1. Mark Grigoleit
Principal Simulation Consultant
Optika Solutions Pty Ltd

2. Contents
 Introduction to iron ore
 Part 1 – the problem
 Part 2 – the solution
 Part 3 – the results

3. Part 1: The problem
 Fun facts about Fe:
 4th most common element in earth’s crust (about 5%)
after Oxygen, Silicon and Aluminum
 Has been mined for about 5,000 years
 Exists mostly as oxides
 Hematite (Fe2O3) and Magnetite (Fe3O4)
 Refined back into metal by removing the oxygen in a
furnace

4. Whyalla mines in SA

5. Infrastructure
 Diggers
 Crushers and screening
 Rail transport to shed
 Ship loading by barge
 All these activities have variability
 No closed form solution
 This is the motivation for using simulation modelling

6. Grade Variability
 Mine blocks have only estimates of grade from drill
cores
 Mine plan is order of digging
 After crushing, an assay to determine actual grades
 What tonnes and grade will ultimately be exported –
this is the devil in the problem

7. The economic facts
 Cost of production is $35 to $75 per tonne, depending
on the operation
 High grade is considered to be ~63% Fe (or 90%
hematite, rest is silica, alumina)
 Lower grade material can be beneficiated (OBP) at
extra cost (wash the sand off)
 Grade penalties apply for deviation from agreed grade
 Shipping delays may incur demurrage charges

8. The Problem
 To work out in advance what ore can be produced
 Time frame of one year or more
 How many tonnes of ore
 And at what grade

9. Part 2: The solution
 We employ a discrete event model that uses built-in
distributions to account for local variability –
processing rates, travel times, down times, weather
delays, etc.
 The model consists of many objects, with each piece of
equipment represented as an object
 The model has over 1,000 input variables

10. The model
 Model built in a simulation language called SLX
 Operates on daily plan
 Plan created by LP formulation of problem
 Model took 4 smart guys 1 year to construct

11. Optimisation Problem
 Goal of the optimisation – supply export tonnes
(169,000) within grade limits
 Types of constraints – time, tonnes, grade
 Three ship lookahead (one month)
 Local optimisation for building feed piles
 Global optimisation for building ship consignments
 Implemented with AIMMS

12. Multi-level grade control
 LP optimisation to select ore
 Local level: ROM piles – crusher – stockpiles
 Local level: stockpiles – rail – shed
 Local level: shed + rail direct load to ship
 Global level: from mine blocks to ship

13. Results 1

14. Results 2

15. Review
 Daily plan by LP solve takes seconds
 Variability of shipped ore grade minimised
 No loss of total exported tonnes
 Detailed modelling allows prediction of tonnes and
grade given changes in actual ore body or external
market conditions

16. Conclusion
 For very large and complex operations, sometimes the
only way to evaluate performance is to use a simulation
model.
 In the case of grade variability of iron ore, this has
proven successful.