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GRADE ENGINEERING STUDIES

Table of contents:  

Framework for Evaluation of Preconcentration Strategies

The following framework has been used to quantify the economic and environmental impacts of preconcentration techniques at numerous metalliferous operations. The framework was also applied to quantify the impacts of mine to mill strategies and the adoption of new processing technologies at operations.

The framework consists of:

  1. Characterisation of preconcentration responses;
  2. Geometallurgy and spatial modelling;
  3. Process design and simulation;
  4. Strategic mine planning; and
  5. Strategy evaluation.

The interactions of the framework in the quantification of the economic and environmental impacts of preconcentration are presented in the figure below.

 

Framework for the quantification of economic and environmental impacts of preconcentration
Framework for the quantification of economic and environmental impacts of preconcentration

 

Characterisation

Characterisation of preconcentration responses throughout the geometallurgical domains of a deposit provides the foundation for technoeconomic evaluation of preconcentration techniques. The process of characterising preconcentration responses requires a combination of laboratory test work and grade heterogeneity modelling to quantify the strength of the drivers for each separation technique.

Characterisation of Natural Deportment

Characterisation of the natural deportment response throughout the deposit requires testwork performed at drillcore and bulk scales. Intact diamond drillcore or coarse assay rejects are used to quantify the natural deportment response in future ore to be mined. The sampling campaign should cover the rock types, grade ranges and spatial distribution of material within, and just outside, the planned mining extents, biasing the next 5-10 years of production. Intact drillcore is crushed to -3.5 mm and screened through a series of sieves with the aim to produce five to six size fractions of similar mass for assay.

Bulk samples are used to determine the production scale natural deportment response of material. These samples may be taken from belt cuts or ROM material and are therefore limited to the material currently exposed or in stockpiles. Bulk samples provide an indication of the scale-up factor required to convert diamond drillcore responses to production scale responses. The bulk samples are screened into five to six different size fractions of similar mass and each size fraction is assayed.

The assay results of the size fractions, for bulk and drillcore samples, can be viewed as a plot of cumulative mass (or percent passing) versus upgrade factor (the grade of the undersize relative to the head grade of the sample) to assess the strength of the natural deportment response. A linear relationship often exists between the natural log of the upgrade factor and the negative natural log of cumulative mass. The slope of this linear relationship can be used to derive a single metric to describe the strength of the natural deportment response. This single metric can be used to quantitively rank the strength of natural deportment responses in different rock types or deposits. It can also be used to interpolate the natural deportment response throughout the resource model to assist in the optimisation of the strategic mine plan with preconcentration by screening.

Characterisation of Induced Deportment

Characterisation of the induced deportment response requires assessment of the natural deportment response, analysis of in-situ grade heterogeneity and engineering of a bimodal blast fragmentation.

In-situ grade heterogeneity is assessed at a blast hole scale using a combination of retrospective data from blast hole assays and forward-looking geostatistical estimates from resource drilling. Blast hole assays provide a high-resolution, 2-dimensional data set that can be combined with grade control polygons to identify specific areas where high or low-grade pockets of material were sent to the incorrect destination due to limitations in the selective mining unit (SMU). These areas can then be assessed for induced deportment response by altering the blast design to achieve finer fragmentation in high-grade and coarser fragmentation in low-grade. Modelled blast fragmentations, natural deportment response and screen aperture size are used to derive the grade and mass of screen oversize and undersize and characterise the induced deportment response.

Forward-looking estimates of induced deportment responses require geostatistical estimation of in-situ grade heterogeneity at a blast hole separation scale. Uniform conditioning (UC) is used to estimate the probability of a blast hole burden and spacing volume being above a series of cut-off grades within a panel of material in the resource model. This estimate is combined with different cut-off grades for high and low-energy blast fragmentation, screen aperture sizes and the natural deportment response to estimate the induced deportment response throughout the resource model. However, UC estimates do not provide the exact spatial location of in-situ heterogeneity but rather an estimate of the average in-situ heterogeneity present for the kriged grade of the panel within the estimation domain. Therefore, the use of the estimated induced deportment response using UC estimates of in-situ grade heterogeneity should be restricted to long-term, strategic mine planning decisions. These decisions are based on larger mining volumes that are more likely to reflect the average estimated response.

The aim of the bimodal blast fragmentation is to achieve the greatest difference in the particle size distribution (PSD) between coarse and fine blast fragmentation to provide the highest separation potential by screening. The results are dependent on the physical rock characteristics and blast design parameters including burden and spacing, hole diameter and charge. Adjustments to blast design parameters are often limited to the charging of holes, as grade heterogeneity of a blast is not confirmed until the blast hole chips are assayed. However, the greatest opportunity to create a wide differential between coarse and fine blast PSDs is through burden and spacing of blast holes. Predictive models using the blast hole data of the bench above and infill drilling can be used to inform adjustments to burden and spacing ahead of time. Alternatively, measure while drilling (MWD) data and real-time grade measurements using sensors on drill chips may be used to inform a change in drill pattern in real time.

Characterisation of Bulk Ore Sorting

Characterisation of the bulk ore sorting response requires estimation of the grade heterogeneity delivered to the sensor at a scale reflecting the separable parcels size of the sorting system and an estimate of sensor and mechanical separation performance. In-situ grade heterogeneity can be estimated using UC, as described in characterisation of induced deportment, using a separation volume approximating the separable parcel size. However, UC has been shown to have limitations in the estimation of in-situ grade variability present at very small separation units, including truck and shovel volumes.

While the in-situ grade heterogeneity of a separable parcel of material can be estimated using geostatistical techniques, the process of mining, loading, transporting and crushing the material prior to presentation of the material to a sensor can significantly reduce measured heterogeneity and the bulk ore sorting response. Modelling the impact of mixing in each stage of mining and material handling is a critical focus to support desktop bulk ore sorting evaluations. Currently, empirical measurements of grade heterogeneity present in separable parcels of material at the point of deployment may provide the best estimation of the delivered heterogeneity to the sensor. In the absence of empirical data, the impact of mixing on in-situ grade heterogeneity estimates can be examined through sensitivity analysis.

Sensor and separation performance of the bulk ore sorting system can be examined using anticipated or measured accuracy and precision results for the sensor technology and modelling of mechanised sorting efficiency. The combined impact of delivered heterogeneity, sensor performance and separation performance can be modelled using discrete event simulation to estimate production scale bulk ore sorting response. This response can then be linked to either insitu grade heterogeneity estimates or material classifications within domains to interpolate a bulk ore sorting response in the resource model.

Characterisation of Particle Sorting 

Characterisation of the particle sorting response requires measurements for the grade distribution of particles present in discrete size fractions of crushed and screened material, the performance of the sensor technology and mechanical separation and the natural deportment response. The grade distribution of particles within different size ranges can be determined through laboratory assay. These assays can be used to provide a theoretical particle sorting response in the absence of particle sorting test work.

The combined sensor and mechanical separation performance of particle sorters can be examined using supplier testing services. This service uses a particle sorter to test a sample from the mine using different sensors. The particles are marked to indicate which stream they reported to with each sensor then each particle is weighed and assayed. The assay results and sorting performance can then be used to determine the particle sorting response for each sensor technology.

Particle sorting requires screening to prepare a specified particle size range to feed the particle sorter. To determine and optimise the integrated particle sorting and screening response of the material, the natural deportment response is required to estimate the grade of the screen undersize. The combined estimates for particle sorting response and natural deportment screening response can be used to characterise the upgrade response of material with particle sorting.

Characterisation of coarse gravity separation 

Characterisation of the coarse gravity response requires laboratory testwork and the natural deportment response. Laboratory and large scale testwork can be used to examine separation performance for dense media separation and inline pressure jigs. Samples from the mine are treated in different densities of media or at different inline pressure jig settings and the float and sink streams are assayed to evaluate and optimise gravity separation performance.

Coarse gravity separation requires pre-treatment by crushing and screening to produce a consistent PSD in the feed and to remove fines. Therefore, the upgrade from coarse gravity separation must also consider the natural deportment response of the fines. An example from an operation where the upgrade to fines is as significant as the upgrade from the inline pressure jig is provide by.

Geometallurgy and Spatial Modelling

Interpolation of preconcentration responses in the resource model require assessment of suitable geometallurgy domains and selection of an appropriate geostatistical estimation method. There is often insufficient data for the development of specific preconcentration domains during early stages of preconcentration evaluations. In these cases, natural deportment and laboratory measured preconcentration responses may be interpolated in the resource model using the current estimation methodology for geometallurgical parameters at the operation. Estimates for in-situ grade heterogeneity using UC modelling (as discussed in the characterisation of induced deportment and bulk ore sorting responses) should also follow the current estimation domains and search parameters for the operation.

Geostatistical estimation methods range in sophistication from simple inverse distance weighting methods to complex multi indicator kriging or conditional simulations. In the absence of adequate spatial data regarding the preconcentration responses, minimal estimation techniques or domanial averages should be applied. As greater knowledge of the preconcentration response develops, more sophisticated estimation methods provide greater control for the population of the preconcentration response in the resource model.

Process Design and Simulation

Process design and simulation considers the integration and interactions of preconcentration techniques with the existing infrastructure and processing facilities of the operation. Process design and simulation is an iterative activity that alternates with the optimisation of preconcentration strategies within the strategic mine plan. 

Process Design 

Process design assesses the equipment requirements, plant layout and material handling strategies for given rock characteristics and capacities to develop an operationally capable preconcentration circuit. During early stages of preconcentration evaluations, several preconcentration designs are developed at a range of preconcentration capacities. These designs provide an understanding of how operational and capital costs change with preconcentration capacity to assist optimisation of preconcentration strategies in the strategic mine plan.

Process designs may include fixed, semi-mobile or mobile deployment options. Fixed options have a higher capital cost and lower operating cost and are best suited to a constant production capacity over a longer period. At the other end of the spectrum, mobile options have a lower capital cost and higher operating cost and offer flexibility to increase or decrease production capacity over shorter periods. Mobile or semi-mobile preconcentration plants are ideally suited to production trials for preconcentration techniques and initial implementation.

Process design governs the operating and capital costs for preconcentration techniques and the location of the preconcentration plant can have a large influence over both costs. The largest operating cost associated with bulk preconcentration techniques is the rehandling of product streams. Selecting a location that minimises product rehandle for one or more product streams can greatly reduce the operational costs of preconcentration. Furthermore, integrating preconcentration within existing material handling transfer or decision points can eliminate rehandle for one or more product streams, greatly reducing operating and capital costs for preconcentration. Critical locations to consider are:

  • Close to the primary crusher, to allow the upgraded product stream to tie into the coarse ore stockpile conveyor;
  • At waste storage facilities or deferred stockpiles, to minimise rehandle of the downgraded product stream;
  • At pit exits or in the mine, to minimise deviations in haulage or handling distances to and from the preconcentration plant; and
  • At the mine face, ideally between or within digging and loading activities to eliminate product rehandling.

Process simulation

Process simulation is used to assess the impact of preconcentration techniques on downstream production processes. These impacts depend on the preconcentration technique. Preconcentration by screening alter the particle size distribution in feed to the processing plant, often resulting in increased capacity in the comminution circuit which may be used to increase throughput or optimise grinding conditions to improve recovery. All preconcentration techniques can increase the grade of the feed to the processing plant which can improve flotation recovery.

Process modelling and simulation is performed using the Integrated Extraction Simulator (IES). IES is a cloud-based simulation covering the comminution and beneficiation activities of the entire operation from drilling and blasting through to final concentrate. IES incorporates the ability to simulate the processing performance of multiple ore types, optimise process settings to maximise plant performance within specified constraints and to perform mass simulation and write responses back to the resource model.

 

Strategic Mine Planning

Optimisation of the strategic mine plan provides a holistic approach to capture all impacts of new technologies and operational strategies deployed across the mining value chain, over the entire life of the operation. The optimisation is value driven, with the primary objective to maximise net present value within specified operational and processing constraints. For these reasons, optimisation of the strategic mine plan is the preferred method to evaluate preconcentration techniques.

Inputs into strategic mine planning include the resource model and operational parameters, constraints and economic assumptions. From these inputs, the value of each block in the resource model is calculated for each processing destination including waste storage facilities. The maximum value of the block is assigned, and the Lerchs-Grossman algorithm is applied to develop a series of nested shells by adjusting the revenue factor applied to each block. These nested shells provide guidance to the selection of the ultimate pit and design of phases to be mined. The material can then be scheduled, and cut-off optimised, for all processing destinations in the operation.

The inclusion of preconcentration techniques offer flexibility and opportunity for optimisation in their application and operational settings, such as screen aperture or sorting cut-off. These options can be included in the optimisation of the strategic mine plan in much the same way trade-offs between throughput and recovery with grind size have traditionally been assessed. The figure below shows the interaction of operational decisions for preconcentration and process plant settings that can be optimised in the strategic mine plan.

Once an optimal schedule and cut-off grade policy is produced, the resultant material movement and cashflow provides the best estimate of economic benefits relating to the scenario modelled. In addition, the material movements can be combined with power, fuel, steel and water consumption (from process and haulage models) to provide environmental metrics relating to planned production and quantity the waste rock and tailings generated. A comparison of the strategic mine plan with and without preconcentration techniques provides a robust quantification of anticipated economic and environmental impacts due to preconcentration.

Complete strategic mine plan optimisations for preconcentration techniques have been performed with Whittle Consulting Enterprise Optimisatio, Geovia Whittle™, COMET Strategy and COMET Scheduler and Datamine NPV Scheduler. However, due to the magnitude of work involved in performing a complete mine optimisation, early opportunity assessments for preconcentration techniques are performed within the operation’s existing ultimate pit and phases. These evaluations operate under the presumption that if sufficient value can be found within the existing mine design, performing a complete strategic mine planning optimisation will only unlock greater value.

 

Interaction of preconcentration and process plant settings optimised in the strategic mine plan.
Interaction of preconcentration and process plant settings optimised in the strategic mine plan.

 

Preconcentration Impact on Operational Capacities

Optimising the strategic mine plan requires balancing constraints to the rate of production imposed by mining, processing and marketing capacities. Preconcentration introduces an additional capacity to optimise that increases the effective processing capacity of an operation and changes the economic value of material, increasing the quantity of ore mined. As a result, the optimal balance between mining, processing, marketing and preconcentration capacities must be established and there may be a need to increase mining and marketing capacities.

The requirement for additional mining capacity with preconcentration is necessary for operations that are, or become, mining constrained. Generally, this is the optimal configuration for underground metalliferous mines where mining capacity is the most capital-intensive constraint on production. As a result, the opportunity cost of production raises the cut-off for mined material and encourages the use of highly selective mining methods. Optimal preconcentration strategies will seek to relieve the pressure on mining bottlenecks by preconcentrating material prior to the constraining activity, such as the preconcentration of ore prior to an underground hoist. As a result, greater capacity in downstream mining activities are required to maintain the quantity of ore processed. Preconcentration may also unlock greater value in mining constrained operations by enabling a transition to lower-cost, less selective mining methods, simultaneously increasing resource utilisation and the quality of ore sent to processing destinations.

Conversely, the requirement for additional mining capacity with preconcentration is less prevalent in operations that are processing constrained. Generally, this is the optimal configuration for open-pit metalliferous mines where processing capacity is the most capital-intensive constraint on production. As a result, the opportunity cost of production raises the cut-off applied to processing, generating an excess quantity of economic ore from the mine. This excess ore is often deferred to stockpiles or allocated to lower-value processing destinations, such as dump leach. The increase in effective processing capacity with preconcentration allows a greater quantity of ore to be treated as it is mined, reducing the quantity of material stockpiled or processed at a lower-value destination. This accelerates both the quantity of metal and the cashflow produced by the operation, significantly increasing NPV.

Strategy Evaluation

Strategy evaluation involves sensitivity, scenario and simulation modelling to find the optimal preconcentration strategy and understand the value drivers for that strategy.

Sensitivity analysis is used to flex individual assumptions regarding preconcentration performance and costs to understand the impact each assumption has on the value proposition. This information is useful in early opportunity assessments to identify critical assumptions for detailed examination in the next stage of evaluation.

Scenario analysis examines the effect of multiple simultaneous changes to preconcentration assumptions. Common examples of scenario analysis applied to preconcentration evaluations include the assessment of:

  • Different individual or combinations of preconcentration techniques;
  • Different deployment capacities and locations for preconcentration techniques; and
  • Staging of capital and scaling-up of preconcentration capacity.

Simulation analysis assigns probabilistic distributions to uncertain variables and through repeat analyses determines the probabilistic range of likely outcomes. During the evaluation of preconcentration techniques, simulation analysis is best suited to examine the impact of uncertainty in the modelled preconcentration responses. However, due to the magnitude of work involved, simulation analysis may be applied during detailed feasibility studies for deployment options of the selected preconcentration technique(s).

Project Evaluation Stages

The time, personnel and investment required to examine new technologies and strategies to deploy at an operation can be substantial. A capital investment model is used to assist in focussing a company’s limited resources on the examination of the highest value options for an operation. This model uses a stage-gate approach to progress the most promising options and strategies from conception to operation at site. CRC ORE have followed this approach to apply the framework for evaluation of preconcentration strategies at varying levels of detail to the different stages of project evaluation. A description of the different project evaluation stages is provided in the following Table. 

 

Project Stage

Description

Opportunity Assessment

Broad assessment of all available options, preliminary ranking by order of magnitude valuation and identify critical assumptions that require further examination.

Concept Study

Top ranked options and critical assumptions identified in the opportunity assessment are assessed in greater detail to provide higher confidence in their ranking and valuation.

Production Trial

Top ranked option(s) from concept study are demonstrated at pilot scale to prove concept in production environment and confirm critical assumptions.

Prefeasibility Study

Detailed assessment of successful options trialled including alternative designs and layouts, capacities and deployment strategies, detailed risk assessment and identify best strategy for feasibility study.

Feasibility Study

Detailed assessment of the selected strategy including definitive designs and cost estimates, environmental and social impact assessments and permitting, licensing and supply contracts for final approval to invest and implement the strategy.  

Implementation

Commission selected strategy, optimise performance and integrate within production.

Operation

Monitor and sustain performance within production.

Results and Discussion

The following results represent a compilation of 32 Grade Engineering® evaluations performed across fifteen sites using the framework described in this paper. The following Table provides relevant information regarding the project, commodities, preconcentration techniques examined, whether the evaluation considered the full or partial optimisation of the strategic mine plan and the change in the NPV for each preconcentration option. The relative change in NPV is also displayed in the Figure below.

 

Chapter4 Table2  
 

Change in NPV with preconcentration for 32 evaluations across fifteen operations
Change in NPV with preconcentration for 32 evaluations across fifteen operations

 

The evaluations are dominated by preconcentration screening assessments and bulk ore sorting assessments. The results show a wide range of possible economic outcomes can be achieved with preconcentration, including the fact that not all orebodies are amenable to certain preconcentration techniques.

 

The average improvement in NPV was 12.8% across all preconcentration techniques; screening techniques showed an average increase of 12.8% NPV and bulk ore sorting 17.0% NPV. Partial optimisation of the strategic mine plan yielded an average increase of 9.3% NPV, while a full optimisation returned 18.5% NPV. These results highlight the additional value from preconcentration that is unlocked with a complete optimisation of the strategic mine plan.

The Figure below presents a boxplot of selected production and environmental metrics for twelve preconcentration screening and bulk ore sorting options across six operations. Not all evaluations presented in the Table above have been examined for changes in environmental metrics.

The results show a wide range of outcomes in production and environmental metrics are possible. The impact that preconcentration has on the quantity of ore processed and tailings and waste rock generated is often ambiguous. Significant increases in these metrics are possible with the expansion of the ultimate pit as a result of preconcentration. Similarly, a reduction in waste rock is possible with increases in ore processed and tailings generated due to a significant lowering of the economic cut-off with preconcentration and little opportunity to expand the ultimate pit. In all scenarios examined, preconcentration increased the grade of ore treated and the total metal recovered from the orebody, indicating greater resource utilisation is achieved with preconcentration.

The results also show significant decreases in the energy, emission, water and steel intensity of metalliferous production are possible with preconcentration. These intensities of production represent the change in the energy, water and steel consumed, and emissions generated, relative to the production of a unit of metal output. On average, a 6% reduction in energy intensity, 8% reductions in emission and water intensities, and a 11% reduction in the steel intensity of comminution resulted from preconcentration. These figures represent the inputs directly consumed by the operation and do not account for embedded energy, emissions and water in inputs consumed.

Furthermore, the results show that preconcentration strategies were able to more than offset the increased energy, water, emission and steel requirements with additional metal production in scenarios where the quantity of ore processed significantly increased.

 

Boxplot of production and environmental metrics for twelve preconcentration options across six operations
Boxplot of production and environmental metrics for twelve preconcentration options across six operations

 

 

Preconcentration at greenfield projects

Greenfields projects have greater flexibility to alter site layouts and adjust operational capacities to capture the full benefits provided by preconcentration. However, there is less ability to trial and test preconcentration techniques at greenfield projects, often creating greater uncertainty whether to invest in preconcentration strategies for greenfield projects. In the results presented in Table 2, preconcentration options applied to greenfield projects returned an average increase of 16.1% NPV, while brownfield projects returned an average increase of 11.2% NPV with preconcentration. The largest increase in NPV from the 32 evaluations presented in Table 2 was also a greenfield project (6C) and provides an excellent example of the additional benefits that can be achieved by altering site layouts and mine designs for preconcentration.

The project is evaluating the development of an intrusive-hosted, structurally controlled stockwork gold and silver deposit. The orebody lends itself to development as three open pits to be processed by SABC comminution, flotation, CIL and electrowinning circuits. The operation is processing constrained and stockpiles a significant portion of lower-value ore at a central location between the three pits. The stockpiled ore will be reclaimed and processed toward the end of the mine’s life.

Although the project is profitable, feasibility studies failed to achieve a threshold return for investment. Preconcentration techniques, including screening and dense media separation, were examined to improve the economics of the operation. Natural deportment testwork from drillcore samples across the three pits revealed a consistent, very high upgrade signature in fines throughout the orebody. Bulk samples taken from a mining adit through the main pit were screened and assayed to confirm the very high natural deportment response was present at ROM PSD. Strategic mine planning used the natural deportment testwork to reoptimise pits, phases and site layouts with preconcentration by screening. The study found:

  • The optimal preconcentration strategy sent high-grade ore directly to the processing plant and upgraded lower-value ore through the screening plant;
  • Preconcentration enabled a significant expansion of the ultimate pits and increased resource utilisation;
    • Lowering the mining cut-off grade by 50%,
    • Increasing mineable resources by 80%,
    • Reducing striping ratios by 55%,
    • Increasing mine life by 20%, and
    • Increasing total gold production by 20%.
  • Preconcentration reduced the quantity of material processed via stockpiles and enabled the processing plant to be relocated to a central location between the three pits;
    • Reducing annual material movement by 15%,
    • Reducing cash costs and total costs by 10%, and
    • Reducing implementation capex by 5%.
  • While a reduction in process plant capacity could maintain a similar production profile with preconcentration, the significant increase in the quantity of economic ore mined warranted a processing plant of similar capacity, or slightly larger, to maximise NPV.  

Preconcentration by screening improved the NPV of the project by 34% and has become the primary plan to develop the orebody.

 

 

 




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