Dimitrios Kateris

Performance and Emission Characteristics of Spark Ignition Engine Fuelled with Ethanol and Methanol Gasoline Blended Fuels

Depletion of fossil fuels and environmental pollution has led researchers to anticipate the need to develop bio-fuels. Alcohols are an important category of bio-fuels. Methanol can be produced from coal, biomass or even natural gas with acceptable energy cost. Also, gasification of biomass can lead to methanol, mixed alcohols, and Fischer–Tropsch liquids (Chum and Overend, 2001). Ethanol is produced from sugars (particularly sugar cane) and starch by fermentation. The biomass industry can produce additional ethanol by fermenting some agricultural by-products (Prasad et al., 2007). Lignocellulosic biomass is a potential source for ethanol that is not directly linked to food production (Freudenberger, 2009). Shapouri et al. (1995) showed that the net energy value of corn ethanol has become positive in recent years due to technological advances in ethanol conversion and increased efficiency in farm production. Corn ethanol is energy efficient, as indicated by an energy ratio of 1.24, that is, for every Btu dedicated to producing ethanol, there is a 24-percent energy gain. Goldemberg et al. (2004) demonstrated, through the Brazilian experience with ethanol, that economy of scale and technological advances lead to increased competitiveness of this renewable alternative, reducing the gap with conventional fossil fuels. Consequently alcohols are particularly attractive as alternative fuels because they are a renewable biobased resource and oxygenated, thereby providing the potential to reduce particulate emissions in spark ignition engines. Kim and Dale (2004) estimated that the potential for ethanol production is equivalent to about 32% of the total gasoline consumption worldwide, when used in E85 (85% ethanol in gasoline) for a midsize passenger vehicle. Such a substitution immediately addresses the issue of reducing our use of non-renewable resources (fossil fuels) and the attendant impacts on climate change, especially carbon dioxide and the resulting greenhouse effect (von Blottnitz and Curran, 2007). The conversion of biomass to bio-fuel has some ecological drawbacks. It is well known that conversion of biomass requires additional energy inputs, most often provided in some form of fossil fuel. Also, agricultural production of biomass is relatively land intensive, and there

Fault severity estimation in rotating mechanical systems using feature based fusion and self-organizing maps

The capability of Self-Organizing Maps (SOM) to visualize high-dimensional data is well known. The presented work concerns a SOM based diagnostic system architecture for the monitoring of fault evolution in bearings. Bearings form an essential part of rotating machinery and their failure is one of the most common causes of machine breakdowns. A SOM based approach has been used to map time series of feature data produced by acceleration sensors in order to capture the process dynamics. The fusion of specific features and the introduction of new features related to fault severity can enable the monitoring of fault evolution. The evolution of system states showing the bearing health trend has been shown to warn of impeding failure.

Automatic Detection of Different Harvesting Stages in Lettuce Plants by Using Chlorophyll Fluorescence Kinetics and Supervised Self Organizing Maps (SOMs)

Agriculture aims at increasing production and provision of high quality products to the market. Most of the times, quality is strongly correlated with harvesting stage of each product. Specifically, lettuce qualitative characteristics and nutrients appear to vary strongly in different development stages. In 46, 60 and 70 days of growth, the plants were harvested at baby, immature and mature stage. Then, the parameters of chlorophyll fluorescence were determined in two middle leaves of 3 plants of each hybrid at different harvest stage by using chlorophyll fluorescence kinetics. The measurements revealed significant differences between harvesting stages. The fluorescence parameters were utilized as inputs for training different models of supervised Self Organizing Maps (SOMs) aiming at the prediction of harvesting stage. It was shown that the prediction of different harvesting stages is h by supervised SOMs due to non-linearity nature of the problem which is owned to the heterogeneity of the fluorescence kinetics parameters.