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By Ashish Kumar Vishwakarma, Vivek Kumar Himanshu, Kaushik Dey

The accurate prediction of blast-induced ground vibration due to underground ring blasting is a prominent need for ensuring the safety of structures. Different site-specific empirical equations are available for the prediction of ground vibration. These empirical equations are best suited when the monitoring and blasting locations are present in the same medium. The change in the medium alters the behavior of wave propagation. Hence, existing empirical equations have limitations in peak particle velocity (PPV) prediction when the blasting location is an underground hard rock mine and the monitoring location is ground surface. This is because the underground metal mine comprises different levels having void in the form of excavated stope or paste-filled stope. It is very difficult to predict the magnitude of PPV on the surface in such instances. Therefore, this study has been carried out to predict the PPV at surface due to underground blasting. In this paper, PPV data was recorded at surface for 207-ring blasts. Furthermore, the PPV has also been measured at different underground locations for 47-ring blasts. Different empirical equations along with k-nearest neighbor (KNN) and random forest (RF) model of machine learning technique were developed for the prediction of PPV. Most of the empirical models have higher accuracy in the prediction of PPV at an underground location. This shows that scaled distance-based empirical predictors are best suited when the monitoring and blasting media are the same. However, the empirical models do not predict PPV accurately when the monitoring location is ground surface and the blast is conducted underground. The machine learning models are better suited for PPV prediction in such cases. Based on the analysis performed for the case study site, RF model predicts PPV at surface with the highest accuracy. The coefficient of determination and root mean square error for RF model used for predicting PPV at ground surface are 0.94 and 0.438 mm/s respectively. The RF-based model is also the best suited among all the models for predicting PPV at underground locations as well.

Apr 20, 2024

By Pratoomkhuan N. Lertsgoon

A large underground cavern with dimensions of 25m × 175m × 49m (W×L×H) was excavated in sandstone and siltstone to house 4 units of hydropower generator and was completed in 2000. In feasibility study stage, the property of the siltstone which governs the ceiling of the cavern was questionable due to slaking phenomenon. Parameters on rock properties for design were back calculated from actual ground response measured from a trial excavation. For safety of construction, comparison between prediction and actual ground responses during excavation had proceeded. The prediction was provided by means of PHASES and PHASE2 software and the actual rock response was detected by the provided monitoring system.

Jan 1, 2003

By D. Dunbar

The flooding of open pits is now receiving considerable attention from the mining industry as an option that may minimize environmental impacts, particularly when anoxia in pit-lake deep waters can effectively eliminate oxidation of pit-wall sulfides. This paper describes a state-of-the-art, coupled physical-geochemical pit-lake model. The model is used to verify that a controlled, two-stage infilling of the open pit at the Kori Kollo gold mine, southeast of La Paz, Bolivia, would result in a cost-effective, environmentally responsible closure strategy. By filling the pit initially with highly saline surface waters and naturally saline deep groundwater and then capping with fresh river water, a very stable and strongly stratified pit lake will be developed. In addition, the stratification will result in the isolation of metal-rich saline bottom waters from surface waters of good quality. Results of this model provide defensible predictions of the physical stability and evolution of water quality in the Kori Kollo pit lake over time. The model results indicate that anoxic conditions will develop below the fresh-saline water boundary within one to two years, followed by the rapid onset of sulfate-reducing conditions in the bottom waters. Hydrogen sulfide produced by sulfate reduction will readily react with dissolved metals to form highly insoluble metal sulfides. The process results in the gradual removal of metals from the water column and the concomitant improvement of water quality over time. A notable benefit of this strategy is that it can be fully integrated with other mine site closure options as well as future environmental-management strategies. The proposed plan allows for the safe isolation of saline waters, the potential for passive treatment in anoxic bottom waters of ARD seepage from other mine site operations, should they arise, and a repository for future reactive wastes that could be effectively isolated in a physically and chemically stable environment.

Jan 1, 2003

"Recently, an effort was made to calibrate the Riedel-Hiermaier-Thoma (RHT) material model to reproducethe behavior of granite under impact and explosive loading conditions. The study concluded that the materialmodel captured the phenomenon of compressional damage in granite due to such loading conditionsfairly well. The present work uses hydrodynamic modeling with the calibrated RHT model to estimate thefines generated around a blasthole. This can improve the calibration of different size-distribution empiricalmodels and enhance the prediction of fines."

Jan 1, 2012

By D. Dunbar

Flooding of open pits now receives considerable attention from the mining industry as an option that may minimize environmental impacts, particularly when anoxia in pit lake deep waters can effectively eliminate oxidation of pit-wall sulphides. A state-of-the-art, coupled physical-geochemical pit lake model is used to verify that a cost-effective, environmentally responsible closure strategy for the open pit at the Kori Kollo gold mine, southeast of La Paz, Bolivia is to adopt a controlled, two-stage infilling of the open pit upon cessation of operations. By filling the pit initially with highly saline waters from evaporation/infiltration basins and naturally saline deep groundwater, followed by capping with fresh river water, a very stable and strongly stratified pit lake will be developed. In addition, the stratification will result in the isolation of metal-rich saline bottom waters from surface waters of good quality. Results of this model provide defensible predictions of the physical stability and evolution of water quality in the Kori Kollo pit lake over time. The model results indicate that anoxic conditions will develop below the fresh-saline water boundary within 1 to 2 years, followed by the rapid onset of sulphate reducing conditions in the bottom waters. Hydrogen sulphide produced by sulphate reduction will readily react with dissolved metals to form highly insoluble metal sulphides. The process results in the gradual removal of metals from the water column and the concomitant improvement of water quality over time. A notable benefit of this strategy is that it can be fully integrated with other mine site closure options as well as future environmental management strategies. The proposed plan allows for: 1) safe storage of saline evaporation basin waters; 2) the potential for passive treatment in anoxic bottom waters of ARD seepages from other mine site operations should they arise; and 3) a repository for future reactive wastes that could be effectively isolated in a physically and chemically stable environment.

Jan 1, 2001

By A J. Moore

Predictive assessment of surface blast vibration requires the identification and quantification of significant factors, and the summation of their combined effect. Methods available for the prediction of significant factors are described, and examples are given of the combined use of these methods to evaluate alternative proposals.

Jan 1, 1999

By Quin Dennis

Process Optimization ?What is it really? It is NOT ?a larger fan to increase draft through a process ?a larger pump on a wet circuit ?a wider conveyor belt for solid materials ?a larger feed elevator into or out of a process These relieve individual constraints ??bottlenecks??and allow operation to the next constraint in the process ?It is also NOT about automating the operator ?which captures consistency but is not continuous optimization

Nov 1, 2011

By Frédéric Flament, Daniel Hodouin, Claude Bazin

A control strategy is proposed to meet the following specifications of flotation control: minimize the variations of concentrate and tails grades and recovery while limiting the amplitude of variations of the manipulated variables. The strategy consists of determining future control actions which optimize a performance criterion over a limited time horizon. The desired dynamic behaviour of the controlled flotation plant is specified using reference models. The strategy is tested on a 2-computer plant simulator working in an environment which emulates the industrial conditions.

Jan 1, 1993

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 Tommee Larochelle, Henry Kasaini

"Simulation of rare earth element solvent extraction processes for plant design and optimization is complex, requires extensive bench scale and pilot scale testing, and is traditionally performed using proprietary algorithms. The complexity of simulation is mainly due to traditional simulation software solvent extraction modules not being designed to simulate rare earth separation processes because of the interaction between each element. This paper presents an alternative method for designing and optimizing rare earth element solvent extraction separation processes using METSIM to converge each stage of the battery individually until the full battery reached ultimate convergence. A feed-forward controller is used to control the organic to aqueous flow ratio feeding the solvent extraction modules. This method minimizes the complexity of simulating such processes by using inexpensive batch solvent extraction data, and allowing for rapid theoretical optimization, by varying the process configuration and parameters from a graphic interface instead of the traditional code-based interface of most proprietary models. Comparison of the model predictions to pilot plant data reveals that the method can be used to accurately model rare earth solvent extraction processes, as illustrated by its application in the development of Rare Element Resources innovative process.INTRODUCTIONThe Rare Earth Elements (REEs) consist of 16 metals, specifically the 15 lanthanide elements and yttrium, which is typically found in the same mineral deposits as the lanthanides. Scandium can be associated with the rare earth elements, but is usually only present in trace amounts, and therefore omitted. The term “rare earths” originates from an old term used to designate minerals (earths) and their believed scarcity upon their initial discovery. The rare earths designation is now considered a misnomer because it was demonstrated that the crustal abundance of some rare earth elements, such as cerium, is known to be higher than many common metals, such as copper. However, enriched deposits are fairly uncommon compared to other similarly abundant metals. The REEs are typically divided into groups commonly consisting of light rare earths (LREEs), medium rare earths (MREE), and heavy rare earths (HREEs). Light rare earth elements include lanthanum to neodymium, medium rare earths include samarium to gadolinium and the heavy rare earth elements include terbium to lutetium, and yttrium. The REEs exhibit similar chemical characteristics and are usually extracted from the ore and processed into a mixed rare earth concentrate. The rare earth concentrate requires further processing in order to separate the REEs from each other and produce a series of single element products."

Jan 1, 2016

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