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By Murray G

This paper outlines the processes involved in acid generation from sulphides and the key factors in identifying and predicting time dependent changes in the nature of acid mine drainage (AMD). The initial characteristics of mine water; whether drainage from exposed mining faces, runoff and seepage from waste dumps or seepage from process tailings; do not necessarily reflect the long term potential for AMD. The ultimate quality of mine drainage is a function of the interaction of many chemical, physical, biological and engineering (operational) factors. Techniques are available that can predict the qualitative nature of acid generation and mine drainage quality but accurate quantitative predictions are essentially impossible at the present time. As more and more mining operations exploit sulphide ore, the AMD and acid waste issue will become a. far greater concern. The significance of time dependent factors in the production of acid mine drainage-.is not just restricted to particuL.ar mine sites but needs to be recognised by the mining industry as a whole.

Jan 1, 1988

By Christian Ludwig, Virginie Silberstein, Rizlan Bernier-Latmani, Simona Regenspurg

"Residues from waste incineration (bottom ashes) form large amounts of heavy metal-rich waste. Theoretically, heating and the addition of chloride to the ashes could remove these metals by formation of volatile metal chlorides. The remaining heavy metal-poor ashes could be used, for example, as cement additives. In the proposed process of our study we used bypass dust from the cement industry as chloride additives to the bottom ashes. We aim to determine experimentally the optimal conditions to remove heavy metals from ashes and to simulate this process by thermo- chemical modeling. In our experiment we investigate heavy metal flows by heating of differently composed mixtures of bottoms ash and bypass dust in dependence of different furnace temperatures (500-1200 °C). Furthermore, we investigate the reactivity (leaching characteristic) of the resulting product in order to predict if secondary residues can be landfilled in agreement to Swiss regulations for inert materials.IntroductionThe incineration of garbage is generally considered as the most environmentally friendly method for the reduction of municipal solid waste. This is because, compared to traditional storage of waste on landfills, incineration produces less greenhouse gases and destroys toxic organic compounds. The released exothermic energy can be recuperated and the residues from the combustion chamber (bottom ash) are strongly reduced in volume. However, they still form about 600 kt of waste per year in Switzerland, which needs to be landfilled. However, these residues could be potentially used as construction materials after mechanically recovering ferrous and non-ferrous metals. However, the resulting ashes (discharged dry or wet) still contain increased amount of heavy metals (exceeding state regulation) requiring them to be finally deposed on a hazardous waste site. A thermal treatment of these ashes could enable both, to separate and recuperate the remaining heavy metals and an inorganic heavy-metal poor material. The evaporation of metals depends on temperature and can be lowered by addition of chlorides, which would lower the boiling point of most metals by formation of metal chlorides (table 1)."

Jan 1, 2008

By Christian Ludwig, Virginie Silberstein, Rizlan Bernier-Latmani, Simona Regenspurg

"Residues from waste incineration (bottom ashes) form large amounts of heavy metal-rich waste. Theoretically, heating and the addition of chloride to the ashes could remove these metals by formation of volatile metal chlorides. The remaining heavy metal-poor ashes could be used, for example, as cement additives. In the proposed process of our study we used bypass dust from the cement industry as chloride additives to the bottom ashes. We aim to determine experimentally the optimal conditions to remove heavy metals from ashes and to simulate this process by thermo- chemical modeling. In our experiment we investigate heavy metal flows by heating of differently composed mixtures of bottoms ash and bypass dust in dependence of different furnace temperatures (500-1200 °C). Furthermore, we investigate the reactivity (leaching characteristic) of the resulting product in order to predict if secondary residues can be landfilled in agreement to Swiss regulations for inert materials.IntroductionThe incineration of garbage is generally considered as the most environmentally friendly method for the reduction of municipal solid waste. This is because, compared to traditional storage of waste on landfills, incineration produces less greenhouse gases and destroys toxic organic compounds. The released exothermic energy can be recuperated and the residues from the combustion chamber (bottom ash) are strongly reduced in volume. However, they still form about 600 kt of waste per year in Switzerland, which needs to be landfilled. However, these residues could be potentially used as construction materials after mechanically recovering ferrous and non-ferrous metals. However, the resulting ashes (discharged dry or wet) still contain increased amount of heavy metals (exceeding state regulation) requiring them to be finally deposed on a hazardous waste site. A thermal treatment of these ashes could enable both, to separate and recuperate the remaining heavy metals and an inorganic heavy-metal poor material. The evaporation of metals depends on temperature and can be lowered by addition of chlorides, which would lower the boiling point of most metals by formation of metal chlorides (table 1). 745"

Jan 1, 2008

By R. G. Shen

The relation curve of strain and acceleration is presented in this paper. The steel pipe pile of 609.6 mm in external diameter driven by IDH-45 diesel motor hammer on the esturial shoal of Yangtze River. The ultimate bearing capacity of pile is analyzed by one dimensional wave equation and the effect of computational parameters of the equation is discussed.

Jan 1, 2010

By Vahid Tajdar

The behavior similarity between pile and cone penetration test, has guided the researchers to use the CPT results in prediction of piles bearing capacity. In this article, artificial neural networks have been used to study about relation between cone tip and sleeve friction strength obtained from cone penetration test and ultimate pile bearing capacity, obtained from static pile load test. Artificial neural networks "ANNS" is a form of artificial intelligence. ANN tries to dramatize the human nerves & brain system biology structure in self-structure. In this article, the results of relevant studies about the prediction of ultimate pile bearing capacity in especial area of soils are presented by use of back - propagation neural networks. The results of static pile load test up to failure on 63 piles with closed end and CPT test results in the place of construction these piles use in order to confirm and creation ANN model. Comparing predicted ultimate pile bearing capacity by utilizing ANN with the numbers which are predicted by 5 common traditional methods shows that using of artificial neural networks is a suitable method for predicted ultimate pile bearing capacity in identified area of texture of soil moreover it has a better operation in comparison with common traditional methods.

Jan 1, 2006

By Sufi Md Gulzar, L B. Roy

Rock engineering tasks like tunnelling, dam and building construction, and rock slope stability rely heavily on properly estimating the rock’s uniaxial compressive strength (UCS), a crucial rock geomechanical characteristic. As high-quality specimen are not always possible, scientists often estimate UCS indirectly. The primary objective of this paper is to assess the efficacy of long short-term memory (LSTM), K-nearest neighbour (KNN), a combination of particle swarm optimisation (PSO) with an artificial neural network (ANN), and adaptive neuro-fuzzy inference system (ANFIS) to estimate the UCS of sandstones from Jharia, Dhanbad, India. Point load index (PLI), porosity (n), P-wave velocity (Vp), density (ρ), and moisture content (%) are the parameters used for the present study. Finally, a comparison was made between the various prediction algorithms outputs. The findings of the study validated the effectiveness of computational intelligence methods in forecasting UCS compared to other models used in this paper. The KNN achieves overall the best results, with an R2 of 0.95 for training, 0.94 for testing, and an RMSE of 0.03 for training and 0.05 for testing.

Nov 23, 2023

By M. E. Grimes

A method of predicting uranium extraction rate in underground bacterial leaching of as-blasted ore has been developed. The method is based on the hypothesis that extraction is directly proportional to the volume of a piece of ore that is penetrated by the leaching agent, and that each piece of ore, regardless of size, has been penetrated radially from the external surface to the same depth at a given leaching time. Consequently, given an initial size and assay distribution, extraction should be predictable for a given penetration. Two laboratory bacterial leaching tests under simulated stope leaching conditions were carried out for a periods of 89 and 96 weeks. On completion, the residual ore was screened and analyzed. The information so obtained was employed to provide an elapsed time base for the penetration model, from which predictions of future extraction were made. The finding of solubilized uranium trapped within apparently sol id ore pieces was unexpected. However, it is not recoverable except after crushing, and hence does not affect calculations of underground leaching .

Jan 1, 1974

By R. G. Reddy

Viscosities of alkaline metal oxide-silicate melts were estimated using our previously developed mathematical model. The structural based model considers depolymerization effects and related breakdown of the silicate network structure, on the addition of metal oxides to the melt. The developed expression: n =4.9x10 9 NO 0 T ½ exp(E/RT) poise was used to calculate the viscosities of various binary silicate systems such as, CaO-SiO,, MgO-SiO, and MnO-SiO,, as a function of temperature and composition. Excellent agreement was obtained between the calculated and experimentally determined viscosities.

Jan 1, 1991

By A H. Bagherieh, S Mesroghli, H Jorjani, S Chehreh Chelgani

The rare earth elements (REEs) assay and recovery in leaching processes is being determined using expensive analytical methods, ICP-AES, and ICP-MS. This paper presents a neural network model to predict the effects of operational variables on the lanthanum, cerium, yttrium, neodymium recovery in the leaching of apatite concentrate. The effects of leaching times: ten to 40 min, pulp densities: 30 to 50 per cent, acid concentrations: 20 to 60 per cent and agitation rates: 100 to 200 rpm were investigated and optimised on REEs recovery in laboratory at a leaching temperature of 60¦C. The obtained data, on the laboratory optimisation process were used for training and testing of a neural network. A feed-forward artificial neural network with 4-5-5-1 arrangement, was capable of estimating the REEs leaching recovery. Neural network predicted values were in good agreement with the experimental results. The correlations of R = 1 in training stages, and R =  0.971, 0.952, 0.985 and 0.98 in testing stages were results for Ce, Nd, La and Y recovery predictions respectively and that theses values are considerably acceptable. It was shown that the proposed neural network model accurately reproduces all the effects of operation variables, and can be used in the simulation of a REEs leaching plant.

Jan 1, 2008

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 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