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This paper describes modern long-term mine scheduling and gives an example of its application. It then discusses the obstacles to the implementation of such schedules.

Sep 26, 2011

By K J. Riihioja, E Nozawa

SmartTag is a radio frequency identification (RFID) based technology designed to allow tracking of ore from its source through blasting, run-of-mine (ROM) pads, crushers, intermediate stockpiles and finally into the concentrator. This paper presents current developments and the future direction of the SmartTagÖ ore tracking system, as well as presenting some case studies. Successful applications of the SmartTag system have encouraged development to allow the system to be used in a wider variety of plants, in particular plants with finer crushing and screening stages. This presented Metso with a number of technological challenges, including RFID tag and antenna design. By reducing the traditional RFID tag diameter from 60 mm to 20 mm the RFID tags can now successfully pass through a secondary crusher. The new smaller RFID tags (referred to as mini RFID tags) have been used in some Process Integration and Optimisation projects with very promising results. The results indicate that the miniature RFID tags have a higher survivability to blasting and through secondary crushing stages. Extending ore tracking further through the process allows more reliable linking of spatial data with time based process performance. Metso is looking at developing even smaller micro RFID tags, and is also investigating other applications for the SmartTag system, including ore tracking from the original source to the final destination (ie from the mine, through the plant and ports to the customer).

Sep 26, 2011

By E Nettleton, A Melkumyan

Identification of geology and mineralogy in situ on the mine face is essential if mining processes are to be automated for tasks such as exploration, grading and reconciliation. Hyperspectral data acquired from field based platforms are ideally suited to these tasks, and are set to revolutionise the automated mining industry. Changing conditions of solar illumination introduces variation into the hyperspectral signal, which is unrelated to geology, complicating analysis of data. Thus, algorithms must be able to deal with changing illumination conditions and operate in a consistent and probabilistic framework, which is a prerequisite for data fusion with other sensors. To produce accurate classification maps of geology, algorithms must be developed that are effective under different conditions of illumination.The efficacy of three algorithms to classify hyperspectral data for geology mapping on vertical mine faces was assessed. These algorithms are the spectral angle mapper (SAM), and two machine learning methods operating within a fully probabilistic Gaussian process (GP) framework û the squared exponential (SE) and the observation angle dependent (OAD) covariance functions (kernel). Two experiments were done to assess the classification performances. The first determined the performance of the algorithms by classifying hyperspectral data of known rock samples acquired using a field spectrometer. The GP-based methods had a similar performance, with average accuracies and F-scores of 97 and 88 per cent (GP-OAD) and 95 and 81 per cent (GP-SE), respectively. Average accuracies and F-scores for SAM were significantly smaller (80 and 40 per cent respectively). The algorithms were then applied to hyperspectral imagery acquired from a vertical mine face at different times of the day, under very different conditions of illumination. In contrast with experiment 1, SAM and GP-OAD gave results which were superior to GP-SE and were entirely consistent with geology mapped in the field. Compared with SAM, GP-OAD was less sensitive to the effects of shadow and produced classified images that were less noisy. The GP-SE method failed to delineate even gross changes in geology on the mine face. These results show conclusively that in order to use independent training data (spectral library) to classify imagery acquired under a different set of conditions, algorithms must take into account variability in incident illumination. Measures based on spectral distance are inferior to those based on spectral angle.The OAD kernel within the GP framework (GP-OAD) is superior to the other two methods because:it provides a probabilistic framework;it is insensitive to variability in illumination within and between imagery acquired under different conditions; andit can identify and map geological zones on a vertical mine face using an independent spectral library, without a priori knowledge.

Sep 26, 2011

The JORC Code contains a mandatory classification system for Mineral Resource and Ore Reserve estimates for public reporting. The classification is a statement of the Competent PersonÆs confidence in the estimate and an attempt to convey that confidence to the various users of the resource estimate. In general the classification process is either largely subjective, relying on the Competent PersonÆs experience, or relies on statistical measures derived from the estimation process, or is derived from an estimated level of confidence in the outcome. However, the statistical measures may not necessarily relate to the overall confidence in the resource or reserve which must take account of the confidence in the geological inputs to the estimation.In addition to the traditional deterministic interpretation and estimations adopted by geologists, semi-quantitative techniques are now available to improve the estimation of confidence while continuing to involve the Competent PersonÆs experience in the process. Methods for quantifying confidence in mineral resource estimates for advanced projects and operating mines have also been developed.Classification is undertaken primarily as a communication tool between the estimators and the other stakeholders in the process. A brief examination of the stakeholdersÆ requirements is included.

Sep 26, 2011

By R J. Hardy

The aim of this study is to examine the volumetric and mass movement characteristics of load and haul operations using statistical modelling of relatively large samples of bucket loads that are transported as small samples in the form of truck payloads. Generally, the factors influencing bucket load variability are random, difficult to predict and to control. In the process of loading trucks the underlying variability of individual bucket loads will determine the variability of truck payloads. Bucket loads of material prepared by drilling and blasting for loading into trucks are fundamental quantities in open pit mine production. A study of payload data from a large surface mining operation indicates that bucket loads and truck payloads that are a small sample of bucket loads are normally distributed. Truck payloads have a reduced dispersion compared with bucket loads that is an inverse 0.5 power function of the number of passes per truck payload. Comparison of statistical modelling results with the æ10/10/20Æ policy promoted by Caterpillar Global Mining Inc, indicate that compliance with the æ10/10Æ part of the policy, applying to truck overloading is facilitated, as the coefficient of variation of truck payloads decreases, as a result of the increasing number of loading passes. Compliance with the more stringent æ20Æ part of the policy, relating to safe steering and braking is also facilitated, albeit at lower coefficient of variation for similar reasons.

Sep 26, 2011

By D M. Silva, J C. Alves

According to the literature, material transportation represents 50 per cent of the operating costs for an open pit mine. In this context, this paper proposes a multistage approach for dynamic truck dispatching in real open pit mine environments, implemented in the commercial package SmartMine®, marketed by Devex SA. The first stage of this approach defines the optimal number of trucks that maximises the tonnage production by means of a robust linear programming model, which considers the operational constraints of a real mining process. The second stage uses a dynamic dispatching heuristic joining computational simulation and multicriterion optimisation techniques for decision making for truck dispatching. Another relevant contribution of this work is the publication of realistic instances collected from a dispatching system managing an iron mine, containing information about the hauling and loading fleet, the equipment situation when the working shift starts, cycle times, the time line of special occurrences (such as equipment stoppages and loading equipment reallocation in order to satisfy the mine planning) and the tonnage produced. These instances were generated with the purpose of submitting the method to the test of a real situation that considers the uncertainties existing in the process studied. The proposed approach was validated by means of a discrete event simulation model applied to the previously designated instances. Using this multistage approach as a mechanism for dispatch decision making, it is shown that the haulage fleet productivity increased when compared with the real values reported from the instances. These results are important because they show that the proposed approach generates efficient dispatch decisions for trucks operating in a real environment, reducing operating costs, increasing production and making the mine more competitive.

Sep 26, 2011

By W Assibey-Bonsu

The paper provides a brief review of the multivariate uniform conditioning and the localised multivariate uniform conditioning techniques and presents a case study based on a porphyry copper gold deposit in Peru. Two other important scenarios have been investigated and will be particularly emphasised: The recovered selective mining unit (SMU)s depend on a combination of copper and gold content, which can be expressed by means of ænet smelter returnsÆ (NSR). Two approaches are compared. Firstly, the application of a composite cut-off on NSR on the joint distribution of each grade and the application of uniform conditioning to three variables is investigated, ie for the first approach NSR is used as the main variable. The second approach considers copper as the main variable. In the case of multiple domains the procedure used to apply the technique to process mixed panels is clarified and recommendations are made.

Sep 26, 2011

By M R. Asef

Although generation of environmentally harmful hydrogen sulfide is considered to be related to sour gas fields, sweet gas reservoirs and waste fluids, travertine springs and geothermal fields may also produce acid gas (H2S and CO2) under certain conditions. The potential parameters influencing such a possibility need to be fully investigated to evaluate the extent of the consequences and predict hydrogen sulfide generation accordingly. Reports on hydrogen sulfide production at different regions and fields were gathered worldwide for the cases when some of them did not have such a problem previously. In order to inspect different aspects of these case studies, some other regions with no hydrogen sulfide content were selected as control. In all cases water samples were taken carefully for chemical analysis in the laboratory. Besides, mineral compositions were characterised in some cases for better evaluation. In order to give a thorough prospect of hydrogen sulfide production, a geochemical model was prepared. In situ engineering geological conditions, especially pressure and temperature, were assigned in the model to investigate the possibility of H2S generation using PHREEQC software. The results of modelling revealed that although many similarities existed between mineral compositions of the cases, certain formations were particularly prone to hydrogen sulfide generation. For better evaluation, important exchangeable cation ratios such as potassium/sodium, calcium/magnesium and ion proportions like sulfate and chloride were calculated as trends of the formations. As expected, the model could successfully monitor a higher ratio of SO42- and Cl- in potential H2S generator formations. Wide variation in percentage of HCO3- was considered as a secondary observatory parameter for better estimation. In addition, it was noticed that higher anion content created an environment ready to absorb any cations and this makes H2S generation easier. Finally, using correlations between predictors and estimators for low depth and deep samples defined the degree of effectiveness for every sample.

Sep 26, 2011

Production scheduling of open pit mining is a key area as it deals with the management of operating cash flows of mining companies. The efficiency of long-term schedules is usually assessed with economic criteria such as net present value (NPV), discounted cash flow rate of return (DCFROR) and payback period, which are not sufficient on their own for revealing the complex picture of the decision-making process.This paper offers a thorough analysis of the most important contemporary criteria of long-term production scheduling, as way to provide the mining practice with a new approach to the decision-making process. The list of criteria includes technological, economic and probabilistic ones. A detailed case study is provided aiming to illustrate the applicability of the criteria for comparing the scheduling approaches regarding the economic interests of a mineÆs owner and contractor. The results obtained can be used for revealing new trends in the current methodology of open-pit production scheduling optimisations.

Sep 26, 2011

By C Tonkin, S D. Howell, P V. Johnson, J Moriarty, P W. Duck

This paper presents the scientific framework underpinning the resource valuation and optimisation model (RVOM). The RVOM is a partial differential equation based real options software package, which helps mine owners optimally plan their operations, understand their project risks, and make defensible valuations. This is achieved in the presence of both financial and physical uncertainty, as well as processing capacity constraints. The RVOM can be applied to any multi-ore mine, open pit or otherwise, where the block-order of extraction has already been planned using existing software tools such as the Gemcom WhittleÖ strategic mine planning package. The RVOM can also take account of multiple commodities within a single mine, and multiple forms of price behaviour. The three key outputs from the RVOM are: valuation, optimal decision and probability of decision. A decision takes account of upfront costs, and can include an unlimited number of transitions between: normal operation, expanded operation, care and maintenance and abandonment (and variants thereof). The RVOM then employs stochastic process theory to determine the probability of reaching these decisions. This gives the mine operators clear indications as to where their risks lie, aiding their mine planning. A clear example of the RVOMs usage to a case-study gold mine is presented, demonstrating its broad applicability, the added value it can create and how users can easily make use of the RVOMÆs state-of-the-art algorithmic engine.

Sep 26, 2011

Large deposits present computational challenges to todayÆs commercial orebody modelling systems that are based on conventional block models. We describe a system that is specifically engineered to model and visualise large deposits. Central to the system is linear octree encoding of conventional block models and real-time conversion of three-dimensional (3D) meshes to linear octrees. We provide both methodological treatment and practical experience in implementing such a system. A commercial implementation is also presented to demonstrate the validity of the approach.

Sep 26, 2011

By J Zhou

This paper discusses the implementation of a mixed integer linear programming model for a coal preparation plant. The plant considered consists of primary and secondary heavy media cyclones, hydrocyclones for intermediate fines and froth flotation for the very fine material. The model is designed to optimise the plant settings and product flows to maximise revenues for a given specification for two product outputs. The model can deal with bonus/penalty aspects of the product pricing for products that exceed or fail to meet specification. We report on a model with two separate coal feeds with parallel circuits for the oversize, intermediate and fines streams. The genesis of this model is more than 30 years old but recent improvements in mixed integer programming solvers make a full mixed integer formulation practical. The original model began as an enumeration of cut points with arbitrary rules for product allocation. The product allocation was improved in a linear programming formulation by Gunn (1982) and Sarker (1991 and 1995). Current mixed integer programming capabilities make it possible to replace the enumeration aspects and solve directly as a mixed integer programming problem. The current implementation uses a general purpose modelling language to formulate the model. Open source and commercial mixed integer programming solvers are compared

Sep 26, 2011

By D Misra, R Ganguli, S Bandopadhyay

The objective of this paper is to assess the accuracy and applicability of recent state-of-art methods such as the machine learning algorithms (MLA) for mineral resource estimation. Conventional methods such as geostatistics have been predominantly used in the mining industry for this purpose with varying success. Recent advances in the use of MLA have provided a fresh approach to obtain improved accuracy in the estimation of mineral resources. Two MLA methods: the neural network (NN) and the support vector machine (SVM) have been used to estimate a polymetallic ore deposit in Alaska.The general characteristic of SVM and NN emphasises the fact that they can approximate any multivariate non-linear relation among variables. Although neural network models are able to capture the nonlinear spatial relationships that may be present in the data, they are usually difficult to optimise under sparse data settings. Of the various NN alternatives, despite their effectiveness, the model selection and estimation process is typically difficult as it involves solving complex integration and optimisation of parameters. In support vector modelling (SVM), estimating of parameters involves optimisation of a convex cost function. The working principle of SVM makes it robust against noisy and extreme value data. At the same time, it can capture the spatial distribution of ore grade more effectively with careful modelling and selection of SVM parameters.The outcome of the MLA methods were compared to those of the geostatistical ordinary kriging (OK) application to study their generalisation ability. The comparison was made using the following goodness of fit criteria: mean-squared error; mean absolute error; mean error; and coefficient of determination. The results indicated that the MLA methods may improve the predictability of ore deposits and thereby reduce the inherent risk in mineral resource estimation.

Sep 26, 2011

By C Claassen, R Balusu

Computational fluid dynamics (CFD) models have been developed based upon field data collected from a longwall mine in New South Wales, Australia to study the behaviour of goaf gas flow in both active and sealed goaf areas. A base CFD model was built to represent the goaf situations when the active longwall retreats near the finish-off line. Results from the base model were calibrated and compared against field goaf gas monitoring data. The base model was then used to carry out parametric studies to investigate a number of operational scenarios and their impact on goaf gas behaviour. CFD modelling results indicate that the overall goaf gas flow pattern changes as the operating longwall retreats towards finish-off line. In all cases, oxygen penetration into the active goaf remains high, reaching 15 per cent or higher, even at some 800 m behind the longwall face.The modelling results also indicated that it would be difficult for early detection of an active goaf heating (on the maingate side) based upon CO readings in the return airflow, as the main stream of the gaseous product will be flowing into the sealed deep goafs in adjacent longwall (LW) panels. Monitoring points (such as tube bundles and bag sampling points) should be selected at deep seated seals along the active goaf so that abnormal CO or C2H6 readings can be picked up for early detection and location of potential heating spots; goaf inertisation can be better achieved by injecting inert gas such as nitrogen at deeper points (>200 m) behind the operating longwall; the injection of in-seam drainage methane into the goaf areas will only have a limited impact on goaf inertisation as much of the injected methane will migrate towards deep and higher parts of the goaf due to its buoyancy effect.

Sep 26, 2011

By A Melkumyan, F Ramos

This paper addresses the problem of prediction of grade variables over volumes (block estimation) for an autonomous mine. Resource estimation is usually concerned with the prediction of the quality of material over volumes (blocks in resource models). This problem is tackled by developing Bayesian block estimation with the Gaussian processes (GPs) framework. GPs are commonly used for nonparametric regression and classification and offer an elegant solution to deal with incomplete knowledge and information. A GP is defined by its mean and covariance function. The parameters of the covariance function can be learnt by maximising the marginal likelihood of the data. This offers a way of automating the covariance fitting process, while at the same time allowing for an assessment of how well the covariance function fits the data. Within the GP framework the calculation of the average grade over a volume can be performed using Bayesian quadrature, which treats an integral as a random variable. First, it is demonstrated that the block kriging equations in geostatistics are the same as Bayesian quadrature. Then, it is determined when closed form analytical solutions for the integrals are possible and quadrature approaches for approximations are briefly discussed.

Sep 26, 2011

By J A. M Luz, A T. Bernardes

HukkiÆs law is an empirical law, which does not take into account several events of energy loss during the fragmentation processes. Since experimental results are very difficult to obtain, for a large range of fragment sizes, the verification of the law is very difficult. The relation between fractures and fragmentation processes with fractal geometry was proposed some decades ago. Empirical laws known in this context show basic features of fractal geometry, mainly self-affinity and power law behaviour. Thus, in this work a model was developed to simulate the fragmentation process and to check the relation between energy consumption and fragment sizes. The model is represented on a regular lattice. Links can represent pathways for fracture processes. The events of fragmentation have energy given by a probability distribution function. In the model there is no mass loss. Propagation of fractures occurs as self-avoiding random walks on the regular lattice.

Sep 26, 2011

Mineral resource classification is essential because it is required for public disclosure. The resources must be binned by a qualified person into measured, indicated and inferred categories using an appropriate methodology. The classification methodologies used have long been a topic of research and discussion. There are various traditional and geostatistical methods used in practice. Some are more popular than others. Because of the uniqueness of each ore deposit, it seems almost impossible to have a single industry-accepted norm for mineral resource classification. Therefore, some experts have adapted the methodologies tailored for the deposits they are working on, but many simply use the methods they and their clients understand and are comfortable with. In this paper, a new approach is presented to classify the mineral resources using a data geometry index. This index combines both traditional and geostatistical aspects of resource classification based on the data configuration around the block interpolated. It is a function of several variables such as kriging variance calculated for a block, distance from the block to the nearest sample and the average distance of all samples used to interpolate the block. Thus the approach using this index takes into account the spatial data configuration around the block being estimated, as well as the traditional distance criteria. The data geometry index and how it is calculated is explained. A case study for a gold deposit is presented to see how the classification based on this index compares to the existing methodology used in this deposit for mineral resource classification.

Sep 26, 2011

By S D. Dessureault

In one of North AmericaÆs largest mines, which contains more than six active coal-producing faces, mine controllers must meet the complex challenge of adhering to the mine plan while also ensuring customer quality. Fleet management systems (FMS) provide many different avenues to control production flows, from the faces to the various pit crushers. Nonetheless, mine controllers must balance a variety of factors such as unplanned downs, differing customer demands, their own daily key performance indicators (KPIs) related to equipment utilisation, among others. Deviating from the short-term mine plan is the most common means of balancing the other factors, since adherence to the plan is traditionally only measured during the weekly variance meeting with engineering staff. However, such deviation may be one of the most costly responses, as long-term scheduling issues may arise such as multi-face dead-heading or insufficient stripping. This paper focuses on the development of a system for real-time adherence to mine plan within a control room environment. This system is part of a larger blending-focused, multi-million dollar project with elements of a centralised remote control room, developed at the University of ArizonaÆs Mining Information Systems and Operations Management (MISOM) Mine Control Center (MC2). The mine plan (MPL) component of the overall blending system integrates both historical and real-time FMS data with mine plan requirements directly sourced from original systems (no additional input required). The measures, displayed largely as graphs, have tools to help controllers manage the production to facilitate adherence to the plan. Some measures are designed to motivate crews to perform by inciting competition between crews, softened through a study of the controller culture. The MPL component was deployed at the mine, and the before and after deployment adherence was measured. This paper discusses the technical development of the new tool, its deployment, and an ethnography (study of culture) to understand controller motivation.

Sep 26, 2011

By E A. Gunn, J Zhou

In this paper a project to develop a joint optimisation/simulation model for production planning in a mine-mill complex is presented. The system involves a mill receiving ore from a number of different locations, each with a unique mineral composition. The mill consists of several crushing and grinding circuits that function in series and parallel. Scheduling the flow of materials through the mill is complex due to constraints on throughput imposed by the millÆs design, the length of time material can be stockpiled on the surface, varying input mineral composition, and throughput targets for a range of output products.An optimisation model is used to create a set of campaigns (ore feed rates) over a given planning horizon under the assumption of deterministic mill throughput. A simulation model is used to evaluate the production plan under assumptions of stochastic machine failure and operational losses created by the need to cycle between input feeds. Campaign scheduling has two important implications for the simulation. First, during any campaign, multiple ore types may need to flow through the mill. However, the mill must be controlled to ensure that only one type of ore can be in a crusher, a grinder or a storage bin at any point in time. Second, as the mill switches from one campaign to another, the set of ores being processed may differ. Thus, the flow of ore through the equipment during a campaign changeover must be controlled to ensure that one or more of the ores from the current campaign can be completely eliminated before the ores from the new campaign are introduced.The simulation architecture employed for this project is Visual Basic for Applications (VBA) combined with MS-Excel as a user interface. A fixed step discrete event orientation is assumed as the simulation framework. A set of model constructs was built for the project with a specific Canadian milling operation in mind, but was designed to be representative of a broad class of milling operations. The constructs are implemented as class objects within the VBA structure, each with a number of properties and methods. The collection of VBA objects forms a composable modelling toolkit. These objects can be linked together by users, via MS-Excel, to represent a broad range of milling operations.

Sep 26, 2011

By B K. Hebblewhite, G Sharrock

The strength and deformation properties of a rock mass are important parameters influencing the design of civil and mining excavations or structures. The mechanical behaviour of jointed rock masses are controlled by the characteristics of discontinuities and the strength of intact rock bridges between discontinuities. To date, analytical, empirical and experimental methods have not captured the key mechanisms controlling the mechanical properties of actual rock masses with non-persistent joints. The emergence of the synthetic rock mass (SRM) in 2007 provides a promising approach for this purpose. In this paper a rock mass with non-persistent joints is analysed with the SRM model based on PFC3D. The influence of joint geometrical parameters on the uniaxial compressive strength (UCS), rock mass deformation modulus (Em) and failure mode is studied. Joint parameters include joint orientation, persistency, spacing and step angle. Prismatic numerical samples with dimensions 300 mm × 150 mm × 50 mm were generated and a sensitivity analysis of each joint parameter on UCS and Em was undertaken. The results show that of the parameters tested, joint orientation has the greatest influence on UCS. Minimum UCS occurs when the angle between the major principal stress and joint surface is approximately 60°. The joint step angle was found to have a significant effect on failure mode and a joint step angle of 90° is the most favourable joint orientation for step-path failure. The importance of joint step angle is highlighted in the discovery of a failure mode bifurcation point at joint step angles of 60° and 90° for joint orientations of 75° and 90°, respectively. For these parameters the failure mode transitions from block rotation to a new failure mode where the coalescence of cracks results in the formation of new small regularised blocks.

Sep 26, 2011