Several correlations have been proposed for bubble point pressure, however, the correlations could not predict bubble point pressure accurately over the wide range of operating conditions. This study presents Artificial Neural Network (ANN) model for predicting the bubble point pressure especially for oil fields in Iraq. The most affecting parameters were used as the input layer to the network. Those were reservoir temperature, oil gravity, solution gas-oil ratio and gas relative density. The model was developed using 104 real data points collected from Iraqi reservoirs. The data was divided into two groups: the first was used to train the ANN model, and the second was used to test the model to evaluate their accuracy and trend stability

The purpose of this study is to measure the levels of quality control for some crude oil products in Iraqi refineries, and how they are close to the international standards, through the application of statistical methods in quality control of oil products in Iraqi refineries. Where the answers of the study sample were applied to a group of Iraqi refinery employees (Al-Dora refinery, Al-Nasiriyah refinery, and Al-Basra refinery) on the principles of quality management control, and according to the different personal characteristics (gender, age, academic qualification, number of years of experience, job level). In order to achieve the objectives of the study, a questionnaire that included (12) items, in order to collect preliminary inform

With a goal to identify, and ultimately removing from the oil fraction, the carcinogenic components, an oil fraction oil has been analyzed into a main three hydrocarbon groups, paraffins, aromatics, and polycyclic saturates. A multi-stage adsorption apparatus has been used. Four units of 300 g alumina each seems to be sufficient for removing the polynuclear aromatics from 75 g of an oil fraction boiling between 365-375 °C from Qurna crude oil. The usefulness of the ternary diagram for analyzing the oil fraction to the three hydrocarbons groups has been studied and verified. An experimentally based linear relationship of density and refractive index was established to enable of identifying the composition of an oil fraction using th

This study investigates the characterization and growth dynamics of a Magnetically Stabilized Gliding Arc Discharge (MSGAD) system, generating non-thermal plasma with argon gas under atmospheric pressure and flow rates of 1-5 L/min. The electrical properties and growth patterns concerning gas flow rates and applied voltages were examined utilizing a magnetic field for stability. Using a digital oscilloscope, a correlation between voltage reduction and increased current was uncovered. An algorithm analyzes digital images to compute arc length, area, and volume. Results reveal how gas flow rate and applied voltage directly impact arc growth. Furthermore, the magnetic field's role in guiding and stabilizing the plasma discharge was explored. T

This work includes the synthesis and identification of ligand {3-((4-acetylphenyl)amino)-5,5-dimethylcyclohex2-en-1-one} (HL* ) by the treatment of 5,5-dimethylcyclohexane-1,3-dione with 4-aminoacetophenone under reflux. The ligand (HL* ) was identified via FTIR, Mass spectrum, elemental analysis (C.H.N.), 1H and 13C-NMR spectra, UV-Vis spectroscopy, TGA and melting point. The complexes were synthesized from ligand (HL* ) mixed with 3-aminophenol (A) and metal ion M(II), where M(II) = (Mn, Co, Ni, Cu, Zn and Cd) at alkaline medium to produce complexes of general formula [M(L* )(A)] with (1:1:1) molar ratio. These complexes were detected via FT-IR spectra, UV-Vis spectroscopy as well as elemental analysis (A.A) and melting point, conductivit