Program A

Increasing certainty on the mill feed for predictable and controllable performance

In this program, we use sensors to enable machines to identify and monitor ore characteristics at several points of the upstream mining chain, from in-place resources to the mill feed. The aim is to achieve feed control and blending in stages to provide a stable, predictable and controllable feed for the plant.

Benefits

  • Monitoring the ore attributes in the Run-of-Mine (RoM) ore can provide rapid feedback to mining operations for dilution control and reconciliation in short time periods.
  • Having a greater certainty on mill feed attributes will allow the value to be optimised in shorter time periods than is currently possible.

Research challenges

  • Resource knowledge updated in real time with sensor information
  • Near real time ore tracking and tagging from mine to mill
  • Near real time mineralogy identification in the mill feed. 

Research Projects

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  • Stage Project A1: BELT SENSING AND ORE SORTING

    Kiet Wong

    Research project 1 (A RP1): Optimising mining operations under different constraints with a focus on in-situ resource to stockpile

    (This project is part of Research Group 1 - Geostatistical Modelling and Value Chain Optimisation)

    Industry challenge: Reduce 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: Kiet Wong

    Principal supervisors: Prof Peter Dowd, A/Prof Chaoshui Xu

    Postdoctoral fellow: Dr Amir Adeli

    Research lead: University of Adelaide

    Translation partner: Datanet

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

     

    Yusha Li

    Research project 2 (A RP2): Resource heterogeneity modelling for optimal ore extraction

    (This project is part of Research Group 1 - Geostatistical Modelling and Value Chain Optimisation)

    Industry challenge: How 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: Yusha Li

    Principal supervisors: Prof Peter Dowd, A/Prof Chaoshui Xu

    Research lead: University of Adelaide

     

    Yerniyaz Abildin

    Research project 3 (A RP3): Constraints and quantifying uncertainty on resource domain boundaries

    (This project is part of Research Group 1 - Geostatistical Modelling and Value Chain Optimisation)

    Industry challenge: Uncertainty 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.

    Student: Yerniyaz Abildin

    Principal supervisors: Prof Peter Dowd, A/Prof Chaoshui Xu

    Postdoctoral fellow: Dr Amir Adeli

    Research lead: University of Adelaide

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  • Stage Project A2: ORE TRACKING

    Hirad Assimi

    Research project 5 (A RP5): Resource heterogeneity modelling from trucking to multiple stockpiles to mill feed

    (This project is part of Research Group 3 - Optimisation Group)

    Industry challenge: Stockpiles can have large grade variations; it 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: Hirad Assimi

    Principal supervisors: Prof Frank Neumann, Dr Markus Wagner

    Postdoctoral fellow: Dr Aneta Neumann

    Research lead: University of Adelaide

    Translation partner: Eka

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

  • Stage Project A3: ORE TAGGING AND FINGERPRINTING

    Research project 4 (A RP4): Correlations of elemental, mineralogical, hyperspectral with sensor data for mineral identification

    (This project is part of research group 2 - Mineralogy Group)

    Industry challenge: Uncertainty 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.

    Postdoctoral fellows: Dr Amir Adeli, Dr Dale Otten

    Principal supervisors: Prof David Lancaster, Prof William Skinner, Prof Nigel Cook

    Research leads: University of Adelaide, UniSA

    Translation partners: Bureau Veritas, Boart Longyear, Scantech, Consilium Technology

  • Stage Project A4: SENSOR INFORMATION AND SORTING

    Yue Xie

    Research project 6 (A RP6): Blend strategy optimisation

    (This project is part of Research Group 3 - Optimisation Group)

    Industry challenge: There 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: Yue Xie

    Principal supervisors: Prof Frank Neumann

    Postdoctoral fellow: Dr Aneta Neumann

    Research lead: University of Adelaide

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

     

    Hu Wang

    Research project 7 (A RP7): Mill feed sorting to optimise throughput and energy usage

    (This project is part of Research Group 4 - Machine Learning Group)

    Industry partner challenge: Hard 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: Hu Wang

    Principal supervisors: Prof Chunhua Shen, Dr Markus Wagner

    Postdoctoral fellow: Dr Junjie Zhang

    Research lead: University of Adelaide

    This project is linked to Translation Project 6 (A TP6).

  • Translation projects

    Dr Amir Adeli

    Translation project 1 (A  TP1) Ore tracking from resource to crusher

    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: Prof Peter Dowd

    Postdoctoral Fellow: Dr Amir Adeli

    Translation Partner: Datanet

     

    Translation project 2 (A TP2): Muck pile fragmentation measurement techniques - from controlled samples to field trials

    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: A/Prof Chaoshui Xu

    Students: Deea Rizki Oziana, Ulfa Riani, Iven Tan

    Translation Partner: Maptek

     

    Dr Dale Otten

    Translation project 3 (A TP3): Develop a new sensor for mineral identification

    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: Prof David Lancaster, Prof Bill Skinner, Prof Nigel Cook

    Postdoctoral fellows: Dr Dale Otten

    Translation partners: Bureau Veritas, Scantech

     

    Shi Zhao

    Translation project 4 (A TP4): ROM stockpile modelling

    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).

    Chief Investigators: Dr Tien-Fu Lu, Dr Larissa Statsenko

    Postdoctoral Fellow: Dr Shi Zhao

    Translation Partner: Eka

     

    Dr Aneta Neumann

    Translation project 5 (A TP5): Advanced ore mine optimisation under uncertainty

    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: Prof Frank Neumann

    Postdoctoral Fellow: Dr Aneta Neumann

    Translation Partner: Maptek

     

    Dr Junjie Zhang

    Translation project 6 (A TP6): Mill feed sorting to optimise throughput and energy usage

    This project will create a concept design of ore sorting to detect and divert problematic material (hard and sub-economic material such as bulldog shale and steely hematite). It 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: Prof Chunhua Shen, Adjunct A/Prof Max Zanin

    Postdoctoral Fellow: Dr Junjie Zhang

    Translation Partner: Scantech, Consilium Technology