• Structural Monitoring and Maintenance
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• v.3 no.4
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• pp.377-393
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• 2016

The present work develops an expert system for detecting and predicting the crude oil types and properties at normal temperature ${\theta}=25^{\circ}C$, by evaluating the dielectric properties of the fluid transfused inside glass fiber reinforced epoxy (GFRE) composite pipelines, by using electrical capacitance sensor (ECS) technique, then used the data measurements from ECS to predict the types of the other crude oil transfused inside the pipeline, by designing an efficient artificial neural network (ANN) architecture. The variation in the dielectric signatures are employed to design an electrical capacitance sensor (ECS) with high sensitivity to detect such problem. ECS consists of 12 electrodes mounted on the outer surface of the pipe. A finite element (FE) simulation model is developed to measure the capacitance values and node potential distribution of ECS electrodes by ANSYS and MATLAB, which are combined to simulate sensor characteristic. Radial Basis neural network (RBNN), structure is applied, trained and tested to predict the finite element (FE) results of crude oil types transfused inside (GFRE) pipe under room temperature using MATLAB neural network toolbox. The FE results are in excellent agreement with an RBNN results, thus validating the accuracy and reliability of the proposed technique.

• Journal of Microbiology and Biotechnology
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• v.18 no.5
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• pp.815-820
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• 2008

Two culture-independent methods, namely ribosomal DNA libraries and denaturing gradient gel electrophoresis (DGGE), were adopted to examine the microbial community of a Malaysian light crude oil. In this study, both 16S and 18S rDNAs were PCR-amplified from bulk DNA of crude oil samples, cloned, and sequenced. Analyses of restriction fragment length polymorphism (RFLP) and phylogenetics clustered the 16S and 18S rDNA sequences into seven and six groups, respectively. The ribosomal DNA sequences obtained showed sequence similarity between 90 to 100% to those available in the GenBank database. The closest relatives documented for the 16S rDNAs include member species of Thermoincola and Rhodopseudomonas, whereas the closest fungal relatives include Acremonium, Ceriporiopsis, Xeromyces, Lecythophora, and Candida. Others were affiliated to uncultured bacteria and uncultured ascomycete. The 16S rDNA library demonstrated predomination by a single uncultured bacterial type by >80% relative abundance. The predomination was confirmed by DGGE analysis.

• Journal of Microbiology and Biotechnology
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• v.8 no.6
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• pp.645-649
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• 1998

A glycolipid type of biosurfactants was obtained from a strain which had been isolated from soil. The cell was identified as Pseudomonas aeruginosa from taxonomic characteristics and was designated as YPJ80. Thin layer chromatography and deoxyhexose detection tests were done to verify the type of biosurfactant. Critical micelle concentration (CMC) of the surfactant was observed to be 50 ppm and the minimum surface tension was 30.1 mN/m. As an emulsifier, YPJ80 biosurfactant was superior to emulsan in the emulsification of crude Arabian light oil.

• Journal of the Korean Society for Marine Environment & Energy
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• v.18 no.4
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• pp.304-309
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• 2015

To evaluate the degree of contamination caused by oil spill accident in the sea, the in-situ sensors which are based on the scientific method are needed in the real site. The sensors which are based on the fluorescence detection theory can provide the useful data, such as the concentration of oil. However these kinds of sensors commonly are composed of the ultraviolet (UV) light source such as UV mercury lamp, the multiple excitation/emission filters and the optical sensor which is mainly photomultiplier tube (PMT) type. Therefore, the size of the total sensing platform is large not suitable to be handled in the oil spill field and also the total price of it is extremely expensive. To overcome these drawbacks, we designed the fluorimeter for the oil spill detection which has compact size and cost effectiveness. Before the detail design process, we conducted the experiments to measure the excitation and emission spectrum of oils using five different kinds of crude oils and three different kinds of processed oils. And the fluorescence spectrometer were used to analyze the excitation and emission spectrum of oil samples. We have compared the spectrum results and drawn the each common spectrum regions of excitation and emission. In the experiments, we can see that the average gap between maximum excitation and emission peak wavelengths is near 50 nm for the every case. In the experiment which were fixed by the excitation wavelength of 365 nm and 405 nm, we can find out that the intensity of emission was weaker than that of 280 nm and 325 nm. So, if the light sources having the wavelength of 365 nm or 405 nm are used in the design process of fluorimeter, the optical sensor needs to have the sensitivity which can cover the weak light intensity. Through the results which were derived by the experiment, we can define the important factors which can be useful to select the effective wavelengths of light source, photo detector and filters.