Development of a 3500-tonne field robot

The mining industry is highly suitable for the application of robotics and automation technology, since the work is arduous, dangerous, and often repetitive. This paper presents a broad overview of the issues involved in the development of a physically large and complex field robotic system—a 3500-tonne mining machine (dragline). Draglines are “walking cranes” used in open-pit coal mining to remove the material covering a coal seam. The critical issues of robust load position sensing, modeling of the dynamics of the electrical drive system and the swinging load, control strategies, the operator interface, and automation system architecture are addressed. An important aspect of this system is that it must work cooperatively with a human operator, seamlessly passing control back and forth in order to achieve the main aim—increased productivity.

Robust sensing for a 3,500 tonne field robot

The mining industry is highly suitable for the application of robotics and automation technology since the work is arduous, dangerous and often repetitive. This paper discusses a robust sensing system developed to find and trade the position of the hoist ropes of a dragline. Draglines are large `walking cranes' used in open-pit coal mining to remove the material covering the coal seam. The rope sensing system developed uses two time-of-flight laser scanners. The finding algorithm uses a novel data association and tracking strategy based on pairing rope data.

Social licence to operate and the coal seam gas industry : lessons from social issues in established mining operations?

The recent growth of the coal seam gas industry has increased pressure on regional communities. Debate surrounding the industry is intense and a social licence to operate has yet to be granted to the industry in its entirety. This article presents an analysis of social issues surrounding the coal seam gas industry, making comparisons between two case studies: the Ranger and Jabiluka mines and the Yandicoogina mine. It presents the results of a desktop study, focussed on three topics: community identity; procedural justice and distributive justice, which provides a means for comparison and draws attention to central concerns. It is found that: power imbalances; changing community identities; potentially inequitable distributions of long term benefits and the process to distribute those benefits and negative perceptions of the industry as a whole serve to undermine the provision of a social licence to operate by communities and has the potential to impose significant negative impacts on companies within the industry.

Mining robotics

Mining is the process of extracting mineral resources from the Earth for commercial value. It is an ancient human activity which can be traced back to Palaeolithic times (43 000 years ago), where for example the mineral hematite was mined to produce the red pigment ochre. The importance of many mined minerals is reflected in the names of the major milestones in human civilizations: the stone, copper, bronze, and iron ages. Much later coal provided the energy that was critical to the industrial revolution and still underpins modern society, creating 38% of world energy generation today. Ancient mines used human and later animal labor and broke rock using stone tools, heat, and water, and later iron tools. Today’s mines are heavily mechanized with large diesel and electrically powered vehicles, and rock is broken with explosives or rock cutting machines.

Application of data mining techniques in the prediction of coronary artery disease : use of anaesthesia time-series and patient risk factor data

The high morbidity and mortality associated with atherosclerotic coronary vascular disease (CVD) and its complications are being lessened by the increased knowledge of risk factors, effective preventative measures and proven therapeutic interventions. However, significant CVD morbidity remains and sudden cardiac death continues to be a presenting feature for some subsequently diagnosed with CVD. Coronary vascular disease is also the leading cause of anaesthesia related complications. Stress electrocardiography/exercise testing is predictive of 10 year risk of CVD events and the cardiovascular variables used to score this test are monitored peri-operatively. Similar physiological time-series datasets are being subjected to data mining methods for the prediction of medical diagnoses and outcomes. This study aims to find predictors of CVD using anaesthesia time-series data and patient risk factor data. Several pre-processing and predictive data mining methods are applied to this data. Physiological time-series data related to anaesthetic procedures are subjected to pre-processing methods for removal of outliers, calculation of moving averages as well as data summarisation and data abstraction methods. Feature selection methods of both wrapper and filter types are applied to derived physiological time-series variable sets alone and to the same variables combined with risk factor variables. The ability of these methods to identify subsets of highly correlated but non-redundant variables is assessed. The major dataset is derived from the entire anaesthesia population and subsets of this population are considered to be at increased anaesthesia risk based on their need for more intensive monitoring (invasive haemodynamic monitoring and additional ECG leads). Because of the unbalanced class distribution in the data, majority class under-sampling and Kappa statistic together with misclassification rate and area under the ROC curve (AUC) are used for evaluation of models generated using different prediction algorithms. The performance based on models derived from feature reduced datasets reveal the filter method, Cfs subset evaluation, to be most consistently effective although Consistency derived subsets tended to slightly increased accuracy but markedly increased complexity. The use of misclassification rate (MR) for model performance evaluation is influenced by class distribution. This could be eliminated by consideration of the AUC or Kappa statistic as well by evaluation of subsets with under-sampled majority class. The noise and outlier removal pre-processing methods produced models with MR ranging from 10.69 to 12.62 with the lowest value being for data from which both outliers and noise were removed (MR 10.69). For the raw time-series dataset, MR is 12.34. Feature selection results in reduction in MR to 9.8 to 10.16 with time segmented summary data (dataset F) MR being 9.8 and raw time-series summary data (dataset A) being 9.92. However, for all time-series only based datasets, the complexity is high. For most pre-processing methods, Cfs could identify a subset of correlated and non-redundant variables from the time-series alone datasets but models derived from these subsets are of one leaf only. MR values are consistent with class distribution in the subset folds evaluated in the n-cross validation method. For models based on Cfs selected time-series derived and risk factor (RF) variables, the MR ranges from 8.83 to 10.36 with dataset RF_A (raw time-series data and RF) being 8.85 and dataset RF_F (time segmented time-series variables and RF) being 9.09. The models based on counts of outliers and counts of data points outside normal range (Dataset RF_E) and derived variables based on time series transformed using Symbolic Aggregate Approximation (SAX) with associated time-series pattern cluster membership (Dataset RF_ G) perform the least well with MR of 10.25 and 10.36 respectively. For coronary vascular disease prediction, nearest neighbour (NNge) and the support vector machine based method, SMO, have the highest MR of 10.1 and 10.28 while logistic regression (LR) and the decision tree (DT) method, J48, have MR of 8.85 and 9.0 respectively. DT rules are most comprehensible and clinically relevant. The predictive accuracy increase achieved by addition of risk factor variables to time-series variable based models is significant. The addition of time-series derived variables to models based on risk factor variables alone is associated with a trend to improved performance. Data mining of feature reduced, anaesthesia time-series variables together with risk factor variables can produce compact and moderately accurate models able to predict coronary vascular disease. Decision tree analysis of time-series data combined with risk factor variables yields rules which are more accurate than models based on time-series data alone. The limited additional value provided by electrocardiographic variables when compared to use of risk factors alone is similar to recent suggestions that exercise electrocardiography (exECG) under standardised conditions has limited additional diagnostic value over risk factor analysis and symptom pattern. The effect of the pre-processing used in this study had limited effect when time-series variables and risk factor variables are used as model input. In the absence of risk factor input, the use of time-series variables after outlier removal and time series variables based on physiological variable values’ being outside the accepted normal range is associated with some improvement in model performance.

Sodium removal from Maramarua CSG waters using Ngakuru zeolites

Coal seam gas (CSG) waters are a by-product of natural gas extraction from un derground coal seams. The main issue with these waters is their elevated sodium content, which in conjunction with their low calcium and magnesium concentrations can generate soil infiltration problems in the long run , as well as short term toxicity effects in plants due to the sodium ion itself. Zeolites are minerals having a porous structure, crystalline characteristics, and an alumino-silicate configuration resulting in an overall negative charge which is balanced by loosely held cations. In New Zealand, Ngakuru zeolites have been mined for commercial use in wastewater treatment applications, cosmetics, and pet litter. This research focuses on assessing the capacity of Ngakuru zeolites to reduce sodium concentrations of CSG waters from Maramarua. Batch and column test (flow through) experiments revealed that Ngakuru zeolites are capable of sorbing sodium cations from concentrated solutions of sodium. In b atch tests, the sodium adsorption capacity ranged from 5.0 to 34.3meq/100g depending on the solution concentration and on the number of times the zeolite had been regenerated. Regeneration with CaCl2 was foun d to be effective. The calculated sodium adsorption capacity of Ngakuru zeolites under flow-through conditions ranged from 11 to 42meq/100g depending on the strength of the solution being treated and on w hether the zeolites had been previously regenerated. The slow kinetics and low cost of the zeolities, coupled with potentially remote sites for gas extraction, could make semi-batch operational processes without regeneration more favourable than in more industrial ion exchange situations.

Characterisation of Coal Seam Gas Waters in New Zealand

Coal seam gas (CSG) exploration and development requires the abstraction of significant amounts of water. This is so because gas desorbtion in coal seams takes place only after aquifer pressure has been reduced by prolonged pumping of aquifer water. CSG waters have a specific geochemical signature which is a product of their formation process. These waters have high bicarbonate, high sodium, low calcium, low magnesium, and very low sulphate concentrations. Additionally, chloride concentrations may be high depending on the coal depositional environment. This particular signature is not only useful for exploration purposes, but it also highlights potential environmental issues that can arise as a consequence of CSG water disposal. Since 2002 L&M Coal Seam Gas Ltd and CRL Energy Ltd, have been involved in exploration and development of CSG in New Zealand. Anticipating disposal of CSG waters as a key issue in CSG development, they have been assessing CSG water quality along with exploration work. Coal seam gas water samples from an exploration well in Maramarua closely follow the geochemical signature associated with CSG waters. This has helped to identify CSG potential, while at the same time assessing the chemical characteristics and water generation processes in the aquifer. Neutral pH and high alkalinity suggest that these waters could be easily managed once the sodium and chloride concentrations are reduced to acceptable levels.

Characterisation of micrinite in the late triassic Callide Coal Measures

Samples from the Callide Coal Measures, Queensland, Australia, containing the minor maceral, micrinite, have been studied using optical and electron-optical techniques to determine the precise compositional and structural nature of micrinite when in association with vitrinite macerals. Emphasis has been placed on direct spatial correlation of optical and electron-optical data due to the fine grain size (<1μm) of micrinite and its relatively low abundance compared with other macerals in the Callide Basin coals. Precise elemental, morphological and structural data, including electron diffraction, provides unambiguous evidence for the presence of kaolinite in the component known as micrinite. Indeed, micrinite consists predominantly of fine-grained kaolinite (>90 per cent of the component) and, as such, should not be considered a maceral.

Noise removal from wave-forms and spectrograms derived from natural recordings of the environment

The work described in this technical report is part of an ongoing project to build practical tools for the manipulation, analysis and visualisation of recordings of the natural environment. This report describes the methods we use to remove background noise from spectrograms. It updates techniques previously described in Towsey and Planitz (2011), Technical report: acoustic analysis of the natural environment, downloadable from: http://eprints.qut.edu.au/41131/. It also describes noise removal from wave-forms, a technique not described in the above 2011 technical report.

A demand-responsive decision support system for coal transportation

In this paper, a demand-responsive decision support system is proposed by integrating the operations of coal shipment, coal stockpiles and coal railing within a whole system. A generic and flexible scheduling optimisation methodology is developed to identify, represent, model, solve and analyse the coal transport problem in a standard and convenient way. As a result, the integrated train-stockpile-ship timetable is created and optimised for improving overall efficiency of coal transport system. A comprehensive sensitivity analysis based on extensive computational experiments is conducted to validate the proposed methodology. The mathematical proposition and proof are concluded as technical and insightful advices for industry practice. The proposed methodology provides better decision making on how to assign rail rolling-stocks and upgrade infrastructure in order to significantly improve capacity utilisation with the best resource-effectiveness ratio. The proposed decision support system with train-stockpile-ship scheduling optimisation techniques is promising to be applied in railway or mining industry, especially as a useful quantitative decision making tool on how to use more current rolling-stocks or whether to buy additional rolling-stocks for mining transportation.

ACE research briefing paper 007 : Ken Talbot - an entrepreneur's contribution to the growth of the Australian Mining Industry

Ken Talbot was one of Australian mining’s most successful entrepreneurs and rose to the top of his industry to become one of Australia’s wealthiest men. Although the nation’s resources industry is synonymous with global names such as Xstrata, BHP Billiton and Rio Tinto, Ken was an individual who made a big impact on the development and growth of the sector. This case study examines Ken’s achievements, his transition from employee to entrepreneur, and the qualities that enabled him to succeed at such a high level. In particular, it focuses on his Jellinbah and Coppabella mining developments that directly led to the creation of Macarthur Coal and the Talbot Group. By the time of his premature death in an African plane crash in 2010, Ken had amassed a fortune estimated at almost $1 billion and was aged just 59. The last publically available Talbot Group annual report for calendar year 2009 showed that the investment portfolio of the group returned 113 per cent that year. Even throughout the global financial crisis the portfolio made a positive return on investment of no less than 10 per cent. Ken’s sense of mateship and his tremendous people skills were keys to his success in the mining industry and the wider community. In addition to excelling in business, he is also remembered for his philanthropy and leaving 30 per cent of his estate to charity through the Talbot Family Foundation.

Modeling and control of a 3500 tonne mining robot

Draglines are extremely large machines that are widely used in open-cut coal mines for overburden stripping. Since 1994 we have been working toward the development of a computer control system capable of automatically driving a dragline for a large portion of its operating cycle. This has necessitated the development and experimental evaluation of sensor systems, machines models, closed-loop control controllers, and an operator interface. This paper describes our steps toward the goal through scale-model and full-scale field experimentation.

Translating road safety research on night-time visibility to the context of mining

In recent years a significant amount of research has been undertaken in collision avoidance and personnel location technology in order to reduce the number of incidents involving pedestrians and mobile plant equipment which are a high risk in underground coal mines. Improving the visibility of pedestrians to drivers would potentially reduce the likelihood of these incidents. In the road safety context, a variety of approaches have been used to make pedestrians more conspicuous to drivers at night (including vehicle and roadway lighting technologies and night vision enhancement systems). However, emerging research from our group and others has demonstrated that clothing incorporating retroreflective markers on the movable joints as well as the torso can provide highly significant improvements in pedestrian visibility in reduced illumination. Importantly, retroreflective markers are most effective when positioned on the moveable joints creating a sensation of “biological motion”. Based only on the motion of points on the moveable joints of an otherwise invisible body, observers can quickly recognize a walking human form, and even correctly judge characteristics such as gender and weight. An important and as yet unexplored question is whether the benefits of these retroreflective clothing configurations translate to the context of mining where workers are operating under low light conditions. Given that the benefits of biomotion clothing are effective for both young and older drivers, as well as those with various eye conditions common in those >50 years reinforces their potential application in the mining industry which employs many workers in this age bracket. This paper will summarise the visibility benefits of retroreflective markers in a biomotion configuration for the mining industry, highlighting that this form of clothing has the potential to be an affordable and convenient way to provide a sizeable safety benefit. It does not involve modifications to vehicles, drivers, or infrastructure. Instead, adding biomotion markings to standard retroreflective vests can enhance the night-time conspicuity of mining workers by capitalising on perceptual capabilities that have already been well documented.

Evaluation of electrocoagulation for the pre-treatment of coal seam water

This work explored the applicability of electrocoagulation (EC) using aluminium electrodes for the removal of contaminants which can scale and foul reverse osmosis membranes from a coal seam (CS) water sample, predominantly comprising sodium chloride, and sodium bicarbonate. In general, the removal efficiency of species responsible for scaling and fouling was enhanced by increasing the applied current density/voltage and contact times (30–60 s) in the EC chamber. High removal efficiencies of species potentially responsible for scale formation in reverse osmosis units such as calcium (100%), magnesium (87.9%), strontium (99.3%), barium (100%) and silicates (98.3%) were achieved. Boron was more difficult to eliminate (13.3%) and this was postulated to be due to the elevated solution pH. Similarly, fluoride removal from solution (44%) was also inhibited by the presence of hydroxide ions in the pH range 9–10. Analysis of produced flocs suggested the dominant presence of relatively amorphous boehmite (AlOOH), albeit the formation of Al(OH)3 was not ruled out as the drying process employed may have converted aluminium hydroxide to aluminium oxyhydroxide species. Evidence for adsorption of contaminants on floc surface sites was determined from FTIR studies. The quantity of aluminium released during the electrocoagulation process was higher than the Faradaic amount which suggested that the high salt concentrations in the coal seam water had chemically reacted with the aluminium electrodes.