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Industry challenge:ÌýReduce uncertainty in the mill feed and meet saleable product specifications.

Project scope:

1. Use the resource model for the coarse-scale characterisation of the orebody on which stopes are designed and the grade and geological uncertainties are quantified
2. Use blast-hole assays to provide the fine-scale grade characteristics of stopes and to reduce uncertainty
3. Use samples from muck pile and from trucks to enhance the fine-scale grade characteristics and reduce uncertainty.

• Student:ÌýKiet Wong
• Principal supervisors:ÌýProf Peter Dowd, A/Prof Chaoshui Xu
• Postdoctoral fellow:ÌýDr Amir Adeli
• Research lead:Ìý³ÉÈË´óƬ
• Translation partner:Ìý¶Ù²¹³Ù²¹²Ô±ð³Ù

This project is linked to Translation Project 1 (A TP1).

Industry challenge:ÌýHow to take advantage of small scale heterogeneity to optimise ore extraction.

Project scope:

1. Assess the heterogeneity of grades within a large block
2. Generate the resource model with the heterogeneity information (resource heterogeneity model)
3. Rapidly update the proposed model with more available data to reflect local heterogeneity in real-time (dynamic heterogeneity model).

• Student:ÌýYusha Li
• Principal supervisors:ÌýProf Peter Dowd, A/Prof Chaoshui Xu
• Research lead:Ìý³ÉÈË´óƬ

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Industry challenge:ÌýUncertainty in the resource model, challenge of identification domain boundaries with varied mineralogy and mine planning decision making.

Project scope:

1. Geostatistical modelling and simulation to high resolution
2. Benchmark uncertainty in resource model for boundaries and attributes including geomet
3. Validate using downhole data on in-situ resource.
4. Commercialisation

• Postdoctoral fellow:ÌýYerniyaz Abildin
• Principal supervisors:ÌýProf Peter Dowd, A/Prof Chaoshui Xu
• Research lead:Ìý³ÉÈË´óƬ

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Industry challenge:ÌýStockpiles can have large grade variations;Ìýit is difficult to respond to problems with the blend due to lack of information on spatial variability within the ROM pile.

Project scope:

1. Optimise blends under constraints
2. Theoretically well-founded settings with different distributions
3. Realistic scenarios using EKAs simulator and End User data.

• Student:ÌýHirad Assimi
• Principal supervisors:ÌýProf Frank Neumann, Dr Markus Wagner
• Postdoctoral fellow:ÌýDr Aneta Neumann
• Research lead:Ìý³ÉÈË´óƬ
• Translation partner:ÌýEka

This project is linked to Translation Project 4 (A TP4).

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Industry challenge:ÌýUncertainty on the mill feed, ore tracking and fingerprinting.

This project is linked to Translation Project 3 (A TP3).

Project scope:

1. Validate the concept the fingerprinting at lab scale simulations
2. Use fingerprinting concept on resource data and mil feed for multi-elements
3. Simulate potential for ore sorting for attributes of value and cost.

• Student:ÌýTBC
• Principal supervisors:ÌýÌýProf Nigel Cook
• Postdoctoral fellow:ÌýTBC
• Research leads:Ìý³ÉÈË´óƬ
• Translation partners:ÌýBureau Veritas, Boart Longyear, Scantech, Consilium Technology

Industry challenge:ÌýDeveloping a new algorithm for predicting metal recovery accounting for non-additivity and preferential sampling designs.

This project is linked to Translation Project 3 (A RP1).

Project scope:

1. Develop a new algorithm for predicting metal recovery accounting for non-additivity and preferential sampling designs.
2. Run various tests of the algorithm using Prominent Hill data.

• Principal supervisors:ÌýÌýProf Peter Dowd
• Postdoctoral fellow:ÌýDr Amir Adeli
• Research leads:Ìý³ÉÈË´óƬ
• Translation partners:ÌýOZM

Industry challenge:ÌýThere is a need to optimise batches of concentrate over the mine plan by linking data and decisions.

Project scope:

1. Understand existing methods of copper concentrate blend strategy optimisation
2. Translate mill models into computer language
3. Improve objective functions and existing algorithms.

• Student:ÌýYue Xie
• Principal supervisors:ÌýProf Frank Neumann
• Postdoctoral fellow:ÌýDr Aneta Neumann
• Research lead:Ìý³ÉÈË´óƬ

This project is part of Research Group 3 - Optimisation Group
This project is linked to Translation Project 4 (A TP4).

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Industry partner challenge:ÌýHard rock types negatively affect mill throughput. Early identification of rock mineralogy that is hard to process in the SAG mill feed is required.

Project scope:

1. Visual and machine learning on gyratory crusher and SAG mill feed to identify mineralogy type
2. Machine learning to tune mill parameters to optimise throughput and energy usage.

• Student:ÌýHu Wang
• Principal supervisors:ÌýProf Chunhua Shen, A/Prof Max Zanin
• Postdoctoral fellow:ÌýDr Junjie Zhang
• Research lead:Ìý³ÉÈË´óƬ

This project is part of Research Group 4 - Machine Learning Group
This project is linked to Translation Project 6 (A TP6).

This project is developing an ore tagging system to track the ore body from blast to the ROM to the crusher. It will translate the research outcomes of Research Project 1 (A RP1).

• Chief Investigator:ÌýProf Peter Dowd
• Postdoctoral Fellow:ÌýDr Amir Adeli
• Translation Partner:ÌýDatanet

This project is developing a new geo-metallurgical model for validating geological loggings to identify and remove mis-logged geological samples.

• Chief Investigator:ÌýProf Peter Dowd
• Postdoctoral Fellow:ÌýDr Amir Adeli
• Translation Partner:ÌýOZM

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The aim of this research project is to assess the performance of the latest laser scanning technique for its application in qualifying the size distribution of muck-pile fragments for optimizing blasting parameters. It is expected that 3D laser scanning technique is faster and more reliable than other methods in evaluating the muck-pile fragmentation. To test this hypothesis, measurements using different methods will be conducted and analysed in a comparison study.

• Chief Investigator:ÌýA/Prof Chaoshui Xu
• Students:ÌýDeea Rizki Oziana, Ulfa Riani, Iven Tan
• Translation Partner:ÌýMaptek

This project aims to develop a new sensor for complex mineralogy identification, and dovetail the data with elemental composition of the rock. It will translate research outcomes of Research Project 4 (A RP4).

• Chief Investigators:ÌýProf David Lancaster, Prof Bill Skinner, Prof Nigel Cook
• Postdoctoral fellows:ÌýDr Dale Otten
• Translation partners:ÌýBureau Veritas, Scantech

This project aims to create a 3D high-resolution near real time model of the run-of-mine stockpiles. It will translate the research outcomes of Research Project 5 (A RP5).

This project is selected for commercialisation.Ìý

• Chief Investigators:ÌýDr Tien-Fu Lu, A/Prof Larissa Statsenko
• Postdoctoral Fellow:ÌýDr Shi Zhao
• Translation Partner:ÌýEka

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This project aims to quantify the effect of uncertainty during resource model creation. It will translate the research outcomes of Research Project 6 (A RP6).

• Chief Investigator:ÌýProf Frank Neumann
• Postdoctoral Fellow:ÌýDr Aneta Neumann
• Translation Partner:ÌýMaptek

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This project willÌýcreate a concept design of ore sorting to detectÌýand divertÌýproblematic material (hard and sub-economic material such as bulldog shale and steely hematite).ÌýIt will also use machine learning to develop recommender tools for operators. It will translate the research outcomes of Research Project 7 (A RP7).

• Chief Investigators:ÌýProf Chunhua Shen,ÌýAdjunct A/Prof Max Zanin
• Postdoctoral Fellow:ÌýDr JunjieÌýZhang
• Translation Partner:ÌýScantech, Consilium Technology