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By E Rubio, N Morales

Currently, mine plans are optimised by using several criteria as objective functions, like profit, life-of-mine, concentration of some pollutants, mining costs, confidence level or ore resources, with consideration of constraints related to production rates, plant capacities and grades. Whilst this approach is successful in terms of producing high value production schedules, it uses a static opening sequence of the drawpoints and therefore the optimisation is made within the level of freedom left by the original opening schedule, and as a result it is far from the true optimal value of the project. This paper presents a model to optimise the sequence of drawpoint opening over a given time horizon. The emphasis is in the precedence constraints that are required to produce meaningful operational sequences considering the exploitation method (panel or block caving), physics considerations and logical rules. Further on, while it applies the standard approach of maximising net present value (NPV), it considers other targets for optimisation, like the robustness and constructability of the plans. Finally, it applies the model to real data to obtain feasible plans by means of this model. The first tests were done varying the production capacity of the mine.

Sep 26, 2011

By I Douglas, L Allen

Localised indicator kriging was developed following a similar approach used by Marat Abzalov (2006) in his paper titled æLocalised uniform conditioning (LUC): A new approach for direct modelling of small blocksÆ. Abzalov introduced the idea of mapping panel distributions from uniform conditioning onto selective mining unit-sized blocks that were created using ordinary kriging. This concept of remapping panel distributions was applied to multiple indicator kriging histograms.At Newmont a program was developed that made the remapping of MIK histograms to SMU sized blocks extremely efficient and was subsequently tested on several gold deposits. The results have been encouraging as the localised indicator kriging (LIK) method has eliminated many issues that have plagued both ordinary kriging and multiple indicator kriging. Use of localised indicator kriging methodology limits the smearing of high-grade and the over-smoothing of grade that is common and difficult to control in OK but retains the ability for localising grades. LIK also simplifies change-of-support corrections, eliminates un-mineable slivers of low or high-grade when dealing with small tonnages, and allows for the mapping of linear and non-linear estimates into the same block.

Sep 26, 2011

By P Moarefvand, M Osanloo

The anticipated economic life of the mine, which defines the rate of mine production, is dictated initially by the design capacity of the mill/concentrator and the production capacity of the mine relative to the grade and tonnage of ore that can be encompassed by the pit design. A small pit will result in smaller investment and cash flow; in contrast, a larger size of pit will increase capital investment and annual revenue. Since open pit mine design and long-term production sequencing are essentially based on three-dimensional block models, changes in block dimensions leads to different mine sizes, and therefore changes in the mine life. The physical characteristics of the deposit, such as reserve tonnage and ore grade, affect the expected future revenues from the mining project, and consequently they affect the life of the mine. Large blocks will increase the recoverable ore reserves and production rates and hence increase the mine life. This paper discusses the effects of the block dimension on the commercial ore reserve, grade-tonnage curve and its effect on the production rate, ultimate pit size, mining cost and consequently the mine life. The result shows that block height is the most effective parameter governing the economical life of the mine.

Sep 26, 2011

By M J. Pejovic, I Miljanovic, A Milutinovic4, T Benovic, A Petrovski

The paper presents a model of a multigrade monitoring-management structure for support to decision making and management in real and extended time in mineral processing (MP). The model was created as a result of a long-lasting research, design and development of mining, computer integrated monitoring-management systems. Apart from the general explanations, the authorsÆ state in the introduction that efficiency of a system for support to decision-making and management in mineral processing is more difficult to achieve by conventional approaches, than by the fuzzy logic approach. Starting from the multiphase structure of process flows in MP, the following are process and monitoring-management oriented graphs. The graphs served as a foundation of the multigrade hierarchical fuzzy model for support to decision making and management in MP. Following the example of the monitoring-management system of the æRudnikÆ mine flotation plant, fuzzy logic implementation was shown. The key conclusions of the paper are that fuzzyfication of layers of the decision making and management model has full justification due to the complexity and dynamics of mineral processing processes, complications and often insurmountable difficulties concerning the precise description of real systems by conventional mathematical tools, variability of external influences, and the (general) non-linearity of the MP processes management criteria functions.

Sep 26, 2011

By S D. Dessureault

The largest surface coal mine in North America requires complex materials handling infrastructure to fulfil the highly variable quality specifications of its contracts. These diverse demands, reflected in the arrival of numerous customer trains every day, necessitate the integration of information in every stage of production. To meet this goal, quality measurement, train line-up and mine planning processes are being integrated into local and remote control rooms. The effectiveness of such a system can only be measured by comparing decisions that are made with, and without, each of the individual integrated control systems. Traditionally, mine controllers use their experience, intuition or a variety of trial-and-error spreadsheets to determine the ideal materials handling flows needed to achieve the correct quality. These balanced decisions are complex since many factors must be considered such as the mine plan, fleet costs, quality variability and others; consequently mine controllers usually have widely varying success between crews.An evaluation model was developed to assess the impact of a decision support system (DSS) integrated in a control room for controlling the coal blending process. To this end, an ARENA-based simulator representing the whole production process can be used to pull performance and current materials handling settings directly either from the real-time systems in the mine, or from historical data. In this way, the same shift (breakdowns, cycle rates, etc) can be simulated and performance compared. Different settings of the DSS, in terms of what information is displayed and the methods for doing so, can be tested for effectiveness. The historical data are extracted directly from a corporate data warehouse, without altering the everyday working databases. At the same time, the simulator writes its results back into the same data warehouse structure, allowing the same KPI reports to be used to measure simulated shifts.

Sep 26, 2011

By A Noack

Today underground infrastructures, like tunnels and especially wide spread underground mines, are often equipped with ethernet based communication systems. These networks are used for the unified communication of data, voice and video. As these networks stretch along the major tunnels and in increasing scale also into the production areas, a high degree of coverage can be reached. For wireless coverage, standard IEEE 802.11 WLAN access points are being used. In order to provide a maximum availability and reliability, the active components are equipped with battery backup power supplies and the network infrastructure is set up in a redundant way using ring or meshed topologies. This provides a much higher availability compared to traditional phone systems or dedicated data links using a conventional star topology.Distributed computing intelligence that is installed on active network nodes throughout the whole network, additionally supported by a central server system, creates the basis for new mine safety functions introduced in this paper for both coal and non-coal mining. These new safety functions cover the detection of potential emergencies, using the status of the network as a æsensorÆ, as well as dynamically guiding people to safe exits or rescue shelters in case of an emergency. Furthermore, these functions are of important benefit for improving the efficiency of search and rescue operations following a major incident or accident underground.

Sep 26, 2011

In this paper a quantitative deterministic risk modelling approach (Quadrim) is introduced. The mathematical framework and equations for quantifying uncertainty within and aggregating across value chain nodes from error propagation techniques is proposed and discussed. A case study and worked examples, based on a hypothetical mine are then presented. This illustrates how uncertainty from the mining and economic value chains can be handled in a typical net cash flow calculation, and how this can be translated to a risk model. It is then illustrated that current valuation techniques do not just result in an information gap for decision makers. Traditional approaches such as discounted cash flow (DCF) analysis and truck and shovel productivity analysis, which do not take into account uncertainty in input values, can result in potential bias in estimates such as net present value (NPV) or equipment hours

Sep 26, 2011

By J V. Mehrabani, A R. Sayadi

Adequate estimation of equipment costs is a key factor in a feasibility study and in evaluation of design alternatives for mining and mineral processing projects. Inaccurate early cost estimation could lead to the elimination of investment benefits. However, this estimation is complex and time consuming because of data accessibility problems and consequently access to a rapid, up-to-date and accurate tool is desirable for carrying out cost estimations.This paper describes the development of models for estimating capital and operating costs for a jaw crusher, which is used as the primary crusher for a wide variety of mineral beneficiation plants, applying single and multivariate regression analyses, together with the design of experiment approach, based on principal component analysis. Explanatory variables include gape size, length of feed opening and power as numeric (quantitative) variables and toggle type (single or double) and the existence of extra heavy duty construction as categorical (qualitative) variables. The performance of each model has been investigated and comparing the estimated R-squared (R2) of each model, the best was selected and its validity examined. The cost models include the capital and operating costs along with the operating cost components. The results indicate that the suggested best cost model is sufficiently accurate for use in feasibility studies.

Sep 26, 2011

By D Wyborn, P A. Dowd

The characterisation of rock fracture networks is an important component of rock engineering applications involving stability assessment or fluid flow analysis. However, the derivation of a reliable rock fracture model remains a very challenging problem in practice. This paper describes a Bayesian framework, in the form of Markov Chain Monte Carlo (MCMC) simulation, for the construction of such a model. Model conditioning using different data sources is discussed including seismic events recorded during hydraulic fracture stimulation, rock face fracture mapping data and downhole geophysical survey data. The freeware FracSim3D is used for the simulations.

Sep 26, 2011

Real-time data collection and plant historians are becoming common place in mining and processing plants. This vast quantity of data must be turned into easily accessible and actionable information for all levels of the organisation.Queensland Nickel use a combination of Microsoft and SAP business intelligence tools to collect, cleanse, aggregate and present plant information to the appropriate people for supporting decision making in the business. SAP Business Objects, for visualisation, and SAP MII, for integration, allows complex and accurate metrics to be delivered as real-time dashboards. Designing a rigid data management architecture to segregate the data collection, integration and logic and visualisation into distinct logical layers, allows for rapid report development and greatly assists with IT maintenance, while also minimising the complexity of any customised programming. This type of architecture ensures a common data source for all business reports, ensuring one version of the truth. The past few years at Queensland Nickel have seen process and maintenance engineers spending considerably less time collecting and integrating data and spending more time analysing what is happening in the plant. Real-time reports and dashboards have been developed around the business needs rather than around the data. Two years ago Queensland Nickel introduced an employee bonus scheme based upon meeting monthly performance targets; an easy to use dashboard that allows everyone, from operators through to management, to monitor these metrics day-to-day.

Sep 26, 2011

By P Knights, M S. Kizil

Truck/shovel operations constitute 50 - 60 per cent of the cost of open pit mining operations. Mining companies, therefore, are constantly seeking to close the gap between actual truck/shovel productive capacity and maximum potential truck/shovel capacity. This paper analyses the performance of a mixed truck/shovel fleet in an open cut coal mine in Australia. It analyses operating delays, and proposes a novel graphical means of determining truck-limited or shovel-limited operations. A goal seeking algorithm is advanced in order to maximise truck/shovel match factors (MF) and hence optimise fleet productivity.

Sep 26, 2011

By A Esfahanipour, M Osanloo

Determination of cut-off grade?strategy is one of the most important stages of open pit mine planning and design. It is the parameter directly influencing the financial, technical, economic, legal, environmental, social and political issues in relation to mining operation. Choosing the optimum cut-off grade strategy that maximises the economic outcome has been a major topic of research workers for nearly one century. Many researchers have contributed in devising methods and algorithms, such as dynamic programming, linear programming, optimal control and so on for various aspects of its determination. In this paper, a non-linear mathematical programming for cut-off grade strategy optimisation is presented, considering the three main stages of mining operation introduced by K F Lane. In this model maximisation of net present value of mining operation, under the three constraints of mining stagesÆ capacities, considered as the optimisation criteria. Due to the discrete representation of the mining resource, the proposed non-linear formulation is approximated by a non-linear signomial geometric programming. According to non-convexity and the complexity of the proposed model, an augmented Lagrangian genetic algorithm was used to find the optimum cut-off grade strategy under varying and fixed price circumstances. To validate the proposed non-linear model efficiency, the results were compared with the results obtained by the K F Lane methodology. It was found that the proposed non-linear model works efficiently in the determination of cut-off grade strategy. According to the simplicity of the structure of non-linear programming modelling in comparison with dynamic programming it is hoped that, further development of this model would certainly provide the ability of considering managerial and technical flexibilities as well as incorporating more real mining conditions in the determination of cut-off grade strategy optimisation.

Sep 26, 2011

By M J. Pejovic, S BoÜevski, I Miljanovic, M Hudej, A Milutinovic, A Petrovski

The paper deals with the problem of managing the supplies of consumables (energy resources, lubricants, explosives, explosive supplies, tires, spare parts, etc) in mines. After the introductory remarks and comments, a general stochastic model of supply, relating in general to the dynamic programming method is presented. A selection of stochastic supply model for experiments and research by the authors of this paper is not a coincidence, based on of their long experience, showing that this type of model is the most suitable to provide support for decision-making and management of supplies of consumables in mines.In order for the problem?of?consumables?supply?management to be reviewed, in whole and objectively from the aspect of risk, experimental tests of the stochastic model in real conditions were performed at the limestone open pit mine æZelenikovecÆ near Skopje, the capital of Macedonia. The scope of the experimental research enabled reliable analysis. The results obtained confirm the efficiency and reliability of the stochastic model and two separate manipulative-operational positions regarding its application, which may have an important influence on the optimisation of supplies.

Sep 26, 2011

By D Thornton

Geologists use large data sets and spend many hours and much computing time modelling the distribution of minerals throughout a rock mass. Significant investments in drilling, assaying and quality assurance and quality control (QA/QC) are made before sophisticated geostatistical techniques are applied to the collected data. A large amount of research has been done in this field such that the ore control model is a reasonably accurate reflection of the mineral that is actually in the ground. However, after all this effort, the mining engineers then work just as diligently to blast the in situ rock mass to a particle size that can be efficiently handled and further reduced by downstream comminution processes. In open cut mines, this invariably requires large amounts of explosive energy that displaces the ore boundaries from where the geologists originally defined them. Failure to accurately account for blast movement will result in misclassification, whether that is ore to waste; low grade to high grade; sulfide to oxide; or other contaminates û collectively referred to as ore loss and dilution. The financial consequence of getting this wrong is substantial and not well quantified. This diminishes all of the good geological QA/QC done in defining the ore. This paper discusses various technologies used to physically measure blast-induced orebody movement and the difficulties in modelling this with a purely theoretical approach. Rock mass structure, blast energy, design and timing contours all have significant and interrelated effects on blast movement vectors. Measured blast movement has a large variance due to the uncertainly of many of the controlling parameters û arguably dominated by the heterogeneous nature of the rock mass. Although there have been significant advances in blast movement modelling capability in recent years, this unpredictable blast movement limits their use in the context of production ore control. Certainly, without physical field measurements, accurate predictions of movement are near impossible.Limitations of traditional surface markers in determining three-dimensional (3D) blast movement within the orebody are also reviewed, and advancements in accurately measuring these vectors within blasted ore discussed

Sep 26, 2011

By P Lukic

The definition of the working sections that will be used in a mine is usually initially made in the field by the geologist supervising the drilling program. These initial definitions are often made on the basis of rules of thumb such as æcoal less than 30 cm thick cannot be mined separatelyÆ; æpartings more than 30 cm thick will be mined separatelyÆ; and æif a coal seam is less than 50 cm thick and the parting above or below is thicker than the coal, the coal will be wastedÆ. These decisions are also often made based on the requirement for a minimum sample mass for analysis, and the desire to collect data at the minimum resolution to conserve the exploration budget.In many cases the geological models based on this data are produced at a resolution that is inadequate to allow the mining engineer to produce a mine plan that will maximise project value. This problem is generally unrecognised.Where data is collected to a higher level of resolution the subsequent processes employed in the production of geological models and mine plans often discards the inherent value that is hidden in this data. Again this problem is generally unrecognised.This paper highlights this problem and then presents a methodology that ensures that the optimal working section is selected and compares the outcomes of applying this methodology versus adherence to the usual rules of thumb.

Sep 26, 2011

By C Augustin, M Hahn, K Nienhaus

Operators of mining machinery like haul trucks or excavators have very limited information about the exact distance to other equipment as well as to the natural boundaries like berms, walls and structures such as crushers. The industry is seeking solutions to provide safety and positioning. The authors have developed a solution approach which is being used in several installations. This solution consists of three parts: first robust and compact two-dimensional (2D) radar scanners that provide range and angular information of the environment especially under difficult conditions like dust, rain, snow and fog; second an intelligent data processing framework flexible enough to adopt to different machines and operation modes like speed, inclination, visibility, tire friction, GPS position, orientation and acceleration; and third a high quality and easy to use interface giving highly condensed data to the operator, not blinding the operator at night and being sunlight readable. The truth of present proximity detection sensors is that they not only have a high false alarm rate but furthermore cover only a fraction of the possible scenarios. This makes them unsuitable for stand-alone collision avoidance systems. On the other side, mine dispatch systems only deliver database and GPS information, but no information regarding the real environment. The indurad company and its Australian partner CTS (http://www.cts.com.au) addressed these aspects by developing smart productivity enhancement driver assistance tools like lane and crusher dumping assistance.An innovative driver assistance tool developed in cooperation with the coal shearer manufacturer Eickhoff and the RWTH Aachen University has been awarded with the annual research award of its customer RAG, being the largest German hard coal producer, as well as with the bauma Innovation Award in 2010.

Sep 26, 2011

By M Armony, J F. C L Costa, J C. Koppe

Linear kriging methods fail to estimate local grades and local reserves for highly skewed variables due to three reasons: it uses only part of the available data information, it has no physical principles associated with the mathematical proceedings to minimise the problems, and it works with continuous variables while data are discrete because of the limited precision of any equipment. The techniques commonly used up to now to solve the highly skewed variables estimation problems were unsuccessful, unless mathematical rigor is abandoned. Even the method of multiple indicator kriging is not satisfactory. This paper presents a new framework, the field parametric geostatistics (FPG) that transforms noisy variograms into well-behaved variograms and justifies mathematically empirical procedures commonly used, as trimming or capping arbitrarily very high values. The method, when applied to non-skewed variables, yields similar results to classic kriging. The methodology is illustrated in a case study at a gold deposit in Brazil, and compared to results obtained by usual techniques.

Sep 26, 2011

By F Mavroudis, M Gaastra

Due to the increasing demand for resources worldwide, efficient utilisation of raw material deposits and recycling technologies become more and more important. Growing industrial nations and increased living standards require a large raw material supply. Hereby responsible handling of existent primary and secondary raw materials is indispensable. By utilising state-of-the-art technologies resources are extracted, recovered and recycled more efficiently which increases sustainability. Hereby sensor technologies combined with intelligent data processing systems in particular provide a considerable research area. Innovative sensor technologies are already implemented during recognition of material-specific characteristics, during elemental recognition as well as during machine control. They are applied to attain increased automation levels of mining machineries, higher recoveries during mining and processing as well as higher recoveries. To be able to face the increased demand for sensor technologies and the integration into raw material industries, an interdisciplinary association of three RWTH Aachen University Institutes has been founded. The association has expertise on increasing productivity and capacity utilisations by automating mining and recovering processes by sensor technologies. Due to the utilisation of sensor systems, materials and structures can now be detected and elementary analysed on a real-time scale. The developed and enhanced techniques are the key for any ambition of autonomous plants and machineries. This paper gives an overview on the regarded sensor technologies for material recognition, machine automation and mineral processing. The authors present concepts based on extensive laboratory and field studies of successful implementation and application the technologies considering special mining conditions. Overall three applications are described to demonstrate the potential of sensor technologies for autonomous and sustainable mining: real-time analysis: recognition of elementary compounds during extraction processes; multi-sensor system EiControlPlus: designed for automation of longwall shearers by complete detection of the machineÆs environment; and sensor-based sorting: applied in recycling and mineralÆs industries.

Sep 26, 2011

The cloud transform simulation is a technique used to generate realisations of correlated attributes. The principle of this technique is to partition the scatter diagram between the input attributes into several interval classes and derive for each class a cumulative distribution function (CDF) using only the data pairs of the scatter diagram in that class. The simulation then proceeds like a traditional Monte Carlo simulation but using the derived CDF in each class.The cloud transform simulation has the advantage of being a simple and fast technique that is able to reproduce the uncertainty in the relationship between the two input attributes regardless of whether the relationship is linear or not. However, the technique has three major drawbacks. First, the number of classes has an impact on final simulated results and there is not a robust approach to define such number. Second, simulated results exhibit some saw tooth variations that are unrealistic and third the reproduction of the spatial continuity of the simulated attribute is not guaranteed.This paper presents the use of the quantile regression approach in order to overcome the first two disadvantages of the cloud transform simulation. Details of the technique and how it can be coupled with the cloud transform simulation are presented along with a real case study.

Sep 26, 2011

By F Ramos

Conventional techniques for resource estimation generally involve the laborious analysis of variograms and the specification of parameters for covariance functions. In large deposits, construction of resource models and their update require significant amount of manual work, expert knowledge and time. In this paper we take a machine learning view to the problem of resource estimation. This is a non-parametric Bayesian method, leading to automatic parameter estimation (learning). Through Bayesian learning, the parameters of the model can be obtained by optimising a quantity known as the marginal likelihood. The main advantage of this approach over conventional maximum likelihood estimators is that the objective function incorporates the OccamÆs razor principle. This allows the comparison of different statistical models û for example, models designed with different covariance functions û and naturally penalises complexity, thus avoiding overfitting. Our technique presents significant flexibility for the definition of mean and covariance functions and permits the usage of more sophisticated models.The second contribution of this work is the formulation of a data fusion mechanism that continuously updates the resource model. As mining progresses, more information becomes available. For example, the hardness of the rocks can be measured while drilling blast holes, grades of ore and contaminants are obtained from cone logging. This information is in general available at a finer spatial resolution but can be more uncertain. The data fusion mechanism can seamlessly estimate the uncertainties of the different types of information and fuse them as new measurements are acquired. This procedure can significantly improve the quality of the resource model, defining new grounds for stochastic mine planning. We demonstrate the effectiveness of our approach in estimating iron ore deposits by integrating information about different grades.

Sep 26, 2011