This work was conducted to study the extraction of eucalyptus oil from natural plants (Eucalyptus camadulensis leaves) by organic solvents. the effects of the main operating parameters were studied; type of solvent (n-hexane and ethanol), time to reach equilibrium, the temperature (45°C to 65°C) for n-hexane and (45°C to 75°C) for ethanol, solvent to solid ratio (5:1 to 8:1 (v/w)), agitation speed (0 to 900 rpm) and the particle size (0.5 to 2.5 cm) of fresh leaves to find the best processing conditions for the achieving maximum oil yield. The concentration of eucalyptus oil in solvent was measured by using UV-spectrophotometer. The results (for n-hexane) showed that the agitation speed of 900 rpm, temperature 65°C with solvent to soli

An experimental work has been done to study the major factors that affect the axial dispersion of some hydrocarbons during liquid-liquid miscible displacement. Kerosene and gas oil are used as displacing phase while seven liquid hydrocarbons of high purity represent the displaced phase, three of the liquids are aromatics and the rest are of paraffinic base. In conducting the experiments, two packed beds of different porosity and permeability are used as porous media.

The results showed that the displacement process is not a piston flow, breakthrough of displacing fluids are shown before one pore volume has been injected. The processes are stable with no evidence of viscous fingering.

Dispersion model as a

 The logistic regression model of the most important regression models a non-linear which aim getting estimators have a high of efficiency, taking character more advanced in the process of statistical analysis for being a models appropriate form of Binary Data.                                                          

Among the problems that appear as a result of the use of some statistical methods I

Hydrocarbon displacement at the pore scale is mainly controlled by the wetness properties of the porous media. Consequently, several techniques including nanofluid flooding were implemented to manipulate the wetting behavior of the pore space in oil reservoirs. This study thus focuses on monitoring the displacement of oil from artificial glass porous media, as a representative for sandstone reservoirs, before and after nanofluid flooding. Experiments were conducted at various temperatures (25 – 50° C), nanoparticles concentrations (0.001 – 0.05 wt% SiO2 NPs), salinity (0.1 – 2 wt% NaCl), and flooding time. Images were taken via a high-resolution microscopic camera and analyzed to investigate the displacement of the oil at dif

Hydrocarbon displacement at the pore scale is mainly controlled by the wetness properties of the porous media. Consequently, several techniques including nanofluid flooding were implemented to manipulate the wetting behavior of the pore space in oil reservoirs. This study thus focuses on monitoring the displacement of oil from artificial glass porous media, as a representative for sandstone reservoirs, before and after nanofluid flooding. Experiments were conducted at various temperatures (25 – 50° C), nanoparticles concentrations (0.001 – 0.05 wt% SiO₂ NPs), salinity (0.1 – 2 wt% NaCl), and flooding time. Images were taken via a high-resolution microscopic camera and analyzed to investigate the displacement of the oil

The toxicity effect of some heavy metals (Lead, Cadmium, Copper, and Zinc) on the growth of alga Scenedesmus dimorphus which belongs to the Division of Chlorophyta was studied and depended on the total cell number . The growth rate and doubling time were also calculated accordingly in present of absent of the the heavy metals . There were differences in toxic effects of the metals (p<0.05) . The growth was decreased gradually with alga when exposured to Lead at 15,20 and 25 mg/l in comparison with the control , mean while 30 mg/l caused an acute decrease in growth . Treating the alga with 0.05,0.1,0.5 mg/l concentration of Cadmium the number of cells decreased while at 1 mg/l the effect was more pronounced . As for Copper the conc

The ionospheric characteristics exhibit significant variations with the solar cycle, geomagnetic conditions, seasons, latitudes and even local time. Representation of this research focused on global distribution of electron (Te) and ion temperatures (Ti) during great and severe geomagnetic storms (GMS), their daily and seasonally variation for years (2001-2013), variations of electron and ion temperature during GMS with plasma velocity and geographic latitudes. Finally comparison between observed and predicted Te and Ti get from IRI model during the two kinds of storm selected. Data from satellite Defense Meteorological Satellite Program (DMSP) 850 km altitude are taken for Te, Ti and plasma velocity for different latitudes during great

The influence of adding metal foam fins on the heat transfer characteristics of an air to water double pipe heat exchanger is numerically investigated. The hot fluid is water which flows in the inner cylinder whereas the cold fluid is air which circulates in the annular gap in parallel flow with water. Ten fins of metal foam (Porosity = 0.93), are added in the gap between the two cylinder, and distributed periodically with the axial distance. Finite volume method is used to solve the governing equations in porous and non-porous regions. The numerical investigations cover three values for Reynolds number (1000 ,1500, 2000), and Darcy number (1 x10^-1, 1 x10^-2, 1x10^-3). The comparison betwee

Image compression plays an important role in reducing the size and storage of data while increasing the speed of its transmission through the Internet significantly. Image compression is an important research topic for several decades and recently, with the great successes achieved by deep learning in many areas of image processing, especially image compression, and its use is increasing Gradually in the field of image compression. The deep learning neural network has also achieved great success in the field of processing and compressing various images of different sizes. In this paper, we present a structure for image compression based on the use of a Convolutional AutoEncoder (CAE) for deep learning, inspired by the diversity of human eye