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By Guido Deissnann, Jennifer L. Kirkaldy, Stephan Kistinger, Carol M. Pettit

Contaminated mine drainage presents a potential hazard for aquatic ecosystems and groundwater resources. Reliable predictions of long-term developments in seepage quality are necessary to select reasonable decommissioning options and remediation measures for waste-rock heaps during mine closure and/or mining site rehabilitation to minimize future risks and environmental hazards. Extensive geochemical modeling work was used to assess the consequences of various remediation options for waste rock heaps at a former uranium mine in Eastern Germany. The predicted contaminant discharges are used for an assessment of long- term benefits of decommissioning alternatives to derive conclusions for sustainable remediation measures using risk-based cost/benefit analyses.

Jan 1, 2000

By D T. Feinstein, M B. Gotkowitz

In Wisconsin, state laws and regulations governing metallic mine permitting stipulate that protection of groundwater quality be assured indefinitely through all phases of the mining project, including development, active mining, reclamation, and long-term closure. By rule, the Wisconsin Department of Natural Resources (WDNR) requires that permit applicants use a numerical model to assist in the assessment of compliance with this requirement for all proposed mine-waste facilities. As a part of the compliance assessment, model calculations are used to predict groundwater solute concentrations through time at designated boundaries for comparison to current groundwater quality standards. In practice, this modelling requirement has involved the application of numerical solute-transport models. Our review of a solute-transport model submitted by the developers of a proposed mine near Crandon, Wisconsin, provides us with an opportunity to assess the usefulness of transport modelling as a permitting requirement. Recent evaluations of solute-transport models by other workers suggest limitations in predictive accuracy. Our review of the model submitted for this project also identified important limitations due primarily to uncertainty in calibration of model parameters at the undeveloped site. The utility of any model to predict project compliance or non-compliance with numerical standards is limited by the range of uncertainty of key model parameters, such as dispersivity and porosity. Uncertainty in the future behaviour of the solute source(s) also reduces the predictive power. Moreover, the site characterisation provided by the model may have limited power to describe the site condition post-development. Although these inherent shortcomings accompany the use of solute transport models at undeveloped sites, the requirement for model development and the ensuing technical review has substantially assisted in our evaluation of site conditions and project design. Through this process we have identified elements of the waste facility design that are crucial to the environmental viability of the project, resulting in modifications to original project proposals. While results from this model review will be used to evaluate compliance, additional benefits come from the identification of critical design features that enhance groundwater protection.

Jan 1, 2003

By Y Xie, D Willingham

Predictive maintenance (also known as ‘condition/health monitoring’) refers to the intelligent monitoring of equipment to avoid future failures. In contrast to conventional preventive maintenance, the maintenance schedule is not determined by a prescribed timeline; instead, it is determined by analytic algorithms, such as machine learning, using data collected from equipment sensors and/or via simulation.Predictive maintenance offers the following benefits for operators and equipment manufacturers:- reduces equipment downtime by identifying issues before failure, thereby enabling convenient scheduling of equipment service and extending equipment lifetime- determines the root cause of the failure, enabling appropriate service to be performed without utilising resources to determine a diagnosis- avoids the unnecessary costs of maintenance and unexpected failures- improves safety and product quality.Algorithms and data are critical to predictive maintenance success. Sensor data is first preprocessed using advanced statistical and signal processing techniques; it is then combined with predictive modelling techniques, such as machine learning, to estimate equipment health.Once tested, predictive maintenance algorithms may be used in an operational setting within an IT environment, such as a server or cloud. Alternatively, algorithms may be implemented in an embedded system directly on the equipment, allowing for faster response times and significantly reducing the amount of data sent over the network.CITATION:Willingham, D and Xie, Y, 2017. Predictive maintenance modelling for bearing-based equipment, in Proceedings Iron Ore 2017, pp 627–630 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Jul 24, 2017

By R Marchant, D Willingham

"Predictive maintenance (also known as PHM or equipment ‘health monitoring’) refers to the intelligent monitoring of equipment to avoid future equipment failures. In contrast to conventional preventive maintenance, the maintenance schedule is not determined by a prescribed timeline; instead, it is determined by analytic algorithms, such as machine learning, using data collected from equipment sensors and/or via simulation.Predictive maintenance offers the following benefits for operators and equipment manufacturers:reduction of equipment downtime by identifying issues before failure, thereby enabling convenient scheduling of equipment service and extending equipment lifetimeautomatic determination of the root cause of the failure, enabling appropriate service to be performed without utilising resources to determine a diagnosis avoidance of costs of unnecessary maintenance and unexpected failures improved safety and product quality.Algorithms and data are critical to predictive maintenance success. Sensor data preprocessing is performed using advanced statistical and signal processing techniques. In the case that data isn’t available for the full range of operating modes, operating scenarios can be simulated. Machine learning techniques are then used to estimate equipment health. Once tested, predictive maintenance algorithms may be operationalised in an IT environment such as a server or cloud. Alternatively, algorithms may be implemented in an embedded system directly on the equipment, allowing for faster response times and significantly reducing the amount of data sent over the network.In this paper, we use both historical and simulated data of the plant under various modes of operation including run to failure, and we then leverage machine learning techniques in MATLAB to estimate degradation and remaining useful life. This technique, of combining both simulation models and machine learning, in the areas of predictive maintenance and equipment health monitoring has been adopted by Safran, makers of aircraft jet engines, since 2007. They now have over 160 modules developed (Lacaille and Aurelie, 2015).Citation:Willingham, D and Marchant, R, 2016. Predictive Maintenance Using Simulation and Machine Learning, in Proceedings 13th AusIMM Mill Operators’ Conference 2016, pp 279–284 (The Australasian Institute of Mining and Metallurgy: Melbourne)."

Oct 10, 2016

By Gwenaële Petit, Ulan Massimkhanov, Gilles Joubert

For its Zuuvch Ovoo uranium deposit in Mongolia, Badrakh Energy (partly owned by Orano Mining) is working on the prefeasibility study for exploitation by ISR (in situ recovery). As part of this project, Orano built a 2-cell (each cell is constituted of one producer well located in the center and surrounded by six injection wells in an hexagonal pattern) and a processing pilot plant that will be used to demonstrate the operability and economic viability of an ISR process. Another main objective of this pilot trial is to demonstrate the ability of Orano’s modeling tools to predict the ore behavior under given leaching conditions. To model uranium production by ISR, Orano uses HYTEC, a reactive transport computational software, which was adapted for its existing operation in Kazakhstan (Katco) to predict uranium recovery and reagent usage based on operating conditions. In order to perform similar simulations for this new deposit, Orano engaged in a comprehensive program including ore sampling, detailed characterization, and metallurgical testwork. The mineralogical conclusions were used to prepare a simplified geochemistry for the ore, in particular carbonate and reactive clay grades, uranium mineralization and penalizing elements such as pyrite. Then, bottle roll leach tests were used to characterize dissolution kinetics for smectite clays. Eventually, column leach test results such as pH, free acid, and uranium recovery were modeled on HYTEC to improve the fit of some of the dissolution kinetic parameters. Parameters fitted using metallurgical test work results will now be used as inputs for predictive model at the pilot scale, in order to anticipate operational results and help with operating decisions for production ramp up.

Jan 1, 2020

By Kim Henley, Chris Batten

This paper discusses the use of the Orica WildFireTM flyrock model to help mining operations in Australia and Asia minimise blast exclusion zones for equipment, increasing utilisation and productivity and delivering downstream value

Feb 1, 2020

By Yucheng Li, De Huang, Yonghong Liu

Mine frictional resistance is one of the essential parameters of mine ventilation systems, and it is of considerable significance for the prevention and control of mine risks and accident hazards. Frictional resistance requires a lot of resources and time and has errors. To reduce the errors of frictional resistance, a regression model of frictional resistance is proposed based on the correlation analysis of the affecting factors. Through the comparative test analysis of seven regression methods, it is found that the classification and regression tree method for solving the proposed regression model has better performance than other methods. The least square error criterion is used to calculate and rank the importance of the factors affecting frictional resistance. The results show that the cross-sectional area of the tunnel, the support method, and the presence or absence of devices or pedestrian ladders have the most considerable influence on the prediction accuracy of frictional resistance. The support methods and cross-sectional areas are essential input variables. Through the combination test analysis of the influencing factors, the results show that the regression model does not have predictive ability when the number of input variables is less than 3 and the best predictive performance cannot be obtained using all the influencing factors. The best prediction model of resistance is to combine the shape of the tunnel, the support method, the presence or absence of devices or pedestrian ladders, and the cross-sectional area. The method proposed in this paper does not require a lot of resources and time investment, and the frictional resistance error is small. At the same time, it solves the problem of resistance prediction with incomplete data of influencing factors and provides a compelling new method for obtaining frictional resistance parameters.

Jan 28, 2022

By Greg Partington, Darryl Thorburn, Charlene Wildman, Elisa Puccioni, Connie Payne, Campbell McKenzie

"Mineral prospectivity modelling of polymetallic nodule deposits has been completed over the Cook Islands Exclusive Economic Zone (CIEEZ) using the GIS based Weights of Evidence modelling technique. Polymetallic nodules, also known as manganese nodules, have been explored as a possible resource for a number of metals including cobalt since the early 1970s. Recent data on the nodule composition and abundance in the CIEEZ has led to a renewed interest in the prospectivity for manganese nodules in the region. The predictive modelling has used the most recently accepted mineral model for polymetallic deep-sea nodule deposition to identify factors that are most likely to map the presence of this type of mineralisation in the CIEEZ.Manganese nodules form on the deep seafloor as concretions formed by concentric layering of iron and manganese hydroxides around a core and they grow at a very slow rate, on the order of millimeters per million years. The available data for the CIEEZ indicates a greater abundance in the area of nodules formed by hydrogenetic processes, which are the result of slow precipitation of metallic components from seawater. The environment in which the nodules are most abundant is at depths between 4,000 m and 6,000 m in areas of low sedimentation rate. Hydrogenous nodules commonly grow on siliceous and pelagic clay sediment, they are less common on other types of sediment."

Sep 1, 2014

By C. Orf. Chukabarah

Erratic behavior in convergency during iterative numerical-method evaluation of mine ventilation fan characteristic curves is one of the common problems encountered in mine ventilation network analysis. Five different sets of mine fan data points, that had resulted in unsatisfactory fan characteristic curves, were re-analyzed in the present study, using an embedded mine network optimization computer program. Current results revealed that all five fan data sets not only can be satisfactorily fitted as Yang (1992) did with MFIRE subprogram, but can further be optimized with unique solutions as implemented in the present study. It was concluded, inter alia, that the improved MFIRE-based mine fan curve-fitting computer subprogram (SPLINE) produces satisfactorily similar fan curves as SPSS-based curve-fitting algorithms, including when the latter method uses either standardized or unstandardized fan curve data sets.

Jan 1, 1993

By A Fitzpatrick

An integrated and dynamic water balance and water quality model for a proposed base metal mine has been created using the GoldSimÖ software package. The model was initially developed to address the water quality of the Tailings Management Facility pond and how pond discharge would potentially affect downstream water quality. However, due to changing project objectives, the modelling has expanded over time through an iterative process to encompass the full mine site and all receiving environment water quality. The model results have been used as part of the mine planning/design process but are also intended to be used for the environmental impact assessment and permitting process. The model has been built to allow for changes to input terms to be readily made as additional data becomes available. The model results serve as a prediction of water quality and the potential project impacts to the receiving environment water quality. This paper presents the process by which data from a wide variety of sources was used to construct the model and how the model results were used to guide development of a conceptual water management plan. In addition, the role of the model in establishing proposed discharge criteria will be highlighted. The model has been delivered as a fully functional application with the intent that the end-user can be other than the modeller, the development of this aspect shall also be covered. CITATION:Fitzpatrick, A, 2012. Predictive site-wide water quality modelling as a tool for mine planning and permitting - A recent Canadian example, in Proceedings Life-of-Mine 2012 , pp 71-78 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Jul 10, 2012

By J Q. Shi, M Karakus, Y Liu

Predicting the spatial distribution of rock mass properties in cave mining is a critical challenge. In this study, we introduce a novel machine learning-based model to advance our comprehension of rock behaviour. This model specifically focuses on the spatial distribution of rock mass and mechanical properties, a crucial aspect of our understanding of rock mechanics. Our model is inspired by the recognition that neighbouring information serves as crucial prior knowledge for predicting the spatial distribution of these properties, wherein closer neighbours are likely to exhibit similar characteristics. To effectively capture and model these neighbour relationships, we design a neural network-based model to map the spatial locations within the data set into a new highdimensional space. Within this transformed space, we utilise Euclidean distance to identify N neighbours. Leveraging these neighbours, we introduce a straightforward yet highly effective approach—label propagation—to construct a graph that illustrates neighbour relationships. This graph not only facilitates the prediction of the spatial distribution of rock mass properties but also offers a measure of uncertainty for these predictions. We validate the proposed method using a real rock data set and showcase its efficacy in capturing intricate relationships and distributions within rock properties, enabling precise predictions across diverse spatial scales. Our results underscore the potential of machine learning-based approaches to revolutionise the field of rock mechanics, providing valuable insights applicable to geotechnical engineering, mining, and other sectors where an accurate understanding of rock properties is paramount. This model has direct practical implications, offering a new tool for geotechnical engineers and mining professionals to enhance their understanding and prediction of rock properties, thereby improving safety and efficiency in their operations.

Sep 1, 2024

By M J. Nugus, F Clark, E J. Hill, J T. Carswell, J S. Cleverley, N H. S Oliver

High-nugget gold orebodies are difficult to model in 3D because samples from diamond drill core are generally too small to provide reliable estimates of the gold content of the larger body of rock, and therefore interpolation of gold assay values between drill holes is also unreliable. As a result, gold resources are generally underestimated in areas of high nugget. In practice, proxies for gold mineralisation, such as presence of alteration, specific structures or high vein density, have been used by geologists to predict rocks which are favourable for mineralisation. However, there has been no automated method for combining the predictive capabilities of several proxies, except by human judgement.We have been developing a method to combine several different types of geological features or geochemical analyses into a single measure using conditional probability. Conditional probability provides a meaningful numerical measure for the favourability of rocks for containing high-grade gold mineralisation. This measure can be interpolated in the exploration space, providing a sound basis for a 3D model of the orebody. We have developed this technique using data from the Sunrise Dam Gold Mine, which is a high-nugget orebody hosted in a structurally complex Archaean greenstone belt and which has historically proved to be a very challenging orebody to model.CITATION:Hill, E J, Nugus, M J, Clark, F, Oliver, N H S, Cleverley, J S and Carswell, J T, 2013. Predictive three-dimensional modelling of nuggety gold orebodies, Sunrise Dam, Western Australia, in Proceedings World Gold 2013 , pp 463-468 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Sep 26, 2013

By M. Sadegh Safarzadeh, Stanley M. Howard

The two-dimensional predominance area (Kellogg) diagram (PAD) surfaces bounding the three-dimensional Cu-As-S-O system were constructed at 900 K. The computations employed gases As4O6(g), O2(g), and SO2(g) as the independent coordinate species to generate diagrams of As4O6(g) − O2(g),O2(g) − SO2(g), and As4O6(g) − SO2(g), while the third gas (As4O6, O2, or SO2) was fixed. The results indicate that there is no shared boundary between enargite (Cu3AsS4) and copper sulfate (CuSO4); however, at PO2 < 10−6 atm, PAs4O6 < 10−10 atm, and PSO2 > 10−4 atm, CuSO4 is the stable phase at 900 K. The chemical driving force for oxidation of intermediate phases between enargite and copper sulfate is substantial except for basic sulfate (CuO · CuSO4).

Reduce longwall equipment transfer time between panels by utilizing a predriven recovery room, and thus eliminate the premove preparation associated with most equipment recovery methods. Background and Approach The recovery point for a retreating longwall is a predetermined position near the end of the panel where mining is terminated and equipment is disassembled and removed. However, to prepare for equipment removal at the recovery point, bolting and wire meshing arc required before the recovery point is reached. Usually, preparations for equipment recovery begin about 35 ft from the recovery point, significantly impeding the normal rate of advance of the longwall and, consequently, production. Recently, BethEnergy Mines, Inc., and the Bureau of Mines cooperated to evaluate the applicability of utilizing predriven recovery rooms to improve longwall equipment recovery. A predriven recovery room is an entry driven parallel to the longwall face at the end of the panel, having standard dimensions (for that particular mine) and supported with bolts, wire mesh, and supplemental support. The preparation of this entry is completed before it experiences mining-induced effects from the approaching longwall face. This enables the face to continue to mine at its normal rate of advance until the panel is completed. The supplemental support is required to control the loads imposed by the front abutment and is the most vital component of the recovery room.

Jan 1, 1989

Jan 1, 1923

Jan 1, 1961

By Ruddle R. W.

Although copper has been smelted for many years, relatively small attempt has been made in the past to examine the fundamental physical chemistry of the processes involved. Therefore, technological developments apart, slow progress has been made with the development of improved or new principles of smelting. The methods at present in use in the majority of the world’s smelters are essentially those employed at the beginning of the present century; it is significant that many of the textbooks on copper smelting, written some 50 years ago, are still regarded as standard works. The book is divided into seven chapters. In the first the fundamentals of modern smelting processes are briefly outlined for easy understanding of the subsequent sections, while the next two chapters are confined to the more theoretical aspects of the constitution of mattes and slags. Some problems of considerable practical importance are discussed in the remaining four chapters; these are (1) magnetite formation, (2) copper losses during smelting, (3) the recovery of sulphur from the smokes produced during smelting, and (4) the elimination of impurities. A collection of thermodynamic data relevant to copper pyrometallurgy is presented in an appendix, and, for the convenience of readers not well acquainted with thermodynamics, a brief explanation of the thermodynamic terms and methods employed is given in a second appendix

Jan 1, 1953

Jan 1, 1916

Jan 1, 1914

Jan 1, 1915