Next Generation Mine Planning:

Advanced Scientific Approach to

Optimize your Extraction Sequence

Daniel Spitty

February 2014

SME Conference, Salt Lake City, USA

The problem

What is the best increment excavation sequence

and the best material blending combination

such that tonnage, quality, cost, and NPV targets

are met?

The challenge

Material complexity

Over 1,000,000 blocks of

material within10,000

increments containing multiple

material types in each.

Business rule complexity

30 year planning horizon broken

up into quarterly buckets, with

the ability to configure 100’s of

business rules differently for

each bucket.

Decision making complexity

Business problems requiring

non-linear approaches to

providing realistic, optimal and

most of all, executable business

outcomes.

Integrated Supply chain complexity

From excavation to haulage to blending

to material destination, the best plan

may be different if the business priority

is tonnage, cost or quality.

Within minutes….

= Material complexity * Supply chain complexity * Business rule complexity * Decision making complexity

Non-Linear Problems and Solutions

• Genetic Algorithms

• Simulated Annealing

• Hybrid Neural Networks

• Evolutionary Strategies

• Hill-Climbers

• Ant Systems

• Tabu Search

• Evolutionary Programming

Non-linear optimisation techniques are required to solve non-linear models:

Mine Planning Inputs

●Geospatial block model of the material within the mine

●Haulage road network nodes

●Stockpile and waste dump locations

●Fixed and mobile equipment configuration

●User defined

●KPI targets (desired)

● area aggregations, dependencies,

● business rules,

●material flow

● optimization objective weightings

Solution Approach

●2 Key Components

●Excavation – Diggers/Shovels

● excavation sequence determination via intelligent grade control

adaptation of excavated material

●Blending – Haulage/Destination

● product target optimisation via blending

●Uses a virtual simulation-based “world” for entities to function in

●Diggers act as agents in the virtual world, each responsible for

determining their own independent actions

●Maintains a central inventory of available ore that influences the high-

level direction of the agents

Solution Approach

●Agents perform adaptive search to determine their best decision

● uses different evaluation heuristics depending on the state of the inventory

● the size of the search is adapted depending on the state of the inventory

●Uses look-ahead to determine future positions in order to assess the

impacts of possible decisions

1. Where does the digger move next?

2. How much does a digger excavate?

3. Is material waste or ore?

4. Is back filling now possible?

5. Which waste dumps do we send the waste to?

6. Is the ore to be sent to crusher or to a stockpile?

7. Which crusher to send to?

8. Which stockpile to send to?

9. What material to draw from which stockpile?

10. How much haulage is needed from pit to crusher?

11. How much haulage is needed from pit to stockpile?

12. How much haulage is needed from stockpile to crusher?

13. Which plant to use?

12

1

8

3

7

2

9

5

10

4

11

6

13

High level decision points

Results

●Multiple scenarios can be run and compared to support decisions

●Block extraction sequence

●Mobile equipment assignments and location

●Mobile and fixed equipment utilization

●KPI planned performance

Benefits and Conclusion

● Exploring more possibilities

through science techniques

● Science adapting to current and

future projected states to change

decisions

● Enabling informed decision making

through the generation of multiple

scenarios in a timely manner

●Matching the optimization objectives

with the business operating model

through dynamic configuration

Thank you!