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By S Lehane, S Pearce

The characterisation and assessment of waste rock has been the subject of considerable research, and many guidance documents have been published both internationally and within Australia. While these documents provide detailed information on how to characterise waste rock, there is not a great amount of detail on how to determine the optimal waste management options from an operational viewpoint. For example, typical questions that a mine operator would seek answers to include: What placement method should be used (eg end tipping versus paddock dumping)? What is an optimal tip head height for waste placement in a storage facility? How should sulfide grade control be carried out?Defining the contributing factors to waste management and waste material placement and assessing the contribution of each variable directs these assessments towards a semi-qualitative and quantitative methodology. Quantitative methodologies show an increase in the confidence of these assessments by providing logical, measureable assessments.As part of a strategic assessment for waste management at an active mine site, a detailed risk-based investigation of the influence of variables common to waste placement was completed. These variables included placement method, placement height, sulfur grade and material conditioning (eg compaction) on the associated risks of waste material storage. The assessed risks include temperature assessments (potential for thermal take-off as a result of spontaneous combustion), toxic gas production and flow and acid production potential. Applying a numerical modelling approach to the assessment of the different waste material placement strategies allows for the variation in risk to be graphically presented and communicated through risk matrices and histograms.CITATION:Pearce, S and Lehane, S, 2015. Quantitative risk assessment tools to assist with waste management and placement guidelines, in Proceedings Tailings and Mine Waste Management for the 21st Century, pp 353–364 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Jul 27, 2015

By V Liebezeit

Data management is a significant, but perhaps unrecognised, consideration for any organisation embarking on a geometallurgy program. While resource geologists are accustomed to dealing with thousands or millions of samples and their associated assays, metallurgists and mineralogists have traditionally carried out only a few expensive and complicated tests and consequently tend to manage the resultant data in spreadsheets. The emerging discipline of geometallurgy arrives with significant challenges as a result of the sheer volume of data generated. As an example, the Olympic Dam geometallurgy program has generated more than:60 000 physical subsamples analysed for up to 30 analytes; 25 000 spreadsheet-based laboratory test results; 30 000 sized fractions analysed via QXRD, MLA and/or QEMSCAN;multiple external providers of data, and more than seven terabytes of data. Considerations for any geometallurgy data management system include storage space, data integrity, accessibility, traceability (sample provenance), maintenance and ease of use, integration with data providers and the ability to modify the system to adapt to changing requirements. Further, aspects of a geometallurgy program, such as conducting a range of tests on one sample, producing subsamples such as concentrates and tailings, compositing and routine QA/QC may not be well handled by existing software and providers geared to standard geochemistry, mineralogy or metallurgical test programs. The development of a geometallurgy database using existing software (AcQuire), and the associated file management system will be described for a large geometallurgy program at Olympic Dam. As with many aspects of geometallurgy there is no approach that meets the needs of every site or program. Recognition of data management as an issue, a structured approach in the program planning phase and commitment to executing the strategy will increase the chances of successfully mining the geometallurgical data.

Sep 5, 2011

Chlorination of organic matter by direct injection of chlorine gas or hypochlorite has been investigated metallurgically worldwide with little understanding of the basic chemistry of the passivation mechanisms in- volved. In this paper, chlorination of carbon is attributed to oxidation-reduc- tions, resulting in surface halogenation of graphitic or amorphous carbon. It is proposed that chlorine and carbon interact to form thin coatings of chioro-carbon complexes that block the adsorption of gold cyanide com- plexes, thus passivating the gold adsorption sites of carbonaceous matter. Analytical, spectrographic, microscopic and electron-optical methods have been applied to demonstrate the proposed mechanism of chlorine-carbon bonding, as well as to evaluate the advantages and limitations of chlorination in the pretreatment of refractory carbonaceous gold ores.

Jan 1, 1991

By F Ramos

Conventional techniques for resource estimation generally involve the laborious analysis of variograms and the specification of parameters for covariance functions. In large deposits, construction of resource models and their update require significant amount of manual work, expert knowledge and time. In this paper we take a machine learning view to the problem of resource estimation. This is a non-parametric Bayesian method, leading to automatic parameter estimation (learning). Through Bayesian learning, the parameters of the model can be obtained by optimising a quantity known as the marginal likelihood. The main advantage of this approach over conventional maximum likelihood estimators is that the objective function incorporates the OccamÆs razor principle. This allows the comparison of different statistical models û for example, models designed with different covariance functions û and naturally penalises complexity, thus avoiding overfitting. Our technique presents significant flexibility for the definition of mean and covariance functions and permits the usage of more sophisticated models.The second contribution of this work is the formulation of a data fusion mechanism that continuously updates the resource model. As mining progresses, more information becomes available. For example, the hardness of the rocks can be measured while drilling blast holes, grades of ore and contaminants are obtained from cone logging. This information is in general available at a finer spatial resolution but can be more uncertain. The data fusion mechanism can seamlessly estimate the uncertainties of the different types of information and fuse them as new measurements are acquired. This procedure can significantly improve the quality of the resource model, defining new grounds for stochastic mine planning. We demonstrate the effectiveness of our approach in estimating iron ore deposits by integrating information about different grades.

Sep 26, 2011

By D C. Ford

The Argyle AK1 orebody was discovered in 1979 and subsequently found to be a large, economically viable reserve with high grade diamondiferous ore. Production began via an open pit operation at the end of 1985 and has continued for some 25 years to present day. This, however, will come to an end in the next few years with production moving to an underground operation via block-cave mining methods.A series of studies from 2001 - 2005 was carried out; diamond drilling commenced after approval in 2006, from an exploration drive to determine the likelihood of the current proposed block cave operation.In 2006, development of the block cave project began, in order to realise the part of the orebody below the extent of the open pit mining operation.Development of the block cave technique, comprising an undercut level beneath the target zone of the orebody with subsequent extraction level 15 - 20 m beneath, relies largely on a number of block-caving parameters (hydraulic radius, rock type, structure and orientation of stress and grade of the orebody). These all have an influence on cave ability and eventual profit of the operation. Evaluation of further potential for taking this orebody beyond the block cave phase with options such as sublevel-caving are being considered for the UG-2 portion of the resource/reserve. Diamond drilling for this work is currently under revise.The challenges faced in transitioning form the surface operations to the underground, comprising a changing workforce, converging development drives, underground aquifers, major excavations and transportation of ore via automation are intriguing.

Aug 22, 2011

After the minim operation, most raw materials that occur in the earth's crust require some form of processing or refining before they can be converted into a final end product. In the mineral industry these remarks apply to materials such as iron ore, bauxite, limestone, copper ore and many others that are found in Australia in large quantities. Processing plants for these raw materials operate more efficiently, however, if the feed to them is consistent in both physical and chemical characteristics. Such uniformity rarely occurs in nature so that some form of homogenisation, or blending, is required ahead of the processing plant. Bending also assists the mining operation itself by reducing; the need for selective mining and, by combining high and low grade materials, a more uniformly graded, commercially acceptable product can be obtained, thereby increasing the commercial life of a mine or quarry. Bed blending is the most efficient of the processes available, and following its development in the United States in 1905, over 100 plants have been installed, or are under construction, all over the world. The original process, the Robins--Nessiter system, has been the subject of continued improvements and refinements, so that economical capacities are possible from less than 100 l.t.p.h. up to 10,000 l.t.p,h. and beyond.

Jan 1, 1969

By S Talu, E Savage, P Sturm, A van As

It is estimated that substandard ground support installations and elements cost the Rio Tinto Copper Group millions of dollars between 2000 and 2014. These issues have also created significant safety and financial risks in Rio Tinto mines. As a result, Rio Tinto Copper has developed CaveCAD, an integrated system to collect, store and analyse all ground support-related data. The CaveCAD system stores geotechnical data and has the ability to automatically import data, collect data through tablet applications, report processed/analysed data and present this data through 3D visualisation software.The CaveCAD Ground Support module closes the design–implementation–verification loop by providing a method of verifying the compliance of all installed ground support, recording manufacturer specifications, recording quality control test compliance and rectifying any non-conformant areas. The module uses innovative technology to create value through cost saving, safety improvements and data collection efficiency. Cost savings will be achieved by a decreased need for excavation rehabilitation, less ‘waste’ due to faulty installation and quality assurance checks reducing the likelihood of substandard support units being used. Data collection will be improved through the use of a mobile tablet application that can be used underground in ‘off-line’ environments.CITATION:Savage, E, Talu, S, Sturm, P and Van As, A, 2016. Development and implementation of the CaveCAD ground support module for cost and safety improvements in block cave mines, in Proceedings Seventh International Conference and Exhibition on Mass Mining (MassMin 2016), pp 769–772 (The Australasian Institute of Mining and Metallurgy: Melbourne).

May 9, 2016

By G Pitkin, D Sorley

The purpose of any consultancy is to provide a superior service for their client. Organisational structure is a key element in providing this service and differentiating between consultants.In an organic organisation the emphasis is on effectiveness, problem solving, responsiveness, flexibility, adaptability, creativity and innovation. Such an organisation is able to respond in a timely manner to change because employees are empowered to be creative, to experiment and to suggest new ideas. The process of innovation is triggered by employees throughout the organisation in a collaborative manner.Problem solving becomes a core competence of the consultancy. A multidisciplined approach arrives at superior solutions that are rigorous, holistic, achievable and often novel. It allows the mining consultancy to extract the most from a very talented and experienced team.This paper highlights the advantages of an organic structure for a mining consultancy and how to implement such a structure. It draws on examples of innovative problem solving from feasibility and strategic studies carried out in Australia and Africa.CITATION:Pitkin, G and Sorley, D, 2016. Organic management structure – its advantages in a mining consultancy, in Proceedings International Mine Management Conference, pp 155-162 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Jun 22, 2016

The major coalfields of Australia are almost unique in the occurrence in some areas of CO partly or almost completely replacing CH4,t1e normal seam gas from metamorphism of coal due to time and depth of burial. This CO2 situation has some benefits but mostly it introduces particular safety problems result- ing from the high density of CO2 relative to air, magnifying problems of gas dilution in ventilation, and the greater affinity which coal has for CO2 than for CH4 increasing proneness to gas-dynamic phenomena. The occurrence of most CO2 seam gas can be directly related to igneous intrusion and to its associated pneumatolytic injections. Whilst intrusions locally increase the rank of coal by their contact metamorphic effect, normal coal metamorphism due to time and depth of burial continues after intrusion and injection together with the ongoing metamorph- ic production of CH . This new CH slowly displaces any CO to leave some present-day intruded coals with seam gas with reduced CO2 or even without any significant CO2 associat- ions. The seam gas maps of the Bowen and Sydney Basins and more detailed maps of specific areas exemplify these situations. Mining progress, plus more searching studies of igneous-coal relationships provide improved understanding of the factors involved.

Jan 1, 1986

By S Hayton

While coal seam gas is considered to be an unconventional gas, the proliferation of areas under exploration and development mean that it is rapidly becoming main stream. The potential of New Zealand's coals to produce coal seam gas has been recognised for some time. However, key market drivers have not been suitable for the progression of this potential into reality. Kenham Holdings Ltd and CRL Energy Ltd, recognising the potential for the gas in New Zealand have initiated what is the largest ever assessment of New Zealand's coal seam gas potential. The premature demise of the giant Maui field has raised the focus of new gas developments and has increased interest in what we are doing. Over the past couple of years Kenham Holdings Ltd and CRL Energy Ltd have approached the assessment of the coal seam gas potential in a staged programme. Preliminary assessment of the data on the permit areas indicates that the potential resource may be up to 500 PJ. To date, preliminary appraisals have been completed or are underway on a significant number of fields. Exploratory drilling has been completed or initiated on several of those fields, with results from this work being fed into the preliminary modelling as it has become available. Given that in many of these areas there is no pre-existing information concerning their gas potential and world wide experience with lignite is practically nonexistent, it has been a steep learning curve with many surprises. However, initial gas content results and preliminary gas flow models have been on the whole, rather pleasing, with the commerciality of some developments already apparent.

Jan 1, 2003

By Kavanagh M. E, Henley R. W

The Meekatharra mining district lies in the northern pan of the Murchison Province of the Archaean Ynlgam block of Western Australia. Since dis- covery of the Paddy's Flat belt in the district in 1897 total gold production has been 67 tonnes and a further 33 tonnes in resources have been defined by drilling. The Paddy's Flat gold deposits occur within the Gabanintha Formation, a basal formation of the Murchison greenstone sequence containing komatiitic basalts, ultramafics and minor banded iron formations. The regional control on the distribution of deposits is an anastomosing shear belt which extends over 350 km and incorporates the Mt Magnet, Reedys and Meekatharra deposits. The following gold deposit styles may be recognized based on lithological association and alteration: Albite porphyry hosted, Carbonate-fuchsite- quartz alteration hosted deposits, Banded Iron Formation hosted sulphide replacement gold deposits and quartz vein deposits. With the exception of the banded iron formation hosted deposits, the Paddy's Flat alteration zone shows strong similarities to the alteration styles at Kerr-Addison (Ontario).

Jan 1, 1991

By Edwards A. B

The occurrence of gold so finely divided as to be invisible under the microscope, even at magnificatioIl; of 1,200 to 2,000 diameters, is rare, but not unknow;n.. Burg (1930) has described an occurrence of such "invisible gold" from the 25/37 vein of the Bradisor mine at Brad in the Siebengeburgen district of Rumania (Transylvania), in which the pyrite contained up to 150 grams of gold per ton (approx. 5 oz. gold per ton). Hoffman (1931) has described an epithermal silver gold ore from the Titiribi district of Columbia (South America), in which "invisible gold" to the extent of 150 grams per ton occurred in the pyrite, arsenopyrite and zincblende; and Haycock (1937) has recorded it in pyrite from the Surf Point mine (Canada), which contained no visible gold, but yielded positive testsfor gold whenever samples drilled from polished surfaces of apparently homogeneous pyrite were tested spectrographically.The examination of a cyanided pyritic concentrate, together with a sample of the ore from which it' was prepared, from the Dolphin East lode at Vatukoula, Fiji, carried out as part of the programme of the Mineragraphic Section of the Council for Scientific and Industrial Research, Australia, has established a further occurrence of "invisible gold" in pyrite and arsenopyrite. The Dolphin pyrite contains about 25 oz. gold per ton, and the arsenopyrite contains more than 33 oz. per ton.The concentrate has been prepared for the Mill Superintendent of the Emperor Gold Mining Co. by L. Mayger...

Jan 1, 1946

By D Corley, J Moore, M Grant, A, W R

Resource estimates are the corner stone of technical and investment decision-making. Prior to mining, resource estimation uncertainty has the greatest potential to lead to poor investment decisions, despite a significant component of resource estimation uncertainty being unknowable at this critical stage in the mining cycle. This presents a conundrum to the resource geologist in terms of risk evaluation and resource classification. Ahead of mining, there is normally considerable focus on drill hole spacing analysis to determine the ‘optimal’ drill hole spacing, taking grade and geological continuity into account; cost-benefit analyses balance improved resource definition against cost; the law of diminishing returns and the exponential cost increases acting together as spacing is reduced. Typically, a lower limit to the drill hole spacing is identified. After completing the resource drilling to an agreed spacing, resource estimation is then undertaken. At this point it is common to attempt to bracket the resource estimation uncertainty. In many cases conditional simulation is used, based on a modelled, but largely assumed, variogram model. It is also assumed that the histogram of the available drill hole sample data is representative of the in-ground mineralisation. The purpose of this paper is not to diminish the importance of drill hole spacing and simulation studies, but rather to illuminate an important aspect of estimation uncertainty that, although previously recognised, is typically overlooked – the question of how representative the available data set is in characterising the true (but unknown) distribution of mineralisation. To do this, we lift the lid on one of OceanaGold’s former operations, the mined-out Globe Progress Mine, by resurrecting a high quality, close-spaced, reverse circulation (RC) grade control data set. The Globe Progress Mine is within the West Coast Region of New Zealand, it was closed and transitioned to rehabilitation in 2016 and is now known as the Reefton Restoration Project. The exhaustive Globe Progress grade control data was used to repeatedly ‘redrill’ the deposit by extracting 35 m × 35 m spaced subsets from the original 5 m × 5 m spaced grade control data. Utilising closely spaced grade control data removed the need for assumptions regarding short range continuity, which are necessary with forward-looking analyses that are based upon broader spaced resource drilling. The extraction process used a nearest neighbourhood algorithm, repeatedly moving the origin in 5 mE, or 5 mN increments. Individual resource estimates were then completed for each of the extracted drill hole data sets (49 in total) as well as a grade control estimate based upon the exhaustive data set. Whilst the data unpinning each of the 49 estimates changed, the geological assumptions, variography, and modelling parameters remained constant. This approach was taken to isolate the impact of changing the input data. The 49 estimates were then compared against each other and the grade control estimate. The mean of all 49 sensitivity estimates was close to that of the grade control estimate in terms of contained gold, tonnes and grade. Whilst it is acknowledged that the grade control estimate itself is subject to some degree of estimation uncertainty, the close match between the average of the 49 estimates and the grade control estimate suggests that the resource estimation methodology is reasonable and appropriate. Whilst this comparison is important, the focus of this study is on the component of estimation uncertainty related to the underlying data, and this is reflected in the spread across the estimates. The spread across the 49 global estimates (highest to lowest) for this particular Mineral Resource Estimation Conference 2023 | Perth, Australia | 24–25 May 2023 142 case study was found to be significant (approximately 20 per cent in grade and metal) and that is attributable solely to the underlying data. This exercise quantifies a component of the estimation uncertainty that is inherent to all drill hole data and is distinct from the uncertainties associated with modelling methodology choices, sample and subsample quality and drill hole spacing-related interpolation uncertainty. Importantly, this uncertainty is unknowable prior to mining; we can only directly compare the resource drill hole data against grade control data after mining has taken place. For the case study, approximately 65 per cent of the estimates fell within 5 per cent of the grade control estimate for contained-gold, suggesting that for many projects the histogram of drill hole data is unlikely to differ noticeably from that of the in-ground resource. However, about 15 per cent of the estimates in this study differed by more than 7.5 per cent, suggesting that a not-insignificant proportion of estimates will be materially compromised. Whether or not the available drill hole data is representative comes down to the ‘luck of the draw’ and cannot be known at the time of resource estimation. Given the challenge of attempting to evaluate forward-looking estimation uncertainty, a component of which that can only be quantified retrospectively, what should we do as resource geologists? What are the implications for risk evaluation, resource classification and reconciliation? Furthermore, without rigorous post-mining data checks, resource geologists may conflate suboptimal modelling with the shortcomings of the underlying data. An example of this problem is discussed in the following section.

May 24, 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 M Rafty, C Lane

Managing water is a key aspect of mining, whether a site is in water surplus or water deficit. Water in tailings storage facilities (TSFs) needs to be managed carefully to maximise the density of the solids, maximise the re-use of available water and to minimise the risk of water overtopping the spillway in the event of heavy rainfall. Developing a water balance models is a standard method for monitoring the major flows of water around a site. However, models created by specialist consultants are often overly complex and are not suited to being used by process plant personnel after they have been handed over to the mine operator. Models typically offer information on water management at the time that they are created, but then ‘sit on the shelf’ and offer no further insight or value for operating mines. Advisian has developed a customised water balance model for a mine in Sub-Saharan Africa, which can be adapted to other locations, to assist in managing water at the site and in particular to predict the current and future water level and volume in the TSF under various rainfall scenarios. The model was developed using Microsoft Excel and requires no specialist skills to operate. Site personnel enter in daily production, water transfer and rainfall data and then view the output dashboards to observe the current water level and volume, decant rates, licensed water abstraction and water supply versus demand. The output dashboards were developed in close consultation with the client to ensure that all of the relevant key performance indicators were included, from the perspectives of all the key stakeholders. This modelling approach focusses on the needs of the people who will actually use the model to actively manage TSFs with the need to involve the model developer as part of ongoing operations eliminated. The simplicity of this approach is now being embraced by other mine sites in Africa and Australia. CITATION:Rafty, M and Lane, C, 2018. Water balance management 101, in Proceedings Mine Waste and Tailings Stewardship Conference 2018, pp 485–490 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Jul 23, 2018

By M Behrens

The Melbourne Main Sewer Replacement (MMSR) is a major urban infrastructure project involving approximately 2.3 km of tunnelling and construction works. The works extend from Docklands Precinct, on the southwest edge of the city, southwards to connect with the Hobsons Bay Main Sewer in Port Melbourne. The tunnel boring machine (TBM) tunnelled section of the works terminates at the Yarra River where the delta geology and alignment requirements precluded the use of underground tunnelling methods. The 132 m long river crossing works involve a cut-and-cover excavation within a staged fl ooded cofferdam construction to construct an immersed tube tunnel as an extension of the TBM works.This paper reports on the development of the staged construction method and highlights the geotechnical and structural design aspects involved in building a new sewer in the Yarra River to close the gap between the new TBM driven and pipe-jacked tunnels.

Mar 8, 2011

By J deVries, A Broodryk, P Kyselica, K McLean

Gwalia gold mine is located close to the town of Leonora in the Goldfields region of Western Australia, some 830 km from Perth. The mine is wholly owned by St Barbara Ltd, an Australian Securities Exchange listed gold producer and explorer. The Gwalia mine is a deep, all-trucking mine and is serviced by a 10 km long decline from surface. Mining is currently 1540 m below surface. Reserves extend to 1820 m below surface and are open at depth. The mine produces in excess of 200 koz of gold per annum.The Leonora area is hot and arid, with a mean annual rainfall of 230 mm and a 95 per cent mean daily maximum temperature of 20.3°C wet-bulb and 35.6°C dry-bulb.The primary ventilation system comprises a 490 kg/s surface exhaust fan, which draws air down a parallel intake system consisting of the decline, and a dedicated fresh-air intake system extending to the bottom of the production zone. Cooling is provided via a surface bulk air cooler serviced by an ammonia refrigeration plant that delivers 150 kg/s of chilled water to the bulk air cooler.The refrigeration plant and bulk air cooler were installed in 2008. The design allows cooling to be increased over time by installing an additional refrigeration plant in a lead lag configuration with the current refrigeration plant when required.Since 2008, expansion and increased depth reached a point where the underground heat load exceeded the installed capacity of the existing 4.0 megawatt rated (MWR) refrigeration plant. In order to maintain the mine design conditions underground, upgrading of the existing refrigeration system was required to bring the cooling capacity to 7.5 MWR.Two expansion options were considered: the addition of an ammonia vapour-compression refrigeration machine similar to the existing one or a lithium bromide absorption refrigeration system using waste heat from the site power station. The original design and layout of equipment on-site meant that either could be used, allowing an assessment of the merits of both when additional cooling was required.The decision was made to install a lithium bromide absorption system on the lead side of the existing ammonia refrigeration plant. This arrangement provides considerable economic benefits, including reductions in site-generated electrical power, generator fuel consumption and carbon dioxide emissions.This paper details the planning, design, installation and commissioning of the absorption chillers.CITATION:Broodryk, A, deVries, J, Kyselica, P and McLean, K, 2015. The design, installation and commissioning of an absorption refrigeration system at Gwalia gold mine in Western Australia, in Proceedings The Australian Mine Ventilation Conference, pp 193–200 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Aug 31, 2015

By J Foo, T Kleinschmidt, B Reynolds, K Kennewell

The consideration of complex supply chains often highlights multiple system bottlenecks that are localised and dynamic – they shift depending on what variability last influenced the system. Using the understanding of a dynamic system model, synergies between items that tackle localised bottlenecks can be understood to generate a suite of solutions. These can be simple solutions, such as a couple of new conveyors placed in series, or more complex ones, including buffer dynamics (capacity, location, behaviour, etc).Each solution in the suite will have an associated uplift in system performance and an associated cost of implementation. Dynamic simulations can address each situation in turn and estimate the projected improvement it would offer. However, these solutions will have different benefits when implemented together, and each solution will give different uplift depending on what other solutions have already been assumed. If the number of solutions is numerous, traditional dynamic simulation analysis becomes the bottleneck, limiting options analysis to the processing power available.This paper will illustrate the use of a new technique applied as part of a capital growth optimisation project with a major iron ore producer. Metamodelling is the creation of a model of a model; in this case study, the focus is on developing a response surface representing the suite of solutions. This can be achieved through a combination of dynamic modelling and an appropriate mathematical process called design of experiments. The result is an approximation surface of the outcomes of the dynamic model, encapsulating interactions between key elements of the value chain.This modelling approach provides a number of advantages compared to traditional dynamic modelling approaches, including but not limited to:early and rapid evaluation of a large range of potential solutionsaccommodation of multiple initial states of the value chainoptimisation of the overall suite of upgrades (down to route-level upgrades) based on business objectiveseasy re-evaluation of outcomes when key inputs such as costs change.CITATION:Kleinschmidt, T, Foo, J, Reynolds, B and Kennewell, K, 2015. Case study of iron ore value chain optimisation, in Proceedings Iron Ore 2015, pp 605–612 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Jul 13, 2015

By D Hatton, S Wilson, F Orrante, M Taghimohammadi, F van der Walt, N Arslan, M Westcott

"Cayeli Bakir Isletmeleri is an underground copper-zinc mine in turkey. The ore is highly variable with feed grades ranging from 1–4 per cent copper and 1–10 per cent zinc. Due to this variability, ore characterisation and blending strategies are crucial for performance optimisation.This paper presents a case study for a multidisciplinary approach to process optimisation, integrating Geometallurgical and advanced control methods to achieve optimised plant performance. The results from comprehensive metallurgical tests and surveys were used to develop a mathematical model utilising SGS’s Integrated Geometallurgical Simulator® (version number 2.5.24.0). The model was employed to determine the most appropriate operational settings required to ensure that optimised grades and recoveries are achieved for each ore type.SGS’s Modular Expert Technology (MET) expert system was then implemented to target these optimum operational ranges utilising online grade and froth velocity measurements. The successful conversion and deployment of the modelled process parameters into an expert system has resulted in significant improvements in overall recovery and circuit stability.Citation:Arslan, N, Westcott, M, van der Walt, F, Hatton, D, Taghimohammadi, M, Orrante, F and Wilson, S, 2016. A Multidisciplinary Approach to the Optimisation and Control of a Copper-zinc Grinding and Flotation Circuit, in Proceedings 13th AusIMM Mill Operators’ Conference 2016, pp 115–122 (The Australasian Institute of Mining and Metallurgy: Melbourne)."

Oct 10, 2016

By D J. Logan, J Jackson

"As of 1 December 2014, the 2012 edition of the JORC Code requires Ore Reserves to be defined by studies at the pre-feasibility or feasibility level. This paper examines how the pathway for most mining developments is determined in the Pre-Feasibility Study and how a trade-off must be made between the need to make the best decision, the constraints of limited time and resources, and the level of the project’s accuracy and uncertainty in terms of value. Issues considered include framing the scope of the study, planning for robust decision-making and ensuring effective communication between the diverse disciplines within the team.An inherent conflict exists between the immediate value added to a mining business from an increase in ore reserves resulting from a ‘quick’ Pre-Feasibility Study and the long-term value of pursuing a development strategy based on proper investigation and evaluation of the options. Recognition and resolution of this conflict is necessary if a project is to progress into future development stages, both in a timely manner and with the optimal development alternative and strategy.The challenge for project developers and those conducting the Pre-Feasibility Study is to understand what the key deliverables are (ie an Ore Reserve statement, option selection, business evaluation, forward work plan or risks) and how to follow good practice to ensure that these deliver value.CITATION:Logan, D J and Jackson, J, 2016. The pre-feasibility study – big choices, little time, in Proceedings Project Evaluation 2016, pp 122–132 (The Australasian Institute of Mining and Metallurgy: Melbourne)."

Mar 8, 2016