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technical > geometallurgy and geometallurgical modelling
By incorporating the principles of geometallurgical modelling into operational design and day-to-day decision making, we can substantially reduce those risks associated with the underlying geological variability that impact on processing performance and product quality. QG offers geometallurgical modelling services to improve the design of the mining and treatment systems, enhance ore/waste decision making and improve on operational profitability.
 What is geometallurgy?
Definitions
How do you define ore? What is waste? These may sound like fairly straightforward questions and are certainly fundamental to a successful mining operation. Conceptually:
• Ore can be defined as material that it is economic to mine, process and sell; whereas
• Waste is uneconomic.
This is, on the surface, a fairly clear-cut distinction.
Geometallurgy is a cross-discipline approach with the objective of addressing some of the complexities associated with determining the value of a resource and therefore if it is economic to exploit. By integrating geology, mining operations, mineral processing and metallurgy, geometallurgy aims to improve the fundamental understanding of resource economics.
Geometallurgy is relevant at both feasibility study and operational phases. A geometallurgical approach to mine planning and plant design is based on identifying the various attributes that contribute to the realised value of a resource. This includes traditional attributes such as the grade of the economic elements as well as less traditional factors including:
• Concentration of deleterious elements;
• Hardness;
• Grindability;
• Mineral species and ‘mineral grade’;
• Mineral liberation;
• Metallurgical recovery;
• Mining recovery;
• Drillability;
• Fragmentation;
• Reagent consumption; and
• Smelter enabling characteristics.
Geometallurgy at feasibility stage
During feasibility studies, the improved ore characterisation combined with the spatial modelling of critical physical characteristics that forms the basis of a geometallurgical approach provides a much improved basis for operational and mineral processing plant design. This approach reduces the risk associated with developing new operations or expanding an existing business.
By increasing ore body knowledge and then using it to design the entire operational flow sheet (from in situ rock to end product) a more reliable and realistic model of the production capacity of the system can be developed. Key bottlenecks, product constraints and low-cost/high value opportunities can be identified. The result can be increased total metal recovery and improved asset utilisation. This in turns allows better decision making, in terms of capital expenditure, to optimise project economics.
Geometallurgy at operational stage
At the operational phase, a geometallurgical approach improves the critical communication between geologists, mine planners and metallurgists. All disciplines work together to understand the value of the deposit and optimise the return to the business by scheduling the most appropriate combination and sequence of material types. This process reduces the risk and uncertainty at the grade control, mining and processing steps. In this respect geometallurgy can be seen as an extension of the mine-to-mill process. In operations that take a geometallurgical approach, the business plan is based on optimising product produced per shift or day at the lowest cost. Thus lower grade material that has better recovery and throughput may be preferred to higher grade but lower throughput material. The realised value (in terms of product produced and cost of production) drive the entire mine plan.
Risk reduction
Applying geometallurgical modelling techniques can directly reduce the risks associated with meeting production targets. Geometallurgy has the potential to act on both the consequences and likelihood axes to decrease risk.
As knowledge of material types, their spatial associations and likely performance parameters increases, operations can improve scheduling and planning outcomes. Production plans can be developed to reduce variability, take advantage of blending to better achieve down stream product specification and deliver a more reliable result. More knowledge equals less production uncertainty and less need to change the plan to deal with unexpected outcomes. This approach reduces the likelihood of an adverse outcome at both the development (project) and operating phases.
The consequences of an adverse event can also be minimised by improving the understanding of geometallurgical parameters. By taking a holistic approach to ore characterisation and spatial modelling of metallurgical parameters, operations are better prepared to recover from unfavourable incidents. Geometallurgy allows increased response speed and opportunities to reduce the duration of unplanned events.
Practical implementation of geometallurgy
Cross Disciplinary Approaches
There are a number of critical factors that need to be in place if a geometallurgical approach is to be successful. The first and foremost factor for success is to breakdown some of the traditional barriers between professional disciplines. The geologists, engineers and metallurgists involved in a project must truly want to work together with a shared goal and recognise the value they can each bring to the program. This type of approach requires geologists, engineers and metallurgists to develop significant understanding of the mining and ore treatment flowcharts. Technical professionals must become ‘multi-lingual’, that is capable of speaking (and understanding) geological, metallurgical and business language and concepts. Because it is a cross-disciplinary approach, geometallurgy cannot work if driven by only one ‘silo’.
Measurable physical characteristics
Once there is a commitment to working in terms of a realised value (as opposed to individual attributes such as grade or recovery) the cross-disciplinary team must determine the physical value drivers for the operation. These physical drivers will include many factors that are already part of the existing management system such as throughput, grade and recovery.
For each physical value driver, the team must drill down to identify measurable physical characteristics in the rocks that impact the value driver. This may be things such as a hardness measure that relates to grindability and drillability or a quantifiable measure of texture that relates to recovery. Ideally the measurable characteristics should be as easy to collect as possible, preferably from drill core. This may require changes to the logging approach to incorporate additional factors using automated logging equipment.
Identification of these physical characteristics involves measurement of behaviour at multiple scales. Routine metallurgical test work including locked-cycle tests, drop weight test are expensive and time consuming. Geometallurgy looks for alternate practices that can act as proxies for these tests. These proxy tests should allow a step-change increase in the volume of data available in order to allow spatial modelling of the physical value drivers. As testing and modelling progresses, using a geometallurgical approach also allows sampling to be focused on the right materials. Relevant and representative samples indicative of all materials (economic and deleterious) can be selected to ensure full ore body characterisation.
Spatial Modelling
For the geometallurgical characterisation to have a real impact on the business, it must enable improved mine planning. For this there needs to be a map of the physical characteristics identified as impacting the value drivers, as discussed above.
Creating a spatial of the geometallurgical properties of a deposit is similar to creating a resource model. The fundamental difference is that the resultant 3D model contains additional variables related to the realised value of each block. Application of appropriate spatial modelling tools is essential. Unlike the grade variables most operations are familiar with, many of these geometallurgical parameters behave quite differently when considered in a spatial context. This means that there must be a well thought out estimation approach for these new variables. In some cases the variables may be non-additive and/or require non-linear estimation approaches. In other cases it may be preferential to simulate rather than estimate. Each deposit will be different and understanding the required business outcome is essential to adopting the correct geostatistical tool.
Geometallugy in mine planning
Once a 3D model of realised value is available, the mine planning approach to optimisation, design and scheduling must be modified to incorporate this parameter as the key driver for the operation. Instead of working directly with grade, the planning team (in conjunction with the geologists and metallurgists) will work with the concept of realised value per shift/day. The production capability of the operation is maximised around this value proposition. If possible, the associated mining, processing and marketing costs should be incorporated into the value proposition with due consideration of the precision of the estimates for these parameters.
This different angle on the traditional geology, mine planning and metallurgical approach to managing an operation has additional down stream consequences. Of critical importance to the success of the approach is the incorporation of the proper checks and balances. That is, all of the key physical characteristics that build up the realised value proposition must be measured at various stages of the operation and there must be a robust reconciliation system that monitors the performance of the estimate against the end result produced. Ensuring this reconciliation system relates to both the day-to-day performance of the operation and the spatially predicted estimates is vital. Tracking and monitoring multiple variables must be incorporated into the operation’s management information systems.
More geometallurgical value
By its nature, geometallurgy has the potential to allow further improvement and innovation within the resources sector. When well executed, geometallurgy should vastly improve the fundamental understanding of the nature of a mineralised system. This understanding will impact on:
• Exploration and discovery;
• Geotechnical characterisation;
• Environmental and waste management; and
• Other technical aspects associated with the characterisation of rocks and minerals.
For example, as a part of a geometallurgical investigation, all drill core may be subjected to an automated logging approach that provides quantified measures of alteration intensity and type. Using multivariate statistics, this alteration may be recognised as key vectors to mineralisation. This could allow exploration programs to be tailored on the basis of the greater understanding derived from geometallurgical characterisation. Target ranking could be optimised and exploration activities directed towards potentially more valuable targets.
How QG can help
Quantitative Group specialises in working with our clients to extract the full potential of their people and their ore bodies. Our philosophy, “Our Skills on Your Team” describes the fundamental skills transfer and mentoring approach we take to projects and continuous improvement for our clients. We believe that it is not enough to give our clients a report and recommendations. We must also work with them to develop and understand the value proposition inherent in our work.
Value chain development
Geometallurgical modelling is a high-leverage management tool that has the potential to materially improve bottom-line operational performance. QG’s Directors and Principal Consultants bring a wealth of experience to help with the implementation of a geometallurgical approach. Our mentoring and coaching skills are well suited to facilitating workshops to identify business value drivers. With our broad commodity expertise we can help fast-track development of a geometallurgical value chain and ensure that your operation avoids common pitfalls and recognises the full spectrum of opportunities.
Multivariate statistics
QG’s expertise in analysing and modelling multivariate spatial variables is ideally suited to resolving the most appropriate approaches for each unique geometallurgical application. Using statistical tools (for example, principal component analysis, cluster analysis, discriminant analysis, etc.) QG can help identify what physical parameters are most highly-related to key value drivers. Our expertise in managing large and complex databases, data-mining and pattern recognition allow the critical variables to be grouped for further analysis and modelling.
Sampling and representativity
Many resource projects have failed to deliver the expected returns on investment. One underlying cause of value destruction with the industry is insufficient or non-representative sampling. Ensuring that all material types (both ore and waste) are adequately sampled for geological and metallurgical parameters is a critical risk management component. QG has experience in using statistical and geostatistical toiols to determine the frequency and spatial distribution of samples required for unbiased modelling and determination of operating parameters.
Sample quality is equally (if not more) important as sample quantity. Poor sample quality can lead to incorrect conclusions and poor decisions. QG can help set up sampling protocols and quality assurance programs to monitor and improve sample quality for geological and metallurgical sampling programs.
Spatial modelling
In order to extract the full value from implementing a geometallurgical approach, the key parameters must be spatially modelled. This involves creating an appropriate block model that can be used as an input for downstream mine planning and scheduling. QG can help characterise the spatial variability of geometallurgical variables. Depending on the properties of each variable QG can assist in identifying the most appropriate modelling approach and parameters, noting that some geometallurgical variables may necessitate different apporoaches to grade modelling if valid results are to be achieved. QG can build your geometallurgical model whilst at the same time training your staff using our ‘co-pilot’ mentoring techniques. This ensures not only a robust spatial model but also enables your people to own and maintain the model and underlying modelling processes.
Mine planning
Implementing a geometallurgical approach requires the integration of geological and metallurgical variables with the mine planning system. Instead of dealing with one or two key variables, your planning system needs to incorporate the full range of value drivers. The basic planning approach is based on maximising value per unit produced in any given time period. QG has experience in developing systematic planning and management information approaches that complement geometallurgy. Measuring, planning and reporting on the performance of geometallurgical drivers is essential to the success of a value-based approach.
Reconciliation and management information
Like any system or project, the success of a geometallurgy program can only be determined by measuring its performance. QG can assist with:
• The reconciliation and analysis of pre- and post-implementation performance;
• Ongoing reconciliation of actual against predicted performance; and
• Determining what (if any) changes occur as more and more data becomes available during the mining process.
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Scott Jackson (left), Scott Dunham (centre), and John Vann (right) are the Directors of Quantitative Group
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