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By W. M. Shaver, W. R. Dengler, F. A. Edwards

INTRODUCTION The Macassa Division of Lac Minerals Ltd. is a high grade gold mine that has been operating in Kirkland Lake, Ontario for the past 51 years. It produces approximately 120,000 tonnes of ore per year, averaging approximately 18.7 grams per tonne (0.45 ounzes per ton) of gold. Over the years, the ore body has been developed to a point where mining takes place below the 1,220 m (4,000 feet) elevation and 1,830 m (6,000 feet) west of the original shaft. The mine is currently serviced by two surface and two winze shafts. (Figure 1). The ore body extends below the 1,930 m (6,400 feet) Level which is the bottom of the existing mining operation. An ambitious underground exploration program has substantially increased these reserves. Concurrent with the discovery of greater reserves, has been rapidly escalating mining costs, particularly labour which constituted 66% of the total cost in 1981. These costs continued to escalate as the major proportion of mining was carried out to the west, far away from the existing hoisting systems. This required three lift hoisting for men, materials and ore. I n addition to the increased travel time and ore handling expense, increased humidity and temperature and ventilation and compressed air losses have added to the inefficiency of the operations. As a result, studies were initiated in 1980 to assess the future of the Macassa operation.

Jan 1, 1985

By Alan Morris

BRASS rod for use in automatic screw machines is one of the major products of the brass mills. A large tonnage is consumed each year in the manufacture of an endless variety of finished articles and parts for assembly into other products. The machines that do this work have been developed to a high degree of efficiency, and their manufacturers are still striving for higher production speeds. This development has brought with it a demand for brass rod to meet the increasingly difficult machining conditions. This paper is a report of the results of tests undertaken in an effort to obtain a measure of the effects of such variables as lead content, microstructure and cold work on the machinability of brass rod. It is realized that the term "machinability" has never been well defined. Power, finish and tool life all should have a place in such a. definition. In practice it is sometimes found necessary to sacrifice some of these in order to obtain a rod that will not tend to throw up a burr, or will give a burr which is easily tumbled off. Again a specially stiff rod may be necessary because of long overhang from the chuck or a rod of special characteristics is needed to take a given broaching operation without chattering. It is only through an understanding of the effect of basic mill operations on the machinability of the rod that such problems as these may be solved most economically. It is hoped that this paper, together with the discussion it may evoke, will prove to be a basis for such an understanding.

Jan 1, 1932

By Scott D. Thomson

The client for this project specializes in the use of state-of-the-art technology to fabricate and assemble composites and metal-bonded structures for commercial and military aircraft programs. One of the key components for this operation at the client's plant is a highly precise computer-numerical-controlled router known as the Shoda Machine. This machine tool was installed at the plant in July 1998 for the manufacture and inspection of aircraft parts, and it is extremely sensitive to deflections. To monitor the occurrence of deflections, an internal laser system continuously checks the machine calibration. Deflections in excess of 5/1000-inch in the foundation on which the machine rests require costly periodic re-leveling and recalibration of this tool. Therefore, a significant problem arose when measured deflections during machine operation were as large as 20/1000-inch. In addition, the foundation was cracked in at least three places as a result of elastic foundation movement caused by dynamic loading as well as shrinkage and loss of base support. Deciding on a remediation procedure to alleviate or eliminate these problems involved the following important considerations: (1) achieving the client's need for reducing deflections to less than 5/1000-inch, (2) minimizing the repair cost as much as possible, and (3) reducing the time required for implementation of the foundation remediation solution. After evaluating the advantages and disadvantages of several alternatives, jet grouting was selected as the most suitable solution. The jet grouting program provided columnar elements of soilcrete to remediate the Shoda Machine foundation.

Jan 1, 2003

By Dean H. Ambrose

This paper presents the results of a study using computer human modeling to examine machine appendage speed. The objective was to determine the impact of roof bolter machine appendage speed on the likelihood of the operator coming in contact with. A contact means two or more objects intersecting or touching each other, e.g., appendage makes contact with the operator?s hand, arm, head or leg. Incident investigation reports do not usually contain enough information to aid in studying this problem and laboratory experiments with human subjects are also not feasible because of safety and ethical issues. As an alternative, researchers developed a computer model approach as the primary means to gather data. By simulating an operator?s random behavior and machine?s appendage velocity, researchers can study potential hazards of tasks where it is not possible to perform experiments with human subjects. Analysis information is helpful to the mining industry in terms of making recommendations that reduce the likelihood that roof-bolter operators experience injury due to contact with a moving boom. Data analysis of roof bolter simulations show that the virtual-operator?s response time has little effect on the number of contacts experienced. Based on frequency and cross-tabulation, regardless of other variables, contact incidents were always greater when the boom was moving up, were always greater on the palm, and were always greater for the boom part of the machine. Also, regardless of boom speed, the 25th-percentile-sized operators experienced more contacts than did other operator sizes. Furthermore, regardless of boom speed, the 152-cm mine seam experienced more contacts than did other seam heights tested. Results of a survival analytic approach suggest that controlling the boom speed is the most important factor in determining the risk of an operator making contact. Based on the data collected, boom speed greater than 41 cm/s results in a substantial increase in risk to the roof bolter operator and should probably be avoided. At speeds less than or equal to 25 cm/s are associated with a more modest relative risk, which represents an acceptable level of risk. Also, at speeds between 25 and 41 cm/s, there could be a particular boom speed in this range at which a significant inflection point in the relative risk estimates occurs. Based on the results, this issue needs to be addressed by future research.

Jan 1, 2003

By Dean H. Ambrose

This paper presents the results of a study using computer human modeling to examine machine appendage speed. The objective was to determine the impact of roof bolter machine appendage speed on the likelihood of the operator coming in contact with. A contact means two or more objects intersecting or touching each other, e.g., appendage makes contact with the operator?s hand, arm, head or leg. Incident investigation reports do not usually contain enough information to aid in studying this problem and laboratory experiments with human subjects are also not feasible because of safety and ethical issues. As an alternative, researchers developed a computer model approach as the primary means to gather data. By simulating an operator?s random behavior and machine?s appendage velocity, researchers can study potential hazards of tasks where it is not possible to perform experiments with human subjects. Analysis information is helpful to the mining industry in terms of making recommendations that reduce the likelihood that roof-bolter operators experience injury due to contact with a moving boom. Data analysis of roof bolter simulations show that the virtual-operator?s response time has little effect on the number of contacts experienced. Based on frequency and cross-tabulation, regardless of other variables, contact incidents were always greater when the boom was moving up, were always greater on the palm, and were always greater for the boom part of the machine. Also, regardless of boom speed, the 25th-percentile-sized operators experienced more contacts than did other operator sizes. Furthermore, regardless of boom speed, the 152-cm mine seam experienced more contacts than did other seam heights tested. Results of a survival analytic approach suggest that controlling the boom speed is the most important factor in determining the risk of an operator making contact. Based on the data collected, boom speed greater than 41 cm/s results in a substantial increase in risk to the roof bolter operator and should probably be avoided. At speeds less than or equal to 25 cm/s are associated with a more modest relative risk, which represents an acceptable level of risk. Also, at speeds between 25 and 41 cm/s, there could be a particular boom speed in this range at which a significant inflection point in the relative risk estimates occurs. Based on the results, this issue needs to be addressed by future research.

Jan 1, 2003

By Sebastian Avalos, Julian M. Ortiz, Willy Kracht

Semi-autogenous grinding mills play a critical role in the processing stage of many mining operations. They are also one of the most intensive energy consumers of the entire process. Current forecasting techniques of energy consumption base their inferences on feeding ore mineralogical features, SAG dimensions, and operational variables. Experts recognize their capabilities to provide adequate guidelines but also their lack of accuracy when real-time forecasting is desired. As an alternative, we propose the use of real-time operational variables (feed tonnage, bearing pressure, and spindle speed) to forecast the upcoming energy consumption via machine learning and deep learning techniques. Several predictive methods were studied: polynomial regression, k-nearest neighbor, support vector machine, multilayer perceptron, long short-term memory, and gated recurrent units. A step-by-step workflow on how to deal with real datasets, and how to find optimum models and final model selection is presented. In particular, recurrent neural networks achieved the best forecasting metrics in the energy consumption prediction task. The workflow has the potential of being extended to any other temporal and multivariate mineral processing datasets.

Jun 16, 2020

By P Stewart, S Cowie, A Offer, J Carpenter, E Jones

The Ban Houayxai Gold-Silver Operation is a producing asset for Australian-based copper and gold producer, PanAust Limited. The Operation lies within PanAust’s 2600 square-kilometre Phu Bia Contract Area in northern Laos. Commencing production in 2012, Ban Houayxai is operated by PanAust’s Lao-registered company, Phu Bia Mining. PanAust owns a 90 per cent interest in Phu Bia Mining. The Government of Laos owns the remaining 10 per cent. The Operation mines an intermediate sulphidation epithermal gold-silver deposit and comprises an open-pit feeding ore to a conventional carbon-in-leach process plant. Metallurgical recoveries for gold and silver are approximately 75-85 per cent and 55 per cent respectively. To year-end 2017, the Operation had poured 0.65 million ounces (oz) of gold and 4.7 million oz of silver. The Ban Houayxai Ore Reserve supports a mine life of approximately four years from the end of 2017. The Ban Houayxai deposit is in an early Permian volcano-sedimentary unit which is part of a late Carboniferous to early Permian (310–270 million years ago) volcanic-plutonic sequence of the Truong Son Fold Belt. The deposit formed when hot saline (240–415°C, 2.7–13.0 weight per cent NaCl equivalent) fluids ascended from a geologically fertile island arc porphyry into overlying volcanics along a fault or faults in the early Permian (290–256 million years ago) (Manaka et al, 2014). CITATION: Carpenter, J, Cowie, S, Stewart, P, Jones E, and Offer, A, 2018. Machine learning at a gold-silver mine: a case study from the Ban Houayxai Gold-Silver Operation, in Proceedings Complex Orebodies Conference 2018, pp 82–85 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Nov 21, 2018

By Carlos Fonseca, Gustavo Usero, Roberto Mentzingen Rolo, Gabriel Moreira, Octavio Rosa de Almeida Guimarães

Geological domain modeling is an important step in mineral resources evaluation. The procedure can be laborious and time-consuming, especially in multivariate settings. However, estimates are significantly improved when carefully limited by geological variables. This paper proposes a workflow for geological domain modeling suited for stratigraphic deposits. The workflow consists in automatically defining the domains by using clustering algorithms, generating the surfaces that represent the contacts by interpolation and subsequent triangulation, and simulating the surfaces. The proposed workflow produces consistent and realistic results and allows the user to test different domain configurations and assess volumetric uncertainty automatically and quickly.

Jun 25, 2023

By B Laidens, D Souza, W Bielefeldt

The CaO-MgO-SiO2-Al2O3 system, characterised by its intricate phases and thermodynamic properties, plays a pivotal role in steel secondary refining processes, encompassing desulfurisation, non-metallic inclusion capture, and refractory protection. Accurate predictions for diverse industrial applications, including metallurgy, ceramics, and materials science, are imperative. To address this challenge, a combination of machine learning techniques will be specifically applied to model the liquid fraction of the slag and the solid fraction of MgO. The development of an artificial intelligence (AI) system, leveraging various machine learning techniques, has gained momentum in this project. The focus of this work is on constructing an AI model, based on machine learning techniques, within the CaO-MgO-SiO2-Al2O3 system, utilising simulation results from FactSage™, version 8.1 (by GTT Technologies). The primary objective is to train the AI model using these simulation outputs to predict the percentage of liquid fraction and MgO saturation based on chemical composition parameters. The AI model will undergo training with a comprehensive data set of simulations within the CaOMgO- SiO2-Al2O3 system, covering a diverse range of compositional at 1873 K. These simulations, conducted through FactSage™ 8.1 software, provide a robust foundation for AI model training, ensuring generalisability and precise predictions for the liquid fraction of the slag and the solid fraction of MgO, the solid fraction of MgO in this case is determined by the difference between the total MgO and the MgO in the liquid fraction, so it is not the objective of this study to determine which phase of MgO is in the solid state.. The predictive capabilities of this AI model hold significant implications for process optimisation, quality control, and decision-making in CaO-MgO-SiO2-Al2O3- dependent industries. Precise estimations of the liquid fraction and MgO saturation empower researchers and engineers to enhance operational efficiency and quality. This paper explores the methodologies employed for AI model creation and training, achieved results in terms of prediction accuracy, and potential applications in the field. The development of this AI system signifies a notable advancement in utilising machine learning for better comprehension and control of complex chemical systems. Furthermore, to align the study with real-world steel production, we introduce FeO and MnO at concentrations of 2 per cent and 1 per cent at 1873 K, respectively, following the validation of model results using the CaO-MgO-SiO2-Al2O3 system. This adjustment aims to bring the study closer to the observed reality in steel mills globally.

Jun 19, 2024

By Maral Malekian, Pat Bellett, Eranda Tennakoon, Fernanda Carrea, Moe Momayez

Slope instabilities in open-pit mines pose a safety risk to workers and a financial burden on production. The direct impact of slope stability on safety and production makes slope failure predictions one of the important challenges in the mining industry. Predicting the precise time of slope failure has been the subject of much research in conjunction with the development of innovative monitoring technology designed to prevent sudden failures. This paper investigates the use of AutoRegressive Integrated Moving Average (ARIMA) model to predict the time of slope failure. Input data such as inverse velocity (IV) and dimensionless inverse velocity (DIV) from 20 slope failures were used to train the model predict the failure time. For comparison purposes, the time of slope failure using the traditional inverse velocity method is also provided. We show that ARIMA provides 90% more accurate predictions than the TIV approach.

Jul 12, 2023

By P M. Hetherington, F A. Pym, M P. Murphy, K E. Crook

Consultants in the mining industry have the opportunity to visit interesting deposits all over the world. Each deposit has its own set of challenges to face when it comes to defining and understanding the deposit geology and the economics to make it viable for extraction. Some of the challenges can include appropriate data capture, sampling techniques, structural characteristics, geochemistry, geophysics, lithology definition, and economic boundaries. It is essential that the practices used at each deposit (eg drilling, sampling, geological modelling, resource estimation etc) are documented and the results obtained are robust and defensible. The Competent Person is not always aware of instances where their resource estimation practices have not been substantiated. ‘You don’t know what you don’t know’ is a phrase frequently used during a mineral resource audit. Technical work to substantiate drilling, sampling, density, assaying, geological modelling and estimation processes is not always completed. Audited Competent Persons often comment: • ‘I was unaware that this was required.’ • ‘I have not had training in this area and do not know how to complete the checks required.’ • ‘This is how we’ve always done it.’ Examples of audits where it was found that geological practices were not substantiated are examined here. A discussion is included on why substantiation of these practices is important, and potential reasons as to why there are instances of unsubstantiated processes and practices are discussed.

Mar 22, 2022

By B P. Voutharoj, R A. Dutch, M Paknezhad, T Ostersen

With the proliferation of new sensor technologies, acquiring multiple data sets over the same ground is becoming cheaper and easier than ever. This new, higher resolution multivariate data provides a significant resource for exploration and resource geologists but comes with the added complexity of effectively integrating the various data sets in useful and meaningful ways to elucidate new geological understanding. Both geophysical data sets and hyperspectral data are extensively used for exploration targeting, providing different information at different resolutions and crustal scales. One of the biggest challenges comes from trying to effectively integrate data sets that record very different physical properties, across the different scales these data are captured at, in a way which can allow for a data-driven analysis of these combined data sets.

Sep 1, 2024

By S. Frimpong, D. Ali

For achieving bulk economic excavation, large dump trucks are being used in majority of the earth moving operations resulting in high impact shovel loading operations (HISLO) which exposes the operators to high levels of whole body vibrations (WBV). As the truck ages, hydro-pneumatic suspension struts loses their capability to effectively attenuate the vibration. A system is required to monitor and predict the performance of the suspension struts in real time. Machine learning and artificial intelligence (AI) has been applied for modeling and predicting the suspension system performance for lighter/smaller vehicles. However, no work has been done to implement machine learning or AI for modelling and predicting the performance of hydro-pneumatic struts in large dump trucks. Therefore, current work is going to be a pioneering effort towards developing machine learning and AI models for solving this problem. Support vector machine (SVM) and regularized dual non-linear regression has been implemented in order to achieve the goal. 3D virtual simulator for CAT793D was used to conduct experiments in MSC.ADMAS environment. The two main recorded outputs were the RMS accelerations in the vertical and horizontal directions at the operator seat for characterizing the performance of the suspension system. During the development and the training of the models, eighty percent (80%) of the total experimental data was used and the remaining 20% was used during the testing and validation of the developed models. SVM model showed the desired accuracy in terms of hydro-pneumatic suspension system performance prediction for large dump trucks. These models can be implemented in the dump truck controller which can then be used to monitor the performance of the suspension system in real-time, and with that proper maintenance and/or replacement can be scheduled by the maintenance personnel. Workplace safety, operator’s health and the overall system efficiency can be greatly improved with an implementation of such an intelligent system. INTRODUCTION Large capacity dump trucks are being employed in any earth moving operation throughout the world for achieving bulk economic excavation [1, 2]. A large dynamic impact force is produced with large capacity shovels dumping material weighing 100 tons or more in to the dump truck under gravity. This dynamic impact force generates high frequency shockwaves which travels throug0068 the truck body, chassis and reaches the operator’s cabin and thus exposes the operator to severe levels of whole body vibrations (WBV). Recommended safe limits have been listed by the International Standards Organization (ISO) in sections 1, 2, 4 and 5 of ISO 2631 (1997, 2003, 2001 and 2004). An operator can got through a long term back, shoulders, arms and neck disability and disorders if exposed to the WBV levels beyond those limits. The dynamic impact force generated as a result of the high impact shovel loading operations (HISLO) was modelled by Ali and Frimpong [2]. Furthermore, the complete methodology for optimizing the dumping process and reducing the impact force has been provided by Ali and Frimpong [1]. The vibration attenuation system present in the dump truck requires improvement in addition to the dumping process optimization. The vibration attenuation system in the dump truck commonly consists of hydro-pneumatic suspension struts. As the dump truck ages, its suspension system deteriorates and therefore loses its capability to effectively attenuate the shockwaves and the resulting vibrations produced by the dumping process. The deterioration tales place due to the contamination of the hydraulic oil or oil and nitrogen mixing or due to the gas accumulator diaphragm rupturing. The monitoring of the output vibration levels can be used to evaluate the effectiveness of any suspension system. With the dump truck suspension system, the vibration levels at the operator’s seat can be used for the evaluation of the effectiven

Jan 1, 2019

By K. KAKLIS, O. SAUBI, Z. Agioutantis, R. JAMISOLA

Machine learning in the form of artificial neural networks was applied to investigate whether specimen load evolution can be predicted as a function of acoustic emission (AE) signals in the case of three-point bending (TPB) marble specimens instrumented with piezoelectric sensors. The ultimate objective of this study is to develop a model that can quantify rock behavior under loading that can lead to rock-failure prediction in underground structures subjected to bending, such as roof failure in development or production openings.

Nov 1, 2022

By K. KAKLIS, O. SAUBI, Z. Agioutantis, R. JAMISOLA

Three-point bending (TPB) tests were conducted on prismatic Nestos marble (Greece) specimens. The specimens were instrumented with piezoelectric sensors, and comprehensive recordings of acoustic emission (AE) signals were obtained. Machine learning in the form of artificial neural networks (ANNs) was then applied in an effort to investigate whether specimen load evolution can be predicted as a function of AE signals. A number of ANN models were developed, and the optimum model was selected based on the highest coefficient of determination (CoD) value as well as the lowest root mean square error (RMSE) value that was calculated for each model. The best performing ANN model exhibits accuracy above 99% with an RMSE value below 4%. It can be concluded that ANNs can potentially be applied to predict rock behavior under load especially when such loads lead to failure.

Sep 7, 2022

By S McGregor, J Sharp, I Entezari, T Boulter

The piezocone penetration test (CPTu) is one of the primary screening tools used by the mining industry to evaluate whether tailings are susceptible to liquefaction (static or cyclic). Liquefaction analysis using the CPTu is influenced by the fines content of the soil, and most CPT screening tools empirically use the behaviour type index (Ic) to correct for fines. The issue with this approach is that such empirical relationships have been developed using data collected from natural soils and their applicability to different tailings deposits needs to be studied. In this paper, we explore the use of machine learning to develop a site-specific model to estimate fines content of tailings using gamma piezocone penetration testing (GCPTu) measurements. The results are then compared to the fines content derived from the soil behavior type index (Ic) proposed by Robertson and Wride (1998). A dataset of paired GCPTu and laboratory results were compiled and a support vector machine (SVM) algorithm was employed to calibrate a model to estimate tailings fines content from in-situ GCPTu data. We calibrated GCPTu data to geotechnical 75 μm fines content measured in the laboratory. The geotechnical fines content is the fines content as a percentage of solids. The development dataset was comprised of paired GCPTu-laboratory results collected in 2009-2019 from a mining area with predominately clay mineral and quartz sand tailings. Results show that the developed model predicts the geotechnical 75 μm fines content of the studied deposits with 7.8% error. The Ic based method estimates the fines content with 31% error. Evidently, the Ic method is not applicable to the studied tailings deposits. It is appreciated that many tailings fines do not contain clay, and that the success on this program may not be repeatable everywhere. However, the authors wish to point out that the application of site-specific machine learning algorithms may be used to significantly improve empirical geotechnical parameters.

Jul 1, 2021

By Littlewood E

We offer these notes as a practical contribution. All the arguments we advance have been tested by practical experience.In its preparation we realised that our subject was one of such importance and covered so wide a field that, dealt with by one of us alone, it might easily have been too much for him, so we have endeavoured, by mutual criticism and advice and a pooling of ideas, to bring forward a few points that may be of advantage to members. If any of the paper is of benefit to mining on the "Golden Mile," or elsewhere, we shall feel amply repaid for our efforts.It is obvious to all that the longer the machine is working productively per shift the greater the return, to all concerned; the machine-man himself and the company. Furthermore, if a man commences a shift with a definite objective in view; for instance, to bore and fire a specific cut, which he recognizes will give him a good financial return, then that man will exercise all his capabilities to attain that objective.In this paper we hope to demonstrate the value to the machine-man of the knowledge of the capabilities of each hole he bores, so that he will be enabled to utilize his drilling time to the greatest advantage. Also, we hope to show that such drilling time can be advantageously extended.After the war, with the influx of new men to the industry, some education on the finer points of machine work may be desirable, and any instruction in this branch must stress the realization of the value of each hole.We realise that most of those present have a sound workable knowledge of machine-mining, and we are not trying to teach anyone the fundamentals of machine work, but we would like to bring to their attentIon some of the finer points which we believe are very important.The machine-man has control of gear of a capital value of over £150, and the use or misuse of that material has a direct bearing on working costs. Therefore it is imperative that the capital outlay is gainfully employed.A "bogger" in charge of a 5/- shovel engaged unproductively is bad enough. How much worse for £150 worth of gear to be not fully productive.Figs. 1 and 2 demonstrate angles of break, and the full realization of the importance of this angle is essential to our paper. Although these Sketches are two-dimensional, we hope they will be sufficient to demonstrate our meaning. We will pass over them as briefly as possible.Fig. 1 shows three holes asked to perform an identical task.(a) 7-ft. hole bored 2 ft. into solid ground-will not come out.(b) 4-ft. hole bored 1 ft. from solid ground-will come out easily.(c) 5-ft. hole bored to solid ground-the best that can be expected under general conditions encountered.The angle of break is a right-angle, normal to direction of hole.

Jan 1, 1944

By J. Peck, F. Brackebusch, M. Scobie

"A number of alternative strategies exist to improve narrow orebody stope productivity and safety through mechanization. Particular attention is given to one of these: the development of an in-stope mining machine. This paper summarizes those characteristics of vein orebodies in Canadian mines which are considered to govern such mining machine design. It then reviews past and current attempts to develop hardrock mining machines which could potentially be applicable to narrow stope widths.IntroductionSerious interest is mounting to exploit new technology in the development of methods and machinery to stope narrow, hardrock, orebodies. The motives relate not only to the foreseen need to maintain or reduce production costs, but also to considerations of manpower availability, safety, ground control, ore recovery and dilution. This also follows the demonstrated success of technology application in larger metal orebody stoping, where machine development has been seen to lead to improved productivity as well as the future prospects of continuous mining systems and eventual automation, e.g. ITH blasthole drill development and bulk mining. This paper deals in particular with machine design for ore fragmentation in narrow, hardrock orebodies. It is considered that such machine design closely interacts with the over-all stoping method design and that both require consideration in parallel to ensure success in mechanization."

Jan 1, 1990

By L Mottola

Three decades ago mining companies, principally in North America, aided by machine and explosive manufacturers, started significant attempts to develop and implement automated mining systems, mainly underground. These tended to be motivated by decreasing cost, increasing productivity, reducing labour requirements, and improving safety. These are important business drivers, however, little attention was given to the underlying goal of mining automation, namely to bring upstream processes in control and reducing variation, as seen in downstream mineral processing plants.This paper first reviews the nature of Lean Thinking and its application in the context of the growing commitments by industry to develop future automated mine production systems. The paper then makes reference to prior and current experiences in open pit blasthole drill and shovel performance monitoring, to demonstrate the principle that machine performance data offers as a means, not only to characterise the dynamic geological and working environment, but also to provide a foundation for automation. In this case, drill and shovel monitoring was originally motivated as a means to optimise blast fragmentation through more effective drill and blast designs that are based on an accurate determination of the bench geological and geotechnical characteristics, hence managing variation in the drilling and blasting process. This paper considers how automating and integrating such components of the mine production system would bring processes into a controlled state, creating value at the face, within a framework of Lean mining.

Nov 22, 2011

By C. Hendricks

This paper is based upon field studies conducted in a western Canadian open pit coal mine. This work has involved the adaptation of microprocessor-based instrumentation to monitor various operating parameters on-board blasthole drills and mining shovels. A review of instrumentation and monitoring methodology will first be presented. Based upon experience from field-based studies, the paper will subsequently focus on the application of monitoring technology within the framework of an integrated Equipment Monitoring System. Such a system will permit the real-time performance evaluation of key equipment involved in surface mille production.

Jan 1, 1991