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Optimisation of Complex Gold Processing Plants with Multiple Unit Operation Constraints – the Newcrest Planning Scheduler


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Author D R Seaman, R P Tooher and B A Seaman
ID P201608022


Several mine scheduling tools exist for use in optimising and scheduling the reserve according to in situ value. These tools generally value the ore on a per block basis according to regressed recovery models based on ore grade and type. It is then the task of the plant metallurgist to ensure that these ore blocks are fed to the plant in a manner that satisfies the prevailing plant constraints. This task is often completed in complex Excel® spreadsheets that are modified over time with factors applied to account for plant modifications or limitations.

It is the authors’ experience that this approach is difficult to audit and replicate in a consistent manner. Furthermore, operational decisions, such as primary grind size or autoclave oxidation are made on the metallurgist or managements’ perceived optimum value and does not necessarily represent a cash flow based optimum.

Many processing plants contain multiple internal constraints. As ore feed grades and properties change over the life-of-mine (LOM), processing-bottlenecks shift from one unit process to another and different operating strategies need to be adopted to maximise cash generation from the process.

This paper presents a unified optimisation approach to scheduling ore based on a circuit mass balance to describe the process plant with individual models for each significant unit operation. The approach allows for optimisation over multiple input variables and the inclusion of linear and non-linear constraints.

This optimisation process has been applied to different complex gold operations. The end result is an automated scheduler that selects the optimum blend of ores as well as plant operating conditions that satisfy all internal plant and shutdown constraints while maximising the objective of choice (metal production or cash flow). The models are also valuable for evaluating expansion or debottlenecking opportunities of individual processing units. These models are coded in Matlab® R2016a provided by MathWorks®, and can be deployed to end users in a compiled version with significant savings in time to run different scenarios over the Excel®-based systems.


Seaman, D R, Tooher, R P and Seaman, B A, 2016. Optimisation of Complex Gold Processing Plants with Multiple Unit Operation Constraints – the Newcrest Planning Scheduler, in Proceedings 13th AusIMM Mill Operators’ Conference 2016, pp 191–198 (The Australasian Institute of Mining and Metallurgy: Melbourne).