Automatic search strategy for ROM stockpile recovery optimisation

Assimi, Koch, Garcia, Wagner, Neumann

Presented at the Preconcentration Digital Conference November 2020

ABSTRACT

Run-of-Mine (ROM) stockpiles are the inventory of valuable materials extracted from the mine. Depending on the customer requests, a selective addition of ore from stockyards should be selected at the required tonnage. In the stockyard, stackers and reclaimers are the machines which load and unload ore, respectively. Currently, humans determine the reclaiming/stacking planning sequence in the stockyard by rules of thumb. However, this is a complex task subject to multiple operational constraints, such as where undesirable mineral properties should not exceed certain limits in a request.

Human planning can lead to limited decision support and lower ability to consider the upcoming blends and requests. This kind of decision making can cause perturbations in the assumed profit and unexpected loss in practice. Therefore, an intelligent decision-maker and sequence planner would be highly valuable to automate stacking and reclaiming operations in practice. The benefits are reducing variability in decision-making and operation costs.

This paper considers a stockyard with available mineral information in the stockpiles using load and dump locations from GPS data feeds. We present an optimisation problem to find a solution which results in reclaiming more ore in a shorter time. To solve the problem, we develop an automatic search strategy based on the greedy search algorithm independent of the type of machine used in reclaiming. The proposed method plans a sequence for stockyard recovery blend optimisation to meet multiple customer demands in a timeframe. Blend optimisation is subject to various constraints such as the elemental composition of the valuable materials and penalty elements. We present three case studies using EKA’s simulator, to fulfil two to four requests from four stockpiles. We compare the obtained results with a pilgrim step reclaiming heuristic and we show how our proposed search strategy outperforms the current strategy used in practice.

AUTHORS

H Assimi1, B Koch2, C Garcia3, M Wagner4 and F Neumann5

1. PhD Student, School of Computer Science, University of Adelaide, SA 5005. Email: This email address is being protected from spambots. You need JavaScript enabled to view it.

2. Vice President, EKA Software Solutions, Adelaide, SA 5000 Email: This email address is being protected from spambots. You need JavaScript enabled to view it.

3. Senior Systems Engineer, EKA Software Solutions, Adelaide, SA 5000. Email: This email address is being protected from spambots. You need JavaScript enabled to view it.

4. Senior Lecturer, School of Computer Science, University of Adelaide, SA 5005. Email: This email address is being protected from spambots. You need JavaScript enabled to view it.

5. Professor, School of Computer Science, University of Adelaide, SA 5005. Email: This email address is being protected from spambots. You need JavaScript enabled to view it.

ACKNOWLEDGEMENTS

This research has been supported by the SA Government through the PRIF RCP Mining Consortium.

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