989 resultados para 290701 Mining Engineering


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Tese (doutorado)—Universidade de Brasília, Instituto de Geociências, Programa de Pós-Graduação em Geologia, 2015.

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Exposure to diesel particulate matter from diesel exhaust has been shown to have adverse health effects in humans. In 2012 The International Agency for Research on Cancer classified diesel exhaust as a group 1 know human carcinogen. Because of the associated health effects, there has been a strong push to reduce the amount of diesel exhaust present in the mining industry. Biodiesel is one to the more common and promising control options used to reduce the amount of diesel particulate matter that is generated during fuel combustion. The use of biodiesel over petroleum diesel has been shown to reduce not only particulate matter, but hydro carbon and carbon monoxide mass emissions as well. Personal and area samples were collected at an underground metal mine in the northwestern United States to evaluate the current blend of B70 biodiesel. The objective of this research was to evaluate the carbon levels associated with diesel particulate matter generated from the combustion of a B70 biodiesel. Data was also compared to past studies on which diesel particulate matter from petroleum diesel was evaluated. Samples were taken on four separate four day campaigns between March and October of 2014. Area samples were taken from 7 different areas in the mine and personal samples were taken from a 20 person cohort. The equipment used for sampling was compliant with the NIOSH 5040 method. Statistical analysis of the results was done using Minitab 17 software. The statistical analysis showed that the total carbon concentrations from biodiesel were well below the MSHA exposure limit. Results also showed that organic/elemental carbon ratios were consistent with past studies as the concentrations of organic carbon were significantly higher than those of elemental carbon.

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Microwave reduction testing using activated charcoal as a reducing agent was performed on a sample of Black Thor chromite ore from the Ring of Fire deposit in Northern Ontario. First, a thermodynamic model was constructed for the system. Activity coefficients for several species were found in the literature. The model predicted chromium grades of 61.60% and recoveries of 93.43% for a 15% carbon addition. Next, reduction testing on the chromite ore was performed. Tests were performed at increasing power levels and reduction times. Testing atmospheres used were air, argon, and vacuum. The reduced product had maximum grades of 72.89% and recoveries of 80.37%. These maximum values were obtained in the same test where an argon atmosphere was used, with a carbon addition of 15%, optimal power level of 1200 W (actual 1171 W), and a time of 400 seconds. During this test, 17.53% of the initial mass was lost as gas, a carbon grade of 1.95% was found for the sintered core product. Additional work is recommended to try and purify the sintered core product as well as reduce more of the initial sample. Changing reagent schemes or a two step reduction / separation process could be implemented.

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Heat management in mines is a growing issue as mines expand physically in size and depth and as the infrastructure grows that is required to maintain them. Heat management is a concern as it relates to the health and safety of the workers as set by the regulations of governing bodies as well as the heat sensitive equipment that may be found throughout the mine workings. In order to reduce the exposure of working in hot environments there are engineering and management systems that can monitor and control the environmental conditions within the mine. The successful implementation of these methods can manage the downtime caused by heat stress environments, which can increase overall production. This thesis introduces an approach to monitoring and data based heat management. A case study is presented with an in depth approach to data collection. Data was collected for a period of up to and over one year. Continuous monitoring was conducted by equipment that was developed both commercially and within the mine site. The monitoring instrumentation was used to assess the environmental conditions found within the study area. Analysis of the data allowed for an engineering assessment of viable options in order to control and manage the environment heat stress. An option is developed and presented which allows for the greatest impact on the heat stress conditions within the case study area and is economically viable for the mine site.

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The main objective of blasting is to produce optimum fragmentation for downstream processing. Fragmentation is usually considered optimum when the average fragment size is minimum and the fragmentation distribution as uniform as possible. One of the parameters affecting blasting fragmentation is believed to be time delay between holes of the same row. Although one can find a significant number of studies in the literature, which examine the relationship between time delay and fragmentation, their results have been often controversial. The purpose of this work is to increase the level of understanding of how time delay between holes of the same row affects fragmentation. Two series of experiments were conducted for this purpose. The first series involved tests on small scale grout and granite blocks to determine the moment of burden detachment. The instrumentation used for these experiments consisted mainly of strain gauges and piezoelectric sensors. Some experiments were also recorded with a high speed camera. It was concluded that the time of detachment for this specific setup is between 300 and 600 μs. The second series of experiments involved blasting of a 2 meter high granite bench and its purpose was the determination of the hole-to-hole delay that provides optimum fragmentation. The fragmentation results were assessed with image analysis software. Moreover, vibration was measured close to the blast and the experiments were recorded with high speed cameras. The results suggest that fragmentation was optimum when delays between 4 and 6 ms were used for this specific setup. Also, it was found that the moment at which gases first appear to be venting from the face was consistently around 6 ms after detonation.

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In this position paper, we claim that the need for time consuming data preparation and result interpretation tasks in knowledge discovery, as well as for costly expert consultation and consensus building activities required for ontology building can be reduced through exploiting the interplay of data mining and ontology engineering. The aim is to obtain in a semi-automatic way new knowledge from distributed data sources that can be used for inference and reasoning, as well as to guide the extraction of further knowledge from these data sources. The proposed approach is based on the creation of a novel knowledge discovery method relying on the combination, through an iterative ?feedbackloop?, of (a) data mining techniques to make emerge implicit models from data and (b) pattern-based ontology engineering to capture these models in reusable, conceptual and inferable artefacts.

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Nowadays, data mining is based on low-level specications of the employed techniques typically bounded to a specic analysis platform. Therefore, data mining lacks a modelling architecture that allows analysts to consider it as a truly software-engineering process. Here, we propose a model-driven approach based on (i) a conceptual modelling framework for data mining, and (ii) a set of model transformations to automatically generate both the data under analysis (via data-warehousing technology) and the analysis models for data mining (tailored to a specic platform). Thus, analysts can concentrate on the analysis problem via conceptual data-mining models instead of low-level programming tasks related to the underlying-platform technical details. These tasks are now entrusted to the model-transformations scaffolding.

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Data mining is one of the most important analysis techniques to automatically extract knowledge from large amount of data. Nowadays, data mining is based on low-level specifications of the employed techniques typically bounded to a specific analysis platform. Therefore, data mining lacks a modelling architecture that allows analysts to consider it as a truly software-engineering process. Bearing in mind this situation, we propose a model-driven approach which is based on (i) a conceptual modelling framework for data mining, and (ii) a set of model transformations to automatically generate both the data under analysis (that is deployed via data-warehousing technology) and the analysis models for data mining (tailored to a specific platform). Thus, analysts can concentrate on understanding the analysis problem via conceptual data-mining models instead of wasting efforts on low-level programming tasks related to the underlying-platform technical details. These time consuming tasks are now entrusted to the model-transformations scaffolding. The feasibility of our approach is shown by means of a hypothetical data-mining scenario where a time series analysis is required.

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Comunicación presentada en las XVI Jornadas de Ingeniería del Software y Bases de Datos, JISBD 2011, A Coruña, 5-7 septiembre 2011.

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Mode of access: Internet.

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Mode of access: Internet.

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With increasing competition and more demanding members, clubs need a tool to help them belter attract and retain members and predict their behavior. Data mining is such a tool. This article presents an overview of how data warehousing, data marting, and data mining can provide the foundation on which clubs can build strategies to outsmart competitors, build Ioyalty identify new members, and lower costs.