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The aim of this study was to investigate the effect of a variety of phenolic type antioxidant additives on the microbial stability of biodiesel and diesel/biodiesel blends. Six synthetic phenolic type antioxidant agents were added in FAME at concentrations up to 1000 ppm. Treated FAME was also blended with Ultra Low Sulfur Diesel (ULSD) fuel at a concentration of 7% v/v in order to examine the activity of the substances in the final blends. The oxidation stability in the presence of the phenolic compounds was determined by carrying out measurements under accelerated oxidation process in the Rancimat unit. The effectiveness of those antioxidant agents against microbial contamination in biodiesel fuel was studied under certain testing protocols for detecting microbiological activity in the fuel supply chain and for evaluating antimicrobials against fuel bio-deterioration.

The fuel supply chain faces challenges associated with microbial contamination symptoms. Microbial growth is an issue usually known to be associated with middle distillate fuels and biodiesel, however, incidents where microbial populations have been isolated from unleaded gasoline storage tanks have also been recently reported. Alcohols are employed as gasoline components and the use of these oxygenates is rising, especially ethanol, which can be a renewable alternative to gasoline, as well. Despite their alleged disinfectant properties, a number of field observations suggests that biodeterioration could be a potential issue in fuel systems handling ethanol-blended gasoline. For this reason, in this study, the effect of alcohols on microbial proliferation in unleaded gasoline fuel was assessed. Ethanol (EtOH), iso-propyl alcohol (IPA) and tert-butyl-alcohol (TBA) were evaluated as examples of alcohols utilized in gasoline as oxygenates.

The system identification procedure is a powerful and flexible tool for the modeling of dynamic systems. This paper implements the theory of parametric identification in order to estimate a valid model of a flexible robotic arm. For this purpose experimental data is used for the estimation of ARMAX SISO models. A two-stages identification procedure (non-parametric & parametric) provides an insight about the system under identification. In the first stage, known signal analysis methods are applied (correlation-spectral analysis) for the estimation of frequencies and frequency response, and in the second stage, the estimation of ARMAX models is performed in order to fit a transfer function model to collected input-output data set. For the estimation of model's coefficients, use of Evolutionary Algorithms is implemented.

Contemporary vehicles have to satisfy high ride comfort criteria. In order to determine appropriate human body model in driving condition field and laboratory investigations on passenger cars have been performed. In field research, dominant vibrations loading of passenger was determined by variance analysis of experimental results. The highest loading is in vertical and the lowest is in lateral direction. These results were base for further laboratory investigations. Group of thirty volunteer subjects was tested. We examined the influence of broadband random vibrations on human body behavior by Seat To Head Transmissibility (STHT) function. The influence of vertical and fore and aft vibrations on occupants were examined separately. In addition to, the influence of multi directional vibration was investigated.