982 resultados para AUTO-COMBUSTION


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The increasing volumes of municipal solid waste produced worldwide are encouraging the development of processes to reduce the environmental impact of this waste stream. Combustion technology can facilitate volume reduction of up to 90%, with the inorganic contaminants being captured in furnace bottom ash, and fly ash/APC residues. The disposal or reuse of these residues is however governed by the potential release of constituent contaminants into the environment. Accelerated carbonation has been shown to have a potential for improving the chemical stability and leaching behaviour of both bottom ash and fly ash/APC residues. However, the efficacy of carbonation depends on whether the method of gas application is direct or indirect. Also important are the mineralogy, chemistry and physical properties of the fresh ash, the carbonation reaction conditions such as temperature, contact time, CO2 partial pressure and relative humidity. This paper reviews the main issues pertaining to the application of accelerated carbonation to municipal waste combustion residues to elucidate the potential benefits on the stabilization of such residues and for reducing CO2 emissions. In particular, the modification of ash properties that occur upon carbonation and the CO2 sequestration potential possible under different conditions are discussed. Although accelerated carbonation is a developing technology, it could be introduced in new incinerator facilities as a "finishing step" for both ash treatment and reduction of CO2 emissions.

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This paper presents a statistical-based fault diagnosis scheme for application to internal combustion engines. The scheme relies on an identified model that describes the relationships between a set of recorded engine variables using principal component analysis (PCA). Since combustion cycles are complex in nature and produce nonlinear relationships between the recorded engine variables, the paper proposes the use of nonlinear PCA (NLPCA). The paper further justifies the use of NLPCA by comparing the model accuracy of the NLPCA model with that of a linear PCA model. A new nonlinear variable reconstruction algorithm and bivariate scatter plots are proposed for fault isolation, following the application of NLPCA. The proposed technique allows the diagnosis of different fault types under steady-state operating conditions. More precisely, nonlinear variable reconstruction can remove the fault signature from the recorded engine data, which allows the identification and isolation of the root cause of abnormal engine behaviour. The paper shows that this can lead to (i) an enhanced identification of potential root causes of abnormal events and (ii) the masking of faulty sensor readings. The effectiveness of the enhanced NLPCA based monitoring scheme is illustrated by its application to a sensor fault and a process fault. The sensor fault relates to a drift in the fuel flow reading, whilst the process fault relates to a partial blockage of the intercooler. These faults are introduced to a Volkswagen TDI 1.9 Litre diesel engine mounted on an experimental engine test bench facility.

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The work in this paper is of particular significance since it considers the problem of modelling cross- and auto-correlation in statistical process monitoring. The presence of both types of correlation can lead to fault insensitivity or false alarms, although in published literature to date, only autocorrelation has been broadly considered. The proposed method, which uses a Kalman innovation model, effectively removes both correlations. The paper (and Part 2 [2]) has emerged from work supported by EPSRC grant GR/S84354/01 and is of direct relevance to problems in several application areas including chemical, electrical, and mechanical process monitoring.

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A consensus view of Irish Clinical Immunologists as to when auto injection syringes of adrenaline should be prescribed