R. Milina, Z. Mustafa, D. Bojilov, S. Dagnon and M. Moskovkina
Pattern recognition method (PRM) was applied to gas chromatographic (GC) data for a fatty acid methyl esters (FAME) composition of commercial and laboratory synthesized biodiesel fuels from vegetable oils including sunflower, rapeseed, corn and palm oils. Two GC quantitative methods to calculate individual fames were compared: Area % and internal standard. The both methods were applied for analysis of two certified reference materials. The statistical processing of the obtained results demonstrates the accuracy and precision of the two methods and allows them to be compared. For further chemometric investigations of biodiesel fuels by their FAME-profiles any of those methods can be used. PRM results of FAME profiles of samples from different vegetable oils show a successful recognition of biodiesels according to the feedstock. The information obtained can be used for selection of feedstock to produce biodiesels with certain properties, for assessing their interchangeability, for fuel spillage and remedial actions in the environment.
Corrosion is the cause of irretrievable loss of huge amounts of metals and alloys. The harmful effects of corrosion can be reduced significantly by applying appropriate methods of corrosion protection. One method to protect metals against corrosion is the formation of diffusion coatings on them. High corrosion resistance is typical for the boride diffusion layers. Aluminothermy is one of the main methods for diffusion saturation of the surface of metal products with various elements, including boron, and under certain conditions with aluminum, too. Samples of steel 45 were put to aluminothermic diffusion saturation with boron in a pressurized steel container at a temperature of 1100K, for 6 hours in powdered aluminothermic mixtures. The content of В2О3 in the starting mixtures decreased from the optimum - 20% to 0%, and the content of Al and the activator - (NH4)2.4BF3 is constant, respectively 7% and 0.5%. Al2O3 was used as filler. The borided samples were tested for corrosion resistance in 10% HCl for 72 hours. The results show that their corrosion resistance depends on the composition of the starting saturating mixture (mainly on the content of В2О3), and respectively on the composition, structure, thickness and degree of adhesion of the layer to the metal base.
The effects of the carbon and nitrogen substrates on the growth of Bacillus sp. SG113 strain were studied. The use of organic nitrogen sources (peptone, beef extract, yeast extract, casein) leads to rapid cellular growth and the best results for the Bacillus strain were obtained with casein hydrolysate. From the inorganic nitrogen sources studied, the (NH4) 2SO4 proved to be the best nitrogen source. Casein hydrolysate and (NH4) 2SO4 stimulated the invertase synthesis. In the presence of Jerusalem artichoke, onion and garlic extracts as carbon sources the strain synthesized from 6 to 10 times more inulinase.
Bacterial EPSs (exopolysaccharides) are believed to play an important role in the environment by promoting survival strategies such as bacterial attachment to surfaces and nutrient trapping, which facilitate processes of biofilm formation and development. These microbial biofilms have been implicated in corrosion of metals, bacterial attachment to prosthetic devices, fouling of heat exchange surfaces, toxicant immobilization, and fouling of ship hulls. In this paper, data on EPS production and the effect of EPS on corrosion of steel produced by Lactobacillus sp. are presented and discussed. Lactobacillus delbrueckii K27, Lactobacillus delbrueckii B8, Lactobacillus delbrueckii KO43, Lactobacillus delbrueckii K3, Lactobacillus delbrueckii K15 and Lactobacillus delbrueckii K17 was obtained from Collection of Department of General and Applied Microbiology, Sofia University. It was tested for its ability to produce exopolysaccharides when cultivated in a media containing 10% sucrose, 10% lacose and 10% maltose. The study of the corrosive stability of steel samples was conducted on the gravimetrique method. The rate of corrosion, the degree of protection, and coefficient of protection have been calculated. The structure of layer over steel plates was analysed by SEM (scanning electron microscopy) JSM 5510. It could be underlined that 10% sucrose, 10% lactose and 10% maltose in the media stimulated the process of protection of corrosion.
Nikoleta Yancheva, Daniela Markova, Dilyana Murdzheva, Ivelina Vasileva and Anton Slavov
The foaming and emulsifying properties of pectins obtained from waste rose petals, citrus pressings, grapefruit peels and celery were studied. It was found that the highest foaming capacity showed pectin derived from celery. The effect of pectin concentration on the foaming capacity of pectin solutions was investigated. For all the investigated pectins increasing the concentration led to increase of the foaming capacity. Emulsifying activity and emulsion stability of model emulsion systems (50 % oil phase) with 0.6 % pectic solutions were determined. The highest emulsifying activity and stability showed pectin isolated by dilute acid extraction from waste rose petals.
T. Petkov, Z. Mustafa, S. Sotirov, R. Milina and M. Moskovkina
A chemometric approach using artificial neural network for clusterization of biodiesels was developed. It is based on artificial ART2 neural network. Gas chromatography (GC) and Gas Chromatography - mass spectrometry (GC-MS) were used for quantitative and qualitative analysis of biodiesels, produced from different feedstocks, and FAME (fatty acid methyl esters) profiles were determined. Totally 96 analytical results for 7 different classes of biofuel plants: sunflower, rapeseed, corn, soybean, palm, peanut, “unknown” were used as objects. The analysis of biodiesels showed the content of five major FAME (C16:0, C18:0, C18:1, C18:2, C18:3) and those components were used like inputs in the model. After training with 6 samples, for which the origin was known, ANN was verified and tested with ninety “unknown” samples. The present research demonstrated the successful application of neural network for recognition of biodiesels according to their feedstock which give information upon their properties and handling.