Economic Modelling of Australian Longwall Ventilation

Mine Ventilation - North American Ninth US Mine Ventilation Symposium

Special issue on advances in data mining and its applications

Data mining is the process to identify valid, implicit, previously unknown, potentially useful and understandable information from large databases. It is an important step in the process of knowledge discovery in databases, (Olaru & Wehenkel, 1999). In a data mining process, input data can be structured, seme-structured, or unstructured. Data can be in text, categorical or numerical values. One of the important characteristics of data mining is its ability to deal data with large volume, distributed, time variant, noisy, and high dimensionality. A large number of data mining algorithms have been developed for different applications. For example, association rules mining can be useful for market basket problems, clustering algorithms can be used to discover trends in unsupervised learning problems, classification algorithms can be applied in decision-making problems, and sequential and time series mining algorithms can be used in predicting events, fault detection, and other supervised learning problems (Vapnik, 1999). Classification is among the most important tasks in the data mining, particularly for data mining applications into engineering fields. Together with regression, classification is mainly for predictive modelling. So far, there have been a number of classification algorithms in practice. According to (Sebastiani, 2002), the main classification algorithms can be categorized as: decision tree and rule based approach such as C4.5 (Quinlan, 1996); probability methods such as Bayesian classifier (Lewis, 1998); on-line methods such as Winnow (Littlestone, 1988) and CVFDT (Hulten 2001), neural networks methods (Rumelhart, Hinton & Wiliams, 1986); example-based methods such as k-nearest neighbors (Duda & Hart, 1973), and SVM (Cortes & Vapnik, 1995). Other important techniques for classification tasks include Associative Classification (Liu et al, 1998) and Ensemble Classification (Tumer, 1996).

A framework for electricity price spike analysis with advanced data mining methods

There are many techniques for electricity market price forecasting. However, most of them are designed for expected price analysis rather than price spike forecasting. An effective method of predicting the occurrence of spikes has not yet been observed in the literature so far. In this paper, a data mining based approach is presented to give a reliable forecast of the occurrence of price spikes. Combined with the spike value prediction techniques developed by the same authors, the proposed approach aims at providing a comprehensive tool for price spike forecasting. In this paper, feature selection techniques are firstly described to identify the attributes relevant to the occurrence of spikes. A simple introduction to the classification techniques is given for completeness. Two algorithms: support vector machine and probability classifier are chosen to be the spike occurrence predictors and are discussed in details. Realistic market data are used to test the proposed model with promising results.

A study of the interaction between operator style and machine capability for a hydraulic mining excavator

This paper presents a case study that explores how operator digging style juxtaposes with mechanical capability for a class of hydraulic mining excavators. The relationships between actuator and digging forces are developed and these are used to identify the excavator's capability to apply forces in various directions. Two distinct modes of operation are examined to see how they relate to the mechanical capabilities of the linkage and to establish if one has merit over the other. It is found that one of these styles results in lower loading of the machine.

Trialing wetlands to treat coal mining wastewaters in a low rainfall, high evaporation environment

The large number of wetlands treating mining wastewaters around the world have mostly been constructed in temperate environments. Wetlands have yet to be proven in low rainfall, high evaporation environments and such conditions are common in many parts of Australia. BHP Australia Coal is researching whether wetlands have potential in central Queensland to treat coal mining wastewaters. In this region, mean annual rainfall is < 650 mm and evaporation > 2 000 mm. A pilot-scale wetland system has been constructed at an open-cut coal mine. The system comprises six treatment cells, each 125 m long and 10 m wide. The system is described in the paper and some initial results presented. Results over the first fourteen months of operation have shown that although pH has not increased enough to enable reuse or release of the water, sulfate reduction has been observed in parts of the system, as shown by the characteristic black precipitate and smell of hydrogen sulfide emanating from the wetlands. These encouraging signs have led to experiments aimed at identifying the factors limiting sulfate reduction. The first experiment, described herein, included four treatments where straw was overlain by soil and the water level varied, being either at the top of the straw, at the top of the soil, or about 5 cm above the soil. The effect of inoculating with sulfate-reducing bacteria was investigated. Two controls were included, one covered and one open, to enable the effect of evaporation to be determined. The final treatment consisted of combined straw/cattle manure overlain with soil. Results showed that sulfate reduction did occur, as demonstrated by pH increases and lowering of sulfate levels. Mean pH of the water was significantly higher after 19 days; in the controls, pH was < 3.3, whereas in the treatments, pH ranged from 5.4 to 6.7. The best improvement in sulfate levels occurred in the straw/cattle manure treatment. (C) 1997 IAWQ. Published by Elsevier Science Ltd.

Critical review of acid in situ leach uranium mining: 1. USA and Australia

The technique of in situ leach (ISL) uranium mining is well established in the USA, as well as being used extensively in Eastern Europe and the former Soviet Union. The method is being proposed and tested on uranium deposits in Australia, with sulfuric acid chemistry and no restoration of groundwater following mining. Test sites in the USA were required to restore groundwater to ascertain the extent of impacts and compare costs to alkaline ISL mines. The problems encountered include expensive and difficult restoration, gypsum precipitation, higher salinity and some heavy metals and radionuclides after restoration. One of the most critical issues is whether natural attenuation is capable of restoring groundwater quality and geochemical conditions in an acid leached aquifer zone. The history of acid ISL sites in the USA and Australia are presented in this study, with a particular focus on the demonstration of restoration of groundwater impacts.

Critical review of acid in situ leach uranium mining: 2. Soviet Block and Asia

The technique of in situ leach (ISL) uranium mining is well established in the USA, as well as being used extensively in Eastern Europe and the former Soviet Union. The method is being proposed and tested on uranium deposits in Australia, with sulphuric acid chemistry and no restoration of groundwater following mining. ISL mines in the former Soviet Union generally used acid reagents and were operated without due consideration given to environmental protection. At many former mine sites, the extent of groundwater contamination is significant because of high salinity, heavy metal and radionuclide concentrations compared with pre-mining and changes in the hydrogeological regime caused by mining. After the political collapse of the Soviet Union by the early 1990s, most uranium mines were shut down or ordered to be phased out by government policy. Programmes of restoration are now being undertaken but are proving technically difficult and hampered by a lack of adequate financial resources. The history and problems of acid ISL sites in countries of the former Soviet Union and Asia are presented in this study.

Establishment of native ecosystems after mining - Australian experience across diverse biogeographic zones

The Australian minerals industry, which is dominated by coal, gold, bauxite, iron ore, base metals and mineral sand operations, is widely scattered across a continent which has a wide range of climatic zones ranging from moist temperate in the south through hot deserts in the centre to moist tropical in the north. There is an emphasis at most mines on establishing native ecosystems after mining, and technologies have had to be developed to ensure successful establishment and stability of these ecosystems under often adverse climatic conditions. This paper describes some of the innovative practices used to establish native ecosystenms in bauxite, mineral sand and coal operations across diverse biogeographic zones. Additionally, brief reference is made to an ecosystem function analysis, which has been developed to assess the success of establishment of these ecosystems. (C) 2001 Elsevier Science B.V. All rights reserved.

Mining wheat germplasm collections for yield enhancing traits

The material in genebanks includes valuable traditional varieties and landraces, non-domesticated species, advanced and obsolete cultivars, breeding lines and genetic stock. It is the wide variety of potentially useful genetic diversity that makes collections valuable. While most of the yield increases to date have resulted from manipulation of a few major traits (such as height, photoperiodism, and vernalization), meeting future demand for increased yields will require exploitation of novel genetic resources. Many traits have been reported to have potential to enhance yield, and high expression of these can be found in germplasm collections. To boost yield in irrigated situations, spike fertility must be improved simultaneously with photosynthetic capacity. CIMMYT's Wheat Genetic Resources program has identified a source of multi-ovary florets, with up to 6 kernels per floret. Lines from landrace collections have been identified that have very high chlorophyll concentration, which may increase leaf photosynthetic rate. High chlorophyll concentration and high stomatal conductance are associated with heat tolerance. Recent studies, through augmented use of seed multiplication nurseries, identified high expression of these traits in bank accessions, and both traits were heritable. Searches are underway for drought tolerance traits related to remobilization of stem fructans, awn photosynthesis, osmotic adjustment, and pubescence. Genetic diversity from wild relatives through the production of synthetic wheats has produced novel genetic diversity.

Engineering closure of an open pit gold operation in a semi-arid climate

Along with material characteristics and geometry, the climate in which a mine is located can have a dramatic effect on the appropriate options for rehabilitation. The paper outlines the setting, mining, milling and waste disposal at Kidston Gold Mine's open pit operations in the semi-arid climate of North Queensland, Australia, before focusing on the engineering aspects of the rehabilitation of Kidston. The mine took a holistic and proactive approach to rehabilitation, and was prepared to demonstrate a number of innovative approaches, which are described in the paper. Engineering issues that had to be addressed included the geotechnical stability and deformation of waste rock dumps, including a 240 m high in-pit dump: the construction and performance monitoring of a “store and release” cover over potentially acid forming mineralised waste rock; erosion from the side slopes of the waste rock dumps; the in-pit co-disposal of waste rock and thickened tailings; the geotechnical stability of the tailings dam wall; the potential for erosion of bare tailings; the water balance of the tailings dam; direct revegetation of the tailings; and the pit hydrology. The rehabilitation of the mine represents an important benchmark in mine site rehabilitation best practice, from which lessons applicable worldwide can be shared.

Electricity market price spike forecast with data mining techniques

Electricity market price forecast is a changeling yet very important task for electricity market managers and participants. Due to the complexity and uncertainties in the power grid, electricity prices are highly volatile and normally carry with spikes. which may be (ens or even hundreds of times higher than the normal price. Such electricity spikes are very difficult to be predicted. So far. most of the research on electricity price forecast is based on the normal range electricity prices. This paper proposes a data mining based electricity price forecast framework, which can predict the normal price as well as the price spikes. The normal price can be, predicted by a previously proposed wavelet and neural network based forecast model, while the spikes are forecasted based on a data mining approach. This paper focuses on the spike prediction and explores the reasons for price spikes based on the measurement of a proposed composite supply-demand balance index (SDI) and relative demand index (RDI). These indices are able to reflect the relationship among electricity demand, electricity supply and electricity reserve capacity. The proposed model is based on a mining database including market clearing price, trading hour. electricity), demand, electricity supply and reserve. Bayesian classification and similarity searching techniques are used to mine the database to find out the internal relationships between electricity price spikes and these proposed. The mining results are used to form the price spike forecast model. This proposed model is able to generate forecasted price spike, level of spike and associated forecast confidence level. The model is tested with the Queensland electricity market data with promising results. Crown Copyright (C) 2004 Published by Elsevier B.V. All rights reserved.