The geological and microbiological controls on the occurrence, mobility and concentration of Se and Te in coal and black shales

© 2016 Institute of Materials, Minerals and Mining and The AusIMM Published by Taylor & Francis on behalf of the Institute and The AusIMM

Session organiser/chair: The architecture of coal and other energies

Coal ignited the industrial revolution. An organic sedimentary rock that energized the globe, transforming cities, landscapes and societies for generations, the importance of ‘King Coal’ to the development and consolidation of modernity has been well-recognised. And yet, as a critical factor in the production of modern architecture, coal—as well as other forms of energy—has been mostly overlooked.

From Appalachia to Lanarkshire, from the pits of northern France, Belgium and the Ruhr valley, to the monumental opencast excavations of Russia, China, Africa and Australia, mining operations have altered the immediate social and physical landscapes of coal-rich areas. But in contrast to its own underground conditions of production, the winning of coal, especially in the twentieth-century, has produced conspicuously enlightened and humane approaches to architecture and urbanism. In the twentieth century, educational buildings, holiday camps, hospitals, swimming pools, convalescent homes and housing prevailed alongside model collieries in mining settlements and areas connected to them. In 1930s Britain, pit head baths—funded by a levy on each ton produced—were often built in the International Style. Many won praise for architectural merit, appearing in Nicholas Pevsner’s guides to the buildings of England alongside cathedrals, village manors and Masonic halls as testimonies to the public good.

The deep relationships between coal and modernity, and the expressions of architecture it has articulated, in the collieries from which it was hewn, the landscape and towns it shaped, and the power stations and other infrastructure where it was used, offer innumerable opportunities to explore how coal produced architectures which embodied and expressed both social and technological conditions. While proposals on coal are preferred, we also welcome papers that interrogate the complexity, heterogeneity and hybridity of other forms of energy production and how these have also interceded into architectural form at a range of scales.

Novel Primate-Specific Genes, RMEL 1, 2 and 3, with Highly Restricted Expression in Melanoma, Assessed by New Data Mining Tool

Melanoma is a highly aggressive and therapy resistant tumor for which the identification of specific markers and therapeutic targets is highly desirable. We describe here the development and use of a bioinformatic pipeline tool, made publicly available under the name of EST2TSE, for the in silico detection of candidate genes with tissue-specific expression. Using this tool we mined the human EST (Expressed Sequence Tag) database for sequences derived exclusively from melanoma. We found 29 UniGene clusters of multiple ESTs with the potential to predict novel genes with melanoma-specific expression. Using a diverse panel of human tissues and cell lines, we validated the expression of a subset of three previously uncharacterized genes (clusters Hs.295012, Hs.518391, and Hs.559350) to be highly restricted to melanoma/melanocytes and named them RMEL1, 2 and 3, respectively. Expression analysis in nevi, primary melanomas, and metastatic melanomas revealed RMEL1 as a novel melanocytic lineage-specific gene up-regulated during melanoma development. RMEL2 expression was restricted to melanoma tissues and glioblastoma. RMEL3 showed strong up-regulation in nevi and was lost in metastatic tumors. Interestingly, we found correlations of RMEL2 and RMEL3 expression with improved patient outcome, suggesting tumor and/or metastasis suppressor functions for these genes. The three genes are composed of multiple exons and map to 2q12.2, 1q25.3, and 5q11.2, respectively. They are well conserved throughout primates, but not other genomes, and were predicted as having no coding potential, although primate-conserved and human-specific short ORFs could be found. Hairpin RNA secondary structures were also predicted. Concluding, this work offers new melanoma-specific genes for future validation as prognostic markers or as targets for the development of therapeutic strategies to treat melanoma.

Data Mining Applied to Harmonic Current Sources Identification in Residential Consumers

This work proposes a method based on both preprocessing and data mining with the objective of identify harmonic current sources in residential consumers. In addition, this methodology can also be applied to identify linear and nonlinear loads. It should be emphasized that the entire database was obtained through laboratory essays, i.e., real data were acquired from residential loads. Thus, the residential system created in laboratory was fed by a configurable power source and in its output were placed the loads and the power quality analyzers (all measurements were stored in a microcomputer). So, the data were submitted to pre-processing, which was based on attribute selection techniques in order to minimize the complexity in identifying the loads. A newer database was generated maintaining only the attributes selected, thus, Artificial Neural Networks were trained to realized the identification of loads. In order to validate the methodology proposed, the loads were fed both under ideal conditions (without harmonics), but also by harmonic voltages within limits pre-established. These limits are in accordance with IEEE Std. 519-1992 and PRODIST (procedures to delivery energy employed by Brazilian`s utilities). The results obtained seek to validate the methodology proposed and furnish a method that can serve as alternative to conventional methods.

Emissions of NO(x) and SO(2) from Coals of Various Ranks, Bagasse, and Coal-Bagasse Blends Burning in O(2)/N(2) and O(2)/CO(2) Environments

Oxy-coal combustion is a viable technology, for new and existing coal-fired power plants, as it facilitates carbon capture and, thereby, can mitigate climate change. Pulverized coals of various ranks, biomass, and their blends were burned to assess the evolution of combustion effluent gases, such as NO(x), SO(2), and CO, under a variety of background gas compositions. The fuels were burned in an electrically heated laboratory drop-tube furnace in O(2)/N(2) and O(2)/CO(2) environments with oxygen mole fractions of 20%, 40%, 60%, 80%, and 100%, at a furnace temperature of 1400 K. The fuel mass flow rate was kept constant in most cases, and combustion was fuel-lean. Results showed that in the case of four coals studied, NO(x) emissions in O(2)/CO(2) environments were lower than those in O(2)/N(2) environments by amounts that ranged from 19 to 43% at the same oxygen concentration. In the case of bagasse and coal/bagasse blends, the corresponding NO(x) reductions ranged from 22 to 39%. NO(x) emissions were found to increase with increasing oxygen mole fraction until similar to 50% O(2) was reached; thereafter, they monotonically decreased with increasing oxygen concentration. NO(x) emissions from the various fuels burned did not clearly reflect their nitrogen content (0.2-1.4%), except when large content differences were present. SO(2) emissions from all fuels remained largely unaffected by the replacement of the N(2) diluent gas with CO(2), whereas they typically increased with increasing sulfur content of the fuels (0.07-1.4%) and decreased with increasing calcium content of the fuels (0.28-2.7%). Under the conditions of this work, 20-50% of the fuel-nitrogen was converted to NO(x). The amount of fuel-sulfur converted to SO(2) varied widely, depending on the fuel and, in the case of the bituminous coal, also depending on the O(2) mole fraction. Blending the sub-bituminous coal with bagasse reduced its SO(2) yields, whereas blending the bituminous coal with bagasse reduced both its SO(2) and NO(x) yields. CO emissions were generally very low in all cases. The emission trends were interpreted on the basis of separate combustion observations.

Assessing the evolution of sustainability reporting in the mining sector

Since the 1990s several large companies have been publishing nonfinancial performance reports. Focusing initially on the physical environment, these reports evolved to consider social relations, as well as data on the firm`s economic performance. A few mining companies pioneered this trend, and in the last years some of them incorporated the three dimensions of sustainable development, publishing so-called sustainability reports. This article reviews 31 reports published between 2001 and 2006 by four major mining companies. A set of 62 assessment items organized in six categories (namely context and commitment, management, environmental, social and economic performance, and accessibility and assurance) were selected to guide the review. The items were derived from international literature and recommended best practices, including the Global Reporting Initiative G3 framework. A content analysis was performed using the report as a sampling unit, and using phrases, graphics, or tables containing certain information as data collection units. A basic rating scale (0 or 1) was used for noting the presence or absence of information and a final percentage score was obtained for each report. Results show that there is a clear evolution in report`s comprehensiveness and depth. Categories ""accessibility and assurance"" and ""economic performance"" featured the lowest scores and do not present a clear evolution trend in the period, whereas categories ""context and commitment"" and ""social performance"" presented the best results and regular improvement; the category ""environmental performance,"" despite it not reaching the biggest scores, also featured constant evolution. Description of data measurement techniques, besides more comprehensive third-party verification are the items most in need of improvement.

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.