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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 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 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 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 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 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 R Dimitrakopoulos, C Paduraru

The optimisation of mine complexes and related value chains is a challenging problem due to the simultaneous presence of a highly-dimensional decision space, integer decision variables, non-linear constraints and recovery functions, as well as geological and market uncertainty. In addition, if the uncertainty is revealed progressively, then decisions need to continuously adapt to new information, which poses additional challenges. Stochastic dynamic programming is a well-known class of methods for addressing problems where a decision-maker reacts to new information. While simplistic applications of dynamic programming using exact representations would be intractable for any realistic mine planning problem, new dynamic programming methods using approximate value function representations have been successfully implemented in the past to address problems of similar complexity. This work formulates the problem of mine complex optimisation as an approximate dynamic program and presents an application of the resulting method to a gold-copper deposit.CITATION:Paduraru, C and Dimitrakopoulos, R, 2014. Mineral supply chain optimisation under uncertainty using approximate dynamic programming, in Proceedings Orebody Modelling and Strategic Mine Planning Symposium 2014 , pp 415–422 (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

A large number of mineral deposits contain regions in which it is economically beneficial to extract ore both via open pit and underground mining techniques. Current industry practice lacks an effective means to establish the extent of the open pit and underground mining areas for an individual deposit. We present a mixed integer programming model for scheduling both the open pit and underground mine that can be used to create a holistic extraction sequence for a specific deposit. The model considers both spatial and temporal elements of the open pit and underground mines in order to maximise the net present value of the entire deposit, while adhering to its production, processing and block sequencing constraints. By doing so, we can schedule the open pit and underground in concert.CITATION:King, B, 2014. Modelling open pit and underground production scheduling in concert, in Proceedings Orebody Modelling and Strategic Mine Planning Symposium 2014 , pp 369–374 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Nov 24, 2014

By J McCluskey, N Barros

"Long-term strategic planning at BHP Billiton’s Western Australia Iron Ore business aims for sustainable value maximisation of its large, long-life, low-cost, expandable mineral inventory and supply chain infrastructure. While mine plans are key components of this value equation, this objective requires a whole-of-business approach through comprehensive consideration and optimisation of the full value chain. Only through a system view do we believe that the value proposition of a set of plans is truly optimised.The fundamental considerations include: mineral inventory information, given its intrinsic uncertainty and risk/knowledge horizon a marketing view of future quality premium/discount based on overall market quality load whole of supply chain optimisation on a throughput accounting basis alternative sequences of capital decisions closure costs with significant magnitude of total movement requiring optimisation as part of the plan. In addition, the plan provides a view of acceptable uncertainty levels at different times in the future.BHP Billiton Iron Ore uses a structured Life of Asset planning process that supports the business with the optimisation of its maximum value proposition through a non-deterministic scenario-based optimisation approach on an annual basis. This is a key input to each year’s medium-term plans and budgets, including activities identified through a value of information approach that resolve the key uncertainties and steadily improve confidence in the plan.CITATION:McCluskey, J and Barros, N, 2014. Life of asset planning, uncertainty and value optimisation of the iron ore business – from exploration, to market, to closure, in Proceedings Orebody Modelling and Strategic Mine Planning Symposium 2014, pp 33–40 (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 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