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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

By Q Qian, M Karakus, J Wang, N Melkoumian, D Goodchild, Q Sui, C Wang

Probabilistic analysis is widely used in slope stability assessment because of its capability to take into account uncertainties associated with geotechnical parameters. There are, however, still many unresolved practical issues to the application of the method. One of these issues is the integration of assessments when multiple sets of fractures are considered for the entire slope. In the present study, this issue is addressed within the framework of Monte Carlo simulation. Probability of failure for pairs of fracture sets are assessed independently and the final assessment of the probability of failure for the entire slope is done using conditional probabilities. Prominent Hill open pit, in particular, the problematic fresh Bulldog shale formation, was used as a case study to test the method proposed. The SWEDGE program of the RocScience software package was used for the analysis.

Sep 26, 2011

By R Razanatsimba, A Galli

A mining company has entered into contractual commitments to supply certain quantities of its product to clients in each time period. Failure to fulfil its obligations would damage its reputation as a reliable supplier. The planned production would normally be sufficient unless major problems such as highwall failures, roof falls, natural disaster, or strikes that interrupt production or delays in obtaining environmental permits, occur. To overcome these difficulties, the company may be able to obtain more of the commodity: from its strategic stockpile (if there is sufficient there); by buying it on the spot market; or for some commodities such as gold and uranium, by leasing it. Two sources of uncertainty, the spot price of the commodity and the occurrence of serious incidents that interrupt production, are considered. For simplicity it is assumed that the commodity price follows a geometric Brownian motion, that productions incidents have a binomial distribution, that they are independent from one time period to another and that they do not affect prices. A branching tree structure is used to model the joint evolution of prices and production incidents. The aim of this research project was to evaluate the companyÆs options for fulfilling its contractual commitments. When evaluating these options, multi-stage stochastic programming with recourse was used to solve this problem because in addition to providing the dollar value of the project, it gives decision-makers the æroadmapÆ to reach the optimal value. That is, how much material should be bought/sold on the spot market, how much to add to/subtract from the stockpile, how much to lease and in the worst case what the shortfall would be, for each time period as a function of the current spot price and breakdown status. A case-study over a five-year period is used to illustrate the proposed procedure. In this study the production would be sufficient to cover the companyÆs contractual commitments unless production incidents occur. Focus is on evaluating the impact of the quantity initially in the stockpile on the value of the project when the commodity market is tight, that is, when only small quantities can be bought on the spot market or by leasing.

Sep 26, 2011

By V Gau, L Overmeyer, M Kesting

Economically efficient mining processes can be significantly assisted by the implementation of sensor technologies for the detection of materials and boundary layers between materials inside the stratum. These sensors are meant to facilitate the machinery operator and illustrate furthermore the prerequisites in assisting tools towards fully automated mining equipment. Therefore, project æSensory Identification of Material Type and Detection of the InterfacesÆ (SIMT) aims at the integration of ground penetrating radar (GPR) and geoelectric sensor technology by endowing with them a bucket of a bucket wheel excavator. For this reason, a sensor bucket was developed and tested in the Inden Mine of RWE Power AG. The feasibility of integration and deployment of such sensor technology has been proven effective for the conditions of open cast mining. The main focus of current research lies on developing algorithms to analyse the acquired measurement data from the sensors to automatically detect layers and materials. A computer network receives the GPR and geoelectric measurement data as well as the positioning data of the sensor bucket. The positioning data mainly consists of GPS and encoder information, which is merged with the sensor data by using the UTC time stamp. With the aid of consecutive processes the relevant information will be extracted and provided to the user. The aim of SIMT is to replace material identification, traditionally carried out by the operator, by implementing a continuous operating interpretation system in mining equipment based on GPR and geoelectric sensor technology, resulting in more detailed information regarding the working face during an excavation. This detailed information provides an opportunity for efficient planning and controlling of selective mining of natural deposits, such as the process of lignite mining.

Sep 26, 2011

The paper provides an overview of the development and content of APCOM symposia since their inception in 1961. This development is set within the context of the academic environment in which much of the research published at APCOM symposia is conducted. This environment includes rigorous external assessment of research quality at national levels and against international benchmarks, which carries significant consequences for funding and for the continued existence of the academic minerals industry disciplines. Adverse consequences that undermine the academic provision of mining and resource engineering will impact negatively on the minerals industry by reducing the level of research and education resources available to it.The traditional reductionist approach to research is contrasted with the inter-disciplinary approach that is required to address the complex systems problems that the minerals industry will have to address to ensure its technical, economical and environmental sustainability. Such an approach offers a means of raising research profiles, of establishing mutually beneficial academic-industry engagement and provides an opportunity for APCOM to take a lead role in identifying the research and development agenda for the future

Sep 26, 2011

By B F. J Kelly

Multiple-point simulation is a family of methods used to generate conditional facies simulations that present the same spatial dependence as a user-defined training image. The main advantage of using a training image is the easy integration of geological concepts in numerical subsurface models. The strength of multiple-point methods is that it allows making the best use of geological expertise when data are too sparse for inferring a model of spatial continuity. However, despite being appealing to geologists, the multiple-point methods are often not used in practice due to the difficulty of building complex 3D training images that contain realistic features.A new framework for multiple-point simulation is presented that involves small and simple training images. The main idea is to use random transformations to expand the range of structures available in the simple training image. The training image is not regarded any more as a global conceptual geological model, but rather a basic structural element of the subsurface. Complex geological structures are obtained, whose spatial structure can be parameterised by adjusting the statistics of the random transformations, based on field data or geological context. In most cases, such parameterisation is possible by adjusting two numbers only. One advantage is that the training images are so simple that they can be easily built even in 3D. The method allows building 3D models that reproduce shapes corresponding to a desired prior geological concept and are in phase with different types of field observations, such as orientation, facies or geophysical measurements. The flexibility of the approach allows for naturally integrating different types of data. This is demonstrated on an example involving seismic data assimilation. Examples where complex 3D folded structures are built from a simple training image and a few orientation data are shown.

Sep 26, 2011

By Q Wang

Net present value (NPV) has been the main criterion in mining project evaluation and production plan optimisation. It is a common practice in NPV calculations to take into account only the tangible cost items in the corporate accounting system. The costs incurred due to damage to the local ecosystem and environment are left out and treated as externalities. Such practice on the one hand underestimates the total cost of a mining operation and, on the other hand, ignores the difference in ecological/environmental impacts between different mine plan alternatives. This paper attempts to measure the ecological costs of mining and to incorporate such costs into the evaluation scheme in mining project evaluation and mine planning. The ecological costs of mining are estimated based on the areas and types of land (eg forest land, grazing land, crop land) damaged by mining activities and the carbon emission from fossil fuel consumption. For the land areas (and their associated ecosystems) directly damaged by mining activities, the ecological costs are measured by the prevention and restoration costs, the lost values of eco-services of the damaged land, including bio-product production, soil erosion control, water run-off control, oxygen release, air pollutant absorption and formation of soil nutrients. The ecological cost of carbon emission from the energy consumption of a mining operation is measured by the cost of cultivating the equivalent forest land required to uptake the emitted carbon. A case study on a large-scale open pit mining project is presented to demonstrate the influence of ecological costs on the mine planning outcome.

Sep 26, 2011

By P Hatherly, A Melkumyan, D Wyman

Chemical species distributions in rock sequences can be modelled using Gaussian processes (GPs) to predict the weight percentage between known data points. This method can be improved by dividing the modelled area into regions of different mineralogy. In the context of many iron ore deposits, marker shales give the initial guidance of where these regions may lie. These shales give rise to distinctive peaks in the natural gamma downhole logs, which are conventionally identified by hand. A GP method has been developed to automate this task.Once the appropriate boundary has been located on the basis of shale occurrences, chemical assays from exploration drill holes are used to find the exact boundaries of interest. These boundaries divide the drill hole stratigraphy into regions of different mineralogy, each of which display their own distinct correlations between the main elements and oxides (Fe, SiO2 and Al2O3). Iron ore shows a negative correlation between Fe and Al2O3, but in banded iron formation (BIF) there is a positive correlation between these species. Similarly, SiO2 and Al2O3 have a positive correlation in the shales and ore but a negative correlation in the BIF. Correlations obtained within ore-, BIF- and shale-dominated regions are therefore better than those obtained using the entire log and can be used to improve the results obtained when modelling.

Sep 26, 2011