The design of hazard risk assessment matrices for ranking occupational health risks and their application in mining and minerals processing

Two hazard risk assessment matrices for the ranking of occupational health risks are described. The qualitative matrix uses qualitative measures of probability and consequence to determine risk assessment codes for hazard-disease combinations. A walk-through survey of an underground metalliferous mine and concentrator is used to demonstrate how the qualitative matrix can be applied to determine priorities for the control of occupational health hazards. The semi-quantitative matrix uses attributable risk as a quantitative measure of probability and uses qualitative measures of consequence. A practical application of this matrix is the determination of occupational health priorities using existing epidemiological studies. Calculated attributable risks from epidemiological studies of hazard-disease combinations in mining and minerals processing are used as examples. These historic response data do not reflect the risks associated with current exposures. A method using current exposure data, known exposure-response relationships and the semi-quantitative matrix is proposed for more accurate and current risk rankings.

The role of homeotic genes in determining the segmental pattern of chordotonal organs in Drosophila

The homeotic genes are instrumental in establishing segment-specific characteristics. In Drosophila embryos there is ample evidence that the homeotic genes are involved in establishing the differences in the pattern of sense organs between segments. The chordotonal organs are compound sense organs made up of several stretch receptive sensilla. A set of serially homologous chordotonal organs, Ich3 in the 1(st) thoracic segment, dch3 in the 2(nd) and 3(rd) thoracic segments and Ich5 in abdominal segments 1 to 7, is composed of different numbers of sensilla with different positions and orientations. Here we examine this set of sense organs and a companion set, vchA/B and vch 1, in the wild type and mutants for Sex combs reduced, Antennapedia, Ultrabithorax, and abdominal-A, using immunostaining. Mutant phenotypes indicate that Ultrabithorax and abdominal-A in particular influence the formation of these sense organs. Differential expression of abdominal-A and Ultrabithorax within compartments of individual parasegments can precisely modulate the types of sense organs that will arise from a segment.

New arsenite-oxidizing bacteria isolated from Australian gold mining environments - Phylogenetic relationships

Nine novel arsenite-oxidizing bacteria have been isolated from two different gold mine environments in Australia. Four of these organisms grow chemolithoautotrophically with oxygen as the terminal electron acceptor, arsenite as the electron donor, and carbon dioxide-bicarbonate as the sole carbon source. Five heterotrophic arsenite-oxidizing bacteria were also isolated, one of which was found to be both phylogenetically and physiologically identical to the previously described heterotrophic arsenite oxidizer misidentified as Alcaligenes faecalis. The results showed that this strain belongs to the genus Achromobacter. Phylogenetically, the arsenite-oxidizing bacteria fall within two separate subdivisions of the Proteobacteria. Interestingly, the chemolithoautotrophic arsenite oxidizers belong to the alpha-Proteobacteria, whereas the heterotrophic arsenite oxidizers belong to the beta-Proteobacteria.

Blast design using measurement while drilling parameters

Measurement while drilling (MWD) techniques can provide a useful tool to aid drill and blast engineers in open cut mining. By avoiding time consuming tasks such as scan-lines and rock sample collection for laboratory tests, MWD techniques can not only save time but also improve the reliability of the blast design by providing the drill and blast engineer with the information specially tailored for use. While most mines use a standard blast pattern and charge per blasthole, based on a single rock factor for the entire bench or blast region, information derived from the MWD parameters can improve the blast design by providing more accurate rock properties for each individual blasthole. From this, decisions can be made on the most appropriate type and amount of explosive charge to place in a per blasthole or to optimise the inter-hole timing detonation time of different decks and blastholes. Where real-time calculations are feasible, the system could extend the present blast design even be used to determine the placement of subsequent holes towards a more appropriate blasthole pattern design like asymmetrical blasting.

Asymmetric blasting: a rock mass dependent blast design method

Blasting has been the most frequently used method for rock breakage since black powder was first used to fragment rocks, more than two hundred years ago. This paper is an attempt to reassess standard design techniques used in blasting by providing an alternative approach to blast design. The new approach has been termed asymmetric blasting. Based on providing real time rock recognition through the capacity of measurement while drilling (MWD) techniques, asymmetric blasting is an approach to deal with rock properties as they occur in nature, i.e., randomly and asymmetrically spatially distributed. It is well accepted that performance of basic mining operations, such as excavation and crushing rely on a broken rock mass which has been pre conditioned by the blast. By pre-conditioned we mean well fragmented, sufficiently loose and with adequate muckpile profile. These muckpile characteristics affect loading and hauling [1]. The influence of blasting does not end there. Under the Mine to Mill paradigm, blasting has a significant leverage on downstream operations such as crushing and milling. There is a body of evidence that blasting affects mineral liberation [2]. Thus, the importance of blasting has increased from simply fragmenting and loosing the rock mass, to a broader role that encompasses many aspects of mining, which affects the cost of the end product. A new approach is proposed in this paper which facilitates this trend 'to treat non-homogeneous media (rock mass) in a non-homogeneous manner (an asymmetrical pattern) in order to achieve an optimal result (in terms of muckpile size distribution).' It is postulated there are no logical reasons (besides the current lack of means to infer rock mass properties in the blind zones of the bench and onsite precedents) for drilling a regular blast pattern over a rock mass that is inherently heterogeneous. Real and theoretical examples of such a method are presented.

Gestão do conhecimento usando data mining: estudo de caso na Universidade Federal de Lavras

A gestão do conhecimento abrange toda a forma de gerar, armazenar, distribuir e utilizar o conhecimento, tornando necessária a utilização de tecnologias de informação para facilitar esse processo, devido ao grande aumento no volume de dados. A descoberta de conhecimento em banco de dados é uma metodologia que tenta solucionar esse problema e o data mining é uma técnica que faz parte dessa metodologia. Este artigo desenvolve, aplica e analisa uma ferramenta de data mining, para extrair conhecimento referente à produção científica das pessoas envolvidas com a pesquisa na Universidade Federal de Lavras. A metodologia utilizada envolveu a pesquisa bibliográfica, a pesquisa documental e o método do estudo de caso. As limitações encontradas na análise dos resultados indicam que ainda é preciso padronizar o modo do preenchimento dos currículos Lattes para refinar as análises e, com isso, estabelecer indicadores. A contribuição foi gerar um banco de dados estruturado, que faz parte de um processo maior de desenvolvimento de indicadores de ciência e tecnologia, para auxiliar na elaboração de novas políticas de gestão científica e tecnológica e aperfeiçoamento do sistema de ensino superior brasileiro.

An architecture for an effective usage of data mining in business intelligence systems

Business Intelligence (BI) is one emergent area of the Decision Support Systems (DSS) discipline. Over the last years, the evolution in this area has been considerable. Similarly, in the last years, there has been a huge growth and consolidation of the Data Mining (DM) field. DM is being used with success in BI systems, but a truly DM integration with BI is lacking. Therefore, a lack of an effective usage of DM in BI can be found in some BI systems. An architecture that pretends to conduct to an effective usage of DM in BI is presented.

Data mining contributions to characterize MV consumers and to improve the suppliers-consumers settlements

This paper deals with the establishment of a characterization methodology of electric power profiles of medium voltage (MV) consumers. The characterization is supported on the data base knowledge discovery process (KDD). Data Mining techniques are used with the purpose of obtaining typical load profiles of MV customers and specific knowledge of their customers’ consumption habits. In order to form the different customers’ classes and to find a set of representative consumption patterns, a hierarchical clustering algorithm and a clustering ensemble combination approach (WEACS) are used. Taking into account the typical consumption profile of the class to which the customers belong, new tariff options were defined and new energy coefficients prices were proposed. Finally, and with the results obtained, the consequences that these will have in the interaction between customer and electric power suppliers are analyzed.

Data mining techniques contributions to support electrical vehicle demand response

The introduction of Electric Vehicles (EVs) together with the implementation of smart grids will raise new challenges to power system operators. This paper proposes a demand response program for electric vehicle users which provides the network operator with another useful resource that consists in reducing vehicles charging necessities. This demand response program enables vehicle users to get some profit by agreeing to reduce their travel necessities and minimum battery level requirements on a given period. To support network operator actions, the amount of demand response usage can be estimated using data mining techniques applied to a database containing a large set of operation scenarios. The paper includes a case study based on simulated operation scenarios that consider different operation conditions, e.g. available renewable generation, and considering a diversity of distributed resources and electric vehicles with vehicle-to-grid capacity and demand response capacity in a 33 bus distribution network.

Data mining techniques to support the classification of MV electricity customers

This paper describes a methodology that was developed for the classification of Medium Voltage (MV) electricity customers. Starting from a sample of data bases, resulting from a monitoring campaign, Data Mining (DM) techniques are used in order to discover a set of a MV consumer typical load profile and, therefore, to extract knowledge regarding to the electric energy consumption patterns. In first stage, it was applied several hierarchical clustering algorithms and compared the clustering performance among them using adequacy measures. In second stage, a classification model was developed in order to allow classifying new consumers in one of the obtained clusters that had resulted from the previously process. Finally, the interpretation of the discovered knowledge are presented and discussed.

Using data mining techniques to support DR programs definition in smart grids

In recent years, Power Systems (PS) have experimented many changes in their operation. The introduction of new players managing Distributed Generation (DG) units, and the existence of new Demand Response (DR) programs make the control of the system a more complex problem and allow a more flexible management. An intelligent resource management in the context of smart grids is of huge important so that smart grids functions are assured. This paper proposes a new methodology to support system operators and/or Virtual Power Players (VPPs) to determine effective and efficient DR programs that can be put into practice. This method is based on the use of data mining techniques applied to a database which is obtained for a large set of operation scenarios. The paper includes a case study based on 27,000 scenarios considering a diversity of distributed resources in a 32 bus distribution network.

A data-mining based methodology for wind forecasting

In many countries the use of renewable energy is increasing due to the introduction of new energy and environmental policies. Thus, the focus on the efficient integration of renewable energy into electric power systems is becoming extremely important. Several European countries have already achieved high penetration of wind based electricity generation and are gradually evolving towards intensive use of this generation technology. The introduction of wind based generation in power systems poses new challenges for the power system operators. This is mainly due to the variability and uncertainty in weather conditions and, consequently, in the wind based generation. In order to deal with this uncertainty and to improve the power system efficiency, adequate wind forecasting tools must be used. This paper proposes a data-mining-based methodology for very short-term wind forecasting, which is suitable to deal with large real databases. The paper includes a case study based on a real database regarding the last three years of wind speed, and results for wind speed forecasting at 5 minutes intervals.