6 resultados para Steam-navigation

em Biblioteca Digital da Produção Intelectual da Universidade de São Paulo (BDPI/USP)


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Research of advanced technologies for energy generation contemplates a series of alternatives that are introduced both in the investigation of new energy sources and in the improvement and/or development of new components and systems. Even though significant reductions are observed in the amount of emissions, the proposed alternatives require the use of exhaust gases cleaning systems. The results of environmental analyses based on two configurations proposed for urban waste incineration are presented in this paper; the annexation of integer (Boolean) variables to the environomic model makes it possible to define the best gas cleaning routes based on exergetic cost minimisation criteria. In this first part, the results for steam cogeneration system analysis associated with the incineration of municipal solid wastes (MSW) is presented. (c) 2007 Elsevier Ltd. All rights reserved.

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In the first paper of this paper (Part I), conditions were presented for the gas cleaning technological route for environomic optimisation of a cogeneration system based in a thermal cycle with municipal solid waste incineration. In this second part, an environomic analysis is presented of a cogeneration system comprising a combined cycle composed of a gas cycle burning natural gas with a heat recovery steam generator with no supplementary burning and a steam cycle burning municipal solid wastes (MSW) to which will be added a pure back pressure steam turbine (another one) of pure condensation. This analysis aims to select, concerning some scenarios, the best atmospheric pollutant emission control routes (rc) according to the investment cost minimisation, operation and social damage criteria. In this study, a comparison is also performed with the results obtained in the Case Study presented in Part I. (c) 2007 Elsevier Ltd. All rights reserved.

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The combined-cycle gas and steam turbine power plant presents three main pieces of equipment: gas turbines, steam turbines and heat recovery steam generator (HRSG). In case of HRSG failure the steam cycle is shut down, reducing the power plant output. Considering that the technology for design, construction and operation of high capacity HRSGs is quite recent its availability should be carefully evaluated in order to foresee the performance of the power plant. This study presents a method for reliability and availability evaluation of HRSGs installed in combined-cycle power plant. The method`s first step consists in the elaboration of the steam generator functional tree and development of failure mode and effects analysis. The next step involves a reliability and availability analysis based on the time to failure and time to repair data recorded during the steam generator operation. The third step, aiming at availability improvement, recommends the fault-tree analysis development to identify components the failure (or combination of failures) of which can cause the HRSG shutdown. Those components maintenance policy can be improved through the use of reliability centered maintenance (RCM) concepts. The method is applied on the analysis of two HRSGs installed in a 500 MW combined-cycle power plant. (C) 2010 Elsevier Ltd. All rights reserved.

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The premature failure of steam turbine rotor blades, manufactured in forged 12% Cr-NiMoV martensitic stainless steel, was investigated using visual inspection non-destructive testing, macro and microfractography, microstructural characterization, EDS microanalysis, chemical analysis, micro hardness and tensile testing. The blades belonged to the last stage of a thermoelectric plant steam turbine generator (140 MV A). The results indicated that the failure of the blades was promoted by foreign-particle erosion, which attacked preferentially the low-pressure side of the lower trailing edge of the blades. The resulting wear grooves acted as stress raisers and promoted the nucleation of fatigue cracks, which probably grew during the transition events of the steam turbine operation. Finally, water drop erosion was observed on the blade upper leading edge (low-pressure side). (C) 2009 Elsevier Ltd. All rights reserved.

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Back in 1970s and 1980s, cogeneration plants in sugarcane mills were primarily designed to consume all bagasse, and produce steam and electricity to the process. The plants used medium pressure steam boilers (21 bar and 300 degrees C) and backpressure steam turbines. Some plants needed also an additional fuel, as the boilers were very inefficient. In those times, sugarcane bagasse did not have an economic value, and it was considered a problem by most mills. During the 1990s and the beginning of the 2000s, sugarcane industry faced an open market perspective, thus, there was a great necessity to reduce costs in the production processes. In addition, the economic value of by-products (bagasse, molasses, etc.) increased, and there was a possibility of selling electricity to the grid. This new scenario led to a search for more advanced cogeneration systems, based mainly on higher steam parameters (40-80 bar and 400-500 degrees C). In the future, some authors suggest that biomass integrated gasification combined cycles are the best alternative to cogeneration plants in sugarcane mills. These systems might attain 35-40% efficiency for the power conversion. However, supercritical steam cycles might also attain these efficiency values, what makes them an alternative to gasification-based systems. This paper presents a comparative thermoeconomic study of these systems for sugarcane mills. The configurations studied are based on real systems that could be adapted to biomass use. Different steam consumptions in the process are considered, in order to better integrate these configurations in the mill. (C) 2009 Elsevier Ltd. All rights reserved.

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Recent studies have demonstrated that spatial patterns of fMRI BOLD activity distribution over the brain may be used to classify different groups or mental states. These studies are based on the application of advanced pattern recognition approaches and multivariate statistical classifiers. Most published articles in this field are focused on improving the accuracy rates and many approaches have been proposed to accomplish this task. Nevertheless, a point inherent to most machine learning methods (and still relatively unexplored in neuroimaging) is how the discriminative information can be used to characterize groups and their differences. In this work, we introduce the Maximum Uncertainty Linear Discrimination Analysis (MLDA) and show how it can be applied to infer groups` patterns by discriminant hyperplane navigation. In addition, we show that it naturally defines a behavioral score, i.e., an index quantifying the distance between the states of a subject from predefined groups. We validate and illustrate this approach using a motor block design fMRI experiment data with 35 subjects. (C) 2008 Elsevier Inc. All rights reserved.