Is there a new norm in mining? That is the question being raised as the commodities cycle takes another turn. To get a sense of the answer, we need to look closely at the challenges and complexities facing new and ongoing mining projects and operations in a changing resources industry landscape.
After eight months of intensive planning and risk assessment, the New Zealand Government Minister Responsible for Pike River Re-entry, the Hon Andrew Little, approved a plan in October 2018 to re-enter the Pike River Mine drift.
Automation has the potential to allow the joint human–automation system to achieve levels of performance and safety that are otherwise impossible. Examples of automation introduced to mining more recently include: software for mine planning and enterprise optimisation; pedestrian proximity detection systems interlocked with underground continuous mining machines; automatic face alignment and horizon control of underground coal longwall equipment; automatic cutting cycles of continuous mining machines; automation of swing, dump and return phases of the shovel loading cycle; automated drilling systems and automated haul trucks at surface mines; and automated haulage in underground metal mines.
Machine learning is generating a lot of hype across many industries - including resources. This article provides some context to the hype and discusses ways that machine learning could be used in geoscience.
With changes in the energy sector occurring rapidly, Australia needs to step up our pre-processing and electrochemical manufacturing to make the most out of our world-class resources.
The year 1917 was a watershed for Australian copper mining and particularly for copper smelting. Prior to this time many copper mines were equipped with an on-site smelter, into which high-grade copper ore or a gravity copper concentrate were fed to produce a copper matte (with 40 per cent copper) for subsequent converting to blister copper (99 per cent copper) and refining at centralised copper refineries in Australia or overseas. During the early 1900s several on-site smelters were also equipped with converters.
An overview of common indicators that highlight potential areas for improvement in processing equipment monitoring and maintenance.
Closure guidance has increasingly recognised the risks and opportunities surrounding pit lakes, and these should be considered in closure planning.
Sensor-based ore sorting is being increasingly used to reduce the amount of low-grade and waste material processed in mineral concentrators. This type of preconcentration provides bottom-line benefits to users by reducing the amount of energy, water and consumables, as well as reducing capital cost. Existing operations can increase metal production, while previously uneconomic deposits and low-grade stockpiles can also be exploited. The technology can also be used to separate ore types for selective processing. The path to implementing sensor-based sorting may include: - geometallurgical evaluation - first inspection testing to investigate sensor response - bench-scale testing where sensor selection is not obvious or for difficult applications - performance testing in full-scale sensor-based sorting machines - larger scale site-based piloting with a temporary semi-mobile plant installation. Sorting requires material to be suitably prepared and presented to the machines, and typically this consists of crushing and screening to limit top size and optimise liberation. However, where material streams are suitably sized and prepared, additional equipment may not be required such as the sorting of semi-autogenous grinding (SAG) mill pebble streams. This paper presents a case study of economic upgrading of gold ore by preconcentrating with sensor-based ore sorting. The case study examines sorting amenability, test work and the feasibility study through to implementation, with associated flow sheet development. The development process is analysed and evaluated with a view to rationalising the process for development of future projects. In addition, limited financial modelling based on expected results is shown to illustrate the benefit to the operation.
Following the huge interest and application of autonomous technology, industry case studies suggest that the true value of automation lies in its potential to deliver precise mining.
As part of the AusIMM’s recognition and celebration of the importance of Aboriginal and Torres Strait Islander peoples to Australia’s resources industry, this article offers a perspective on the global significance of 30 years of agreement making between resource developers and Aboriginal and Torres Strait Islander peoples in Australia.
Using robots to accelerate mine mapping, create virtual models, assist workers and increase safety.
About the Author Peter Zarris is CEO of the OPIC Group and Director of OPIC Leadership. He is an Organisational Psychologist with over 18 years’ experience in the development of individual, team and organisational capability. Peter is the National Chair of the Australian Psychological Society’s College of Organisational Psychology, Past Convenor of the Interest Group in Coaching Psychology and an Honorary Vice President of the International Society of Coaching Psychology. Peter has been recognised by Standards Australia for his contribution towards the ‘Guidelines on Coaching in Organisations’ Handbook, is the cofounder of the International Congress of Coaching Psychology and is an International speaker on Leadership Coaching.
