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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 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 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 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 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 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 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 L Blackwell

Strategic mine scheduling is an industry best practice for increasing the long-term value of mining operations and supply chains. The application of optimisation technology such as mixed integer linear programming (MILP) is able to achieve a mathematically optimal result for a given scheduling model. Such a model will typically define the available source material, processing options and desired constraints for accessing and processing the material. Most strategic scheduling models, however, do not include a sufficiently detailed model of capital expenditure options related to capacity expansion.Typical strategic schedule models do not include important capital expenditure features such as capacity decay or expiration (limited lifespan of trucks), precedence relationships over time (rail construction before a new plant or non-linear expansions due to economies of scale) or mutually exclusive option sets (choices of plant size). By not including these features, a strategic mine scheduling model for a capital-sensitive operation is unable to be optimised to deliver maximum value or profitability.This paper presents several results of MILP optimisation using advanced capital expenditure modelling for mining operations and supply chains. Results of the study demonstrate that optimising capital expenditure at the same time as optimising the mining schedule allows for minimisation of capital purchasing, optimal timing for those purchases and optimal scheduling around them. Overall, this results in an increase in net present value.CITATION:Blackwell, L, 2014. Capital expenditure modelling for strategic mine scheduling, in Proceedings Orebody Modelling and Strategic Mine Planning Symposium 2014 , pp 315–322 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Nov 24, 2014

By B King

Processing expansion options and mining fleet purchases are major cost items that receive due focus in most cost management analyses. If these items are not needed, they can significantly reduce a project’s short-term capital spend and improve profitability and value to shareholders. When conditions change prior to purchasing equipment, the purchase may no longer represent the best decision for the project.This paper outlines a structured approach for making major cost decisions to improve the value of a project, determining which items should be kept and which should be eliminated.CITATION:King, B, 2014. Cost management optimisation, in Proceedings Orebody Modelling and Strategic Mine Planning Symposium 2014 , pp 7–12 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Nov 24, 2014

By H Askari-Nasab, E Torkamani, M M. Badiozamani, S P. Upadhyay, M Tabesh

"Decision-making in mining is a challenging task. Optimal decisions regarding shovel and truck allocations, aligned with the optimal short-term production schedules, are crucial in keeping the operations in line with the company’s long-term objectives. In this paper, we present two main modelling components as the means to achieve near-optimal operational plans: a mixed integer linear goal programming model to allocate trucks and shovels to mining faces over a shift-by-shift basis so that operating costs are minimised and resource utilisations are maximised through the planning horizon a discrete-event simulation model to investigate the behaviour of a truck-and-shovel haulage system in the presence of uncertainty within mining operations. The goals integrated into the mathematical model are to: maximise production minimise deviations from the target head grade minimise deviations in tonnage feed to the processing plants from the desired feed minimise operating cost.The mathematical model provides shovel assignments and the target productions as an input to the discrete event simulation model while meeting the desired goals and constraints of the mining operation. We illustrate the applications of the discrete event simulation model by presenting a large-scale oil sands case study. The main contribution of this work thus far is the development, implementation and verification of a simulation model with a link to optimal short-term mine schedule and extraction sequence of mining faces, which assists in achieving a high level of mine plan compliance across short-term planning and operational planning time horizons.CITATION:Askari-Nasab, H, Upadhyay, S P, Torkamani, E, Tabesh, M and Badiozamani, M M, 2014. Simulation optimisation of mine operational plans, in Proceedings Orebody Modelling and Strategic Mine Planning Symposium 2014, pp 299–312 (The Australasian Institute of Mining and Metallurgy: Melbourne)."

Nov 24, 2014

By Y Li, S Ramazan, E Topal

A mine waste dump progression plan can provide a clear outlook of the mining operation. To produce such an output requires detailed placement schedule of the mined material, including the volume (or tonnage) and the allocated dumping location. However, current practise mainly focuses on the ore production, over-simplifying the waste material scheduling. As a result, a rock dump is often treated as a single point in long-term planning, making it difficult to predict the waste dump progression pattern over the life-of-mine. Without such a guidance, it is almost impossible to carry out progressive rehabilitation on the waste rock dumps. The lack of dumping schedule could cause delay in development construction, ie tailing storage facility and run-of-mine pad. Other downstream effect due to the over-simplification is inaccurate estimation of required truck hours, which could have huge financial impact on the operation.In this paper, mixed integer programming (MIP) models of different objective functions, ie maximise truck productivity by minimising the overall haulage distance, minimise required truck deviation between adjacent years, and a hybrid between the two objectives, are utilised to generate the long-term optimum rock placement schedules under the criteria of satisfying site specific conditions. All three MIP models are implemented in a large scale open pit mine. The numerical solutions from the models forms three different rock placement schedules, based on which, the yearly truck requirements are easily calculated and compared. The graphical results show the three corresponding waste dump progression patterns over the life-of-mine, providing the optimised long-term forecast of the operation.CITATION:Li, Y, Topal, E and Ramazan, S, 2014. Optimising the long-term mine waste dump progression and truck hour schedule in a large-scale open pit mine using mixed integer programming, in Proceedings Orebody Modelling and Strategic Mine Planning Symposium 2014 , pp 335–344 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Nov 24, 2014

By M Astorga, J Jackson, J Gaunt

The Ban Houayxai mine (BHX) is a relatively low-grade, low-cost, open pit gold-silver deposit in Laos operated by Phu Bia Mining, a subsidiary of PanAust. The ore production rate at BHX is 4.5 Mt/a, with direct tipping to a semi-autogenous grinding/ball mill circuit with a carbon-in-leach process plant. Approximately 100 000 oz of gold is produced per annum.The operation is located in mountainous terrain with minimal run-of-mine stockpiling. This results in limited capacity for blending from stockpile, with the mill instead reliant upon direct feed ex-pit. Since commissioning in 2012, the plant has seen significant variation in milling rates due to variability in the feed properties of the oxide, transition and primary ores. As part of an ongoing continuous improvement program, an integrated approach was initiated, focusing on maintaining and enhancing production in the future as the proportion of harder primary ore increases. This will specifically focus on direct blending from the loading face using the ore properties and blast fragmentation to maintain mill throughput.This approach was based on the concept of physical assets management, commencing with improving information and knowledge of the condition of the orebody through modelling the characteristics, variability and performance of the feed for processes relevant to throughput. These models are used to support both mine and process plant production planning.The key ore feed characteristics and parameters modelled for the life-of-mine are blastability index, powder factor, crushability or impact resistance and grindability. These predictive spatial models were based on the data from diamond drill holes used in resource definition and geotechnical drilling programs by integrating geotechnical, geological, geochemical and metallurgical data.At the early stages of implementation, the models are being utilised for ore blending decisions to provide guidance and support for budgeting, long-term mine planning, blast design for mill feed and providing the mill with an expectation of performance.CITATION:Jackson, J, Gaunt, J and Astorga, M, 2014. Predicting mill ore feed variability using integrated geotechnical/geometallurgical models, in Proceedings Orebody Modelling and Strategic Mine Planning Symposium 2014, pp 165–176 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Nov 24, 2014

By G S. Esterhuizen, M M. Murphy, T S. Bajpayee

The strength reduction method (SRM) has been implemented in the FLAC3D finite difference software and applied to coalmine entry support evaluations. The FLAC3D numerical models have been calibrated against instrumented field sites in varying geologies, depths and field stress conditions. The calibration between the field and model results obtained a satisfactory match for rock deformation and support response. Previous research has also developed good correlations between numerical model stability estimations using the SRM and empirically derived stability estimations using the analysis of roof bolt systems (ARBS) software and the coalmine roof rating (CMRR). The purpose of this paper is to further apply the SRM to investigate how specific geotechnical and support parameters can impact coalmine entry stability. Results from different studies demonstrate how the calibrated models were used to investigate a few of the well-known questions in both mine design and support design. For mine design the impact of strong and weak beds in the roof strata, rotating the principal stresses and weakening the floor on excavation stability is investigated. For support design the impact of support length, support spacing and adding cable bolts to the design is demonstrated. These scenarios are evaluated in developmental coalmine entries for a variety of geology and field stress conditions. Stability factors are calculated for each scenario using the SRM. The SRM allows the impact of each parameter to be quantified and allows the support system to be optimised for given geotechnical conditions.CITATION:Esterhuizen, G S, Murphy, M M and Bajpayee, T S, 2014. Analysis of geotechnical and support parameters on coalmine entry stability using the strength reduction method, in Proceedings AusRock 2014: Third Australasian Ground Control in Mining Conference , pp 383–392 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Nov 5, 2014

By B Jones, M Chappell

Installing ground support correctly is not an easy task, whether lack of operator training, poor ground conditions or underperforming equipment. Confirming the quality of installation can help with planning and remedial works and help to maintain a safe working environment. A 2?m bolt with 700?mm of encapsulation will pass a 10?t pull test (assuming ground conditions permit); however, it will not provide any data of the unencapsulated length and thus the ability to withstand corrosion. Complimentary testing in the form of non-destructive testing (NDT) can provide factual data that leads to the assessment of the quality of the encapsulation. By improving the quality of the encapsulation, the risk of corrosion is reduced, the ground support durability is improved and hence the asset life is enhanced.This paper presents a case study of a trial using the NDT system RokTel™. Installations of varying quality were installed and tested. The characteristics tested were corrosion, load and free length. These characteristics can be tested and assessed for cable bolts, thread bar bolts and split sets. The aim of this paper is to present NDT testing as a viable method for assessing installation quality and ongoing performance of the ground support.CITATION:Jones, B and Chappell, M, 2014. A non-destructive testing method for measuring bolt condition, in Proceedings AusRock 2014: Third Australasian Ground Control in Mining Conference , pp 307–314 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Nov 5, 2014

By M J. Dunn

The geotechnical model is the cornerstone of any mining geotechnical design and as such the confidence level and reliability of the geotechnical model is paramount. The geotechnical model confidence level needs to be commensurate with the level of design that is being applied or study that is being undertaken. The geotechnical model is made up of several components and the reliability of the data underlying these components ultimately determines the reliability of the geotechnical model.Data reliability is linked to the quantity of data collected, the spatial distribution of the data collected, quality of the data collection and the interpretation of the data. In a typical project, there is an opportunity to improve data reliability through the scoping, prefeasibility, feasibility and final design and implementation stages.Currently, very little quantitative guidance exists in the literature on assessing the confidence level of geotechnical studies, although there have been attempts by various authors (Haile, 2004; Haines, Swart and Kruger, 2006; Read and Stacey, 2009; Dunn, Basson and Parrott, 2011) to qualitatively describe what level of geotechnical data is required. Recently a number of authors (Read, 2013; Fillion and Hadjigeorgiou, 2013; Thomas, 2013) have outlined methods that could be applied to assess the reliability of some types of geotechnical data that are used in the development of geotechnical models.This paper briefly discusses some of the factors influencing data reliability and the geotechnical model confidence and some of the tools available.CITATION:Dunn, M J, 2014. Geotechnical models and data confidence in mining geotechnical design, in Proceedings AusRock 2014: Third Australasian Ground Control in Mining Conference , pp 127–134 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Nov 5, 2014