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By P Hartlieb, M Noeger, M Varelija

Rock bolts are important in underground mining as they provide support to the rock mass around the excavation. They are the most common reinforcement method in mining and civil engineering excavations. The length of the bolts, types and spacing between them can be varied, depending on the reinforcement requirements and the use case (Kolapo, Onifade and Praise, 2022). The rock bolt provides safety and stability for the rock mass in the excavation area and they can be mechanically anchored, grouted, or friction anchored. They normally don’t have a measurement option and there is a possibility that it failed without showing visible signs on the surface, especially in grouted rebar bolts where they can still have some residual strength. Therefore, support functionality can be lost without recognising it. To overcome that, sensors can be installed on all three previously mentioned bolt systems. Intelligent bolts with implemented measuring systems have a function of showing changes and indicating if there is some damage on the bolt. So, it can be said that intelligent rock bolts are regular bolts equipped with additional instruments which are helping us detect potential instabilities inside of the rock mass or malfunction of the bolt itself. Changes in rock mass can be tracked, by measuring rock bolt reaction concerning the excavation response. Rock mass damage and instabilities are therefore measured in terms of rock mass deformation. Moreover, other additional information depending on the type of the sensor device could be collected. Based on such measurements, information about rock mass state can be derived. Different rock bolt types work at different reaction principles. Therefore, limitations on sensor application and information that can be extracted must be considered.

Sep 1, 2024

The Mount Whaleback iron ore deposit at Newman, Western Australia comprises the largest known continuous iron ore deposit in Australia, originally with a Measured Resource in excess of 1700 Mt of hematite ore. The deposit occurs within the Hamersley Province which forms part of the Pilbara Block in the NW of Western Australia (Figure 1). The Mount Whaleback iron ore deposit, together with a number of discrete iron ore bearing satellite orebodies in the Newman district, contributes to the Newman Lump and Fines products. Extensive quality control systems are utilised from mining to shipping to ensure that each product meets a narrow geochemical target grade range.

Jan 1, 1997

By Mike Sharwood

This subject is to be discussed in greater depth by Mike Sharwood. It has also been the subject of a mini-symposium by the Melbourne Branch of the AusIMM in October, 1993. The papers from this symposium were distributed to the membership late last year "to raise their consciousness and to promote greater awareness and understanding of the wide range of liabilities that may arise". An introductory paper by Nicholson outlined the many areas in which liability may arise with particular emphasis on expert reports, eg: Takeovers Schemes of arrangement Acquisitions from associates Prospectuses Related party transactions Directors' duties Continuous disclosure Other influences (environmental, occupational health and safety, trade practices)

Jan 1, 1994

By S Carr-Cornish, K Moffat

Social licence to operate is developed and kept through managing both operational impacts and the relationship between local community and company. However, CSIRO research has demonstrated that in determining how accepting community members are of a mining operation, relational factors, such as procedural fairness and trust, are much more important than operational impacts, such as impacts on employment and the environment (Moffat and Zhang, 2015). Companies seek to develop relational factors through their engagement with local community; such engagement has many aspects, including, initially identifying stakeholders and positioning the company as part of the local community (Lacey et al, in prep; Thomson and Boutlier, 2011). These relational factors involve the social identities of people living in the community. Social identities are a part of people that shape their beliefs, based on their connections with various groups in their lives (Hogg et al, 2004).An engagement process, intentionally or not, inevitably interacts with the social identities of stakeholders (Crane and Ruebottom, 2011). For example, traditionally, at the very beginning of engagement processes, community members are assigned to categories using conventional stakeholder mapping and categorisation methods, often based on assumptions about stakeholders’ interests, which equates to assumptions about stakeholders’ social identities (Colvin, Witt and Lacey, 2016; International Council on Mining and Metals, 2012). Yet, there has been limited empirical inquiry into whether such categories reflect the social identities of stakeholders and if there is a bearing on relational factors, including, the perceived fairness of consultations and trust in mining companies (Crane and Ruebottom, 2011). Also at this time, companies commonly seek to create a presence in communities, for example, joining Chambers of Commerce and participating alongside community members. Again, there has been limited empirical inquiry into whether community members and companies identifying with the same overarching group and thus sharing aspects of social identity, influences relational factors (Crane and Ruebottom, 2011). In this study, we examined the effects of changing aspects of social identity by changing how stakeholders were identified and companies were positioned, on how stakeholders’ perceived a new mining operation, specifically how they perceived the procedural fairness of the proposed consultation process and trusted the company, two factors that underlie a company’s social licence to operate.CITATION:Carr-Cornish, S and Moffat, K, 2016. Out of the box – the power of social identities in engagement processes, in Proceedings Life-of-Mine 2016 Conference, pp 180–182 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Jun 28, 2016

By D Stephenson, N R. Steward

“TerraFlowingTM” is a dewatering process that addresses both the value proposition of Tailings as a Resource™, and the safe storage of tailings. “TerraFlowingTM” incorporates two stages of cyclone dewatering followed by centrifugation of the final dilute tailings stream. In this process three dewatered tailings streams are produced; a primary cyclone underflow, a secondary cyclone underflow and a centrifuge pulp. These three streams can be combined, or used, in various configurations to achieve different outcomes. The ability to vary the cyclone and centrifuge operations and configuration enables this process to deal with tailings feed variations in particle size distribution and mineralogy and deliver consistency, or design variations, in particle size distribution, tailings solids concentrations and recoveries. Water recovery can be up to 85% of the total water in the tailings with a stackable final tailings product achieved if the three tailings streams are combined in the maximum solids concentration configuration. The primary cyclone can be operated to deliver a particle size distribution suitable for tailings storage facility embankment construction or alternatives such as concrete structures or shotcrete for support. The secondary cyclone underflow and centrifuge pulp is suitable for beaching deposition within the tailings storage facility.

Jul 1, 2021

By C Xu, B Li

Multi-sensor fusion visual perception technology significantly enhances visual perception capabilities in complex and harsh environments by combining visual data from different sensors. This technology is especially useful for scenarios where traditional single visible light sensors, such as standard cameras, struggle due to poor lighting, adverse visual conditions, or other visual obstructions. In environments with low light, smoke, or dust, the performance of conventional visible light sensors degrades, limiting their application. By integrating data from various sensors like radar, infrared thermal imaging, and others, multi-sensor fusion technology provides a more comprehensive and complementary visual solution. This fusion not only enhances the robustness of visual systems but also greatly expands their potential applications in fields like visual surveillance. In recent years, deep learning methods have become the mainstream technology for addressing multi-sensor fusion issues, demonstrating significant advantages in adaptive feature selection compared to traditional machine learning methods. However, deep learning-based multi-sensor fusion algorithms still face several challenges. The design of network structures is often redundant, lacking effective screening of useful components within multimodal information. Mainstream fusion algorithms focus excessively on improving display effects without adequately considering the needs of downstream applications and tasks. Existing fusion perception algorithms are generally designed for open visual scenes and lack targeted design for complex visual degradation factors present in tunnel environments of mines. Current deep learning methods for designing multi-sensor fusion networks heavily rely on manual experience to create fusion modules. This reliance increases the complexity of network design and can lead to redundancy. Redundant network modules are difficult to identify in end-to-end learning tasks at this stage, significantly slowing down network inference and potentially interfering with the output of fusion perception. Additionally, training these fusion modules typically requires large amounts of well-annotated multimodal data, further increasing implementation difficulty and cost. For instance, the fusion network design proposed by Li and Wu (2018) focuses on extracting multimodal fusion features, while Zhao et al (2020) introduced a feature decomposition mechanism in the feature fusion and extraction modules. Researchers like Zhang and Ma (2021), Xu et al (2020), and Liu et al (2017) have attempted to improve network structures through dense cascades and the introduction of residual connections. Although these methods have improved the handling of multimodal information to some extent, they still face limitations in distinguishing between useful and redundant information, with redundancy persisting in network designs. Ideally, the design of fusion networks should be more intelligent, guiding network structure design heuristically based on the importance of different modal information for the current task. This would reduce information redundancy and network weights, requiring the development of new algorithms or frameworks capable of automatically identifying and optimising fusion strategies, thereby reducing dependence on manual experience while improving fusion efficiency and network performance. Current deep learning designs for multi-sensor fusion algorithms often overlook the

Sep 1, 2024

By Frfas C, Parrilla F, Regife J. M

A project to develop the Zinclor electrowinning cell to industrial size is being conducted at Tecnicas Reunidas' Research and Development Centre with Spanish Ministry of Industry support. This project is the final development phase in zinc electrowinning from a chloride medium, the highest ZINCLOR technology development level prior to the industrial scale having already been reached. This paper covers the outcomes and conclusions corresponding to project Phases 1 and 2: Process Selection and Industrial Electrodes (1 m2) design and testing. The most important achievement reached is that there are presently available industrial cathodes and anodes for the Zinclor diaphragm cell giving an optimum response for zinc chloride electrowinning with unit power consumptions between 2.65 and 2.80 kWh/kg Zn, depending on the diaphragm type. Standard electrolysis conditions were: 65 g/L ZnC12, 3 g/L HCl, 160 g/L NaCl, 350 g/t Zn arabig gum as the addition agent, 36¦C, and 400 A/m2 current density. The selection of electrodes, diaphragms and organic additives for the industrial scale tests have been optimised. The complete electrowinning cell development, with electrodes having an active area of I m2, enables the evaluation of Zinclor Technology application for industrial processes.

Jan 1, 1993

By S N CARRAS

Ore reserve estimation is a procedure involving data collection, data storage and judgement and assumptions about geology mining and metallurgy. Calculations are based on these assumptions and an economic analysis on projected cash flows is carried out. Ore reserve estimation must therefore be subjective and will always depend on the extent to which the assumptions used in arriving at the estimate are correct. This paper is an attempt to summarise the many aspects that can contribute to ore reserve problems and to offer some suggestions on how to minimise these problems. For further details on specific concepts readers are referred to the detailed descriptions in the referenced papers.

Jan 1, 1992

By S D. Dessureault

In large coal strip mines, there are many different processes at work: stripping, overburden removal using trucks/shovels, cast blasting, slot dozer pushing, more fragmentation and further truck/shovel benches. A key component to each of these steps is the use of machines. In modern operations, most machine costs are tracked within Enterprise systems that transact costs and repair work order management. Similarly, in contemporary mines most machines are tracked using technology that provides precise performance measures for each key process. Such technologies include productivity monitors on draglines, dozers and drills and fleet management systems for trucks and shovels. Linking these two sources of seemingly disparate data û costs and performance by machine û would allow for the precise calculation of unit costs by specific machine. This is similar to an activity-based costing system, except in this machine-based system, calculations are made at an even finer granular level. There are many uses for such an integrated information system in mine planning and performance management, both in real-time and through historical analysis.A real-time fleet costs (FLC) measurement process was developed as part of a larger blending-related, multi-million dollar project centred on a centralised remote control room developed at the University of ArizonaÆs Mining Information Systems and Operations Management Mine Control Center. The FLC component of the overall blending system would allow a mine controller to manage the equipment fleet by viewing its cost in real time and, when mixed with other revenue-related components, the exact profit. For example, a mine controller may show high utilisation while still maintaining a production surplus. This could occur, for example, if the equipment is driven long distances. This might appear in traditional performance metrics as æhigh production performanceÆ, but in reality may prove less profitable. This paper discusses the technical development of the FLC, its deployment and its impact on controller performance, and compares mine profitability before and after its application.

Sep 26, 2011

By Stanojevic R, McGlinn PJ, Day RA, Levins DM

This paper reports the results of a number of studies that Ansto has carried out for mineral sands companies concerned about low-levels of naturally-occurring radioactivity in their products. Where measured levels of radioactivity have exceeded allowable levels, Ansto has used specialised techniques to identify the source and distribution of the radioactivity and investigated processing strategies to remove the Contamination.

Jan 1, 1993