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By A Melkumyan, K Silversides

The banded iron formation-hosted iron ore deposits located in the Hamersley Ranges of Western Australia contain marker shales that are used to distinguish lithology for geological and orebody mapping. These marker shales produce distinctive signatures in the natural gamma downhole logs, which are conventionally manually interpreted. This paper proposes an alternative method of automatically identifying these shales using dynamic time warping (DTW). A Gaussian processes (GPs)-based method for automatic identification is also considered. Both methods are tested on a deposit that contains the Marra Mamba sequence of the Hamersley Province.Both methods were trained to predict the location of the desired shales, and the resulting outputs were compared with a manual interpretation of the results to determine the accuracy. The GP method can identify signatures with an accuracy between 87.0 per cent and 92.0 per cent, which increases to 92.9 per cent and 95.7 per cent where the GP is certain. The DTW method has an accuracy between 90.0 per cent and 91.6 per cent, which increases to 94.0 per cent and 95.4 per cent where the DTW is certain.The GP method has several advantages, including a more automated library building process, a probabilistic output and a shorter run time. However, the DTW method was capable of accepting more distortion in the signature and therefore identified more signatures. This is important in a deposit where the signals are frequently distorted by events such as hydration and folding. Both methods provide a viable alternative to the current manual detection, but their differences make them applicable to different cases.CITATION:Silversides, K and Melkumyan, A, 2014. Comparison between Gaussian processes and dynamic time warping for classification of marker shales in iron ore deposits, in Proceedings Orebody Modelling and Strategic Mine Planning Symposium 2014 , pp 177–184 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Nov 24, 2014

By M W. N Buxton, M S. Shishvan, J Benndorf

The flow of information, and consequently the decision-making along the chain of mining from exploration to beneficiation, typically occurs in a discontinuous fashion over long timespans. In addition, due to the uncertain nature of the knowledge about deposits and the inherent spatial distribution of material characteristics, actual production performance in terms of produced ore grades and quantity and extraction process efficiency often deviate from expectations. Reconciliation exercises to adjust mineral reserve models and planning assumptions are performed with timely lags of weeks, months or even years.With the development of modern information and communication technology over the last decade, a flood of data about different aspects of the production process is available in a real-time manner. For example, sensor technology enables online characterisation of geochemical, mineralogical and physical material characteristics on conveyor belts or at working faces. The ability to utilise this additional information and feed it back into reserve block models and planning assumptions opens up new opportunities to continuously control the decisions made in production planning to increase resource recovery and process efficiency. This leads to a change in paradigm from a discontinuous method to near real-time reserve reconciliation and model updating, which calls for suitable modelling and optimisation methodologies to quantify prior knowledge in the reserve model, process and integrate information from different sensor sources, back propagate the gain in information into reserve models and efficiently optimise operational decisions in real-time.This paper introduces the concept of an integrated closed-loop framework for real-time reserve management, incorporating sensor-based material characterisation, geostatistical modelling under uncertainty, modern data assimilation methods for sequential model updating and mining system simulation and optimisation. The effectiveness of the framework and the value added will be demonstrated in an illustrative case study.CITATION:Benndorf, J, Buxton, M W N and Shishvan, M S, 2014. Sensor-based real-time resource model reconciliation for improved mine production control – a conceptual framework, in Proceedings Orebody Modelling and Strategic Mine Planning Symposium 2014, pp 223–234 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Nov 24, 2014

By C Fricke, R Wood, P Velletri

"In a climate characterised by an ever increasing focus on cost reduction and maximising the utilisation of existing assets and infrastructure, understanding the uncertainty associated with alternative investment and operational choices becomes paramount to minimising the risk of making an ineffective decision. To assist in this process, TSG Consulting has developed an analytic framework to capture the underlying complexity of a mineral supply chain, and then quantify the impact of the uncertainty associated with the key drivers of business productivity. The technical basis of this approach follows three distinct steps: analyse the current system and identify the major factors that drive value build detailed models that can measure the response of the system to potential changes in the factors identified quantify the sensitivity of financial and capacity forecasts to these factors.Each of the above steps draws on a different area from the field of applied analytics, namely: data analytics dynamic modelling and designed experimentation meta-modelling and optimisation.Following the three-step approach described above ensures a rigorous modelling and analysis process is executed to support strategic mine planning decisions. The approach is equally applicable to the analysis of fully integrated system supply chains (pit-to-port), the evaluation of incremental capital investments to targeted areas of a supply chain, or when considering revisions to the operating philosophy of a system in the short term. The basis of each of the three analytic steps is explored in detail in this paper. The result is the definition of a structured mechanism for a business to actively manage risk and drive productivity as a component of the strategic mine planning process.CITATION:Fricke, C, Velletri, P and Wood, R, 2014. Enhancing risk management in strategic mine planning through uncertainty analysis, in Proceedings Orebody Modelling and Strategic Mine Planning Symposium 2014, pp 275–280 (The Australasian Institute of Mining and Metallurgy: Melbourne)."

Nov 24, 2014

By R Dimitrakopoulos, R Senécal

This paper presents a metaheuristic solution to the optimisation of open pit long-term production scheduling with a stockpile and geological uncertainty. The optimisation formulation is a two-stage stochastic integer programming (SIP) model that determines the optimal mining sequence that maximises the total discounted cash flow, while penalising for high deviations from production targets. A parallel implementation of Tabu Search is proposed to accelerate the solution of the SIP formulation and take full advantage of multicore computer processing. Different variants of the proposed algorithm are applied to a case study to assess the performance of the parallel approach. The proposed algorithm and variants are benchmarked using linear relaxation of the complete problem to determine robustness and provide a better overview of the related performance. The results show a net improvement over the sequential solution, and the new proposal seems to be promising when working with a large-scale data set.CITATION:Senécal, R and Dimitrakopoulos, R, 2014. Parallel implementation of a Tabu search procedure for stochastic mine scheduling, in Proceedings Orebody Modelling and Strategic Mine Planning Symposium 2014 , pp 405–414 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Nov 24, 2014

By J MacNeil, R Dimitrakopoulos

As open pit mining of a deposit deepens, the cost of extraction may increase up to a threshold where transitioning to mining through underground methods is more profitable. This paper provides an approach to identifying an optimal depth at which a mine should transition from open pit to underground mining. The value of a set of candidate transition depths is investigated by optimising the production schedules for each depth’s unique open pit and underground operations. By considering the sum of the open pit and underground mining portions’ value along with the cost of transitioning corresponding to each candidate transition depth, the optimal transition depth can be identified. The optimisation model presented is based on a stochastic integer program that integrates geological uncertainty. As an input, the stochastic integer program utilises a set of several stochastic simulations that represent equally probable scenarios of the mineral resource. This group of simulations describes the uncertainty in the deposit, while the optimiser aims to maximise value based on discounted profits of both the open pit and underground components of the deposit.CITATION:MacNeil, J and Dimitrakopoulos, R, 2014. A stochastic optimisation formulation for the transition from open pit to underground mining within the context of a mining complex, in Proceedings Orebody Modelling and Strategic Mine Planning Symposium 2014, pp 363–368 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Nov 24, 2014

By K Dagdelen, I Traore

This paper presents an iterative net present value (NPV) maximisation method to determine the optimum surface to underground transition depth for an orebody to be mined by multiple open pits and an underground mine. The determination of transition depth from open pit to underground mining is based on global production scheduling optimisation of open pit and underground mines using mixed integer linear programming. The method is applied to a case study coming from a gold mining complex with six open pits and a large underground mine using longhole open stoping. The results indicate potential improvements of the NPV of global operations when compared to the traditional techniques based on independently optimised open pit first, followed by underground mining.CITATION:Dagdelen, K and Traore, I, 2014. Open pit transition depth determination through global analysis of open pit and underground mine production scheduling, in Proceedings Orebody Modelling and Strategic Mine Planning Symposium 2014, pp 195–200 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Nov 24, 2014

By M W. N Buxton, M T. Bootsma, J Benndorf, C Alford

Research in the field of cut-off grade optimisation has shown a relationship between cut-off grade, project life and net present value (NPV). Lane’s theory demonstrates that cut-off grades can be optimised in order to maximise project profitability. Although the theory forms the basis for many open pit mining projects, application of the theory in underground mining remains limited to-date. The main reason for this is the complex interaction between all processes in underground mine planning, which makes it difficult to apply Lane’s mathematical optimisation approach.Recently a new stope optimiser product was released. The AMS Stope Optimiser automates the design of underground stopes at user-defined cut-off grades and allows for rapid evaluation of mine designs at different cut-off grades. Using this software, an optimisation approach was developed and validated on an underground gold deposit in northern Sweden. Potential project NPV increased by approximately 30 per cent when using this new approach.Spatial grade uncertainty in mineral resources was identified to be a major risk in underground stope design. The optimisation approach was further extended to account for grade risk using estimated and stochastic simulated resource models. The resulting optimisation process accounts for grade risk early in the design process and reduces the risk of a stope not meeting the cut-off grade with subsequent financial loss.CITATION:Bootsma, M T, Alford, C, Benndorf, J and Buxton, M W N, 2014. Cut-off grade-based sublevel stope mine optimisation – introduction and evaluation of an optimisation approach and method for grade risk quantification, in Proceedings Orebody Modelling and Strategic Mine Planning Symposium 2014 , pp 281–290 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Nov 24, 2014

By M Wortley, J Korteniemi, D La Rosa, L Rajavuori

Geometallurgical modelling of an orebody provides benefits to a mine by gaining a better understanding of the ore characteristics and how they affect the performance of the concentrator. With this knowledge, plant operating conditions can be adjusted to optimise throughput and recovery in advance of the arrival of particular ore types. Therefore, it is extremely important that the origin of the ore being processed is known as accurately as possible. Depending on the homogeneity of the ore characteristics, reliance on assumptions about stockpile residence times, scheduling and material handling can render the best geometallurgical models useless.The solution adopted at Agnico Eagle’s Kittilä mine was to utilise Metso SmartTags™ and in-house expertise to develop a system that continuously and accurately links geotechnical and lab data from the mine to the performance of the plant. This application presented several unique opportunities and challenges. For example, this was the first installation of a SmartTag™ system for geometallurgical modelling in an underground mine. Challenges included the fact that the system installation is routinely subjected to temperatures below -20°C.The system was installed and commissioned in early 2013 and has been operating continuously since. Kittilä has begun to see the benefits of the system, with an increased understanding of how different ores are processed in the concentrator. Other advantages include the ability to alert operators about the arrival of difficult ores and a better understanding of the ore handling systems.This paper describes the installation and use of the system at Kittilä and details some of the geometallurgical relationships that have been developed using the data collected so far.CITATION:La Rosa, D, Rajavuori, L, Korteniemi, J and Wortley, M, 2014. Geometallurgical modelling and ore tracking at Kittilä Mine, in Proceedings Orebody Modelling and Strategic Mine Planning Symposium 2014 , pp 291–298 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Nov 24, 2014

By C A. Myburgh, S Craig, K Deb

In focusing on open pit mine scheduling, one of the biggest areas that a mining engineer can add value is in the area of cut-off grade optimisation. It is not atypical to add up to an extra 20 per cent in value by bringing forward higher-grade ore and deferring costs into the future.There are many techniques available to maximise net present value (NPV) when applied to open pit mine scheduling. That in itself raises the question of why so many of these techniques exist. The short answer is that none of these techniques are robust. Every time the problem changes, the algorithms also have to change.Instead of trying to make a problem fit the constraints of an existing technique, there is much to be said for usefully hybridising different approaches. This paper describes an innovative approach where evolutionary algorithms are combined with both local search and linear programming. The goal is maximising NPV through optimisation of cut-off grade policy and extraction sequence for an open pit mine. The cut-off/cut-over grade policy produced caters for multiple processing streams and stockpiles.Finally we demonstrate the quality of the algorithm by comparing some results with a well-known mathematical solution.CITATION:Myburgh, C A, Deb, K and Craig, S, 2014. Applying modern heuristics to maximising net present value through cut-off grade optimisation, in Proceedings Orebody Modelling and Strategic Mine Planning Symposium 2014, pp 155–164 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Nov 24, 2014

By A Jewbali, R Perry, R Inglis, L Allen

"The use of conditional simulation to characterise mine plan uncertainty is increasing for the assessment of risk in mining projects. While the development of grade uncertainty profiles is relatively straightforward and can be validated using standard geostatistical techniques, the addition of geological uncertainty to evaluate total risk remains problematic. Some of the problems associated with geological uncertainty methods include the clustering of data in favourable geologic units, difficulty in training image definition and the inability to address change of support issues for categorical variables. Despite these obstacles, the importance of geological uncertainty as a contributor to total uncertainty has prompted Newmont to explore and evaluate the use of various techniques (and combinations of techniques) on different deposit types.Two orogenic deposits of different geological complexity were selected for the study: Subika, a shear zone-hosted deposit, and Merian, a deposit containing gold mineralisation associated with quartz vein zones and stockwork within which are found higher-grade quartz breccia zones. Newmont trialled various categorical simulation approaches to determine the applicability of these methods for each deposit type and the effect of parameter choice on the width of the uncertainty interval. Some of the techniques that were trialled include multiple point statistics methods, sequential indicator simulation using local probabilities as well as variations of these methodologies.The goals of this study included: gaining an understanding of which techniques may work best in which deposit types gaining an understanding of the intricacies of each method gaining an understanding of the effect that each method has on total uncertainty analysis.This paper presents a comparison of the various techniques and makes recommendations for their use in uncertainty analysis.CITATION:Jewbali, A, Perry, R, Allen, L and Inglis, R, 2014. Applicability of categorical simulation methods for assessment of mine plan risk, in Proceedings Orebody Modelling and Strategic Mine Planning Symposium 2014, pp 85–98 (The Australasian Institute of Mining and Metallurgy: Melbourne)."

Nov 24, 2014

By P A. Grossman, J H. Rubinstein, M Brazil

The installation of truck turntables at above-ground crushers has the potential to yield gains in efficiency and productivity by eliminating the need for the trucks to turn around and reverse up to the crusher to dump their loads. The benefits include savings in time, fuel consumption and tyre wear. In addition, having a smaller area near the crushers dedicated to manoeuvring the trucks can be a significant benefit at some sites. Safety is also enhanced using turntables.This paper describes a project with the Australian Turntable Company to develop software that generates near-optimal layouts for the paths taken by the trucks in the vicinity of the crushers when turntables are present or absent. The geometry of the paths is constrained by the turning radius of the trucks and by the boundary of the manoeuvring area. The software quantifies the benefit of installing turntables by generating a design for paths with turntables and comparing it with the layout in current or planned use by the mining company. Through the use of this software tool, a mining company can readily assess the benefits of using turntables at their site.CITATION:Brazil, M, Grossman, P A, Rubinstein, J H and Thomas, D A, 2014. Demonstrating efficiency gains from installing truck turntables at crushers, in Proceedings Orebody Modelling and Strategic Mine Planning Symposium 2014, pp 259–266 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Nov 24, 2014

By D Kentwell

A resource model serves two main purposes depending on the stage of a project. The first is to provide an early indication of likely tonnages and grades at certain cut-offs so that decisions can be made on whether continued spending on the project is justified. The second is to provide a basis for mine planning, feasibility studies and reserves. Resource models are not designed for selection of actual blocks in the ground for processing. This is the purpose of a grade control model, which is created at a stage beyond both resources and reserves.Inferred, Indicated and Measured Resources contain different levels of uncertainty with respect to the accuracy of the resulting grade and tonnage curves. These uncertainties arise from three broad areas: quality of information, geological assumptions and estimation technique. This paper focuses on known problems with the ordinary kriging (OK) estimation technique. Resource estimates often produce smoothed block grade distributions with much lower block grade variability than true selective mining unit (SMU) block grade distributions. There are two main factors that are involved in this smoothing. Firstly, where the block size used for estimation is larger than the SMU block size, this will smooth the block grade distribution and move the grade and tonnage curves away from the true SMU block grade and tonnage curves. Secondly, and less well recognised, is that smoothing is inherent in the OK estimator. This also moves the grade and tonnage curves away from the true SMU grade and tonnage curves, even when the estimate is done at the SMU block size. There are a number of techniques, such as simulation, uniform conditioning and degraded search neighbourhoods that can provide a better approximation of the true SMU block grade and tonnage curves at the expense of individual block accuracy.This paper aims to explore the extent to which OK resource estimates designed to maximise individual block accuracy can differ from (an estimate of) the true resource at SMU block size in terms of grade and tonnage curves. The differences at economic cut-offs and above are examined. In some cases these differences are significant. Both the OK estimation technique and block size play a part in the differences observed. To do this we will examine a number of commodities and deposit types using real project data.The conclusion is that the current industry standard default practice of targeting maximum individual block accuracy (conditional unbiasedness) using OK at resource stage, without consideration or evaluation of the extent of associated smoothing at SMU scale, can lead to significant errors in fundamental mine planning decisions. This then has implications for resource classification if the classifications are based heavily on the OK quality without consideration of the true SMU distribution.CITATION:Kentwell, D, 2014. Aligning resource estimates with mine planning, in Proceedings Orebody Modelling and Strategic Mine Planning Symposium 2014 , pp 185–192 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Nov 24, 2014

By J Deraisme, P Epinoux, O Bertoli

"Société le Nickel exploits the Dôme lateritic nickel orebody at its Tiébaghi operations in New Caledonia. The site geology has an obvious bearing on recovery and metallurgical performance, with controls that are extremely complex at all scales, including lithology, oxydo-reduction conditions, mineralogy and multivariate geochemistry. In this context, establishing an adapted recoverable resource estimation method that is efficient and transparent proves an interesting challenge. The method must address the key notions of: support effect – the exploration data set can only warrant the estimation of large panels of 20 × 20 × 3 m3 that are much larger than the selective mining units (SMUs) of 5 × 5 × 3 m3 information effect – the selection at the production stage uses estimates based on information that is much denser than that available for mine planning.The problem is rendered more complex in this type of deposit by the fact that the additive variables used for the estimation (metal accumulation and ore tonnage) are not the ones used to establish the selection at the mining stage (ie the nickel grade of SMUs, which is the ratio of accumulation on tonnage). Eventually, the selection criteria not only involve Ni grades, but also other elements such as Al2O3, Fe2O3, MgO and SiO2.The solution presented in this paper is to construct an SMU multivariate simulation in the saprolitic horizon and construct an efficient post-processing aimed at producing multivariate recoverable resource estimates. The efficiency constraints imposed by the number of blocks to be simulated (1.5 million) lead to resorting to direct block simulations. The underlying multivariate discrete Gaussian model, which had its validity tested beforehand, is put to good use to mimic the selection process that happens at the mining stage by offering the ability to simulate a composite value located at random within each block.The paper also presents the application of a scenario reduction algorithm to pick a representative subset of a few simulations to help appraise the risk attached to the downstream (reserve optimisation and mine sequencing) phases of the project. The original version of the paper (see Deraisme, Bertoli and Epinoux, 2014) used an early version of the scenario reduction plug-in (named S2RM) built in Isatis® software, where measuring the distance between the initial set of scenarios and the reduced set was based on Ni only. The implementation presented here is for the second generation of S2RM, where measuring the distance between the initial and the reduced set of realisations is based on all study variables. The benefits of the measure adapted to the true multivariate nature of the problem are highlighted in this updated version of the paper.CITATION:Bertoli, O, Deraisme, J and Epinoux, P, 2014. Multivariate block simulations of a lateritic-type nickel deposit and post-processing of a representative subset, in Proceedings Orebody Modelling and Strategic Mine Planning Symposium 2014, pp 99–108 (The Australasian Institute of Mining and Metallurgy: Melbourne)."

Nov 24, 2014

By M Recabarren, A Echeverria, J Ronda, J I. Guzman, R Dodds, G Juarez, E Vila-Echague

A strategic mine plan is a fundamental part of any mining operation. It must provide a realistic and operative extraction and processing schedule, and yield the highest value for the business. For open pit mines, the problem can be modelled as a set of blocks representing the deposit, for each of which we need to decide if it is going to be mined, if so, when and where it will be sent (process, stock or dump).This problem can be proven to be of NP-Hard complexity. The traditional approach used by mining engineers to tackle this complexity is to separate the problem into more tractable components, solving each separately. The first step is to use the Lerchs-Grossman (LG) algorithm to obtain the final pit limit, as well as a set of optimised pit shells (nested pits), using different input prices. Then, using this input, operational phases are designed. Third, the set of phases can be used as an input for a phase-bench style optimisation, usually using a mathematical programming-based algorithm.There are, however, several problems with this approach. First, dividing the problem into simpler parts has the consequence that any compromises made at early stages limit the potential value left for the following steps. Second, LG is a static algorithm, with no concept of the time-value of money, capacity limits, opportunity costs, etc. Phase design is essentially a manual step, for which nested pits provide insufficient information in practice, and therefore the quality of the result in terms of potential net present value (NPV) depends strongly on the experience and expertise of the designer. This means that key input data such as commodity prices, different processes capacities and mine movement limits, are considered only at scheduling, when there is little room for optimisation. This sequential approach limits the potential value in each step, where a simplified problem is addressed, instead of addressing the real, and more complex problem, which is dynamic and should be solved as a whole.We present in this paper an algorithm, and its commercial software implementation, that addresses the dynamic problem as a whole, simultaneously and automatically creating a high-value strategic mine plan that adheres to basic operational constraints, determining dynamic cut-off grades and delivering a set of phases which can serve as a much stronger guide than nested pits for the final design. We discuss some of the key elements of the algorithm and propose a new methodology for strategic mine planning, where phase design dynamically responds to mining strategy definition. Results using different mining and operational parameters on a fictitious copper deposit are used as a demonstrative example.CITATION:Juarez, G, Dodds, R, Echeverria, A, Guzman, J I, Recabarren, M, Ronda, J and Vila-Echague, E, 2014. Open pit strategic mine planning with automatic phase generation, in Proceedings Orebody Modelling and Strategic Mine Planning Symposium 2014 , pp 147–154 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Nov 24, 2014

By M Spleit

A long-term production schedule for the LabMag iron ore deposit in northern Quebec, Canada is derived using stochastic integer programming. The optimisation formulation maximises the schedule’s net present value while simultaneously managing the risk of deviations in production tonnages and qualities by considering stochastic simulations of the orebody instead of a single deterministic model. The formulation also smooths and minimises haul truck requirements and ensures that as mining progresses, space is created within the mined out pit for the return of waste material.CITATION:Spleit, M, 2014. Production schedule optimisation – meeting targets by hedging against geological risk while addressing environmental and equipment concerns, in Proceedings Orebody Modelling and Strategic Mine Planning Symposium 2014, pp 397–404 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Nov 24, 2014

By R Dimitrakopoulos, K Kawahata, L Montiel

A method that optimises mining complexes that are comprised of multiple processing destinations, open pits and underground operations is presented. Mining, blending, processing and transportation decision variables are simultaneously optimised while accounting for geological uncertainty. The method uses a simulated annealing algorithm at different decision levels to generate a stochastic-based extraction sequence and processing policies. A case study shows its ability to generate a higher net present value while facing a reduced amount of risk when compared to traditional optimisation methods.CITATION:Montiel, L, Dimitrakopoulos, R and Kawahata, K, 2014. Globally optimising open pit and underground mining operations under geological uncertainty, in Proceedings Orebody Modelling and Strategic Mine Planning Symposium 2014, pp 201–212 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Nov 24, 2014

By P Malkin, S Thomas, P M. Stone, D Kostyuk

In open pit mining, it is common that the bottleneck to productivity and profit lies in the truck-shovel mining operation, especially as strip ratios and mining intensity have increased with resource depletion and advances in mining equipment capabilities. This paper outlines a study of truck-shovel productivity using a new proprietary discrete event simulation platform for truck-shovel operations called MineDES. The study will specifically address the issue of whether truck queuing at shovels should be considered productive or not, and how mining engineers can determine the optimal truck fleet size for specific operational configurations.The MineDES simulation platform will be described and illustrated, highlighting the features that make it an appropriate tool for conducting the experiments at the heart of this study. The study encompasses a set of experiments, consisting of a number of simple truck-shovel operational scenarios for a benchmark open pit mining operation, which are simulated using MineDES. Each experiment is designed to highlight different factors, which are most important in calculating the ideal truck fleet size and for understanding the marginal productive impact of each additional truck added to the fleet. The scenarios will principally address the issues of how different types and levels of unscheduled equipment downtime and process variation, combined with the traffic delays/congestion arising in more intense mining operations, will affect productivity. The results of the experiments are interpreted using a simple fit-for-purpose cost and revenue model in order to highlight the indicative cost of over- or under-trucked configurations.The study has found that the optimal fleet size that maximises productivity and value will, in the presence of even small process variation, lead to non-zero trucks queuing at shovels on average. As process variations and unscheduled downtime events increase in the truck-shovel system, the required fleet size to optimise productivity and value increases. MineDES provides a convenient platform for quantifying this for real operations.CITATION:Kostyuk, D, Thomas, S, Malkin, P and Stone, P M, 2014. Is truck queuing productive?, in Proceedings Orebody Modelling and Strategic Mine Planning Symposium 2014 , pp 267–274 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Nov 24, 2014

By D J. Thobaven

Project financing challenges caused by declining commodity prices have heightened the need to improve project metrics such as minimising capital payback and maximising net present value (NPV). Khnaiguiyah Mining Company (KMC) recognised the need to improve early cash flows at their Khnaiguiyah zinc-copper project after initial studies showed the project could not provide the desired financial outcomes necessary to compete for investment dollars in the current market.A significant improvement to the project cash flow in early years was realised through simultaneous optimisation of cut-off grade, mining sequence and strategic stockpiling. This improvement was achieved by accelerating mining in the pits, stockpiling lower value material and feeding higher value material directly to the plant. The schedule required higher mining and stockpile capacities than the sequential mine plan although minimal change occurred in the definition of ore and waste as measured by tonnes and grade. The practicality of the strategic schedule was demonstrated by implementation in the production plan with excellent conformance.CITATION:Thobaven, D J, 2014. An application of simultaneous optimisation – improving the payback period of a zinc-copper open cut project, in Proceedings Orebody Modelling and Strategic Mine Planning Symposium 2014 , pp 137–144 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Nov 24, 2014

By E Lima, D Gregory, C Faria, A Lapworth

Open pit mine enterprises are complex systems that depend on multiple parameters such as mineral grade distribution, geotechnical characteristics, economic conditions and engineering requirements, all of which vary in time and space with a different degree of uncertainty. The unpredictable nature of these inputs imposes a risk to mine owners and shareholders which in the worst case can lead to highly undesirable financial outcomes.Desktop studies commonly examine a set of scenarios based on adjusting variables such as commodity prices, mining costs, processing costs, and metal recoveries to understand their relative impact on the economics of a project. It is less common to systematically explore and simulate hundreds of alternative physical designs to understand and compare the financial risk associated with one or more mining projects.This paper presents a risk assessment tool set and methodology that uses web based cloud computing to enable multiple simulations of mine designs to be rapidly and efficiently executed. The methodology includes generating random sets of prices and costs values within their respective probability distributions. Pit designs are then created to maximise the profit for each set of input variables. Various approaches to analysing the results are discussed. Use of the same high performance computing environment for understanding the financial risk associated with the variability of the underlying geological resource model is also discussed, with provided examples using case studies from two of the major mineral producers.CITATION:Lima, E, Gregory, D, Lapworth, A and Faria, C, 2014. Simulating extraction shapes to understand financial risk using high performance computing, in Proceedings Orebody Modelling and Strategic Mine Planning Symposium 2014 , pp 351–360 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Nov 24, 2014

By K G. van den Boogaart, U Mueller

"An accurate prediction of benefit for some types of ore may require not just the ore grade, but a whole compositional characterisation of the estimates; that is, its full mineral composition including waste composition and nuisance elements. An example is Nickel laterite ores, where Ni/Co content must be complemented by estimates of many other elements, as these might have a notable impact on ore processing.Estimates are often obtained with (co-)kriging or co-simulation; however, geostatistics applied to compositional data may yield spurious and inconsistent results because of the constant sum constraint. The log-ratio approach avoids such problems. It proposes a three-step procedure: data are mapped to a set of log ratios of components; for example, the additive log-ratio transformation (alr) transformed scores are modelled with an appropriate fully multivariate geostatistical toolbox; for example, direct/cross-semivariograms and co-kriging or co-simulation results are back-transformed to the original units.Some practical aspects may make the application of this technique difficult: the assumption of Gaussianity and the high dimensionality of the compositions combined with the need for using multivariate methods. This contribution compares several ways of treating compositional data (combining normal score transformation, log-ratio transformations, and minimum/maximum autocorrelation factor (MAF) decomposition) with respect to their ability to generate sensible simulations. The various approaches considered are illustrated with a Nickel laterite data set for which ten variables are available. Both theoretical considerations and illustration results suggest that the best combination is (log)ratio-normal score transform-MAF, in this order. The factors so obtained are closer to Gaussian, approximately spatially decorrelated and can be simulated independently. Simulations can later be recombined to alr-simulations, which in turn may be converted back to point compositions. The resulting simulated compositions are compared with a full Gaussian co-simulation of the raw data. The log-ratio methods show reasonable to good reproduction of mean values, and distribution of the data set and by construction satisfy the total sum constraint, in contrast to the simulations based on raw data for which the total sums fluctuate strongly.CITATION:Mueller, U, Tolosana-Delgado, R and van den Boogaart, K G, 2014. Approaches to the simulation of compositional data – a nickel-laterite comparative case study, in Proceedings Orebody Modelling and Strategic Mine Planning Symposium 2014, pp 61–72 (The Australasian Institute of Mining and Metallurgy: Melbourne)."

Nov 24, 2014