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Cyclone Process for Heavy Metals Decontamination of Sediments

Sep 13, 2010

By Melcher G

The challenge to the zinc industry is shifting from maximum metal recoveries to the environmentally safe processing of the ore concentrates. The main source of solid residues from hydrometallurgical zinc processing is the iron content of the concentrates. Unfortunately iron precipitates from leach liquors contain various compounds which cause them to be considered as environmentally unsafe, unless they have been cleaned in subsequent process steps. This calls for separating the iron from hydrometallurgical processing as early as possible. For this purpose a process has been designed to eliminate the iron directly after neutral leaching the calcine. This is being done by direct smelting the neutral leach residue with a cyclone/top blowing unit. The zinc is recovered from the neutral leach residue as a metal oxide to be recycled to the zinc process. The iron is converted to a slag, which is resistant to atmospheric attack. Key data of the integrated hydro-/pyrometallurgical process are presented as derived from pilot plant tests by cyclone smelting and top blowing the slag.

Jan 1, 1993

Cyclonic Separation Effect of Metallic Ores in Cyclonic-Static Micro-Bubble Flotation Column

Sep 13, 2010

There is considerable economic incentive to increase the angle of open pit walls so as to reduce the total volume of waste to be moved to gain access to an orebody. However, a number of slope failures has increased the awareness of mine operators to the potential and consequences of slope failure. Sophisticated methods for the analysis of slope stability have been developed but they rely on an understanding of the appropriate parameters to describe the structural properties of the slope materials. Unfortunately, these properties change with time.The excavation of an open pit mine removes the constraint and weight of the overlying material and allows the newly formed slope to release previously locked in stress. The release of this stress allows the opening of joints and leads to a significant reduction in bulk modulus. The rock mass is also further affected by damage from nearby blasts. Such damage is cumulative and further reduces the modulus of the near surface material and weakens the bonds between structural elements in the slope.

Jan 1, 1992

By A Radici, S Russell

The Dampier Port Upgrade was completed in 2007 and is the largest project in Rio Tinto Iron Ore's expansion program. The project doubled Dampier Port's iron ore shipping capacity over a five year period. Sinclair Knight Merz (SKM) was appointed by Rio Tinto as the Engineering, Procurement and Construction Management (EPCM) contractor to manage Phase A and Phase B of the Dampier Port Upgrade. The aim of the project was to increase the Dampier Port capacity from 74 to 140 million tonnes per annum (Mt/a). This increase in shipping capacity tripled the capacity of the Parker Point facility from 34 to 94 Mt/a, whilst the East Intercourse Island facility increased from 40 to 46 Mt/a. The Dampier port remained fully operational throughout construction and achieved record yearly export tonnages over the period of the project. The $US1.4B project was completed ahead of schedule, on budget and with an outstanding safety record. Significantly the project team set an industry benchmark by achieving five million man-hours Lost Time Injury (LTI) free. The project has been recognised by the Engineers Australia 2008 Western Australian Engineering Excellence awards as category winner in resources and the overall winner in WA, and went on to win a National Engineering Excellence award. This paper provides an overview of the EPCM project management and an insight into some of the key drivers that have delivered this outstanding project.

Jan 1, 2009

Any project carries a number of challenges and risks. Inappropriate design, poor project management, time, industrial disputation and capital over runs are all areas which can impact on a project cash flows and return on investment. In the resource Industry you have the added complexity and risk of geology which is difficult to predict and sometimes unforgiving. The Dartbrook Mine was designed and constructed as a high output longwall mine and has overcome a number of hurdles to produce over 2.3 million tonnes for the first year of operation. A number of problems were encountered during construction which resulted in the inseam development being delayed for six months. Dartbrook people have demonstrated they can manage adversity and produce world class results. The mine has been through a very steep learning curve during 1997 and with this experience behind the mine is now producing at an annualised rate of 3.4 MTPA.

Jan 1, 1998

Dartbrook mine is a new greenfields thermal coal longwall mine located n the Upper Hunter Valley ten kilometres north of Muswellbrook Figure 1). Construction began in April 1993 with coal extraction starting n October 1994. Longwall production is scheduled to commence in the first quarter of 1996 with production expected to exceed 3.5 million onnes per annum. The permanent workforce will number 150 full-time ,mployees and productivity rates will be 22 000 per employee per annum m a ROM basis. Being a greenfield site Dartbrook has had the opportunity to develop and operate the mine using the best practices and latest technology available to meet high standards of safety, quality and production.

Jan 1, 1995

By D Bruce

OZ Minerals’ Carrapateena mine is a Sublevel cave mine located in South Australia, approximately 160 km north of the regional centre of Port Augusta. Carrapateena commenced underground development in 2016, with production commencing in 2019 and ramping up to 4.25 Mtpa by December 2020. In pursuit of continual improvement, the Carrapateena mining team sought to identify how it could better interrogate its current productivity levels and its drivers (root causes). During this process the team were confronted by a disconcerting number of different opinions (which often conflicted) on our actual productivity performance which led to a need for review. Although reliable data and a well-designed time utilisation model (TUM) existed, it was still unclear what actions needed to be put in place to improve productivity. This was for the following reasons: • Data sources were disparate unless significant effort was applied to compile them. • Data sources were not available in a format that could be easily viewed or analysed. • General understanding of the data which was collected and how it could be used was poor. • Process of regularly analysing performance and forming tangible actions to improve was lacking rigour. A project was then started with the goal to fix the areas stated above. An initial requirements gathering exercise was undertaken to identify key data sources for site which returned a staggering 42 known systems which were then shortlisted to the key systems that contained operational data and could generate tangible improvements when analysed. This paper discusses the approach to selecting the OZ Minerals Insights Hub as the chosen platform, and the effort and collaboration between cross-functional departments that was involved in transforming the data so it could be displayed in a useful way. The data sources identified included a fleet management system, two planning databases, a control system historian, and a mobile fleet machine data system. New processes were also introduced to digitally capture daily commentary; this was achieved utilising SharePoint Forms. Data pipelines were then developed in the Insights Hub platform to automate the ingestion and transformation of the raw mining data into curated data models for end user consumption. Tableau dashboard packages have been created to support facilitation of short interval control, daily review meetings, and weekly review meetings as well as data analysis of key equipment and locations. These dashboards visualise the mining data models in a form that provides the user a clear picture of what has occurred, why it has occurred, and where to focus improvement efforts. This has resulted in a team which is more accountable, more equipped to assess the root cause of performance, and more regularly able to make data driven decisions. The next step? To build this out from Mine to Mill and create a data indexing tool which will drive self-service reporting, a data agnostic platform and predictive tools that help our people continue to be one step in front.

Mar 29, 2023

The Olympic Dam Mining Operation involves the supply of some 2.2 million tonnes per annum of ore feed for the metallurgical plant. To achieve this approximately 9 km of tunnels are developed per year to gain access to the long hole open stopes that supply the majority of the production tonnes.Vast amounts of data are produced in the mining activity from the geology, mine design, scheduling, surveying, shift reporting through to the statistical reporting and analysis of the performance of the mine activities. Care must be taken when obtaining the data to maximise the usefulness of the data to other users so not to impose constraints or additional processes on future users.

Jan 1, 1992

By I Canbulat, J Emery, L Yao and C Zhang

This paper documents a process of data analytics and machine learning applied to coalmine roof convergence data. It follows a publication documenting the formation of the database. For each set of analyses Python scripts were utilised to extract data from an SQL database, with subsequent analyses also executed in Python, utilising open-source tools such as scikit-learn. Preliminary analyses with classic machine learning algorithms included multiple logistic regression, K-nearest neighbours, decision tree, random forest, support vector machine, kernel support vector machine, artificial neural network (ANN), naïve Bayes and multiple linear regression. Results indicated that relationships were complex and non-linear, with the artificial neural network most suitable. An ANN model was then refined through a standard process of hyperparameter optimisation and data augmentation to arrive at a final model. Due to the black box nature of the ANN further insight into how the parameters interacted was sought and found with Shapley additive explanations (SHAP values). SHAP values utilise Game Theory, where each input parameter or feature is a ‘player’ while the data set is the ‘team’. The SHAP value is the impact of each player on the target value, essentially establishing the contribution of each input parameter. The results from all analyses are delivered in the paper, together with insights, applications, strengths, and limitations of the methodology.

Nov 29, 2022

By Y Tang, G Si

The analysis of rock fragment sizes plays a pivotal role in various stages of geotechnical and mining engineering. Traditional case-based methods often falter in addressing the variability in rock fragment images due to fluctuating geological conditions and imaging environments. To counter this challenge, AI-based models necessitate training on expansive and diverse data sets, encompassing a wide range of conditions and fragment types. However, the creation of such data sets is often hampered by financial and environmental constraints. This paper introduces a novel approach for rock fragment recognition, which leverages the power of AI for data augmentation, automated annotation and prediction. The methodology integrates three key components: Style-Based Generator Architecture for Generative Adversarial Networks (StyleGAN) for data augmentation, the Segment Anything Model (SAM) for automated annotation, and the multiscale version of you only look once (YOLO-MS) real-time instance segmentation model. The proposed method begins by training a new StyleGAN model using the original data set. It then delves into exploring the latent vectors of different rock fragment images to generate specific ‘style representations’. These latent vectors form the foundational elements of each image in a high-dimensional space, encapsulating critical features such as size, shape, texture, and colour variations. The next step involves conducting quality checks on the generated images to ensure their realism and suitability as training data. Subsequently, the augmented image set is annotated using modified SAM module, followed by training the YOLO-MS real-time instance segmentation model. The performance of this model, trained on the augmented data set, is compared with a model trained on the original data set, particularly in terms of generalisation abilities in new scenarios. The results exhibit a significant enhancement in accuracy, robustness, and generalisability of the model trained on the augmented data set, underscoring the potential of StyleGAN as a powerful tool for data augmentation in geotechnical engineering image analysis.

Sep 1, 2024

By S Versace, D Arnott, D Bojcevs, A Shaw

The George Fisher deposit is a significant sediment hosted silver-leadzinc resource located 2 km north of the Hilton Mine in NW Queensland. The George Fisher feasibility project involved the completion of underground and surface drilling programs to delineate Measured and Indicated Resource which could be converted to an Ore Reserve through mining and commercial studies. The geological information which was collected and used for the feasibility study included a major program of diamond drilling, predominantly from underground sites, but with some drilling also completed from surface. All drillcore was geologically and geotechnically logged, sampled and assayed. Data was also collected from underground cross-cut and drive development which was mapped for rocktype, ore distribution, structure and geotechnical condition. This new geological data, generated during the Pre-Feasibility and Feasibility stages, was combined with results of all previous drilling campaigns in the George Fisher area to produce a comprehensive database on which geological modelling and Resource estimation could be based. To allow fast and accurate collection and utilisation of data from the drilling program, digital collection processes were used where possible. This resulted in rapid data entry into the project database, significantly enhanced data quality, 100 per cent data ownership within the project team and increased productivity levels. Processes used during the project data collection program are described, lessons learned are related and suggestions for ænext timeÆ are listed using the benefit of hindsight.

Jan 1, 1998

This paper presents a new idea in field data collection for geological and scientific data. The computer keyboard has been replaced by a barcode wand driving a spreadsheet style editor. To use the full capabilities of computer hardware currently available, geotechnical and geological data can be entered and validated in the field, in a structured format, for later transfer into modem geological modelling databases. An example, using a geotechnical borehole log, explains the principles discussed. The generic nature of the Datcol system is illustrated by the application developed using the Norwegian Geotechnical Institute Rock Mass Classification system. A brief description of a modem spatial relational database is also given with particular reference to underground engineering.

Jan 1, 1993

By D W. Evans

Research into the dynamic properties of ground support elements continues to grow at a global level, given the increasing importance of this field of application within underground hard rock mining. Measuring the mechanical response of such elements under the rapid onset of simulated rock burst loads forms the basis for this experimental work, typically utilising gravitational drop testing methods. This field of research is fundamentally driven by an increasing need for dynamic performance data, given the constant evolution of rock bolting product designs, materials selection, combination testing and specific application needs based on ground types and loading scenarios. A smaller number of globally recognised dynamic test facilities have predominantly lead research within this arena, typically in association with academic institutes, larger industry groups and government research institutes. However, due to the increasing requirement for dynamic performance data, additional facilities have now emerged in order to satisfy the overall demand for test work. These newer facilities are more typically associated with suppliers to the underground hard rock industry, with experimental designs looking to replicate, at least in part, the work of the primary facilities. For standardisation, repeatability and calibration purposes, the cross validation of test data between primary and secondary facilities becomes an obvious and important consideration across this field of research. This paper outlines the development of a new dynamic drop test facility for testing rock bolts, including the structural design and associated high speed data capture system. Based on the utilisation of identical bolt types, material grades, anchoring systems and decoupled ‘free-lengths’, comparative data has been generated to provide cross validation of the new test facility against data generated by a globally recognised primary facility. The cross validation of data provides increasing confidence in the repeatability and accuracy of the new facility as activity continues towards the provision of further experimental test work.

Nov 29, 2022

By B L. Dickson, J H. Hodgkinson

The self-organising map (SOM) is an exploratory data mining technique that is both non-traditional and underutilised. Methodologies based on SOM tend to be data-driven and unsupervised, which makes them ideal to assist in the integrated analysis and interpretation of complex and disparate æmineral explorationÆ data sets. While traditional statistical multivariate approaches have difficulty with relationships that are non-linear and data distributions that are not normal, SOM-based data mining procedures are useful in these circumstances. In a SOM analysis each sample is treated as a vector in a data space defined by the variables; and measures of vector similarity, such as the dot-product or Euclidean distance, are used to order or segment a data set into naturally occurring populations. These groupings are positioned as nodes, or groups of nodes, on a 2D rectilinear representation of the multi-dimensional ædata spaceÆ, which is the æself organized mapÆ. While it is common not to include a sampleÆs locational information in the actual SOM analysis, such information can be used to display the spatial location of samples coded by their SOM node or cluster. If one finds there are coherent spatial patterns belonging to samples from particular nodes, or groups of nodes, then there is strong evidence that the technique is determining patterns and relationships within the data that have natural significance. Variations of the SOM technique may be used to perform broad categories of operations, including: 1. outlier identification û for samples belonging to various nodes or grouping of nodes; 2. function fitting, prediction, estimation or imputation û for calibrating relationships between variables and determining replacement values for missing, null or censored values; 3. clustering, pattern recognition or noise reduction û for determining the natural patterns within a data set; and 4. classification û for classifying samples into the clusters determined in three. An EXTENDED ABSTRACT is available for download. A full-length paper was not prepared for this presentation.

Jan 1, 2008

By M Reisi, H Waterer, M Savelsbergh, F Engineer

The Hunter Valley Coal Chain Coordinator (HVCCC) Limited is the primary planning organisation responsible for the export of coal through the Port of Newcastle, Australia. HVCCC stakeholders include producers (mining companies), rail track owners, above rail operators, and terminal operators. The HVCCC is tasked with coordinating the activities of all the corresponding parts of the coal export supply chain so as to maximise the system efficiency. The HVCCC also has a forward looking planning role: with demand for coal expected to double in the next decade, strategic capacity planning is a core activity for the HVCCC. Optimisation and simulation models are key aids in such activity. They are used to suggest and evaluate infrastructure expansions and operating policy changes. These models require input data in the form of shipping stems, which are arrival streams of ships at the port, together with their cargo types and composition, and date and time of arrival.Although everyone is familiar with the expression ægarbage in equals garbage outÆ, relatively little research is dedicated to the issues surrounding input data generation. In this research we seek to redress this, and have developed a method for generating high quality shipping data. Shipping stems used as input should be realistic, reflecting key characteristics of historical patterns. They should accurately represent future demand scenarios. Yet they should also have controllable parameters that can be used to test what-if scenarios, such as are required by the HVCCC to answer questions about future infrastructure or operational plans.Here we review our process for achieving this, and give a case study analysing the effects of different shipping stems on the performance of the coal chain, as measured by a capacity assessment tool. The latter is an optimisation model that takes shipping stem and infrastructure data as input, and calculates key supply chain performance metrics. The potential benefits of alternative demand management strategies are analysed by measuring the performance of the coal chain given different shipping stems obtained via the stem generatorÆs controllable parameters.

Sep 26, 2011

By A D. T Goode

Data Metallogenica (DM) is unique as a global mineral sample collection and deposit database with over 70 000 rock samples and a wide range of descriptive, photographic and measurement data. Subscribers have access to approximately four gigabytes of on-line data with many examples included on the public pages. DM commenced its life on the Pacific rim in 1971 and much recent growth has focused on countries of the Pacific margin, particularly Australia, Fiji, Chile, Indonesia and Peru. Approximately 600 separate deposits and mineral occurrences in this region are represented in DM. The sets of rock samples have been photographed at high resolution and included on the web site, together with unpublished descriptions, deposit cross-sections, bibliographies, field and mine photographs, petrology and reflectance mineralogy. Figures and abstracts from PhD theses are a new addition and the donation of proprietary databases by companies and individuals is an encouraging trend. DM increasingly is being viewed as an acceptable and important repository for æfragileÆ and irreplaceable personal and company data, it is also being recognised in its true light as a high level information system, not solely a collection of rock samples. Data Metallogenica is a not-for-profit venture of AMIRA International that requires self-funding from December 2004. Growth and development must be funded entirely out of subscription and sponsorship revenues because no government subsidy is provided. The Pacific rim is in the mainstream of mining and exploration activities worldwide and those involved have much to gain from a dynamically sustaining DM. The home page at www.datametallogenica.com contains much example data and details of subscription and membership rates.

Jan 1, 2004

By E Ben-Dor, D Bakun-Mazor, Y Ben-Ari

Determining the mechanical properties of rocks is important for various civil engineering fields, including the mining of raw materials for aggregates used in the construction and paving industries. Traditionally, the mechanical properties of rocks are obtained through in situ and laboratory tests during geotechnical surveys. However, these time-consuming surveys involve many resources. In contrast, hyperspectral remote sensing methods make it possible to identify the mineralogical composition and crystallographic structure of the rock; properties that control the mechanical properties of the rocks. In this work, we characterise the mechanical properties of carbonate rocks by using a hyperspectral sensor in laboratory conditions. We collected about 150 cylindrical samples of carbonate rock, with a wide range of strength values from several rock outcrops in Israel. We used a point spectrometer in the range of 0.4–2.5 μm and a spectral image sensor in the range of 3.0–12.0 μm, scanning the samples to obtain the signature of their light reflections and spectral emissivity. We then measured the samples’ density, porosity, water absorption, and uniaxial compressive strength (UCS). We used sophisticated data mining to find statistical relationships between the hyperspectral signatures of the samples and their mechanical properties. We used this data to identify the most dominant wavelengths for predicting mechanical properties. We found that the density, porosity, and water absorption of carbonate rocks could be confidently predicted based on spectroscopy data, while the UCS of the rock could also be predicted, but less significantly. The results of the study pave the way for the development of measuring tools for the mechanical properties of rock, based on non-destructive tests of quarrying materials.

Nov 29, 2022

By B L. Dickson, E, M F. Lee, P A. Mikula

Computational techniques are needed to assist in the analysis and interpretation of the increasing amounts of geoscientific and mining related data and information that are routinely gathered at mine sites. New interpretation methods are needed to provide an integrated approach so as to establish relationships (cause and effect) or to be predictive about possible outcomes based on acquired data. Traditional multivariate statistical methods are often confused by variable relationships that are non-linear, data distributions that are non-normal, and by the data themselves that may contain missing values, text and both continuous and discontinuous numeric values. Modern data mining methods are available to understand the relationships within and between diverse data sets, and identify relationships or trends associated with processes, measurements and mine design parameters in mines. The self-organizing map (SOM) is a data mining approach with the advantage that all input data samples are represented as vectors in a data-space defined by the observations (variables). The SOM procedure is an exploratory analysis tool that highlights patterns and relationships. Results are internally derived, in an unsupervised fashion based on measures of vector similarity. Our SOM outputs are highly visual, which assists in understanding and illustrating the dataÆs structure and internal relationships. Two studies are presented. The first uses SOM to investigate the relationships between a range of measured and derived rock property parameters and their observed behaviours during mining. Our aim was to determine whether a SOM analysis would allow a better understanding of observed and predicted rock behaviours from their rock properties. The SOM analysis showed that there were indeed relationships between æbrittleÆ, æsugaryÆ, æslabbyÆ and æsqueezingÆ rock behaviours and a number of the input parameters. The computed SOM æstructureÆ was then used to assess the likely behaviour of unknown samples by assigning them into behavioural fields based on their measured and derived geotechnical responses. The second study was aimed at assessing whether a SOM analysis of microseismic data at the Kalgoorlie Consolidated Gold MineÆs (KCGM) Mt Charlotte mine can contribute towards understanding and potentially forecasting/minimising seismicity at the mine-design stage. The database comprised 40 sample æclustersÆ of microseismic parameters from the MS-RAP software package, which were derived from 2500 individual seismic events. The SOM analysis showed that some of the initial mining conditions are related to the seismic parameters. In each of the studies, the SOM procedures assisted in understanding the data and provided new insights. Because of its æordered vector quantisationÆ foundation, the SOM procedure is a substantial improvement over traditional statistical methods in three main areas:its ability to analyse data from disparate sources, including different data types; its ability to analyse sparse data sets with missing values; and its ability to analyse and visualise non-linear relationships within a data set.

Jan 1, 2006

By C Zhang, R Liang, I Canbulat, S Saydam, B Li

The integration of digital twins in mining represents a significant advancement towards enhancing operational efficiency and decision-making. Central to this development is the role of visualisation, a crucial element in mining digitalisation. This paper aims to develop a data-driven visualisation solution for the construction of mining digital twins, highlighting how this innovation revolutionises the representation of physical mining environments. It spotlights the shortcomings of conventional visualisation methods, particularly their inability to accurately depict complex underground environments and their predominantly historical orientation. To address these challenges, this paper proposes a novel, data-driven approach. This approach extensively utilises CAD files, encompassing data from geological surveys to intricate mine plans, thereby significantly elevating the precision of digital twins. It supports real-time updates and enables flexible interaction with a variety of data types. Moreover, it introduces a comprehensive data diagram alongside the adaptation of a 3D tile index. This strategy ensures the efficient rendering of extensive mining environments, allowing for a more dynamic and interactive digital twin model. In conclusion, the implementation of data-driven visualisation in the construction of mining digital twins marks a pivotal advancement in the industry’s digital transformation. It promises a more detailed, accurate, and dynamic depiction of mining operations.

Sep 1, 2024