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An Experimental Determination of Gross Calorific Value of Different Agroforestry Species and Bio-Based Industry Residues

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Natural Resources 2016年第1期7卷 57-68页

作者： Ioannis Gravalos Panagiotis Xyradakis dimitrios kateris Theodoros Gialamas dimitrios Bartzialis Kyriakos Giannoulis Department of Biosystems Engineering School of Agricultural Technology Technological Educational Institute of Thessaly Larissa Greece Laboratory of Agronomy and Applied Crop Physiology Department of Agriculture Crop Production & Rural Environment University of Thessaly Volos Greece

Solid biomass fuels are useful and cost effective renewable energy source. The energy content of biomass is determined by its calorific value. The objective of this study was to determine experimentally the gross calorific value (GCV) of different agroforestry species and bio-based industry residues that could be used by: a) companies specialized in processing raw biomass solid biofuel production, b) small-scale consumers (households, medium-sized residential buildings, etc.). The fuel samples used were from agricultural residues and wastes (rice husks, apricot kernels, olive pits, sunflower husks, cotton stems, etc.), energy crops and wetland herbs (cardoon, switchgrass, common reed, narrow-leaf cattail), and forest residues (populus, fagus, pinus). The GCV of the bio-mass samples was experimentally determined based on CEN/TS 14918:2005, and an oxygen bomb calorimeter was used (Model C5000 Adiabatic Calorimeter, IKA?-Werke, Staufen, Germany). The GCV of different agroforestry species and residues ranges from 14.3 - 25.4 MJ?kg–1. The highest GCV was obtained by seeds and kernels due to higher unit mass and higher lipid content. Pinus sylvestris with moisture content 24.59% obtained the lowest GCV (13.973 MJ?kg–1).

关键词： Gross Calorific Value Bomb Calorimeter Biomass Bioenergy Agroforestry Residues

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Fault Detection of Fuel Injectors Based on One-Class Classifiers

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Modern Mechanical Engineering 2014年第1期4卷 19-27页

作者： dimitrios Moshou Athanasios Natsis dimitrios kateris Xanthoula-Eirini Pantazi Ioannis Kalimanis Ioannis Gravalos Agricultural Engineering Laboratory Aristotle University of Thessaloniki Thessaloniki Greece Department of Biosystems Engineering Technological Educational Institute of Thessaly Larissa Greece Department of Exploitation of Natural Resources and Agricultural Mechanics Agricultural University of Athens Athens Greece

Fuel injectors are considered as an important component of combustion engines. Operational weakness can possibly lead to the complete machine malfunction, decreasing reliability and leading to loss of production. To overcome these circumstances, various condition monitoring techniques can be applied. The application of acoustic signals is common in the field of fault diagnosis of rotating machinery. Advanced signal processing is utilized for the construction of features that are specialized in detecting fuel injector faults. A performance comparison between novelty detection algorithms in the form of one-class classifiers is presented. The one-class classifiers that were tested included One-Class Support Vector Machine (OCSVM) and One-Class Self Organizing Map (OCSOM). The acoustic signals of fuel injectors in different operational conditions were processed for feature extraction. Features from all the signals were used as input to the one-class classifiers. The one-class classifiers were trained only with healthy fuel injector conditions and compared with new experimental data which belonged to different operational conditions that were not included in the training set so as to contribute to generalization. The results present the effectiveness of one-class classifiers for detecting faults in fuel injectors.

关键词： Fuel Injectors Fault Detection Acoustics Neural Networks One-Class Classifiers

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