to determine what is the full cutoff function.


LOBOS – a math programming platform for the minerals industry

The previous examples presented common limitations of commercial black box mine planning systems and how these were overcome in actual projects using the Life of Business Optimisation System (LOBOS) in combination with other OR technologies. LOBOS facilitates the rapid implementation of complex mine plans at a level of detail that supports operational mine planning by producing both feasible and optimal production strategies at the required level of granularity. LOBOS facilitates the generation of LP/MIP models and their optimisation using high-end commercial Solver engines (Cplex and Gurobi). Inputs, outputs and user defined model components allowing the problem to be both site specific and solved to a level of detail and complexity that meets operational requirements. Superior model formulation, solution strategies and state of the art solvers results in solutions that look far into future operations so that today´s decisions are optimal with respect to tomorrow´s production needs.


Alternative Methodologies: a Difference in Emphasis Math Programming (LOBOS)

Model formulation: • Variables • Constraints • Objective

Evaluate model structure: • Linear/Nonlinear • Integer/Convex/Separable • Network/Sparse

Select appropriate algorithm: • Primal/Dual Simplex • Network/Barrier • Steepest gradient

Optimise: • Improve algorithm settings • Improve formulation • Add hueristics

Specialty Algorithms & Black Boxes (GMPs)

Write Solver Engine: • “Model” not accessible • End product is a compiled

executable

Choose technology appropriate for problem: • Ideally LP • Otherwise custom built engine (DP,

GA, SA, other)

Tightly restrict inputs to specific problem to be solved

Provide UAI for specific inputs and outputs

New model components?

Revise en

gine

New model components?

Rev

ise

mo

del


LOBOS: Recent Projects in Active Operations

• PanAust Phu Kam Copper Gold operation – Cutoff optimisation – Waste production and storage integrated

with LoM plan • DeBeers Cullinen Diamond Mine

– Draw control optimisation with a LOBOS prototype (IDCS)

• Rio Tinto Lisheen UG Copper Mine – Primary through tertiary stope scheduling – Alternative cutoffs

• Zinifex, Century Open Pit Lead Zinc Mine – Stage/Bench/Block sequencing – Earth moving fleet minimisation – Haulage and dump construction

• XSTRATA Zinc UG Lead Zinc Mining Complex – LoM production plan for multiple products – Open cut and UG mines – Stockpile usage

• Beaconsfield Gold UG Gold Mine – Alternative reserve scenarios – Cutoff grade by stope – Development, fill and production schedules

• OTML OK Tedi Copper – Gold Mining Complex – Extend OC production scheduling to

production panels – Inclusive of metallurgical model – Fleet size and allocation – Dump and stockpile usage – Cutoff and stockpile optimisation – Strategies for sulphur reduction in mine

wastes • BHPB Olympic Dam Uranium-Copper UG Expansion

– High level LoMP production schedule – Inclusive of metallurgical model – Regional Cut-off grade optimisation

• Roche Mining Kogan Creek Coal Open Cut – Pit and production schedule optimisation – Blend optimisation

• Kaltim Prima Coal, Open Cut Mining Complex – Multi-pit optimisation – Contractor v. owner haulage – Mine Reserve optimisation

• Consolidated Minerals, Open Cut Manganese Complex – Multi-pit business optimisation – Development rate/dewatering strategy

• Hecla Mining, Greens Creek (PRIMO) – Strategy optimisation (cutoff, methods, reserves)

• Oz Minerals, Prominent Hill UG Mine (PRIMO) – Strategic and tactical mine plan optimisation

• Oz Minerals, Prominent Hill Open cut and UG Complex – Stockpiling strategy and cutoffs – Open pit schedule optimisation – Blending, concentrate grade optimisation – Multiple underground mines

• Codelco, San Antonio open cut copper project – Option analysis for a large oxide-sulphide copper

open cut including alternative stage configurations and mineral processing options and capacities.

– Fleet optimisation – Scheduling

• Barrick Lumwana open cut copper complex – Medium term to Life of Mine production

scheduling – Stockpiling and cutoff strategies – Reserve optimisation – Production fleet optimisation


MineSmith : types of project

• Any project requiring the integration of multiple business units in a mining complex: multi-mine, multi-product, surface to underground transition

• Cutoff optimisation at a level supporting actual implementation (what goes where and when) inclusive of complex stockpiling strategies

• Implementation of complex geometallurgical constraints, ore hardness, complex recovery relationships, concentrate contaminants limits & ore-waste blending

• Control of environmental constraints in tailings, emissions and waste dumps.

• Determination of production fleet requirements and routing

• Integration of waste production with dump construction

• Coordination of development with production schedules in underground mines

• Evaluation of risk (geologic, financial and production) in an optimisation paradigm

Effectively, any problem that cannot be solved by a GMP solution and which must be put into operational practice and when a superior solution is required.


MineSmith Pty Ltd Mine Optimisation Services

ABN: 58 137 563 136

www.minesmith.com.au

Australia Martin Smith, Managing Director [email protected]

LOBOS: Life of Business Optimisation System

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