Search Documents

By A. E. Landsberg, K. C. Corin, G. Stroebel, J. A. Miller, M. Becker, H. J. J. Gordon

Geometallurgy represents an integrated approach to mine planning and operation whereby the naturally occurring ore variability and its effects on all key downstream activities in the mine life cycle are considered. Process mineralogy is one of the key components of this integrated approach. This paper focuses on understanding some of the mineralogical variability across the Swartberg Cu-Pb-Zn sulfide ore in South Africa and its consequent effects on flotation performance. This process mineralogy project is motivated by the planned changeover and expansion of the Black Mountain Mines concentrator to process solely Swartberg ore compared to the run-of-mine blend currently being treated at the plant. Eight samples spanning the main lithological units of the ore body are described using quantitative mineral-textural data and bulk sulfide batch flotation performance to establish five process mineralogical or geometallurgical units comprising: quartz-pyroxmangite-magnetite, quartz-amphibolemagnetite, quartz-magnetite, garnet-magnetite-quartzite and mica-quartzite ores. Important mineralogical characteristics affecting Cu, Pb and Zn recovery, grade and selectivity specific to the different ore samples are discussed. Ultimately, the use of ore-specific process mineralogical and geometallurgical knowledge allows for better mine planning and plant optimisation. Keywords: Flotation, polymetallic base metal sulfide ore, process mineralogy, ore variability

Jan 1, 2020

By Z. Levinson, R. Dimitrakopoulos

A mining complex is an engineering system where raw materials are extracted from several mines, processed, transported, and sold to the market. The uncertainty and local variability of material grades and their locations within the related mineral deposits are critical for determining the optimal life-of-mine extraction sequence, destination policy, operating modes, and capital investment decisions, which define the production schedule of a mining complex. Infill drilling is used to further characterise a mineral deposit and reveals information that changes the representation of the resource model and potentially the resulting optimal long-term production schedule. A self-learning reinforcement learning approach is proposed to determine the optimal drilling locations that maximise the net-present value of the long-term production schedule and the associated financial forecasts by adapting the schedule to additional information. A case study at a copper mining complex demonstrates a $70 M increase in project value by collecting drilling information with a $1 M budget.

Sep 1, 2021

By T. N. Matveev, V. V. Getman, A. Yu. Karkeshkina, L. B. Lantsova, N. K. Gromova

Sulfide-type tin deposits in Russia are characterised by the complex mineral composition of ores and dump products of their processing. Tin-sulfide ores contain industrial quantities of copper, lead, zinc, tungsten, silver, rare and trace elements (indium, scandium, etc.). In addition, the ores are characterised by uneven dissemination of sulfide minerals (chalcopyrite, galena, arsenopyrite, etc.) and tin minerals (cassiterite, stannine, etc.). X-ray phase and spectral analysis, scanning electronic microscopy (SEAM), laser SIM and AFM microscopy helped to identify the selective fixing of sodium dibutyl dithiocarbamate (DBDTC) on chalcopyrite and galena, as well as the tall oil fatty acids (TOFA) and IM-50 on cassiterite. For the first time, collectors and flocculants of slurry fractions of silver and tin, thermomorphic polymers TMPF were used to isolate more than 80% silver from the tin tailings of the Solnechny mine into a copper-silver rough concentrate. It has been established that preliminary ultrasonic treatment and desliming according to the 0.02 mm class removes up to 20% of sludge material, which impairs the process of gravity separation. The combined flotation-gravity scheme using new reagent compositions helped to obtain tin concentrate with a tin content of 5.42%, which meets the requirements for further metallurgical processing (at least 5%). This work was supported by the RSF according to the research project No. 17-17-01292. Keywords: Flotation, flotation reagents, tin-sulfide ores, casiterite, adsorption, flocculation

Jan 1, 2020

By R. B. Khumalo, F. K. Mulenga

Sampling in a continuous flotation plant is always a challenge as these plants are meant for production and not research work. The continuous flotation operation used in this work produced three sets of data: the first was based on overall plant performance, done by sampling the feed, concentrate, and tails; the second was based on the roughers row cell-by-cell; the third set of results came from the cleaners/recleaners row cell-by-cell. The highest recovery results in all sets of data obtained lie in the particle size ranges of 40 μm and 100 μm, which is in agreement with results by other researchers in the same field. King’s theoretical (model) recovery proved to be able to fit the experimental data but there is still room for improvement as the sum of squared error ranged between 400– 1200. Data obtained in continuous flotation operation could be used with batch flotation test-work to characterize scale-up of flotation cells.

Jan 1, 2023

Rock mass classification systems are extensively used in rock engineering design work, and mine design is no exception. Among the systems most widely used for mining-related design work are the NGI 0-system (Barton et al., 1974), the RMR system (Bieniawski, 1976), the MRMR system (Laubscher and Taylor, 1976) and, more recently, the GSI system (Hoek et al., 1998). Classifying the rock mass is widely seen as being the fieldwork required to characterize the rock mass and enable the application of empirical design methods associated with the different classification systems. This paper argues that it is fundamentally important to recognize the distinction between rock mass characterization and rock mass classification. These two processes should, in most cases, be separated from each other. Rock mass characterization should be used to determine the intrinsic properties of the rock mass independently of the application; i.e. independent from the infrastructure to be designed, the size, shape, and orientation of the excavation(s) or pillar(s), etc. Rock mass characterization should also be compatible with most classification systems and empirical design methods to be used. Rock mass characterization is the background fieldwork required to perform rock mass classification and/or engineering design work. Rock mass classification is the subsequent step to the characterization, and an integral part of the design process. Parameters that vary according to the design, such as the relative orientation of geological structures compared to the opening or the mine-induced stresses, should be calculated as part of the rock mass classification and design process, rather than during the rock mass characterization process. The failure to distinguish between rock mass characterization and rock mass classification can lead to major design errors and poor results.

Jan 1, 2012

By P. J. H. de Graaf, S. D. N. Wessels

"Rio Tinto’s Western Australian expansion, combined with mining within structurally complex geology and increasingly below the water table, presents challenges in effective slope management to ensure safe and economic mining.The Geotechnical Management System (GMS) was developed by Rio Tinto Iron Ore (RTIO) to manage geotechnical risks identified during the design process and implementation, and to ensure feedback based on the ‘as-found’ conditions. The GMS utilizes a risk-based approach to geotechnical risk management and is centred on the geotechnical risk and hazard assessment management system (GRAHAMS). GRAHAMS is used to assess pre- and post-control risk for future potential risks (planned slopes), current risks (as-built slopes), and actual geotechnical hazards (realized risks) identified in the pit. This serves as a core operational risk management tool in identifying and prioritizing key risk sectors and management of critical controls. The system’s database reporting functionality supports effective communication of operational risks to operational personnel, as well as reporting the risk profile across operations to management.A rigorous engagement process between design engineers and sitebased engineers is implemented to ensure that key design assumptions, limitations, risks, and opportunities are understood by the site teams. This information, together with mine plan schedule details, is used to assess the design risk and develop appropriate controls. These controls typically include slope performance monitoring and slope reconciliation. The design feedback loop is closed through sharing of key slope performance and reconciliation data with the design teams.The GMS has been successfully implemented in RTIO pits and is fundamental to successful geotechnical slope management. Improved characterization of design assumptions has allowed for re-assessment of the pit design and improved hazard management in high-risk pits. The GMS, GRAHAMS, and other processes reduce the incidence of unexpected slope instability. Improved understanding of rock mass conditions has allowed for economic optimization through redesign of slopes, allowing for an improved understanding of risk and fewer unexpected conditions (surprises), hence an increased realized value."

Jan 1, 2016