No full-text available
To read the full-text of this research,
you can request a copy directly from the author.
Abstract
INTRODUCTION The aim of the paper is to document the research and case studies that have enhanced the understanding of spatially modelling and applying geometallurgical/geotechnical attributes. Some of these case studies progressed all the way through to mine planning and economic optimisation (drill core to cash fl ow) and the benefi ts of this fully integrated geometallurgical approach are also documented. Information from seven case studies is documented to illustrate the various approaches to different data sources and how the data are not only spatially modelled but are also used to change day-to-day mining and processing decisions. There are two case studies from Australia, two from Africa, one from South America, one from South-East Asia and one from North America. Three of the projects had a strong geometallurgical focus while the other four predominantly focussed on geotechnical data. Two of the studies involved fully comprehensive core to cash fl ow analysis. Traditional open pit mine design and optimisation has been limited by the amount of detailed geotechnical and geometallurgical information available. The spatial modelling case studies are discussed to highlight the approaches and benefi ts of building 3D Geometallurgical models. The case studies show signifi cant potential for the geometallurgical mine planning models to add value to mining and processing operations. These included: • signifi cantly better understanding of the orebody variability and impact on the production schedule and what constitutes economic ore, • leads to risk based strategic planning that can reduce the incidence of failed or underperforming projects, and • was based on a holistic approach to mine planning that incorporates the full spectrum of disciplines. The paper documents a broad range of industry case studies which demonstrate strategies for gaining value from geometallurgical initiatives. An additional important aspect of geometallurgical is to provide more robust geometallurgical information that can be incorporated into corporate governance procedures. INDUSTRY CONTEXT Given the increased energy and water required to process Australia's growing lower quality deposits, mine sites are not well equipped to meet the environmental legislation, water shortages and carbon trading systems that are changing the mining economic environment. Mine wide process optimisation that considers the generation of CO 2 and consumption of energy and water in mining operations is a critical issue for mining companies. New tools and models are required to effectively incorporate these new metrics into mine economic assessments. In addition site engineers need to be ABSTRACT The mining industry is facing some key challenges that provide a compelling case for geometallurgy initiatives. Over the last 30 years, the average grade of Australian orebodies being mined has halved while the waste removed to access the minerals has more than doubled. In the last eight years, the industry's energy consumption has increased 70 per cent while multi-factored productivity has fallen 24 per cent. The paper documents a broad range of industry case studies which demonstrate strategies for gaining value from geometallurgy. The mining industry constantly faces the key questions of how signifi cant ore variability is on mine valuation and how would knowledge of the variability change the mining schedule, mine to mill design and operation. Using case studies the paper describes the methods used for incorporating mineral extraction attributes into orebody models and their technical integration into daily operation as well as mine planning optimisation. While pit optimisation from a fully attributed geometallurgical model highlights areas of opportunity and risk it is shown that during the scheduling phase of mine planning that signifi cant value can be added. The benefi ts from these projects included reducing geotechnical and environmental risk, improving mine to mill performance, option analysis and quantifi ed cash-fl ow risk. There is growing acceptance within the minerals industry that 'realistically assumed modifying factors' defi ned by a competent person/s are not suffi cient to mitigate the risk of funding mining ventures. Geometallurgy is moving away from factored ore reserves to data-rich block models providing reliable information for mining, metallurgical and environmental considerations.
... The prime objective of geometallurgy is to improve the profitability of mines through the use of spatial models of rock properties that have a significant impact on value [1,2,5]. It aims to correlate geology and mineralogy with data from testwork and develop a model to predict variability [1,2,5]. ...
... The prime objective of geometallurgy is to improve the profitability of mines through the use of spatial models of rock properties that have a significant impact on value [1,2,5]. It aims to correlate geology and mineralogy with data from testwork and develop a model to predict variability [1,2,5]. A key output is 3D geometallurgical mapping, where diverse attributes from core logging, mineralogical/textural determination and smallscale tests are used to resolve grade, process parameter and rock mass variability. ...
... • The geometallurgical study aimed to link ore characteristics with key operating parameters, principally grade and gold recovery, but also geochemistry and mineralogy, rock mass properties and plant throughput. The outcome was the production of 3D block models to describe variability [1,2,5]. • The selection of whole-core variability samples for grade determination and testwork was driven by the need to quantify gold grade and recovery variability. ...
Vein gold deposits are often characterised by multiple sub-parallel veins and free-milling coarse gold. Inherent heterogeneity results in grade and process parameter variability, which increases project risk if not quantified and controlled. The geometallurgical approach can be broadly split into two activities: strategic and tactical. The strategic approach focuses on the whole orebody and long-term life-of-mine view, whereas tactical geometallurgy relates to a more short- to medium-term view during mining. The geometallurgical approach requires spatially distributed samples within a deposit to support variability modelling. A variability sampling and testwork protocol was developed to quantify gold grade and recovery. Additional attributes from core logging, mineralogical determination were integrated with grade and recovery data. This contribution presents a case study of strategic and tactical geometallurgical programme application to a narrow-vein deposit. It exemplifies how data can be used to support resource estimation, a pre-feasibility study, trial mining and production. Subsequent to production commencing, a tactical geometallurgical/ore control programme was introduced to optimise mine scheduling and process activities.
... Companies that embrace the geometallurgical approach will benefit from increased net present value and shareholder value. then there is increased project risk and potential for revenue loss through reduced Net Present Value (NPV) [2][3][4][5]. ...
... Geometallurgy is an interdisciplinary activity that integrates geology, mining/geotechnical engineering, metallurgy, mineral economics, and geoenvironmental parameters to maximise project economic value, reduce risk, build resilience, and demonstrate good management of the resource [1,3,[6][7][8][9][10][11]. Resilience relates to the capability of a mine operation to respond and recover from a disruptive event. ...
... Two principle aspects of the geometallurgical approach are the quantification of variability and uncertainty. Their understatement may have negative impacts on mining, blending, and processing [3,12,21]. "Variability" reflects fluctuations in successive values (e.g., grade) either in space (spatial) or time (temporal), whereas uncertainty refers to any value for which there is incomplete knowledge (e.g., sparse sample data). Variability is a physical phenomenon that can be measured and analysed, whereas uncertainty is an aspect of knowledge [12]. ...
Geometallurgy is an important addition to any evaluation project or mining operation. As an integrated approach, it establishes 3D models which enable the optimisation of net present value and effective orebody management, while minimising technical and operational risk to ultimately provide more resilient operations. Critically, through spatial identification of variability, it allows the development of strategies to mitigate the risks related to variability (e.g., collect additional data, revise the mine plan, adapt or change the process strategy, or engineer flexibility into the system). Geometallurgy promotes sustainable development when all stages of extraction are performed in an optimal manner from a technical, environmental, and social perspective. To achieve these goals, development of innovative technologies and approaches along the entire mine value chain are being established. Geometallurgy has been shown to intensify collaboration among operational stakeholders, creating an environment for sharing orebody knowledge and improving data acquisition and interpretation, leading to the integration of such data and knowledge into mine planning and scheduling. These aspects create better business optimisation and utilisation of staff, and lead to operations that are more resilient to both technical and non-technical variability. Geometallurgy encompasses activities that utilise improved understanding of the properties of ore and waste, which impact positively or negatively on the value of the product, concentrate, or metal. Properties not only include those that impact on processing efficiency, but also those of materials which will impact on other actions such as blasting and waste management. Companies that embrace the geometallurgical approach will benefit from increased net present value and shareholder value.
... To the authors' knowledge, all studies conducted on predictive geometallurgy by mathematical geoscientists (Bye, 2011;Boisvert et al., 2013;Rossi and Deutsch, 2014;Hosseini and Asghari, 2015;Ortiz et al., 2015;Deutsch et al., 2016) consisted on appropriately predicting the secondary properties at each block of a mining block model, and proposing the mining and processing engineers to conduct their mine planning and plant scheduling based on those properties instead of on metal grades. The first step (Vann et al., 2011) is the geometallurgical analysis of the ore body with respect to its primary properties. ...
... These values can be used instead of grade as better proxy of cashflow in further calculations, like the mentioned ultimate pit or mine scheduling. Value is generated by minimizing capital costs, due to early exploitation of highly valuable parts of the deposit, and by an improved distinction between ore and waste (Bye, 2011). Second, the predicted properties can be used as well to find matching ore partners in blending to reduce feed variability in the plant, and ensure constant plant operation conditions. ...
Predictive geometallurgy tries to optimize the mineral value chain based on a precise and quantitative understanding of: The geology and mineralogy of the ores, the minerals processing, and the economics of mineral commodities. This chapter describes the state of the art and the mathematical building blocks of a pos- sible solution to this problem. This solution heavily relies on all classical fields of mathematical geosciences and geoinformatics, but requires new mathematical and computational developments. Geometallurgy can thus become a new defining chal- lenge for mathematical geosciences, in the same fashion as geostatistics has been in the first 50 years of the IAMG.
... Some of this underperformance has arguably come about because the underlying project evaluations were based on singular, smoothed orebody estimates. This has understated or ignored spatial variability and uncertainty of mineralisation (Bye, 2011) and the critical impacts this has on the mining, blending and processing steps. In addition, many steps in project evaluation involve further averaging of values over time, for example averaging mine planning or processing steps over annual or at best quarterly increments. ...
... For example, the opportunity to increase cash fl ow by exploiting grade variability to change mine planning strategies. The motivation to fi nd better approaches, where spatial variability is properly modelled, in order to avoid losses and identify (and price) opportunity is thus high and the potential payoffs can be considerable (Bye, 2011). ...
A new approach to mineral project evaluation is described called Scenario-Based Project Evaluation (SBPE). SBPE incorporates well established 'scenario planning' and 'scenario thinking' approaches, which are used extensively in other industries, most notably the petroleum industry. While most scenario planning is qualitative and non-probabilistic, the approach described here includes both quantitative and qualitative aspects. SBPE is complementary to optimisation approaches and allows mining firms to build and operate projects that are more robust in the face of adverse changes as well as proactively respond and exploit the upside offered by a realistic assessment of variability. The approach has potential to improve project performance and decrease operational risk. It also has important applications for currently operating mines. Conventional evaluation of metalliferous mineral deposits uses estimates of metal grades and deleterious attributes in singular 'orebody models', usually 3D block models, which fail to capture two critical aspects that underpin the realised value of mining projects: 1. the inherent uncertainty of the estimated attributes 2. the spatial variability of attributes, at the scale of mining selection, which impact on downstream processing steps and fundamentally drive realised value. While singular models are appropriate for estimating block averages, conditional simulation (CS) models of the relevant attributes are needed to represent uncertainty and capture spatial variability. Any attributes that can be either built into CS models and/or modelled in the envisaged mining/processing steps, can be evaluated fully through to highly 'granular' (ie detailed and un-smoothed) cash flows. A complete view of SBPE relies on selected strategies being tested against plausible 'scenarios' of factors external to the project and thus outside management control. 'Externalities' captured in such scenarios include market prices, costs and taxes. Via SBPE, project managers may demonstrate that there is material value in preserving or purchasing options that provide future flexibility to respond to the changing externalities or changes in orebody performance. Such flexibility generally comes at a cost but is very difficult to value. It is argued that full value chain evaluation using SBPE can help to value these options for a mining company. A case study for a gold mine is presented.
... Rock hardness affects the unit operations in both the mine and the mill. With an in-depth understanding of the spatial variation of rock hardness, drill and blasting patterns can be adjusted, and the powder factor can be optimized (Bye, 2011). In a mining operation, knowing the spatial variation of rock/ore hardness is essential to predict the rock/ tool interaction and consequently improve the operational efficiency of the mine. ...
A comprehensive understanding of the hardness of ore being handled and processed in a mining operation can significantly improve operational efficiencies. This is feasible by providing valuable data to support decision-making through the mining value chain (drilling, blasting, loading, comminution). This study presents the results of a machine learning (ML) approach for rock hardness prediction using rock's geophysical and geochemical features. Core samples from several mine sites were logged using a multi-sensor core logging (MSCL) system. Measurements include ultrasonic P-and S-wave velocity, elemental concentration via portable X-Ray fluores-cence analyzers (pXRF), and Leeb rebound hardness, measured every 30 cm along 564 m of core samples. K-Means and PCA were used for better interpretation of the data. Supervised ML models (XGBoost and Random Forest) were utilized to predict rock hardness using the elemental concentrations and ultrasonic velocities as predictors. Since the data was collected automatically with predefined intervals, some of the measurement points were near fractures or veins. The Gaussian weighted moving average (WMA) was used to smooth out variations in geochemistry or hardness caused by local features that do not reflect the overall rock characteristics. This approach is effective for building ML models to become less susceptible to local rock features. It was concluded that the rock hardness could be effectively predicted using only geochemistry, and the process of collecting P-and S-wave velocity for hardness prediction can be skipped.
... Simulation studies on the relationship between fragmentation and throughput of a SAG mill [15,16]; Ore hardness indicators [17] and geotechnical parameters in mine block models and subsequent optimization of mill productivity [18]; Integrated communication modeling systems for blasting, grinding and flotation [19,20]. ...
The conditions of declining gold grade in the ore, increasing depth of excavation, and de-creasing unallocated stock of deposits make it necessary to develop efficient solutions for the mine-to-mill process, which have to be adapted to each specific mining plant and will optimizes production costs. Current research focuses on a case study that demonstrates how indicators of mining losses and dilution influence the variation of costs chain in the production cycle. The article examines the topical issue of determining the effects at the mine-to-mill stages due to changes in losses and dilution. The author’s approach to the formation of a mine-to-mill cost chain is proposed by integrating several cost estimation methods into the general cost estimation methodology. The estimation methodology is a compilation of factor analysis and cost engineering methods that take into account the change in costs due to the variation of losses and dilution. It was proven that with variations in losses and dilution, cost savings arise due to changes in the volume of work on ore averaging, ore transportation, and beneficiation. For the case of the Kuranakh ore field, there are no effects at the mining stage. The use of internal reserves by means of managing ore quality parameters allows reducing the costs per ton of processed rock mass along the entire production chain.
... 61 Practically, these concepts were coupled with the geometallurgy framework resulting in more holistic 62 geometallurgical approaches. Thus, merging advanced mineralogical surveys and mine waste 63 management promotes the geo-environmental assessment of mine waste (Brough et al., 2017;Bye, 2011;64 Chopard, 2017;Elghali et al., 2018;Erguler and Erguler, 2015;Paktunc, 1999;Parbhakar-Fox et al., 65 2013; Weisener and Weber, 2010). This mineralogy-based assessment usually requires the use of time-66 consuming and cost-intensive techniques. ...
Three-dimensional geological modeling is an efficient tool to visualize ore body features during both the exploration and operation phases of mines. Repurposing the 3D geological modeling for mine waste management allows for the visualization of hazardous metals distribution throughout an ore body and its host rock. With this information, a mine manager could seamlessly carry out waste rock management based upon their classification. The major prerequisite to such an approach is to procure sufficiently large datasets in order to ensure high interpolation quality and suitable resolution. Apart from metals of economic interest, other elements, and more precisely the deleterious elements, usually do not undergo exhaustive geochemical analyses throughout the footwall and the hanging wall of ore bodies. Based on that premise, the Éléonore mine site provided restricted grades of arsenic, the most hazardous element within the mine setting, to create a 3D spatial model of arsenic content. A stochastic process coupled with the geological logging of drill cores was created to fulfill the 3D modeling prerequisite with known margins of error. The outcome of this work consists of multi-realization 3D spatial model of arsenic content across the ore deposit and the hosting rock. Each realization was assessed using available chemical analyses to underline the model’s reliability. The results revealed a spacious geochemical halo of arsenic that could reach up to 500 m away from the gold deposit, with up to 94% of arsenic grades exceeding 50 ppm. The process developed in this work will enable mine waste classification before stripping, thereby providing the opportunity for proactive upstream mine waste management options that could prevent future environmental liabilities.
... This work presents a detailed comminution, mineralogical and experimental cyanide leaching study using a geometallurgical approach for three blended reef ores from the Carletonville goldfield, South Africa (Fig. 2). Geometallurgy is an interdisciplinary activity that integrates geology, mining, metallurgy, mineral economics and geo-environmental parameters to maximize project economic value, reduce risk, build resilience and demonstrate good man-agement of the resource (Walters 2008;Bye 2011;Jackson et al. 2011;Ehrig 2013;Keeney 2013;Williams 2013;Lund and Lamberg 2014;Glass 2016;Dominy et al. 2018a;Lishchuk et al. 2019a, b). Geometallurgy is an important strategy for any mining project, in which the prime objective is to improve the profitability of mines through the use of spatial and/or a spatial predictive models of rock properties that have a significant impact on economic value (Dunham and Vann 2007;Lamberg 2011;Vann et al. 2011Vann et al. , 2012Jackson et al. 2014;Lund and Lamberg 2014;Coward and Dowd 2015;Lishchuk et al. 2019a, b). ...
Gold production in South Africa is projected to continue its decline in future, and prospects for discovery of new high-grade deposits are limited. Many of the mining companies have resorted to mining and processing low-grade and complex gold ores. Such ores are technically challenging to process, which results in low recovery rates, excessive reagent consumption and high operating costs when compared to free-milling gold ores. In the Witwatersrand mines, options of blending low-grade gold ores with high-grade ores exist. Although it is well known that most of the Witwatersrand gold ores are highly amenable to gold cyanidation, not much is known on the leachability of blended ores, especially the effects of mineralogical and metallurgical variability between different gold ores. In this study, we apply a geometallurgical approach to investigate mineralogical and metallurgical factors that influence the leaching of blended ores in a set of bottle shaker and reactor column tests. Three gold-bearing conglomerate units, so-called reefs, i.e., Carbon Leader Reef, Ventersdorp Contact Reef and the Black Reef, all in the Carletonville goldfield, were sampled. The ores were prepared using a terminator jaw crusher followed by vertical spindle pulverizer (20 kg aliquot) and high-pressure grinding rolls (80 kg aliquot). Mineralogical analysis was conducted using a range of complementary tools such as optical microscopy, QEMSCAN and micro–XCT. The results show that Witwatersrand gold ores are amenable to the process of ore blending. Some of the ores, however, contain impervious inert gangue and reactive ore minerals. Leach solution can only access gold locked in impervious gangue minerals through HPGR-induced pores and/or cracks. The optimum ore blending ratio of the bottle shaker experiments (p80 = − 75 μm) comprises 60% Carbon Leader Reef, 20% Ventersdorp Contact Reef and 20% Black Reef and yields 92% recovered Au over a leach period of 40 h. Blended ores with high carbonaceous material (> 1 wt% carbonaceous material, (Black Reef = 36–60%) yield lower recoveries of 60–69% Au). Ore leaching at the mixed-bed reactor column (− 75 μm and − 5.6/+ 4 mm) yields about 70% over a leach period of two weeks. We therefore suggest that the feasibility of ore blending is strongly controlled by the mineralogy of the constituent ores and that a mixed-bed reactor may be a viable alternative method for leaching of the low-grade Witwatersrand gold ores. Material from certain reefs, such as the Black Reef, has synergistic/antagonistic (nonadditive) blending effects. The overall implication of this study is that ore blending ratios, effects of comminution on mineral liberation, an association of gold with other minerals, and gold adsorption behavior will greatly inform future technology choices in the area of geometallurgy.
... The emerging science of geo-metallurgy provides tools and methodologies for this process (Bye, 2011). The use of alternative and renewable energy resources will also have great potential for future mineral processing. ...
SOMP created the “Mines of the Future” project to produce high quality, internationally focused reference report and established an editorial committee which invited academic and industry thinkers, and technologists to contribute to the report. The editorial committee and contributors are listed on Pages IX & X.
This report provides a vision of the mines of the future, for 2030 and beyond, and the impacts on required skills and future educational curricula and research needs.
The Mines of the Future report has five main chapters, with the topics in each chapter focusing on the Current Status, the Future, and Transitioning to the Future:
• Operational efficiency
• Novel mining systems
• Sustainable mining practices
• Education
• Research
... The emerging science of geo-metallurgy provides tools and methodologies for this process (Bye, 2011). The use of alternative and renewable energy resources will also have great potential for future mineral processing. ...
... ** Machine additivity -if the value represents a measure of property A which has a deleterious effect on mill performance then it is possible that when two samples I and II are combined, the higher value will have a disproportionate effect with the composite sample having a value different than the arithmetic mean of the individual samples. This type of additivity is referred to as machine additivity and represents blending (Bye, 2011;Newton and Graham, 2011). ...
A spatial model for process properties allows for improved production planning in mining by considering the process variability of the deposit. Hitherto, machine-learning modelling methods have been underutilised for spatial modelling in geometallurgy. The goal of this project is to find an efficient way to integrate process properties (iron recovery and mass pull of the Davis tube, iron recovery and mass pull of the wet low intensity magnetic separation, liberation of iron oxides, and
) for an iron ore case study into a spatial model using machine-learning methods. The modelling was done in two steps. First, the process properties were deployed into a geological database by building non-spatial process models. Second, the process properties estimated in the geological database were extracted together with only their coordinates (x, y, z) and iron grades and spatial process models were built. Modelling methods were evaluated and compared in terms of relative standard deviation (RSD). The lower RSD for decision tree methods suggests that those methods may be preferential when modelling non-linear process properties.
... ** Machine additivity -if the value represents a measure of property A which has a deleterious effect on mill performance then it is possible that when two samples I and II are combined, the higher value will have a disproportionate effect with the composite sample having a value different than the arithmetic mean of the individual samples. This type of additivity is referred to as machine additivity and represents blending (Bye, 2011;Newton and Graham, 2011). learning methods was not a part of the study, since machine-learning methods used here are well established. ...
A spatial model for process properties allows for improved production planning in mining by considering the process variability of the deposit. Hitherto, machine-learning modelling methods have been underutilised for spatial modelling in geometallurgy. The goal of this project is to find an efficient way to integrate process properties (iron recovery and mass pull of the Davis tube, iron recovery and mass pull of the wet low intensity magnetic separation, liberation of iron oxides, and P80) for an iron ore case study into a spatial model using machine-learning methods. The modelling was done in two steps. First, the process properties were deployed into a geological database by building non-spatial process models. Second, the process properties estimated in the geological database were extracted together with only their coordinates (x, y, z) and iron grades and spatial process models were built. Modelling methods were evaluated and compared in terms of relative standard deviation (RSD). The lower RSD for decision tree methods suggests that those methods may be preferential when modelling non-linear process properties.
... When the methane concentration drops (often to less than 1%) it is possible to use this gas stream as 5 greenhouse gas emissions, 3 including some reported data on copper from the literature. While our r smelting and refining stage for 0.5% copper ore grade is lower than that of Giurco et al [9], it is similar to by Lunt et al [10] adjusted to the same concentrate grade, viz. 1.40 cf. ...
These IGCC reports examine climate change risks and adaptation opportunities as well as energy cost and carbon risks and mitigation opportunities for four industry sectors.
Designed as guides for funds managers, ESG analysts and company analysts, the reports provide a first time, comprehensive analysis of climate change issues for integration into company analysis and engagement. The reports will help investors go beyond assessing disclosure practices and carbon prices in their analysis of climate change exposure.
Developed by lead author Dr Michael Smith of the Fenner School the Australian National University, in conjunction with IGCC’s Research Working Group.
Cbus sponsored the development of these reports.
These reports were
a) launched at the major annual conference for superfunds,
b) received national media coverage and now
c) are promoted by the Investor Group on Climate Change here @ http://www.igcc.org.au/assessing_risks
... The geometallurgical approach emphasises early stage intervention and progression across the mine value chain (Baumgartner et al., 2011Baumgartner et al., , 2013 Bye, 2011Table 1). It can be broadly split into two key approaches: project and operational (or strategic versus tactical geometallurgy: McKay et al., 2016). ...
Geometallurgy is an important addition to any evaluation project or mining operation. As a discipline, its seeks to maximise the Net Present Value (NPV) of an orebody, while minimising technical and operational risk. It also aims to promote sustainable development and initiatives by ensuring that all stages of extraction are performed in an optimal manner from a technical, environmental and social perspective. To achieve these goals, development of innovative technologies and approaches along the entire commodity value chain are being established. Geometallurgy has been shown to increase operational stakeholder collaboration, creating an environment for knowledge sharing and improved data acquisition and interrogation, with the end result being the integration of such data into mine planning and scheduling. All of these aspects create better business optimisation, utilisation of staff and targeted and realistic key performance indicators.
... A key output is 3D geometallurgical mapping, where diverse attributes from core logging, mineralogical/textural determination and small-scale tests are used to resolve grade, process parameter and rock mass variability (Keeney, 2013; Keeney and Nguyen, 2014; Tungpalan et al., 2015; Walters, 2009). The geometallurgical approach emphasises early stage intervention and progression during the project to optimise the mine plan (Baumgartner et al., 2011Baumgartner et al., , 2013 Bye, 2011; Dunham and Vann, 2007; Ehrig, 2013; Leichliter, Jahoda and Montoya, 2013; Walters, 2009). Geometallurgy can be broadly split into two key approaches: project and operational. ...
Sheeted vein gold deposits are characterised by multiple sub-parallel veins and free milling gold. In-situ heterogeneity of mineralisation results in grade and process parameter variability, which will increase project risk. Measured variability is often enhanced by poorly designed sampling and testwork protocols. Protocols that are optimised within the framework of the Theory of Sampling to suit the ore type, together with quality assurance/quality control systems, will reduce variability and provide fit for purpose results. The geometallurgical approach requires spatially distributed samples within a deposit to support variability modelling. Diverse attributes from core logging, mineralogical/textural determination and small-scale tests are used to measure variability. This contribution emphasises early stage geometallurgical intervention during project development. It presents a discussion of representative sampling in the context of the Theory of Sampling. A case study presents a protocol which exemplifies how data can be gained from a well-designed and planned programme to support a resource estimate and pre-feasibility study.
The Mine-to-Mill (M2M) approach aims to enhance efficiency and reduce costs in the mineral processing industry by optimizing the mining and processing stages. M2M integrates orebody characterization, blasting, and downstream processes, such as grinding and flotation, demonstrating that material fragmentation directly impacts downstream efficiency. This review studies the development and applications of fragmentation models in M2M integration and optimization, finding that their study is divided into three phases. In the first, the potential of M2M is investigated through simulation models that improve fragmentation in blasting to optimize grinding. The second focuses on the practical application of these models in mines, while the third phase integrates geometallurgi-cal data into mine block models, enhancing production planning and selective ore extraction. The M2M integration has demonstrated significant improvements in plant performance, particularly in increasing grinding efficiency through optimized blast fragmentation. The literature also emphasizes the role of optimizing crushing and grinding conditions through models and circuit adjustments to enhance performance and reducing energy consumption. Geometallurgy plays a crucial role in plant optimization by identifying areas with better processing characteristics and adjusting operating parameters to maximize efficiency. Recent studies have shown how the implementation of integrated models can increase the profitability and sustainability of mining operations.
Consumption rates of reagents and consumables are typically accounted for in mine production scheduling by simple cost adjustment factors per rock type or mining block, ignoring geological uncertainty, blending, and geometallurgical information. To overcome these limitations, this article first creates empirical geometallurgical prediction models of reagents and consumables by tracking blended rock properties that are matched with observed consumption rates at the operating processing plant. The prediction models are then integrated in a simultaneous stochastic optimisation model for short-term production scheduling. Improvements over a conventional short-term production schedule are demonstrated in a case study at the Tropicana Gold mining complex.
Editor’s note: The aim of the Geology and Mining series is to introduce early career professionals and students to various aspects of mineral exploration, development, and mining in order to share the experiences and insight of each author on the myriad of topics involved with the mineral industry and the ways in which geoscientists contribute to each.
Abstract
Economic geology and geometallurgy are intimately linked. Geologists understand the value in knowing the details of ore variability, the formation of mineral deposits, the continuity and the spatial distribution of ore types, and the mineral and textural characteristics that control grades. Beyond exploration and discovery, however, explorers may not recognize that the geologic knowledge developed around a mineral prospect is also essential to miners and metallurgists, reclamation and environmental specialists, and economists and investors who are interested in developing the discovery. Geometallurgy is the interdisciplinary method that links geologic, mineralogical, and geochemical characteristics of mineral deposits to the mining, processing, and metallurgical activities that are involved in the development of mines. Geometallurgy is not a new field, but recent developments in analytical capabilities and the ability to conduct statistical analysis and predictive modeling of large data sets have resulted in geometallurgy becoming a widely used method for optimizing mining operations. While there are many approaches, depending upon the nature of the ore deposit and the mine operating conditions and goals, the most important step explorers can take is to establish partnerships with the other areas of specialization in the project (mining, metallurgy, environmental, economics) and work together to understand the critical factors in order to best develop the deposit. Representative sampling to determine geologic variability and uncertainty and understanding the controls of throughput and recovery in the mining operation are fundamental to optimizing projects. For exploration and prefeasibility timelines, information on ore characteristics and spatial variability can provide a preliminary assessment of how material in a potential ore deposit can be processed.
This book is a comprehensive manual for decision-makers and policy leaders addressing the issues around human caused climate change, which threatens communities with increasing extreme weather events, sea level rise, and declining habitability of some regions due to desertification or inundation. The book looks at both mitigation of greenhouse gas emissions and global warming and adaption to changing conditions as the climate changes. It encourages the early adoption of climate change measures, showing that rapid decarbonisation and improved resilience can be achieved while maintaining prosperity. The book takes a sector-by-sector approach, starting with energy and includes cities, industry, natural resources, and agriculture, enabling practitioners to focus on actions relevant to their field. It uses case studies across a range of countries, and various industries, to illustrate the opportunities available. Blending technological insights with economics and policy, the book presents the tools decision-makers need to achieve rapid decarbonisation, whilst unlocking and maintaining productivity, profit, and growth.
This book is a comprehensive manual for decision-makers and policy leaders addressing the issues around human caused climate change, which threatens communities with increasing extreme weather events, sea level rise, and declining habitability of some regions due to desertification or inundation. The book looks at both mitigation of greenhouse gas emissions and global warming and adaption to changing conditions as the climate changes. It encourages the early adoption of climate change measures, showing that rapid decarbonisation and improved resilience can be achieved while maintaining prosperity. The book takes a sector-by-sector approach, starting with energy and includes cities, industry, natural resources, and agriculture, enabling practitioners to focus on actions relevant to their field. It uses case studies across a range of countries, and various industries, to illustrate the opportunities available. Blending technological insights with economics and policy, the book presents the tools decision-makers need to achieve rapid decarbonisation, whilst unlocking and maintaining productivity, profit, and growth.
The heterogeneous nature of orebodies introduces large uncertainties into all quantitative evaluations, process design and process predictions. Measuring the extent of the variability of ore competence will allow the design process to account for variation in process performance through a quantitative knowledge of its uncertainties related to ore hardness.
The conventional JKMRC drop-weight test (JKDWT) establishes the relationship between input energy (Ecs) and product fineness (t10) from which the breakage potential parameter A × b can be estimated, by combining the broken progeny of groups of particles. A new method, the Extended Drop Weight Test (ExDWT), has been developed which is applied to individual particles and is therefore capable of capturing breakage heterogeneity at high resolution. This paper explores a number of features of the new method, based on breakage tests on individual particles from several different rock types.
The results showed that more accurate descriptions of particle size resulted in higher (softer) A × b values which suggests that the standard method may have been over-estimating rock competence. Regular-shaped cores broken diametrally were found to have higher (softer) A × b values than axially broken cores and irregular shaped particles. These tests also suggested that the true ore intrinsic heterogeneity is the main source of
breakage variability measured by the ExDWT. The mean A × b values determined by the ExDWT showed no statistical difference to those determined by the standard JKDWT method, but the standard deviation of the estimate was much lower.
The results have demonstrated the potential of the new method for capturing the inherent heterogeneity of individual ore particles. Such information could be used to populate multi-component models of comminution.
La Herradura mine in Sonora is one of the most important gold districts in Mexico producing more than 5.5 million ounces of gold over 17 years. It is part of an orogenic gold deposits belt with a northwest-southeast direction for 300 km long and 50 kmwide. The mineralization consists of veins and quartz networks veinlets formed in a brittle-ductil geologic environment and it is hosted in Proterozoic quartz-feldspar gneisses. The ore bodies, defined by a 0.3 g/t Au cut-off grade, have tabular forms up to 1 km in length, 1 km in depth and 100 m in width. The visual control for mineralization is the abundance of quartz veins and veinlets, and a persistent sericitic hydrothermal alteration. Different techniques were used in this work with special focus on the Mineral Liberation Analizer (MLA) program to prove the possibility of predicting recoverable gold in the mine for the primary sulphide zone. Three geometallurgical zones (Zones A, B and C) were defined by ore composites and gravimetric concentrates from the same composites. Modal composition of the concentrates is quartz, feldspar and muscovite (sericite), and a metallic mineralogy of pyrite, sphalerite, galena, magnetite, gold and tellurides of gold and silver. Gold is identified as inclusions in pyrite or in gangue minerals like quartz, albite, orthoclase or ankerite, as well as coating pyrite crystals. The gold composition is electrum with 74 % Au and 26 % Ag; the presence of petzite (Ag3AuTe2) and stutzite (AgTe) were also identified. Recovery constants were calculated for each geometallurgical zone, which were introduced to the resource model of more than 14 million ounces of gold, indicating that error range in recoverable gold is less than 4 % for Zone A, 6 % for Zone C and 13 % for Zone B, in relation with gold recovery calculated with traditional methods. These results could be acceptable to applicate this methodology to La Herradura deposit. The most important error range in the Zone B is interpreted as due to a nugget effect, which isvery common in such mineral deposits. It is also concluded that the secondary milling process currently incorporated to the metallurgical plant is probably unnecessary, so its removal would result in a significant saving in energy and therefore in the economy of the mine.
Kansanshi mine, located in northwestern Zambia, uses conventional open-pit mining to produce primarily copper, with gold as a by-product. The ore is extracted from a deposit with highly complex mineralogical suites that are difficult to process, sometimes leading to poor recoveries. The mine was using an ore classification system called 'Mat-Type', which classified various ore types into 22 different quality categories, ranging from poor to good quality. Ore with poor recovery was classified as 'poor quality' ore and directed to long-term stockpiles. Due to a lack of proper integration between the technical and functional areas forming the mine value chain, Mat-Type unintentionally misclassified ore. A geometallurgical analysis was therefore undertaken to review Mat-Type and a new geometallurgical ore classification system, called 'OXMAT ', was developed to replace Mat-Type. When implemented, OXMAT eliminated nine of the 22 ore categories and the remaining 13 ore categories could now be more accurately and consistently defined, leading to a significant change in the quantity of ore being mined. OXMAT demonstrated that within a defined test volume of the geological model, waste tonnes could be reduced by 19% while ore tonnes could be increased by 17%. This paper therefore demonstrates the value created through integrated geometallurgical modelling.
چکیده:
عدم درک درست از تغییرپذیریهای کانیشناسی، سنگشناسی و فرآوری یک ذخیره معدنی سبب میشود تا تصمیمگیریهای بعدی توأم با ریسک و خطا بوده و سرمایهگذاری پروژه را با خطر جدی روبرو نماید. غالباً در مدلسازی یک کانسار تمرکز بر روی شناخت تغییرپذیری عیار بوده و پارامترهای زمینشناسی نظیر کانیشناسی، لیتولوژی، دگرسانی و ویژگیهای فرآوری نظیر بازیابی مورد توجه قرار نمیگیرد. در سالهای اخیر ضرورت نیاز به یک نگرش جامع به زنجیره ارزش کانیها سبب توسعه شاخه جدیدی بنام ژئومتالورژی شده که با تلفیق دادههای اکتشافی و ویژگیهای فرآوری سبب کاهش ریسک پروژههای معدنی میگردد. کاربرد صنعتی مطالعات ژئومتالورژی یک برنامه ژئومتالورژیکی نامیده میشود. خواص سنجیهای انجام شده در یک برنامه ژئومتالورژیکی شامل اندازهگیری متغیرهای اولیه و متغیرهای پاسخ است.
The risks of starting, operating and closing mining projects have become higher than ever. In order to stay ahead of the competition, mining companies have to manage various risks: technical, environmental, legal, regulatory, political, cyber, financial and social. Some of these can be mitigated with the help of geometallurgy. Geometallurgy aims to link geological variability with responses in the beneficiation process by a wide usage of automated mineralogy, proxy metallurgical tests, and process simulation. However, traditional geometallurgy has neglected the non-technical aspects of mining. This has caused wide-spread discussion among researchers on the benefits of geometallurgy and its place in industry.
In order to improve predictability in geometallurgy, such programs should cover planning, and the testing of hypotheses, and only then should there be an attempt to develop suitable technical tools. Such approach would ensure that those tools would be useful and are needed, not only from the technical point of view, but also from the users’ perspective. Therefore, this thesis introduces methodology on how to decrease uncertainty in the production planning and thus determine how much effort to put into decreasing uncertainty in geometallurgical programs.
The predictability improvement of a geometallurgical program starts at the planning stage. The classification system developed here, through the survey (interviews) and literature review, indicates different ways to link geological information with metallurgical responses, and suggests areas where technical development is called for. The proposed developments can be tested before the start of the geometallurgical program with synthetic data. For the iron ore reference study (Malmberget), it was shown that implementation of geometallurgy is beneficial in terms of net present value (NPV) and internal rate of return (IRR), and building geometallurgical spatial model for the process properties (recovery and total concentrate tonnages), and that it requires fewer samples for making a reliable process prediction than concentrate quality. The new process and proxy for mineralogical characterisation models were developed as part of the geometallurgical program for the iron ore case study (Leveäniemi): an estimator of ore quality (X_LTU), a model for iron recovery in WLIMS, a model for iron-oxides liberation prediction. Additionally, it was found that DT may be applied only for studying marginal ores. The evaluation of different spatial process modelling methods showed that tree methods can be successfully employed in predicting non-additive variables (recoveries).
Multiple categorical variables such as mineralization zones, alteration zones, and lithology are often available for geostatistical modeling. Each categorical variable has a number of possible categorical outcomes. The current approach for numerical modeling of categorical variables is to either combine the categorical variables or to model them independently. The collapse of multiple categorical variables into a single variable with all combinations is impractical due to the large number of combinations. In some cases, lumping categorical variables is justified in terms of stationary domains; however, this decision is often due to the limitations of existing techniques. The independent modeling of each categorical variable will fail to reproduce the collocated joint categorical relationships. A methodology for the multivariate modeling of categorical variables utilizing the hierarchical truncated pluri-Gaussian approach is developed and illustrated with the Swiss Jura data set. The multivariate approach allows for improved reproduction of multivariate relationships between categorical variables.
Optimisation studies on integrating different procedures in mining and mineral processing have an increasing popularity in recent years, and most of them focus on the mine-mill or mine-flotation integration. However, few optimisation studies on integrating mining with gravity separation are found. The gravity separation plays a vital role in coal preparation, which also makes the related integration study necessary. In this work, an optimisation study on integrating mining with gravity separation is first carried out, which contributes to proposing the optimal cut point (the density at which 50% of the feed report to underflow) for gravity separation and the coal seam mass ratio for mining. Specifically, a simplified optimisation model includes separations in dense medium cyclones, dense medium shallow grooves, and spiral separators is developed. Then the Differential Evolution algorithm is employed to obtain the solution that has the maximum economic profit. Finally, the organic efficiency is utilised to evaluate this optimisation. The validation results indicate that the developed optimisation model and the adopted algorithm are applicable. Even though it is a simplified model, it is supposed to provide a preliminary optimisation scheme for integrating mining with gravity separation.
Geometallurgy integrates many aspects of geology, mineralogy, resource modeling, mine planning, metallurgy, and process control to optimize mining operations. Small-scale metallurgical samples that determine the natural variability of the processing response in the deposit are the building blocks of a geometallurgical model. In order to take representative samples for ore characterization and metallurgical testing, it is necessary to partition a deposit into homogeneous regions in terms of processing properties, called geometallurgical domains. Quantitative rock characteristics such as chemical assay, petrophysical properties, mineralogy, and texture are used to form similar groups with regard to processing properties. This study explores a body of multivariate data to detect classes with similar inherent multivariate characteristics. As a reliable and fast method, cluster analysis identifies geometallurgical classes within a multivariate framework in the deposit, which helps in choosing and characterizing samples and performing small-scale test. The eastern part of the Dardvey iron ore deposit was selected as the case study. Three model-free clustering approaches including hierarchical clustering, k-means clustering, and self-organizing maps (SOMs) were investigated. The results show that k-means and SOM performed similarly and outperformed hierarchical clustering. The resulting domains were confirmed by geological logging and were separated well both in attribute and geographical spaces.
An accurate characterization and modelling of rock mass geomechanical heterogeneity can lead to more efficient mine planning and design. Using deterministic approaches and random field methods for modelling rock mass heterogeneity is known to be limited in simulating the spatial variation and spatial pattern of the geomechanical properties. Although the applications of geostatistical techniques have demonstrated improvements in modelling the heterogeneity of geomechanical properties, geostatistical estimation methods such as Kriging result in estimates of geomechanical variables that are not fully representative of field observations. This paper reports on the development of 3D models for spatial variability of rock mass geomechanical properties using geostatistical conditional simulation method based on sequential Gaussian simulation. A methodology to simulate the heterogeneity of rock mass quality based on the rock mass rating is proposed and applied to a large open-pit mine in Canada. Using geomechanical core logging data collected from the mine site, a direct and an indirect approach were used to model the spatial variability of rock mass quality. The results of the two modelling approaches were validated against collected field data. The study aims to quantify the risks of pit slope failure and provides a measure of uncertainties in spatial variability of rock mass properties in different areas of the pit.
The objective of this educational resource is to start to address the carbon skills and training needs, in the broadest sense, for the mining and mineral processing industry sector. Therefore the Guide brings together and integrates current knowledge of how mining and mineral processing industry businesses can better 1) assess climate change related risks and 2) assess and climate change adaptation and mitigation opportunities.
This particular focus arises from surveys that identified significant gaps in carbon skills and knowledge of climate change adaptation and mitigation across the mining and mineral processing industry sector. Relatively recent studies found, for instance, that there was
- a poor understanding of the potential for energy efficiency strategies to cost-effectively reduce energy usage below current benchmarks and BAU trends.
- some mining and mineral processing industry companies are not using recent advances in comminution strategies and technologies, with new mine sites able to achieve up to 50% energy efficiency improvements.
This Guide starts to address these identified gaps in knowledge and skills by providing an easily understood summary with sufficient detail to start the process of cost-effective and timely climate change adaptation and mitigation action plans and implementation. This is accomplished by providing:
- 1) the business case for action on climate change for the mining and mineral processing industry sector
- 2) the risks of inaction on climate change for the mining and mineral processing industry sector
- 3) climate change adaptation assessment to manage these risks based on leading practice and
- 4) climate change mitigation opportunity assessment.
This guide provides an overview of all these topics and directs educators and students to still more detailed resources in these four key areas. This is the first educational resource to address all four topics listed above in the one module. This guide will enable students, professionals and mining companies to undertake climate change risk assessment and develop climate change adaptation and mitigation strategies.
In 2014, 2015, and 2016 the OECD, IMF, and World Bank have published warnings that a slowdown in global economic productivity is threatening to usher in a new low-growth era. This report addresses this problem by showing that a focus on energy and resource productivity can;
· Simultaneously boost labour, capital and multi-factor productivity through improved rates of production, greater labour participation, quicker returns on capital expenditure, as well as reduced energy and resource input costs.
· And thereby achieve total productivity benefits up to 2.5 times greater than the simple productivity benefits from reduced energy and water input costs from energy/resource productivity investments.
Utilising these insights, these reports show, for the first time, how a focus on energy and resource productivity could boost cumulative global GDP >US$25-30 Trillion by 2030 compared to business as usual (BAU) whilst enabling a transition to a low carbon future. The implications of this result are significant for everything from a) helping nations achieving stronger climate change targets post Paris COP and the UN Sustainable Development Goals, b) business strategy and c) national productivity/policy debates.
A Productivity Enhancing Climate Change Mitigation Strategy - Doubling Energy and Resource Productivity by 2030
First of Three Reports by Dr Michael H Smith (ANU) launched at the World Resources Forum – Asia Pacific 2015
Background
In 2014, and again in 2015, the OECD, IMF, and World Bank have published warnings that a slowdown in global economic productivity is threatening to usher in a new low-growth era. These 3 reports address this problem by showing how doubling energy and resource productivity by 2030 can
· Simultaneously boost labour, capital and multi-factor productivity through improved rates of production, greater labour participation, quicker returns on capital expenditure, as well as reduced energy and resource input costs.
· And thereby achieve total productivity benefits up to 2.5 times greater than the simple productivity benefits from reduced energy and water input costs from energy/resource productivity investments.
Utilising these insights, these reports show, for the first time, how a focus on energy and resource productivity could boost cumulative global GDP >US25-30 Trillion by 2030compared to business as usual (BAU) whilst enabling a transition to a low carbon future. The implications of this result are significant for everything from achieving progress at the next UNFCCC Paris COP on Climate Change, to building support for the UN Draft Sustainable Development Goals, to business strategy and national productivity/policy debates.
Four Page Executive Summary– downloadable here
Report #1 – Smith, M (2015) Doubling Energy & Resource Productivity by 2030 - Unlocking a 25-30 Trillion Cumulative Increase to Global GDP Whilst Transitioning to a Low Carbon Future. ANU Discussion paper – downloadable here.
Report 1 is complimented by Report 2 - a “how to guide” for policy makers - and Report 3 - a guide for business leaders.
Report #2 – Smith, M (2015) Doubling Energy & Resource Productivity by 2030 – A “How to Guide” for Policy Decision Makers. ANU Discussion paper.
Report #3- Smith, M (2015) Doubling Energy & Resource Productivity by 2030 – Improving Business Competitiveness and Profitability Whilst Transitioning to a Low Carbon Future. ANU Discussion paper
This chapter provides an overview of various innovations and technology developments in mineral processing that have shaped the current mining industry. A glimpse of the present and future challenges in mining are also presented. A holistic approach to problem solving involving various stakeholders is gaining momentum and this has been reflected in this chapter. Various developments that are being pursed in mineral processing focusing on a change in the existing mining paradigm are also presented in this chapter.
There are several challenges confronting the gold mining industry. One of the key challenges is the trend of increasing complexity of gold deposits with decreasing gold grades and gold-to-sulfur ratios along with higher proportions of carbonaceous matter, arsenic, copper-bearing minerals, and deleterious elements such as mercury. Many of these deposits are economically marginal as the capital and operating cost requirements are relatively high and the metal recoveries are suboptimal in addition to the need to address various environmental issues associated with these deposits. A holistic approach to innovation in mining and processing, that challenges the existing mining paradigm, is becoming more important.
This chapter reviews some of the major innovations and developments in different areas of gold and silver processing that have made a major impact in the gold industry. The areas of focus for this chapter are ore body knowledge (gold mineralogy), comminution, pre-concentration and ore beneficiation, cyanidation, oxidative pretreatment, heap leaching and alternative leaching technologies. Commercial application of Barrick’s new thiosulfate technology will be briefly discussed. In addition, innovations and technology developments that have the potential to shape the future of gold and silver processing are also presented.
A refinement of the traditional Mine-to-Mill integration opportunity for copper block cave mines is introduced here as a Cave-to-Mill production management concept. This is essentially the integration of underground mine production scheduling and monitoring with surface mineral processing management based upon fragment size and geometallurgical ore characteristics. Cave-to-Mill defines ore block models with respect to both mine and mill performance. Linkages between key cave and mill parameters have been established so that coordinated efforts towards maximizing net present value (NPV) can be made. Discrete fracture network (DFN) based methods were found to hold significant value within the Cave-to-Mill approach. The variable and relatively uncontrollable nature of cave fragmentation is considered to be a key distinguishing feature of Cave-to-Mill when compared with typical Mine-to-Mill strategies established for open-pit mines. It is envisioned that Cave-to-Mill will be an important design and operational strategy for block cave mines.
ResearchGate has not been able to resolve any references for this publication.