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MAC and the AFN: Taking Up the Corporate ChallengeTaking Up the AFN Corporate Challenge ?? About MAC ?? ?Towards Sustainable Mining? ??Mining and Aboriginal Peoples Framework ?? AFN-MAC Relationship ?? A New MAC Northern Initiative ??Social and
May 1, 2009
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Mac porphyry molybdenum prospect, north-central British ColumbiaBy G. R. Cope, C. D. Spence
"The Mac porphyry molybdenum prospect is located 100 km east of Smithers in central British Columbia. The identification of anomalous levels of molybdenum, copper, and silver in three adjacent lakes,
Jan 1, 1995
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Macassa No. 3 Shaft - Deep Shaft Sinking By Conventional Methods ? IntroductionBy 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, a
Jan 1, 1985
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Macassa Number Three Shaft Deep Shaft Sinking By Conventional MethodsBy 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
Jan 1, 1985
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Maceral/Microlithotype Analysis Of The Hardgrove Grindability Of Lithotypes From The Phalen Coal Bed, Cape Breton, Nova ScotiaBy J. H. Calder, J. C. Hower
Three lithotypes of Phalen coal from Cape Breton County, Nova Scotia, Canada, were subjected to a modified Hardgrove grindability index (HGI) test. The testing scheme differed from conventional (ASTM
Jan 1, 1998
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Maceral/microlithotype analysis of the progressive grinding of a Central Appalachian high-volatile bituminous coal blendBy A. S. Trimble, J. C. Hower
The petrographic composition of sized coal produced through the progressive grinding of a Central Appalachian high-volatile bituminous coal blend was analyzed for multiple sets either by removing fine
Jan 1, 2000
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Machinability of A356 and A319 Aluminum AlloysBy 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 thi
Jan 1, 2011
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Machinability of Free-cutting Brass RodBy 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 p
Jan 1, 1932
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Machinability of Free-cutting Brass Rod, IIBy 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 auth
Jan 1, 1933
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Machine Design Parameters For High Seam Truss Bolting ApplicationsBy G. Bucelluni
An essential element of truss bolt installation requires machines to angle a hole in the top of approximately 39° to 45° from its vertical axis. Parameter which inherently effect the drill position ar
Jan 1, 1982
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Machine Foundation RepairBy 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
Jan 1, 2003
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Machine Injury Prediction by Simulation Using Human ModelsBy 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 li
Jan 1, 2003
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Machine Injury Prediction by Simulation Using Human Models (0111a15c-4251-44e2-bc90-9d29854de8ad)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 li
Jan 1, 2003
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Machine Learning and Deep Learning Methods in Mining Operations: a Data-Driven SAG Mill Energy Consumption Prediction Application "Mining, Metallurgy & Exploration (2020)"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 forecasti
Jun 16, 2020
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Machine learning at a gold-silver mine: a case study from the Ban Houayxai Gold-Silver OperationBy 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 A
Nov 21, 2018
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Machine Learning Driven Domain Modeling for Stratigraphic DepositsBy 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 signific
Jun 25, 2023
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Machine learning for predicting chemical system behaviour of CaO-MgO-SiO2-Al2O3 steelmaking slags case studyBy 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-metalli
Jun 19, 2024
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Machine Learning for Slope Failure Prediction Based on Inverse Velocity and Dimensionless Inverse Velocity - Mining, Metallurgy & Exploration (2023)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
Jul 12, 2023
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Machine learning in resource geology – why data quality is criticalBy 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
Mar 22, 2022
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Machine learning integration of hyperspectral and geophysical data for improved exploration targetingBy 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
Sep 1, 2024