Azerbaijan State Oil Academy

About: Azerbaijan State Oil Academy is a education organization based out in Baku, Azerbaijan. It is known for research contribution in the topics: Fuzzy logic & Computer science. The organization has 195 authors who have published 225 publications receiving 2713 citations.

Experimental study of the effect of temperature, pressure, and concentration on the viscosity of aqueous SrCl2 solutions in the range of (293 to 473) K and (0.1 to 20) MPa

Abstract: The viscosity of five [(0.27, 0.58, 0.92, 1.73, and 2.63) mol·kg-1] binary aqueous SrCl2 solutions has been measured with a capillary flow technique. Measurements were made at pressures up to 20 MPa. The range of temperature was (293 to 473) K. The total uncertainties in viscosity, pressure, temperature, and composition measurements were estimated to be less than 1.6 %, 0.05 %, 15 mK, and 0.02 %, respectively. The effects of temperature, pressure, and concentration on the viscosity of SrCl2(aq) solutions were studied. The temperature and pressure coefficients of the viscosity of SrCl2(aq) were studied as a function of concentration and temperature. The measured values of viscosity were compared with data, predictions, and correlations reported in the literature. The viscosity data were used to accurately calculate the physical meaning parameters (viscosity A and B coefficients) in the extended Jones−Dole equation for the relative viscosity (η/η0). Various theoretical models [absolute rate theory, TTG (Tam...

Pressure- and Temperature-Dependent Density Change of Juices During Concentration

Abstract: Density of seven fruit juices (melon, plum, peach, black currants, cherry-plum, pear, and tangerine) have been measured at temperatures from 283 to 403 K and at pressures from 0.1 to 10 MPa for the concentrations of soluble solids from 10.7 to 70°Brix. Measurements were made with a hydrostatic weighing technique. The uncertainty of the density measurements was estimated to be less than 0.075%. The effect of temperature, pressure, and concentration on the fruit juice density was studied. The applicability and predictive capability of the various models for the density of fruit juices were studied. Various polynomials, power, exponential, logarithmic, and their combinations correlation models were used to represent the combined effect of temperature and concentration on the density. Models which represent the density of juice relative to pure water density were considered.

Experimental Vapor Pressures and Derived Thermodynamic Properties of Aqueous Solutions of Lithium Nitrate from 423 to 623 K

Abstract: Vapor pressures of six aqueous lithium nitrate solutions with molalities of (0.181, 0.526, 0.963, 1.730, 2.990, and 5.250) mol-kg−1 have been measured in the temperature range 423.15–623.15 K with a constant-volume piezometer immersed in a precision liquid thermostat. The static method was used to measure the vapor pressure. The total uncertainty of the temperature, pressure and composition measurements were estimated to be less than 15 mK, 0.2%, and 0.014%, respectively. The vapor pressures of pure water were measured with the same apparatus and procedure to confirm the accuracy of the method used for aqueous lithium nitrate solutions. The results for pure water were compared with high-accuracy PS–TS data calculated from the IAPWS standard equation of state. Important thermodynamic functions (activities of water and lithium nitrate, partial molar volumes, osmotic coefficient, excess relative partial molar entropy, and relative partial molar enthalpy values of the solvent) were derived using the measured values of vapor pressure for the solution and pure water. The measured and derived thermodynamic properties for solutions were compared with data reported in the literature. The present results are consistent with most previous reported thermodynamic data for the pure water and H2O + LiNO3 solutions at low temperatures.

An effective method for increasing the activity of nickel boride catalyst nano-particles in hydrogenation reactions: Low-temperature hydrogen treatment

Abstract: It is shown that low temperature (<100 °C) hydrogen treatment of nano nickel boride catalysts is an efficient process for the enhancement of their activity in the p-nitrophenol (PNP) to p-aminophenol (PAP) hydrogenation reaction. It has been shown that such a process excludes initial borate species present on the surface and within the catalyst nano-particles by promoting their dissolution in the liquid phase. The latter phenomenon is enhanced by increasing the temperature. Treatment in the absence of hydrogen results in no reaction rate enhancement. Instead, the activity falls significantly below that of the as-synthesized catalyst. The effect of hydrogen treatment on the catalyst physical properties was investigated using FTIR, XRD, nitrogen adsorption and FESEM analysis. Mathematical simulation of the PNP hydrogenation reaction rate supports the hypothesis that the rate enhancement is mainly due to the increase of the catalyst specific surface area and partial reduction of surface nickel oxide species. The mechanism by which hydrogen increases the specific surface is discussed.

Recurrent Fuzzy Neural Network Based System for Battery Charging

Abstract: Consumer demand for intelligent battery charges is increasing as portable electronic applications continue to grow. Fast charging of battery packs is a problem which is difficult, and often expensive, to solve using conventional techniques. Conventional techniques only perform a linear approximation of a nonlinear behavior of a battery packs. The battery charging is a nonlinear electrochemical dynamic process and there is no exact mathematical model of battery. Better techniques are needed when a higher degree of accuracy and minimum charging time are desired. In this paper we propose soft computing approach based on fuzzy recurrent neural networks (RFNN) training by genetic algorithms to control batteries charging process. This technique does not require mathematical model of battery packs, which are often difficult, if not impossible, to obtain. Nonlinear and uncertain dynamics of the battery pack is modeled by recurrent fuzzy neural network. On base of this FRNN model, the fuzzy control rules of the control system for battery charging is generated. Computational experiments show that the suggested approach gives least charging time and least T end -T start results according to the other intelligent battery charger works.