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By A. Melkumyan, K. Silversides

Chemical assays collected from blast holes have the potential to greatly improve the local accuracy of grade models. Assays must be assigned to the correct domain or they will introduce errors. Boundary surfaces created from the widely spaced exploration holes (~50m) do not provide sufficient resolution to correctly delineate domain boundaries between closely spaced (3-5m) blast holes. This paper demonstrates a probabilistic method for classifying production blast holes near domain boundaries. Gaussian processes models are applied to identify the assays and assay ratios most suited to distinguishing particular domains. These models are combined with the exploration based surfaces to classify the blast holes that can be confidently assigned to a particular domain. A reiterative method is used, where information from the blast holes closest to the exploration holes is used to label blast holes further away. This is tested on two domain boundaries in a banded iron formation-hosted iron ore mine.

Sep 1, 2021

By T Mattiske

The Fimiston Open Pit (commonly referred to as the æSuper PitÆ) is a part of the Kalgoorlie Consolidated Gold Mines Pty Ltd (KCGM) operation in Kalgoorlie-Boulder. KCGM manages the Fimiston Open Pit, the Mt Charlotte underground mine, the Fimiston Mill and the Gidji Roaster for its Joint Venture Owners, Newmont and Barrick Australia. KCGM produces up to 850 000 ounces of gold per year. The KCGM operations are located on the historical Golden Mile on the outskirts of the City of Kalgoorlie-Boulder, about 600 kilometres east of Perth Western Australia. This traditionally underground mining centre has been worked since 1893, yielding in excess of 50 Moz of gold from ore with an average head grade of 10 g/t. This represents approximately ten per cent of the gold produced in Australia and one per cent of the gold produced in the world. The Fimiston Open Pit is essentially an aggregation of what were known as the Judd, South, Paringa, Croesus/Eclipse, Central, Brownhill, Drysdale, Morrison, North and Horseshoe pits into one continuous large-scale operation. The old Fimiston Underground Operations (included the Main, Croesus, Chaffers, Lake View, and Perseverance shafts) were also located within the area of the Fimiston Open Pit. The underground operations closed in 1992 due to high mining costs.

Jan 1, 2002

By Robert Fritzsch, Ragnhild E. Aune, Mark William Kennedy, Behzad Mirzaei

"The quantitative particle concentration can give important information about the cleanliness of melts for quality control in primary and secondary production of aluminum. Manual quantification of the particle concentration is normally a time consuming process and human control can bias the acquired images and particle count.The present paper explains the automated image-processing steps for the quantification of SiC-particles, with equivalent diameters from 2 to 25 µm, in solidified A356. A total of 700 micrographs, acquired with a standard white light microscope with 10 x magnification, were analyzed. The applied software (Image Pro-Plus 7.0 from MediaCybemetics®) allows for programming of macros which in tum provides the user with a higher degree of control. The automated results are compared with the results obtained by manually counting the particles in the same micrographs. The impact of the automated results on the estimated filtration efficiency was established to be only -3%.IntroductionInclusions are both indigenous and exogenous particulates that are present in commercial metals such as aluminum. If large individual or large numbers of inclusions are present, they can have a significant negative impact on the appearance, mechanical and chemical behavior of products produced from the metal. Aluminum is known to contain large numbers of inclusions: =50 µm in size, e.g. typically <l ppm by volume or <100,000 inclusions of>20 µm per kg of metal as detectable using a Liquid Metal Cleanliness Analyzer, LiMCA [1]. Depending on the final application of the aluminum, inclusions can render the metal 'not fit for purpose', and result in serious economic consequences to metal producers.It is not surprising that the aluminum industry has developed a number of treatment processes to improve metal cleanliness, given the large number of high value products whose quality can be adversely affected by inclusions. CFFs are the most commonly applied filtration process and have been used to filter >50% of the world production of aluminum since the 1990's [2].Inclusions in aluminum may be particles, bifilms [3] or clusters of: oxides (Al2O3 , SiO2), spinels (MgO· Al2O3), carbides (SiC, Al4C3), nitrides (AlN), borides (TiB2), sulfides, phosphides and intermetallics [ 4]."

Jan 1, 2013

Characterization of ore and waste-rock material using automated quantitative micro-mineralogical techniques (e.g., QEMSCAN® and MLA) has the potential to complement traditional acid-base accounting and humidity cell techniques when predicting acid generation and metal release. These characterization techniques, which most commonly are used for metallurgical, mineral-processing, and geometallurgical applications, can be broadly applied throughout the mine-life cycle to include numerous environmental applications. Critical insights into mineral liberation, mineral associations, particle size, particle texture, and mineralogical residence phase(s) of environmentally important elements can be used to anticipate potential environmental challenges. Resources spent on initial characterization result in lower uncertainties of potential environmental impacts and possible cost savings associated with remediation and closure. Examples illustrate mineralogical and textural characterization of fluvial tailings material from the upper Arkansas River in Colorado.

Feb 27, 2013

By Karl Nienhaus

The BGMR has developed a method for an online-analysis during drilling processes in cooperation with Fraunhofer Institute for Laser Technology in Aachen and various industrial partners. This allows for efficient utilization of deposits, maximum recovery of desired minerals and frequent and costefficient updates of deposit models. The used method, namely laser-induced breakdown spectroscopy (LIBS), was implemented into a compact demonstrator in order to analyze drill dust and loosened material within mining environments and directly on drill rigs. The consequently developed OFURanalyzer (OFUR ? online-analysis of mineral deposits) provides possibilities for exploration and visualization of deposit models in real-time and thus creates an opportunity for innovative applications, such as contactless, continuous quality control of minerals throughout the extraction process. A major challenge in the execution of this project was the necessity to develop a laser system that could withstand rough mining environments dominated by dust, shocks, vibration and therefore would be suited to operate under such conditions.

Oct 1, 2009

By C. Kim

The integration of three-dimensional (3D) data with infrared thermography has received much attention for its potential use as input data for building energy simulations. Therefore, researchers have proposed methods to integrate 3D data with infrared thermography obtained by different sensors. These studies show the potential of the integration of 3D data and infrared thermography using various systems. However, these studies have ignored property aspects of infrared thermography, such as the low resolution of infrared thermography and external environmental influences. Thus, their proposed methods to integrate 3D data and infrared thermography are inadequate because these problems can affect the resulting building energy simulation. This research proposes a framework that integrates infrared thermography and 3D data while accounting for the resolution and shadow effect of infrared thermography. The proposed framework consists of three steps. In the resolution enhancement step, bi-cubic interpolation is proposed. In thermal data correction from the shadow effect, the shadow area is detected from the visible image, and then the shadow effect is removed from the registered infrared thermography. Finally, enhanced infrared thermography is mapped to the 3D data obtained by a laser scanner. The performance of the proposed framework was evaluated using test images obtained from a building in operation. The experimental results show that the proposed framework provides a 3D thermal model that is useful and accurate in simulating building energy. It is expected that the 3D thermal model can be used for accurate energy simulation of existing buildings. In addition, the proposed framework can be applied in defect detection for buildings. Furthermore, this novel approach enhances infrared thermography using visible images.

Aug 1, 2013

By Pentti Niskanen

"The ventilation system of the Pyhasalmi Mine is described. The mine is located in an area where the annual atmospheric temperature may vary from +30°C to -30°C. Natural ventilation causes serious difficulties because the underground mine is connected by drifts and finger raises to the bottom of the 120 m-deep open pit.The natural ventilation tends to make the ventilation net unstable. The paper describes how the instability characteristics of the ventilation net can be studied and how they have been taken into account when automating the control of the ventilation. In addition, the software, hardware and costs of the system are described. The system consists of 12 automated ventilation doors, two mine fans, 8 elements for sensing the air flow and temperatures, a control panel and a process computer."

Jan 1, 2014

By F. M. Ramadhan, M. R. Pratama

Monitoring open-pit mine road conditions has become very critical nowadays. There are several benefits to in conducting research on this field such as opportunity to improve truck’s speed which directly influences truck’s productivity. This research aims to provide a semi real-time, and sensors-based automatic road damage detector that predicts the road quality through road damage exact location tracing, road damages auto-classification, and real-time instruction in the form of work order format to mining road maintenance, and repair authorities through mobile apps and maintenance unit’s in-cabin display. It will also provide the maintenance/repair activity performance evaluation via integrated dashboards. The artificial neural network algorithm is applied during the process of data training regarding classifying road damages and severities which resulted in a valid model. The road damage classification model has a precision level of 80.5% in predicting whether the road is damaged or not. Using this approach, we are expecting to visualise a road quality map of all active roads on mine site.

Sep 1, 2021

By R. L. Tobie

The degree of automation that has evolved in the dumping and hoisting of ore at the 3A and 3B hoisting shafts of the San Manuel mine is discussed. Motormen underground now dump their own trains at shaft pockets with rotary car dumps which operate semiautomatically. The ore is then drawn out of the 1200-ton shaft pockets into measuring baskets in skip loading stations, loaded into bottom-dump skips, hoisted, and dumped into coarse ore bins on the surface, in a fully automated operation. In an underground control room on the 2075 level midway between 3A and 3B shafts, car dumping, skip loading, and hoisting systems are monitored on lighted control panels which mimic each step in the operation. Here skip loading and dumping are viewed on closed-circuit television.

Jan 1, 1972

By M Fimeri, R Williamson

Sample sizing in the iron ore industry requires significant manual effort to collect, transport, and sieve the samples as well as record, process and report the data. Efforts have been made to automate this process, which essentially emulate the manual process, except that a person is replaced by a robot. Even in these cells, the sieving and sizing operation consumes a significant proportion of robot time. The proposed doubling of production from AustraliaÆs iron ore mines over the next five years will place increasing pressure on sample sizing and interest is increasing in alternative automated methods. One such method is the adaptation of computer vision technology which has been successfully introduced into the mining industry in a range of applications including feed size distribution to SAG mills and oversize detection from screening stations. The limitation of this technology is that only the top surface is visible and this is used as an indicator of total size distribution. Obviously in a sample plant, where 100 per cent of the sample must be measured for acceptable quality assurance, this approach is unacceptable. In order to capture 100 per cent of the sample using computer vision technology a monolayer must be formed either by spreading the sample across a moving surface, such as a conveyor or vibrating platform, and passing this under a camera. This will become a æsurgingÆ load which presents specific problems for some types of computer vision systems, namely those relying on a 2D approach. In this paper a 3D computer vision technology will be described which overcomes the problems associated with measuring particle size in a monolayer and experimental results will be presented which demonstrate its effectiveness for lump and fine iron ore sample size measurement.

Jan 1, 2008

By P Hofmeyr, D Pretorius

Iron ore, which is transported by rail from the iron ore mines at Sishen in the Northern Cape, to the Saldanha Iron Ore Terminal in the Western Cape, needs to be analysed while loading to be exported. Every shipment will be sampled and an average analysis of the ore will be produced which is required by the purchaser at the receiving port. On arrival at the iron ore terminal at Saldanha, the ore is tipped from the rail trucks onto conveyor belts to be either stockpiled or direct-loaded. The conveyor belts run under the sampling plant and the ore is sampled periodically by means of an automated cross-belt sampler. This is the first iron ore port sampling facility in which the automated sampler is situated above the conveyor belts and not adjacent to them. A portion of the primary sample taken off the belt is moved to the dedicated sampling laboratory situated behind the cross-belt sampler and over the conveyor belt lines. The sample is split in the laboratory, one split being used for moisture and particle size determination and the other part is dried and then analysed by means of X-ray fluorescence spectrometry (XRFS) on fusion beads for major and minor elements. Particular care is taken to obtain high quality data for penalty elements.This sampling plant is completely automated and from the primary cross-belt sampler to final analysis by XRFS no manual steps are involved. This has meant that some processes are used which are suitable for automation and are ISO compliant but not necessarily ISO-certificated. The ISO manuals were developed with manual laboratory processes in mind. Precautions are also taken to maintain the integrity and representativity of the samples. This paper is a case study?describing?the automated sampling systems used at the Saldanha Iron Ore Terminal sampling facility and laboratory from conveyor belt to analysis.CITATION:Hofmeyr, P and Pretorius, D, 2012. Automated sampling of iron ore for analysis at the Saldanha Iron Ore Terminal in South Africa, in Proceedings Sampling 2012 , pp 25-32 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Aug 21, 2012

By J. M. Rebstock

Recent re-classification of crystalline silica as a potential human carcinogen has resulted in a new limit of 0.1% free silica in bulk powdered minerals. Detection of silica at this level can be expected to severely challenge or exceed the capabilities of existing protocols for x-ray diffraction, the traditional method for free silica determination. Automated SEH-EOS analysis provides an attractive alternative method, with direct analysis of individual particles for the determination of the concentration of free silica, and a direct measurement of silica particle sizes. This paper will discuss automated SEH analytical procedures, sample preparation, and statistical considerations.

Jan 1, 1990

By D. Chetty

The characterization of platinum group minerals (PGMs) from concentrator flotation circuits provides valuable information towards understanding PGM recovery under given milling and flotation conditions. Additional mineralogical characterization also provides information on ore variability, which may affect recovery. Considering the low grades involved, automated scanning electron microscopy (SEM) characterization of PGMs has enabled relatively quick analyses, compared with older, time consuming and less accurate optical microscopy methods. Over the years, parameters, such as species, size distribution, liberation, gangue and base metal sulfide (BMS) associations, have been used to characterize the PGMs in a process mineralogical context, to aid metallurgical interpretation. This paper considers whether flotation indices determined for PGMs can be empirically validated, using initial results from analysis of PGMs in a concentrate bank from a South African concentrator treating UG2 ore from the western limb of the Bushveld Complex. The aim is to determine whether floatability is adequately described by these parameters, or whether other mineralogical factors must be considered when assessing PGM floatability, and hence accounting for recovery vs. losses to tails. The data obtained to date, show that flotation parameters (including liberation index, BMS/gangue relationships and size distribution) have merits where large changes in PGM concentration occur, as observed in the first four cells of the concentrate bank. Larger data sets, however, are required to assess floatability, particularly where concentrate cell grades are similar, as observed in the last six cells of the concentrate bank. Furthermore, the floatability indices could be enhanced by the incorporation of selected associated gangue mineral information, chief amongst these being the content and mode of occurrence of naturally floatable talc and associated orthopyroxene.

Jan 1, 2009

By Matthew A. Baumann

Tooth wear on mining shovels presents a challenge for productivity. A shovel with worn teeth suffers from increased digging forces, longer fill times, and an increased likelihood of lost or broken teeth and adapters. Efficient maintenance planning relies on knowledge of tooth decay rates and patterns. A fully automated wear monitoring system can record detailed logs of tooth wear, permitting G.E.T. personnel to optimize maintenance scheduling. Studies have demonstrated that planned tooth change-outs result in significantly reduced costs as compared to unplanned maintenance. A machine-vision-based tooth-wear monitoring method is introduced. A rugged camera is installed on the boom of cable shovels, or on the stick of hydraulic shovels. The images are processed using a photogrammetric algorithm to determine the individual tooth lengths. Tooth length data is collected at intervals during shovel operations, forming a profile of the current wear state, speed of decay, wear pattern, and expected lifespan of teeth. A case study in a South American mine on a Komatsu PC8000 hydraulic front shovel is presented. Tooth wear is tracked over a 6-day period and wear rate statistics are computed for 6 teeth. This validates the system and provides an example of how wear patterns can be recorded and analysed by an automatic system. Armed with this data, mine personnel can reduce the cost and lost time due to unplanned maintenance, and observe the effects of mine conditions and operator behaviour on tooth wear.

May 1, 2011

By M. K. Elmouttie, G. V. Poropat

Mapping of exposed rock walls provides data on geological features such as joints, bedding planes, faults etc critical to the economics and the safety of mine operations. Traditional, manual methods of mapping can be time consuming, error prone and limited in their coverage. New methods have been developed to map exposed faces remotely and safely however the benefits of these methods are only fully realised when exposed structures can be quickly and easily mapped using automated methods. This paper describes the application of an automated method of analysis of 3D images that provides users with the capacity to map exposed structures automatically. The method is based on the use of vector classification methods used in some neural network systems and provides rapid and reliable identification of exposed planes. CSIRO Exploration and Mining is applying these methods to 3D images for a range of applications from pit mapping to machine automation.

Jan 1, 2006

By Gary Schaffer, Anthony Stentz

Surveying of mines is a necessary step in coal mining to assemble maps which are used for tracking progress in the development of the mine and to plan future coal removal. A first approach for automatically building high-resolution two-dimensioml maps of underground coal mines is presented. The technique assembles laser rangefinder scans obtained from several locations within the mine into a consistent global map of the mine. Each range scan comprises thousands of data points collected in one second, thereby achieving higher resolution and much faster measurement compared to a human using a transit. The raw data (range scans) are processed to fuse them into a consistent representation. This paper describes a first approach to automated surveying and presents initial results of merging range scans. Improvements to the scan-merging algorithm are proposed.

Jan 1, 1993

Eliminate the need for roof bolter operators and their helpers to work under unsupported roof while setting or removing temporary supports before or during the roof bolting cycle-a task that annually accounts for approximately 20 percent of all roof fall fatalities. Approach The Bureau of Mines designed, built, and field tested an auto-mated temporary roof support (ATRS) that can be carried from place to place on the drill head of a single, fixed-head roof bolting machine (squirmer) used in low coal seams (less than 42 inches thick). How It Works The ATRS is designed for installation on any streamlined squirmer. It consists of a ten? foot-long, steel, wide-flange beam supported by two double-acting, telescoping hydraulic cylinders. A steel sleeve, mounted on the bottom center of the beam, is designed to fit over top of the

Jan 1, 1983

By William Gray

It has been ten years since the Mine Safety and Health Administration (MSHA) promulgated a safety standard requiring the use of Automated Temporary Roof Support (ATRS) systems in underground coal mines. The use of ATRS systems with roof bolting machines and continuous miners equipped with integral bolters has all but eliminated fatal roof fall accidents associated with manually setting temporary roof support. The last ten years have seen an evolution of the ATRS systems themselves and of MSHA&apos;s policy regarding ATRS systems. These changes are a response to operational and safety issues regarding ATRS systems often brought about by the ever-changing coal mining environment. As a result, MSHA&apos;s Technical Support personnel receive numerous inquiries from all segments of the coal mining industry on various ATRS topics. These requests for technical assistance range from an interpretation of a facet of the ATRS standard to an in-depth evaluation of a prototype ATRS system. This paper will address the aforementioned evolution of ATRS systems and will also summarize the factors MSHA Technical Support uses to evaluate these systems in the field. In addition, some of the more common misconceptions regarding the design, use, and regulatory requirements regarding ATRS systems will be covered.

Jan 1, 1998

By J M. F Clout

Geological, textural and mineralogical classification of iron ores is routinely conducted by mine and exploration geologists on reverse circulation percussion, blasthole cone and bulk samples for grade control, defining ore and waste, determining lump:fines ratio and predicting downstream processing characteristics. Classification is based on physical hardness, colour, streak and other visual characteristics. However, logging is generally restricted to the five to 20 per cent of chips above about 2 mm. This means that most of the sample is too fine to be reliably logged for anything but colour or streak and the textural types must be inferred from other information. A recently developed software package called æRecognitionÆ allows automated identification and classification of iron ore textural types and gangue on a particle basis in polished section down to 0.01 mm using processed digital images. It is not designed to be used as a routine logging tool, but is applicable where higher definition is required at the resource evaluation stage on selected percussion, drill core or bulk samples. æRecognitionÆ processes data obtained after optical image analysis of fine iron ore samples. It contains several parts, like æRecognitionÆ, æCompositionÆ, æLiberation by Total IronÆ, æLiberation by PhasesÆ and æStatisticsÆ, which allow vast and diverse information to be obtained about the iron ore samples being studied. Identification and classification of the textural type of each particle is performed according to the CSIRO-Hamersley Iron Ore Group Classification Scheme. The recognition procedure encompasses a good knowledge of different ore types and a logical knowledge tree, managing fuzzy boundary problems and the potential for a few different ore types in the same particle to be considered. This new technology introduces strict criteria to make the process of identifying particles more objective. Automated recognition standardises the identification procedure and significantly saves time. It enables users without a strong background in mineralogy to perform accurate texture identification. The æRecognitionÆ package has a modern user-friendly interface and Word compatible output. The combination of all the features mentioned makes the package a convenient tool for detailed studies of fine iron ores.

Jan 1, 2005

By G. K. Palshikar

Mining operations offer many opportunities for the use of IT and communication technologies to improve automation and thereby deliver higher levels of productivity, cost, quality and safety. In this paper, we discuss a particular application, namely, building a fully automated train control and operation system within a mining area. We first outline the basic architecture and functionality of such a system and then present a real-life case study where the railway system within a remote iron ore mine in Canada was converted to a fully automated train operation system. This hard real-time safety-critical system required the use of advanced technologies such as embedded systems (e.g., on-board train controller), radio communications, formal methods, optimal scheduling, and extensive simulations. We focus on the design aspects for maintaining the system in safe states.

Sep 1, 2012