Browsing by Author "Simeonov S."

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    Assessment of Nd2-xSrxNIO4-d as a cathodic material for solid oxide fuel cell applications
    (2013-05-21) Simeonov S.; Kozhukharov S.; Grenier J.; Machkova M.; Kozhukharov V.
    The features and performance of each device and equipment is directly related to its composing materials. The efficiency and durability of the Solid Oxide Fuel Cells (SOFC) also depend on their composing elements. Other important factors for the SOFC behavior are the working conditions (compositions, and humidity of the fuel and oxidant gaseous mixtures, temperature, pressure, etc.). These facts predetermine the necessity for detailed investigation of the behavior of each composing layer of SOFC. For this reason, the electrical characteristics of Nd2-xSrxNiO4-d as SOFC cathodic material were evaluated by high temperature electrochemical methods, durability tests, and posterior XRD and SEM characterization. As a result, the optimal composition and working conditions for the investigated material in assembled SOFC were determined. The obtained material reveals excellent durability and compatibility with the rest SOFC components.
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    Correlations of HTSD to TBP and Bulk Properties to Saturate Content of a Wide Variety of Crude Oils
    (2023-02-01) Stratiev D.; Dinkov R.; Tavlieva M.; Shishkova I.; Nikolov Palichev G.; Ribagin S.; Atanassov K.; Stratiev D.D.; Nenov S.; Pilev D.; Sotirov S.; Sotirova E.; Simeonov S.; Boyadzhieva V.
    Forty-eight crude oils with variations in specific gravity (0.782 ≤ SG ≤ 1.002), sulphur content (0.03 ≤ S ≤ 5.6 wt.%), saturate content (23.5 ≤ Sat. ≤ 92.9 wt.%), asphaltene content (0.1 ≤ As ≤ 22.2 wt.%), and vacuum residue content (1.4 ≤ VR ≤ 60.7 wt.%) were characterized with HTSD, TBP, and SARA analyses. A modified SARA analysis of petroleum that allows for the attainment of a mass balance ≥97 wt.% for light crude oils was proposed, a procedure for the simulation of petroleum TBP curves from HTSD data using nonlinear regression and Riazi’s distribution model was developed, and a new correlation to predict petroleum saturate content from specific gravity and pour point with an average absolute deviation of 2.5 wt.%, maximum absolute deviation of 6.6 wt.%, and bias of 0.01 wt.% was developed. Intercriteria analysis was employed to evaluate the presence of statistically meaningful relations between the different petroleum properties and to evaluate the extent of similarity between the studied petroleum crudes. It was found that the extent of similarity between the crude oils based on HTSD analysis data could be discerned from data on the Kw characterization factor of narrow crude oil fractions. The results from this study showed that contrary to the generally accepted concept of the constant Kw characterization factor, the Kw factors of narrow fractions differ from that of crude oil. Moreover, the distributions of Kw factors of the different crudes were different.
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    Prediction of Molecular Weight of Petroleum Fluids by Empirical Correlations and Artificial Neuron Networks
    (2023-02-01) Stratiev D.; Sotirov S.; Sotirova E.; Nenov S.; Dinkov R.; Shishkova I.; Kolev I.V.; Yordanov D.; Vasilev S.; Atanassov K.; Simeonov S.; Palichev G.N.
    The exactitude of petroleum fluid molecular weight correlations affects significantly the precision of petroleum engineering calculations and can make process design and trouble-shooting inaccurate. Some of the methods in the literature to predict petroleum fluid molecular weight are used in commercial software process simulators. According to statements made in the literature, the correlations of Lee–Kesler and Twu are the most used in petroleum engineering, and the other methods do not exhibit any significant advantages over the Lee–Kesler and Twu correlations. In order to verify which of the proposed in the literature correlations are the most appropriate for petroleum fluids with molecular weight variation between 70 and 1685 g/mol, 430 data points for boiling point, specific gravity, and molecular weight of petroleum fluids and individual hydrocarbons were extracted from 17 literature sources. Besides the existing correlations in the literature, two different techniques, nonlinear regression and artificial neural network (ANN), were employed to model the molecular weight of the 430 petroleum fluid samples. It was found that the ANN model demonstrated the best accuracy of prediction with a relative standard error (RSE) of 7.2%, followed by the newly developed nonlinear regression correlation with an RSE of 10.9%. The best available molecular weight correlations in the literature were those of API (RSE = 12.4%), Goosens (RSE = 13.9%); and Riazi and Daubert (RSE = 15.2%). The well known molecular weight correlations of Lee–Kesler, and Twu, for the data set of 430 data points, exhibited RSEs of 26.5, and 30.3% respectively.