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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

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 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 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 S Van Duin, I Porter

This paper discusses how advanced manufacturing technology (AMT), which is fundamentally reliant on and developed via multi-disciplinary skills, has benefitted non-mining industries, and what lessons can be derived from this experience to benefit the mining industry, particularly underground coal mining. Examples of AMT use in æconventionalÆ industry, some drawn from the more than 25 years experience in many different industries of the Automation and Robotics team at the University of Wollongong, are described to show typical benefits, risks and opportunities expected in using AMT. Applying AMT to mining brings many unique challenges and this paper expands on these and then provides practical examples of how a multi-disciplinary team can develop new processes and systems leading to productivity, safety and other benefits for underground coal mining.

Sep 26, 2011

Longwall mining operations have been in place in Australia since the early 1960s. This mining method is currently used to extract the bulk of coal obtained from underground mines in Australia (CSIRO, 2010). Despite the high tonnages extracted the expected utilisation of much equipment remains relatively low. To some extent this level of utilisation is influenced by the variability in conditions inherent in the mining process. This variability in production rates presents challenges to those attempting to design a coal clearance system. The difficulty is to design a system that has sufficient capacity to support production, without over capitalising or incurring high operating costs. The need to allow for a series of largely unplanned events in the design of a coal clearance system makes simulation modelling an ideal technology to employ. Simulation modelling is a methodology that allows a system with interacting components to be modelled over time, while explicitly taking into account unplanned events that may disrupt operations.This paper discusses how dynamic simulation models have been used to assist in the design of coal clearance systems. Typically this type of modelling has been used to look at the likely profile of production rates from the Longwall shearer and to determine how well various proposed combinations of belts, bins and stockpiles would support production.

Sep 26, 2011

By A Huebner, A Noack

Companies all over the world are very sensitive concerning their IT and network security. In most network connected technical installations and machines however, network security is not taken into account to a significant extent: insecure protocols are run, unencrypted information is exchanged freely and even broadcasted wirelessly and networked machines are accessible via insecure file transfer protocol (FTP).What happens in case of malfunctions of such technical installations? Who is responsible? Who can be held liable in case of accidents? The first computer worm for automation systems (æSTUXNETÆ) (Symantec, 2010) was discovered during 2010.The paper is intended to create sensitivity among decision makers for potential security and resulting safety leaks and points out the current status quo in technical network security. It outlines best practices for secure networking and procedures for remote access and remote software updates on stationary equipment and mobile mining machinery. It describes how to ensure authentic and genuine configurations and software targets and how to ensure secure communication for autonomous and remote controlled equipment. For such equipment in particular, the following statement should apply: æThere is no safety without network securityÆ.

Sep 26, 2011

By E Rubio, N Morales

A common practice in the mining industry is to decompose the planning process into different tasks, so the overall process and specific schedules can be constructed easily. Nevertheless, currently there are some adverse effects, such as not capturing the real value of a project by not taking into account the construction of several underground drifts and general infrastructure. Then the common practice is to compute mining sequence and cut-off grade profiles over the life of the mine without constitutively integrating the development schedule as a means to measure the constructability of a given mine design and sequence. Thus, the investigation summarised in this paper shows a novel, integrative way of treating an underground mine sequence and production schedule while considering drift development scheduling. Moreover, it is thought that, because of this original model, the whole underground production scheduling exercise must concentrate on combining the development and the production scheduling to compute a robust and a feasible production schedule.In order to quantify the effect of the conventional way of viewing the planning, the concept of the building information model (BIM) needs to be used, which was postulated by Charles Eastman and has been used extensively since the late 1970s. BIM takes other aspects of a project into account, considering at the same time the spatial relationships, quantities and properties of building components, but especially some kind of model visualisation, which is the advantage to be incorporated. This paper presents the basis for an optimisation model that allows for defining the sequence of the mining development so that the production plan can be achieved. For this, the model considers the space dimensions, time, pattern or strategy, cost or benefit, precedence and resources available. Tests were performed considering a block caving and sublevel open stoping method. Finally these tests showed the main conclusion, that production sequence changes when the building capacity is considered.

Sep 26, 2011

By J Ferreira

To speed up multivariate geostatistical simulation it is common to transform the set of attributes into spatially uncorrelated factors that can be simulated independently. One decorrelation method is the method of minimum/maximum autocorrelation factors (MAF), where a two structure linear model of coregionalisation is assumed. The transformation decorrelates the theoretical model exactly, but the actual data are only approximately decorrelated. The MAF transformation can therefore be seen as a special case of a non-orthogonal approximate diagonaliser of a set of symmetric matrices. A more general approach for approximate joint diagonalisation (AJD) has been developed in the context of blind source separation. For these AJD algorithms no assumptions are made beyond symmetry of the individual matrices and so they can be applied to a family of experimental semivariogram matrices. In their application there are no restrictions on the number of structures in the linear model of coregionalisation (LMC) thus removing one of the conditions placed on the subsequent modelling of the spatial structure of the factors. In this paper the general background for the U-WEDGE AJD method is presented. It is shown that the U-WEDGE method achieves better spatial decorrelation than the MAF method. The factors derived from U-WEDGE are simulated using conditional turning bands simulation and then backtransformed to attribute space.

Sep 26, 2011

By F Grobler

Many projects fail because project performance in unfavourable economic conditions was not anticipated at the investment decision stage. This type of failure may have been avoided through the selection of a strategy that enables the project to perform well under a range of possible future economic conditions (a ærobustÆ strategy); rather than an æoptimalÆ strategy for expected future conditions that will never eventuate. This paper describes a method of rapidly generating mine plans for a number of project strategies, and then evaluating it against a representative range of equally likely economic conditions. The key performance indicators derived for each strategy are the mean value and the distribution of values (variance). A prudent investor will identify prospective strategies not only on the merit of highest mean values (as a proxy for economic reward), but also on the probability of not meeting specified investment hurdles (a proxy for risk). A robust strategy will achieve its financial objective even under adverse economic variations. This paper focuses on the effect of commodity price uncertainty; particularly around the choice of mining and processing capacity levels. Results from enumerating 2000 prices scenarios against 63 strategy combinations show how processing and mining capacity can be manipulated to manage the risk-reward trade-off.

Sep 26, 2011

By H Khoshrou, A Siamaki

Blast designs can have productive and non-productive impacts on downstream stages, mine productivity and operating costs. On the other hand, ground vibration, fragmentation, and back break caused by blasting impose damage and financial penalties and which must be controlled by blast design. One of the significant variables in blast design is the burden. In this study, the potentials of artificial neural network are investigated in prediction of burden in the Sungun copper open-pit mine. Input data were assembled through 18 blasting blocks according to different levels and experimental geomechanical investigation. To construct the model blastability index, uniaxial compression strength, hole diameter, specific weight of rock, rock quality designation and cohesion strength are taken as input parameters, whereas, burden is considered as an output parameter. Mean square error was used as the performance function and back propagation algorithm as the training function, containing four hidden layers and 14 data sets. Four sets of data were used to make sure that correct training had been carried out. This produced the coefficient correlation of 0.662.

Sep 26, 2011

By B Olsson, R Norman, M Volz

Australia has seen unprecedented demand for coal exports in the last ten years and this demand is set to expand further still according to many analysts. This rapid increase in demand has placed pressure on multi-user coal chains to guarantee the capability to meet the required demand, in terms of both current operations and capacity expansion.The projects required to deliver these forecast tonnages take place in an increasingly complex commercial and technical environment. These challenges include brownfield and greenfield projects, multiple rail and terminal service providers as well as a myriad of mine operators, all needing to operate in an increasingly integrated manner to get the most out of expensive shared assets.Increasing complexity and integration drives the need for more holistic and sophisticated analysis tools. The capacity of coal chain subsystems, such as mine, rail and port, can no longer be determined effectively in isolation. Understanding the nature of the interactions between these subsystems and the resultant capacity of the integrated system is the key to maximising value. TSG advocates the use of a total integrated system approach to understand the behaviour and performance of complex coal chains.Recently, TSG has worked in conjunction with the Integrated Logistics Company and a number of stakeholders in the Dalrymple Bay Coal Chain to deliver the first independent system master plan. This master plan has provided a quantitative and rational assessment of coal chain capacity in its current state and a prediction of its capacity once a pipeline of committed capacity improvements is in place. It is underpinned by a highly detailed discrete-event simulation model of the complete coal chain as an integrated system, including mine load-outs, rail, terminal and port operations.This paper provides an overview of the study and describes the differences between subsystem and total integrated system model performance.

Sep 26, 2011

By A Galli

Traditionally pit optimisation and scheduling is carried out assuming that the resources, the commodity prices and production costs are all known. Once the pit contour and the mining schedule have been determined, geostatistical simulations can be used to assess the impact of the uncertainty in the resources on the pit design and the schedule, and likewise stochastic simulations can be used to test its robustness to fluctuations in costs and prices. But it would be more interesting to have a dynamic procedure for optimising the pit as the resources become better known and as economic factors such as commodity prices and costs evolve over time. This paper presents a novel approach for doing this. Rather than working with millions of small selective mining units, it was decided to work with large blocks, called macro-blocks. Mining sequences are defined as the quantities to be extracted from each macro-block in each time period. Feasible mining sequences are mining sequences that respect the accessibility constraints, and also the geometric constraints on the amounts that can be extracted from any one block over time, or from all the active blocks at a given time. Having defined feasible mining sequences, their mathematical properties were studied. It was found that natural breakpoints occur in the sequences when one set of blocks have been completely mined out, thereby opening up access to others. It was discovered that families of sequences exist, and that they are convex and closed under certain types of row permutation operations. Convexity is an advantage when optimising. The closure property opens up the possibility of mixing and matching subsequences of rows in the sequences. Another interesting property is that the sequences form branching families, rather like the binomial and trinomial trees used to model prices in finance and in real options.It is easy to generate large numbers of feasible mining sequences randomly. A two-step procedure is used to find the best one (or the best few) using three economic criteria: expected NPV,the probability of a negative NPV, and the percentage of years when the cash flow is negative. The procedure was tested on a synthetic gold mine based on the characteristics of the Essakane mine in Burkina Faso. The results were encouraging.

Sep 26, 2011

By C Niemann-Delius

The problem of long-term open pit mine planning is a large combinatorial problem, which canÆt be solved easily by mathematical programming models because of the large problem size. In this paper a new metaheuristic algorithm, which has been developed based on the ant colony optimisation (ACO) and its efficiency, have been discussed. To apply the ACO process to a mine planning problem, a series of variables is considered for each block as the pheromone trails that represent the desirability of the block for being the deepest point of the mine in that column for the given mining period. During implementation several mine schedules are constructed in each iteration. Then the pheromone values of all blocks are reduced to a certain percentage and additionally the pheromone value of those blocks that are used in defining the constructed schedules are increased according to the quality of the generated solutions. By repeated iterations, the pheromone values of those blocks that define the shape of the optimum solution are increased, whereas those of the others have been significantly evaporated. By using this algorithm a high degree of complexities could be considered in objective function and constraints of optimisation model. In addition it can improve the value of the initial mining schedule up to 34 per cent for some cases in a reasonable computational time.

Sep 26, 2011