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By P A. Grossman, J H. Rubinstein, M Brazil, K G. Sirinanda

To date, the scheduling and access design of an underground mine have only been considered as two separate optimisation problems. First, access to the mine is designed and then the scheduling is completed. One drawback of this approach is that the costs of access construction fail to be reflected in the net present value (NPV) calculation. In this paper, designing the access and scheduling its construction are formulated as a single optimisation problem.The underground mine access construction process can be classified according to the number of faces being developed concurrently. An underground mine with a single decline branching at a junction point into two declines is considered. After construction reaches the junction, the two faces of the decline can be constructed sequentially or concurrently.This paper proposes an efficient algorithm for optimally locating a junction point to maximise the NPV where two faces are being developed concurrently. The NPV is defined by taking the locations of orebodies and their values, decline construction costs, decline development rate and discount rate into account. The variation of the NPV and the optimal locations of the junction point for one and two concurrent development faces for a range of discount rates are discussed and compared. The proposed algorithm is applied in a simulated case study based on hypothetical values for an underground mine.CITATION:Sirinanda, K G, Brazil, M, Grossman, P A, Rubinstein, J H and Thomas, D A, 2014. Strategic underground mine access design to maximise the net present value, in Proceedings Orebody Modelling and Strategic Mine Planning Symposium 2014 , pp 235–242 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Nov 24, 2014

By M de Freitas Silva

One of the main steps during the decision-making process of long-term mine planning is the definition of the optimal sequence of extraction, which usually is synonymous with maximising the discounted cash flow of the project subjected to several constraints arising from aspects of technical, physical, and economic limits.Open pit mine production scheduling (OPMPS) comprises several intricacies related to its size and uncertainty of input parameters. Due to its complexity and prohibitive size, traditional mine planning usually relies on heuristic or metaheuristic methodologies which are able to provide good solutions in a reasonable amount of time. However, most of the uncertainty that surrounds the mining complex is ignored leading to non-realistic results.In this paper, a new heuristic approach is explored in order to solve a stochastic version of the OPMPS problem accounting for geological uncertainty in terms of metal content, multiple processing streams, and stockpiling option. The methodology involves generating an initial solution by solving a series of subproblems and this initial solution is improved using a network flow based algorithm.The algorithm was applied to a relatively large gold deposit with more than 119 thousands blocks. Results have shown that the methodology is promising to deal with large-size mine instances in reasonable time.CITATION:de Freitas Silva, M, 2014. Solving a large stochastic integer programming model for production scheduling at a gold mine with multiple processing streams and uncertain geology, in Proceedings Orebody Modelling and Strategic Mine Planning Symposium 2014 , pp 389–396 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Nov 24, 2014

By T Wambeke, J Benndorf

A risk-robust development of a heavy mineral resource requires an assessment of the geological uncertainty and spatial variability of the key factors impacting the mining and processing operation. Attributes of interest are the total heavy mineral grade, the slime content and the amount of oversized particles. The modelling of a mineral sand deposit presents two main challenges. Firstly, the exact location of the geological boundaries are obscure due to the complex interaction of the deposit formation processes. Secondly, the properties of interest exhibit a rather complex transitional behaviour across the so-called soft boundaries. Traditionally, the modelling exercise is restricted to a consecutive stochastic simulation of distinct geological facies and orebody properties. This results in different zones separated by hard boundaries, ignoring the typical transitional behaviour.This contribution presents an alternative approach which models the entire sand body as a single domain while accounting for the complex trends present. Error curves are used to infer covariance model parameters without the need of a prior removal of the trend. The principle is based on minimising the differences between empirical and theoretical errors resulting from two methods of prediction calculated for different rings of influence. The added value results from the unambiguous decomposition of the spatial variation into a deterministic trend and a stochastic residual. The trend model neither overfits nor underfits the data. The calculated residuals are thus unbiased and the uncertainty can be correctly assessed.The effort results in a characterisation of the resource uncertainty which provides a more realistic image of the spatial variability of the key properties in and across the geological domains of an existing heavy mineral sand deposit.CITATION:Wambeke, T and Benndorf, J, 2014. Optimal inference of modelling parameters to simulate complex trends across soft boundaries – a case study in heavy mineral sands, in Proceedings Orebody Modelling and Strategic Mine Planning Symposium 2014 , pp 73–84 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Nov 24, 2014

By S Coward, C Xu, P A. Dowd

"This paper provides an assessment of the current challenges in strategic mine planning and design and suggested approaches for addressing them. The specific challenges covered are: realistic quantification of downstream processes applied to orebody models to provide an integrated approach to mine design and optimisation modelling, estimation and simulation of geometallurgical variables and their integration into resource and reserve estimation and mine planning modelling, estimation and simulation of new variables for new forms of mining – deep mining, particularly block caving, and solution mining flexibility in planning and design to manage risk and minimise its impact information technology (IT) infrastructure and platforms for rapid online data collection, storage, access and processing.Most of these challenges require new types of data, variables, modelling and estimation methods. Foremost among the new types of variables and data are geometallurgical and dynamic rock mass characterisation variables. New types of data and data collection include rapid generation of very large amounts of online sensor data and the consequent need for rapid processing and modelling of these data.This paper outlines the challenges and strategies in each of these areas and uses examples of models and outputs to illustrate approaches and potential solutions.CITATION:Dowd, P A, Xu, C and Coward, S, 2014. Strategies for mine planning and design, in Proceedings Orebody Modelling and Strategic Mine Planning Symposium 2014, pp 41–50 (The Australasian Institute of Mining and Metallurgy: Melbourne)."

Nov 24, 2014

By J Whittle

This paper describes what is involved in carrying out a comprehensive optimisation of a long-term operating schedule for a mining complex. It details the data that must be assembled and discusses the different approaches to optimisation and what limitations they put on the variables and constraints that can be handled.CITATION:Whittle, J, 2014. Not for the faint-hearted, in Proceedings Orebody Modelling and Strategic Mine Planning Symposium 2014 , pp 3–6 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Nov 24, 2014

By M Bloss, B Roberts

BHP Billiton Olympic Dam required a well-tested software product suitable for strategic optimisation and scheduling of options for a potential underground mining project. The company has a standard software package called Blasor® that is used for open pit optimisation of complex deposits such as iron ore, copper-gold-uranium and silver-lead-zinc. It applies a mixed integer programming formulation (MIP) to produce conceptual pushback designs and an optimised pushback sequence. Schedules can then be run on the resulting mine designs. Existing pushback designs can also be imported into the model and an optimum schedule generated. One of the key advantages of Blasor is that multiple strategic mine schedules can be quickly generated according to the various scenarios, options and constraints that are being investigated.It was realised that Blasor, if carefully configured, could be adapted for use in underground planning. The concept of using Blasor as an optimiser for underground strategic scenarios is to represent the underground stope and development data in the form and structure of an open pit mine. In simple terms, underground stoping areas are modelled in the form of pushbacks (phases) with vertical walls. The phase dependency tools in Blasor are used to link mine blocks and their access development drives. These underground phases are imported into the optimiser. Artificial block models are constructed using stope design tonnage and grade data at various fixed cut-offs. A variable cut-off grade model has also been developed using a method of parcels to represent each cut-off grade increment.So far, this approach has been successfully used to investigate the optimum stope design cut-off for various strategic options, the economic potential of a series of mining and processing configurations and to provide guidance on the optimum sequence for developing and mining the various stoping blocks in the resource. Examination of the results has highlighted the most suitable development paths for more detailed study.CITATION:Roberts, B and Bloss, M, 2014. Adaptation of an open pit optimiser for underground strategic planning, in Proceedings Orebody Modelling and Strategic Mine Planning Symposium 2014 , pp 249–256 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Nov 24, 2014

By M W. A Asad, E Topal, D S. S Sandanayake

Underground mine planning requires an optimal stope layout as a prerequisite for the definition of an optimal production schedule. The procedure to generate an optimal stope layout considers an orebody model and economic parameters as inputs. It evaluates all possible combinations of mining blocks and creates a set of stopes by satisfying the physical mining and geotechnical constraints. Thus, the procedure is computationally complex, and generating a solution to the problem is a challenge. This paper presents an implementation of a new heuristic algorithm that solves the stope layout problem. In two case studies, the algorithm achieves 11.2 per cent and 22.5 per cent superior solutions to the industrially available maximum value neighbourhood approach.CITATION:Sandanayake, D S S, Topal, E and Asad, M W A, 2014. Implementing a new heuristic approach to the underground mine stope layout problem, in Proceedings Orebody Modelling and Strategic Mine Planning Symposium 2014 , pp 243–248 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Nov 24, 2014

By R Dimitrakopoulos, R Goodfellow

One of the primary objectives when optimising a mining complex is to maximise its value for the primary stakeholders. In order to achieve this objective, it is necessary to holistically optimise all aspects of the mining complex, including decisions of when to extract materials from the available sources, how to blend or stockpile these materials and how to best use the available processing streams to satisfy customer demand. Existing methods for global, or holistic, optimisation ignore the compounded effects that risk has on the performance of a mining complex. Over the past decade, several stochastic optimisation approaches have been proposed to integrate various forms of uncertainty into the open pit mine design and production scheduling. These methods, however, are limited in their ability to simultaneously optimise the production schedules for the portfolio of mines, material destination policies, the use of the available processing streams and the various products that are produced at each location of the mining complex.This paper aims to discuss a new method for the global optimisation of open pit mining complexes with geological uncertainty. The proposed generalised methodology is capable of modelling and holistically optimising mining complexes, including aspects related to production scheduling, blending, stockpiling and non-linear interactions that often occur in practice, but are over-simplified in existing models. Two case studies are discussed to highlight the need for these complex, stochastic optimisers. First, a case study for a nickel laterite blending operation highlights the need to integrate geological uncertainty into the optimisation in order to ensure product quality constraints are respected. Second, a case study for a copper-gold mining complex highlights the added value when simultaneously optimising the production schedule and the stockpiling and treatment of extracted materials.CITATION:Goodfellow, R and Dimitrakopoulos, R, 2014. Stochastic optimisation of mineral value chains – developments and applications for the global optimisation of mining complexes with uncertainty, in Proceedings Orebody Modelling and Strategic Mine Planning Symposium 2014 , pp 13–22 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Nov 24, 2014

By H M. Parker, G Verly, T Postolski

Assessing the uncertainty and risk in mineral resource estimation is required for best practice and is implied by the public reporting codes in the categorisation of mineral resources and reserves. One industry best practice is quantifying the risk on future production for a given period, which can be done using two geostatistical approaches: conditional simulation and drill hole spacing studies based on estimation variances. Conditional simulation can provide a more complete and flexible answer to the problem, but it takes a fair amount of time to complete. Drill hole spacing studies are simpler, faster and can provide reasonable assessments of mineral resource uncertainty.This paper is a presentation of the principles, theory and practice of drill hole spacing studies. Various types of risk assessment are considered. Difficulties encountered in practice are discussed, and solutions are proposed. The important parameters impacting the results are reviewed. Examples of results and sensitivities are shown.CITATION:Verly, G, Postolski, T and Parker, H M, 2014. Assessing uncertainty with drill hole spacing studies – applications to mineral resources, in Proceedings Orebody Modelling and Strategic Mine Planning Symposium 2014, pp 109–118 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Nov 24, 2014

By J Balzary, A Mohais

Dynamic market and operating conditions, coupled with an environment in which multiple objectives and trade-offs are common, pose major challenges for planners and schedulers working in any mining entity. Many mining companies recognise the need to shift from a siloed mining-focused push model to an integrated value chain, demand-driven approach, but there are still fundamental barriers in business process and the supporting technology preventing a consideration of end-to-end optimality.This paper presents some elements of experiences working with companies to adopt such advanced approaches. In addition to algorithmic elements, an approach to phased and gradual deployment of progressively more sophisticated optimisation models is described. From a practical software adoption perspective, it is believed that this last concern is also of primary importance. Next generation approaches to the optimisation of complex bulk commodity demand chains, namely iron ore and coal are presented, with case studies in the world’s largest integrated operations in Western Australia and Queensland from the raw material mined through to market. Utilising accurate simulation models supported by metaheuristic optimisation techniques, a range of ways to engineer a dynamic decision support framework that can adapt and change with the inevitable changes in commodity markets is explored. Objectives such as total revenue, margin, cost, NPV, throughput, asset utilisation, contractual penalties and bonuses, and energy consumption can be managed simultaneously across the mine, plant, logistics network, port operation, shipping and sales domains.CITATION:Balzary, J and Mohais, A, 2014. Consideration for multiobjective metaheuristic optimisation of large iron ore and coal supply chains, from resource to market, in Proceedings Orebody Modelling and Strategic Mine Planning Symposium 2014, pp 213–222 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Nov 24, 2014

By S Q. Liu, E Kozan

This paper proposes a new multiresource multistage scheduling problem for optimising the open pit drilling, blasting and excavating operations under equipment capacity constraints. The flow process is analysed based on the real-life data from an Australian iron ore mine site. The objective of the model is to maximise the throughput and minimise the total idle times of equipment at each stage. The following comprehensive mining attributes and constraints have been considered: types of equipment; operating capacities of equipment; ready times of equipment; speeds of equipment; block-sequence-dependent movement times of equipment; equipment-assignment-dependent operation times of blocks; distances between each pair of blocks; due windows of blocks; material properties of blocks; swell factors of blocks; and slope requirements of blocks. It is formulated by mixed integer programming and solved by ILOG-CPLEX optimiser. The proposed model is validated with extensive computational experiments to improve mine production efficiency at the operational level. The model also provides an intelligent decision support tool to account for the availability and usage of equipment units for drilling, blasting and excavating stages.CITATION:Kozan, E and Liu, S Q, 2014. An Open pit multistage mine production scheduling model for drilling, blasting and excavating operations, in Proceedings Orebody Modelling and Strategic Mine Planning Symposium 2014 , pp 329–334 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Nov 24, 2014

By M Aros, L Reilly

Iron ore is normally sold in bulk at certain specifications. These include iron grade and specific contaminant grades, typically silica, alumina, phosphorous and sulfur. These target grades are normally agreed between the mining company (producer) and the steelmaking company (customer).This study presents an alternative to traditional cut-off grade calculation, using linear programming that maximises the total ore inventories within a predefined mining envelope while meeting the specified targets. Providing a feasible mining sequence that maintains the target grade is not covered in this study.The method was applied to Fortescue’s operations. The complexity arising from differing ore quality within each mining horizon, variable yields and upgrades and the need to control iron and contaminant grades provided a good opportunity to test an alternative method of ore selection. The new method significantly reduced the time spent in cut-off selection and sensitivity analysis, and provided an optimised solution.CITATION:Aros, M and Reilly, L, 2014. Ore selection optimisation for an iron ore deposit using linear programming, in Proceedings Orebody Modelling and Strategic Mine Planning Symposium 2014, pp 345–350 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Nov 24, 2014

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 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 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