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By F. A. Edwards

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 ounces 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 elevation (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 a three lift hoisting system for men, materials and ore. In addition to the increased travel time and ore handling expense, increased humidity, temperature, ventilation and compressed air losses have added to the inefficiency of the operation. As a result, studies were initiated in 1980 to assess the future of the Macassa operation. The results of the studies indicated that a new deep shaft with a potential to hoist from 2,135 m (7,000 feet) or below was the most attractive option. Detailed design of the shaft and associated surface plant commenced in January, 1982 and the following milestone dates were achieved: 1980 - initial study of hoisting and haulage facilities; January, 1981 - ore reserves in excess of 1 million tonnes of ore at 18.7 g/tonne (0.45 oz/ton) confirmed; March, 1982 - preliminary study of Macassa No. 3 Shaft completed; April, 1982 - permanent hoisting plant purchased; June, 1982 - site clearing commenced; September, 1982 - shaft collared; May 1, 1983 - shaft sinking commenced; February 11, 1985 - shaft reached 1,600 m (1mile); December 1, 1985 - shaft scheduled for completion.

Jan 1, 1985

By J. Kouam

Al-Si-Cu and Al-Si-Mg alloys are widely used in several applications. Although they can be produced near-net-shapes, products made of these alloys very often require some machining. The purpose of this study is to evaluate the machinability of A319 and A356 under TO and T6 conditions. The machinability was assessed using during dry drilling machining process and the machinability indexes used are the chip form, the chip breakability and the cutting forces. It is found that 319 and 356 exhibit different machinability behaviors, especially in the T6-condition in terms of forces requirements, chip breakability and chip segmentation. It is also found that both the microstructure of the workpiece materials and the machining conditions significantly affect the chip formation mechanism, and therefore, the machining parameters could be selected to promote a chip form that is easily manageable in production.

Jan 1, 2011

By Alan Morris

IN a previous paper1 the results of cutting tests on free-cutting brass rod were reported. Investigation was made of the effects of variation in lead content, microstructure and cold drawing. The author's reply to the discussion of that paper includes a study of the directional cutting properties of drawn and annealed rod. The present paper records the results of tests made on series of samples in which the tin, iron and copper contents have been varied. The apparatus, used and method of testing are described in detail in the previous paper. The machine is of the pendulum-operated milling type used and described by Airey and Oxford.2 Angular readings of the height to which the pendulum rises after falling from a fixed position, both when a cut is made and when the pendulum swings freely, give the basis for calculation of the energy consumed in making a cut. Careful weighing of the sample before and after cutting gives a measure of the amount of metal removed. The results are expressed in foot-pounds per cubic inch of metal removed, and have been called "unit cutting energy." In the course of the discussion of the author's earlier paper, W. B. Price reported large-scale tests involving some 50,000 lb. of material. These tests indicated that the addition of as much as 0.6 per cent of iron and 0.25 per cent of tin had apparently no adverse effect on the high-speed cutting characteristics of the rod. The rod also contained a small amount of nickel. The test results reported here tend to confirm his observations.

Jan 1, 1933

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 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 George Jones

MACHINE mining on the pitch plays an important part in the produc-tion schedule at the Salem Hill Colliery of the Haddock Mining Co. This mine is just outside the city of Pottsville, in Schuylkill County, along the southern border of the Anthracite Region. The entrance to the mine is through a cross-measure tunnel 15 ft. above water level and running north 1500 ft. until it strikes the Rabbit Hole vein, where the machine mining is carried on. The main slope, dipping south, was sunk 1200 ft. in the Rabbit Hole vein with levels turned off at 300-ft. intervals. The vein is from 24 to 30 in. thick on a varying pitch of 20° to 40°. The overburden has differed from 50 to 466 ft., the most recent work having the maximum overburden. Immediately over the Rabbit Hole vein is a seam of soft sand slate from 1 to 4 ft. thick which presents a problem at times because it dis-integrates rapidly when exposed to the air. It will crack up and fall without any audible warning, therefore the workmen must always be on the alert, watching for loose slabs. The main top is sandstone, vary-ing in thickness from 8 to 20 ft. The bottom rock is similar to the top, but is finer grained and subject to a more complete disintegration. The vein has a 3 to 4-in. shelly band along the bottom, and all under-cutting has been done in this soft band.

Jan 1, 1935

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

By G. R. Forbes

Flotation froth machine vision systems provide consistent real-time measurements pertaining to the state of the flotation cell being monitored. Typical measurements include froth velocity, froth colour, froth texture and froth stability. This paper presents the results from test work conducted on the molybdenite rougher circuit at the Kennecott Utah Copper Concentrator during February 2006. Machine vision results show that it is not possible to accurately determine bubble size distribution information for the molybdenite froth, despite using state-of-the-art methods. Instead, it is necessary to make use of texture measurements to classify the various different froth states. Numerous combinations of texture measurements and classifiers are used to determine the best combination. Results show that the Fourier ring based texture measurements perform the best when both K nearest neighbour and Gaussian mixture model classifiers are used. The concentrate grade of the flotation cell is modelled using combinations of froth velocity and froth texture descriptors. The results show that by using a linear combination of these descriptors rather than either one by itself it is possible to account for a much larger percentage of the variation observed in the concentrate grade data. Adjusted R2 values of 92% and 84% are achieved when modelling the copper and the molybdenite concentrate grade respectively.

Jan 1, 2014

By A. Adhikari, P. Tukkaraja, S. Jayaraman Sridharan

Shafts are critical components of the mine ventilation systems and contribute significantly to the mine ventilation pressure. Estimation of shaft pressure losses is an important aspect of mine ventilation planning and shaft frictional pressure losses are extensively studies. Shock losses contributed by shaft buntons are estimated using the interference factor model developed half a century ago. This study aims to develop a better performing prediction model for shaft buntons using state of the art experiment and numerical data, and machine learning techniques. Results show that the present prediction model is performing poorly, and the prediction performance can be improved by incorporating additional predictor variables. The Artificial Neural Network standardized Machine Learning model developed in this study is able to predict the shaft bunton shock loss with high accuracy (R2 = 0.956).

Jun 25, 2023