A new nano-sized NiMo/TiO2-γ-Al2O3 was prepared as a Hydrodesulphurization catalyst for Iraqi gas oil with sulfur content of 8980 ppm, supplied from Al-Dura Refinery. Sol-gel method was used to prepare TiO2- γ-Al2O3 nano catalyst support with 64% TiO2, 32% Al2O3, Ni-Mo/TiO-γ-Al2O3 catalyst was prepared under vacuum impregnation conditions to loading metals with percentage 3.8 wt.% and 14 wt.% for nickel and molybdenum respectively while the percentage for alumina, and titanium became 21.7, and 58.61 respectively. The synthesized TiO2- γ-Al2O3 nanocomposites and Ni-Mo /TiO2- γ-Al2O3 Nano catalyst were then characterized by XRD, AFM, and BET surface area, SEM, XRF, and FTIR. The performance of the synthesized catalyst for removing sulfur compounds was conducted through the pilot HDS laboratory unit, various temperatures range 275oC to 375°C, LHSV 1 h-1 were studied; moreover, the effect of LHSV 1 to 4 h-1 on the percentage of sulfur removal was also studied at the temperature of the best removal with constant pressure 35 bar and H2/HC ratio 200cm3/200cm3. The sulfur content results generally revealed that there was a substantial decrease at all operating conditions used, while the maximum sulfur removal was 87.75% in gas oil on Ni-Mo/TiO2-γ-Al2O3 catalyst at temperature 375˚C and LHSV 1h-1.
TiO2 thin films have been deposited at different concentration of
CdO of (x= 0.0, 0.05, 0.1, 0.15 and 0.2) Wt. % onto glass substrates
by pulsed laser deposition technique (PLD) using Nd-YAG laser
with λ=1064nm, energy=800mJ and number of shots=500. The
thickness of the film was 200nm. The films were annealed to
different annealing (423 and 523) k. The effect of annealing
temperatures and concentration of CdO on the structural and
photoluminescence (PL) properties were investigated. X-ray
diffraction (XRD) results reveals that the deposited TiO2(1-x)CdOx
thin films were polycrystalline with tetragonal structure and many
peaks were appeared at (110), (101), (111) and (211) planes with
preferred orientatio
Transition metal complexes of Y(III), La(III) and Rh(III) with azo dye 2,4-dimethyl-6- (4-nitro-phenylazo)-phenol derived from 4-nitroaniline and 2,4-dimethylphenol were synthesized. Characterization of these compounds has been done on the basis of elemental analysis, electronic data, FT-IR,UV-Vis and 1HNMR, as well as conductivity measurements. The nature of the complexes formed were studies following the mole ratio and continuous variation methods, Beer's law obeyed over a concentration range (1x10-4- 3x10-4). High molar absorbtivity of the complex solutions were observed. From the analytical data, the stoichiomerty of the complexes has been found to be 1:3 (Metal:ligand). On the basis of Physicochemical data octahedral geometries were as
... Show MoreThe principal components analysis is used in analyzing many economic and social phenomena; and one of them is related to a large group in our society who are the university instructors. This phenomenon is the delay occurred in getting university instructor to his next scientific title. And as the determination of the principal components number inside the principal components depends on using many methods, we have compared between three of these methods that are: (BARTLETT, SCREE DIAGRAM, JOLLIFFE).
We concluded that JOLLIFFE method was the best one in analyzing the studying phenomenon data among these three methods, we found the most distinguishing factors effecting on t
... Show MoreThe role of the climate in the development of the performance of the administrative bodies of sports clubs
Prediction of penetration rate (ROP) is important process in optimization of drilling due to its crucial role in lowering drilling operation costs. This process has complex nature due to too many interrelated factors that affected the rate of penetration, which make difficult predicting process. This paper shows a new technique of rate of penetration prediction by using artificial neural network technique. A three layers model composed of two hidden layers and output layer has built by using drilling parameters data extracted from mud logging and wire line log for Alhalfaya oil field. These drilling parameters includes mechanical (WOB, RPM), hydraulic (HIS), and travel transit time (DT). Five data set represented five formations gathered
... Show MoreNimodipine (NMD) is a dihydropyridine calcium channel blocker useful for the prevention and treatment of delayed ischemic effects. It belongs to class ? drugs, which is characterized by low solubility and high permeability. This research aimed to prepare Nimodipine nanoparticles (NMD NPs) for the enhancement of solubility and dissolution rate. The formulation of nanoparticles was done by the solvent anti-solvent technique using either magnetic stirrer or bath sonicator for maintaining the motion of the antisolvent phase. Five different stabilizers were used to prepare NMD NPs( TPGS, Soluplus®, HPMC E5, PVP K90, and poloxamer 407). The selected formula F2, in which Soluplus
has been utilized as a stabilizer, has a par
... Show MoreIn this study, titanium dioxide (TiO2) nanoparticles incorporated with cement were synthesis by a simple casting method as a function concentration of TiO2 (0.2, 0.4, 0.8, 1, and 2 wt%). The prepared samples were characterized using the technique of Field Emission Scanning Electron Microscope (FESEM) and UV-Visible spectrophotometer, which was used to measure the adsorption spectra. The observed photocatalytic efficiency of TiO2 nanoparticles (NP) incorporated with cement was investigated by decomposing the dye methyl blue (MB) solution under sunlight irradiation. According to the slope, the value of the k constant at the best sample is 0.8wt%, k=0.8265 min-1. FESEM image of the TiO2
... Show MoreThis paper proposes a new structure of the hybrid neural controller based on the identification model for nonlinear systems. The goal of this work is to employ the structure of the Modified Elman Neural Network (MENN) model into the NARMA-L2 structure instead of Multi-Layer Perceptron (MLP) model in order to construct a new hybrid neural structure that can be used as an identifier model and a nonlinear controller for the SISO linear or nonlinear systems. Weight parameters of the hybrid neural structure with its serial-parallel configuration are adapted by using the Back propagation learning algorithm. The ability of the proposed hybrid neural structure for nonlinear system has achieved a fast learning with minimum number
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