Next Generation Mine Planning

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Information about Next Generation Mine Planning
Technology

Published on March 13, 2014

Author: SchneiderElectric

Source: slideshare.net

Description

Mine planning optimization supports planners to determine ‘What are the best increment excavation sequence and the best material blending combination such that throughput, quality, cost, utilization and Net Present Value (NPV) targets are met?’ It is achieved through using an advanced scientific approach that has been specifically adapted to address the mine planning dilemma.

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!

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