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By L van Latum, A Koh, I Thomas

Haul truck drivers are at a high risk of developing fatigue due to the monotonous nature of the work and demanding shift rosters. In the mining industry, fatigue is identified as a major contributing factor in several fatal accidents a year. A number of fatigue detection devices are now available, that can contribute to improved fatigue risk management in the mining industry. This paper describes the technology and algorithms behind one particular product which is both predictive and un-obtrusive to the operator. The Advisory System for Tired Drivers (ASTiD), was developed by Fatigue Management International (FMI) in collaboration with the Loughborough Sleep Research Centre (LSRC), both in the United Kingdom. While this system includes a model of the human circadian rhythm as a bias for predicting expected fatigue risk by time of day, it also uses yaw data generated by a solid-state gyroscope in the horizontal plane. Algorithms analyse this data to determine the level of monotony of actual driving conditions and to detect changes in steering control caused by driver fatigue. During normal levels of attention, a haul truck driver applies continuous, small adjustments to the steering wheel. As the level of fatigue increases, steering adjustments become less frequent and more erratic. Differentiating between these modes of control in the highly variable mining environment is accomplished by chromatic processing of the gyroscope data. This paper will go into some detail explaining the principles behind chromatic processing and the specific application of these in detecting fatigue in haul truck operators.Technology in itself is not the solution to addressing operator fatigue, but it plays an important role in ongoing monitoring and maintaining awareness with mining personnel. Furthermore, the specific principles behind ASTiD technology support a risk-based approach to fatigue management, allowing optimisation of the mining operation for productivity as well as safety.

Sep 26, 2011

By E Ben-Awuah

Mine planning is the backbone of all mining enterprises and determines the profitability or otherwise of any mining investment. Providing high quality ore and tailings containment at the right time are the main drivers in oil sands mining. Recent environmental and regulatory requirements also makes waste management an integral part of mine planning in the oil sands industry (Directive 074). Due to the limitation of lease area, the pit phase advancement in oil sands mining is carried out simultaneously with the construction of tailings dykes in the mined out areas of the pit. These in-pit dykes are constructed to hold tailings that are produced during the processing of the oil sands ore after an initial external tailings facility. Most of the materials used in constructing these in-pit and ex-pit dykes come from the oil sands mining operation, making it necessary to have a plan for supplying the dyke material. The research problem here is categorised as: determining the order of extraction of ore, dyke material and waste to be removed from a predefined ultimate pit limit over the mine life that maximises the net present value of the operation û a strategic schedule; anddetermining the destination of dyke material that minimises construction cost depending on the construction requirements of the various dykes as per their designs û an operational schedule. There has been developed, implemented and verified a theoretical optimisation framework based on mixed integer goal programming (MIGP) to address the oil sands long-term production scheduling and waste management problem. The research introduced the development of multiple material types and multiple destination optimisation techniques using MIGP in the context of mine planning. This study presents an integration of mixed integer programming and goal programming in solving large-scale mine planning optimisation problems using clustering and pushback techniques.

Sep 26, 2011

Reconciliation involves the collection of tonnage, grade (quality) and contained metal (product) data from disparate and hopefully independent sources. Examples are exploration data, production sampling data from blastholes or drawpoints, and process plant data. These data may be compared by means of ratios (factors). The F1 factor usually relates short-term (ore control) model tonnages, grades and metal content to ore reserves depleted. The F2 factor usually relates received-at-mill tonnages, grades and metal content to delivered-to-mill = production tonnages, grade and metal content. The F3 factor is F1 × F2 and enables a comparison of a mineÆs ability to recover the tonnage, grade and metal content estimated in ore reserves. The F1 factor measures the accuracy of orebody knowledge in the ore reserves to the demarcation of ore and waste by ore control (short-term model). The F1 factor may be used to check and calibrate the selectivity of mineral resource models and/or planned dilution assumed in transfer from mineral resources to ore reserves. The F2 factor enables a check on unplanned dilution entering the ore stream between ore control and the mill. Using the factors it is possible to calculate a monetary value on improvements in the accuracy of orebody knowledge, selectivity and the effects of dilution and ore loss. Reconciliation should be an implicit part of the mining process, and reconciliation targets should be a key performance indicator for well-run mines.

Sep 26, 2011

The Rio Tinto Iron Ore group currently estimates grade attributes for mineral resources using ordinary kriging (OK). OK produces a smoothed globally unbiased estimate, but does not capture local variability, which can have significant impact on mine planning and scheduling. The dimensions and the multivariate nature of iron ore deposits make point simulations of whole deposits impractical. With a direct block simulation method the size and speed limitations of point simulations can potentially be overcome, resulting in a viable method for capturing local variability and for providing risk measures into the mine plan and schedules. Some iron ore deposits are highly folded (post mineralisation) and require preprocessing using an unfolding algorithm to properly define the continuity parameters and directions of anisotropy before simulation can proceed. Block co-simulation proceeds in unfolded space and since mine planning require a block-model with regular block-sizes; a regular block-model is unfolded first. This paper discusses a block co-simulation approach that can be used to simulate the irregular blocks resulting from the unfolding process. A section of an iron ore deposit is used for testing the block co-simulation algorithm and the simulation results are compared against the Isatis direct block simulation method. Available blasthole data is also used to validate the behaviour of the block co-simulation algorithm.

Sep 26, 2011

By B J. Wilde

Evaluating the resources and reserves in a mineral deposit requires many interdependent subjective decisions. The decision of stationarity is arguably the most important. This decision includes: the choice of domains that subset the entire deposit, modelling the boundaries of each domain, and understanding and considering the nature of the contacts between domains. Reasonable choices are made in consideration of the geological context, the available data, the goals of the study and the available resources for the study. This paper reviews aspects of the decision of stationarity in modern resource assessment with a focus on quantifying uncertainty and using numerical/statistical tools to help the geomodeller. The use of volume/distance functions to quantify uncertainty in domain limits is particularly important and is presented in detail. The use of contact plots provides help with the continuity of grades across domain boundaries. Realisations are not assembled as a branching combinatorial of all possibilities, but as a set of realisations that provide a measure of uncertainty. This paper amounts to a definition of stationarity for modern resource assessment.

Sep 26, 2011

By J F. C L Costa, J C. Koppe, C Diedrich

Currently mineral resources evaluation is firstly expressed in terms of volume. Next the resources are transformed into tonnes by using an average bulk density applied to the whole deposit or, in some other circumstances, various different densities to distinct geological domains. Previous work in uranium, copper and nickel deposits used linear correlation between grades and bulk density to derive the density. Geophysics methods are also another good way to obtain density indirectly.These approaches do not take into account that two samples with the same grade but with different bulk densities can characterise different sample supports, leading to severe bias in grade and tonne estimates. It is important that the systematic measurements of bulk density are included into the estimation process, particularly when density is not correlated with the grades. Additionally, the extra time and costs involved in obtaining the bulk density data have made the mining industry disregard it.This study reports the impact in resource estimates by using a methodology that includes the bulk density variability. It involves the determination of a variability model for the bulk density and another model for the composite that was weighted by density as well as by its length. Next, these variables are estimated by kriging and the final grade estimates are obtained. The tonnages are obtained with the bulk density estimates. The conventional methodology was also applied and both estimates are compared to the reconciliation data from a major copper deposit. The results show a significant difference between the tonnage and grade estimates obtained from these two methodologies.The results show that considering density variability when compositing leads to a more accurate methodology resulting in better grade and tonnage estimates locally and globally.

Sep 26, 2011

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

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