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Prediction of Hydrate Phase Equilibrium Conditions for Different Gas Mixtures
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Abstract<p>Gas hydrate formation poses a significant threat to the production, processing, and transportation of natural gas. Accurate predictions of gas hydrate equilibrium conditions are essential for designing the gas production systems at safe operating conditions and mitigating the problems caused by hydrates formation. A new hydrate correlation for predicting gas hydrate equilibrium conditions was obtained for different gas mixtures containing methane, nitrogen and carbon dioxide. The new correlation is proposed for a pressure range of 1.7-330 MPa, a temperature range of 273-320 K, and for gas mixtures with specific gravity range of 0.553 to 1. The nonlinear regression technique was applied to develop the correlation based on 142 experimental data points collected from literature, validated with 85 data points not used for developing the correlation. The statistical parameters analysis showed an average absolute error (AAPE) of 0.2183, a squared correlation coefficient (R2) of 0.9978 and standard deviation (SD) of 0.2483. In addition, comparing the new correlation results with the experimental data and with those calculated by other correlations show an excellent performance for the investigated range.</p>
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Publication Date
Fri Oct 01 2021
Journal Name
Journal Of Engineering
The Use of SBS-Modified Binder to Eliminate the Aggregate Gradation Deviation Effects in Asphalt Mixtures
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Asphalt Hot Mix (HMA) is mainly applied in highway construction in Iraq because of its economic advantage and easy maintenance. Various factors impact the performance of HMA in the field. It is one of the significant impacts on aggregate gradation. The Universal Specification for Roads and Bridges in Iraq (SCRB) limits the different types of asphalt layers and allows for designed tolerance aggregate gradation. It is quite hard for contractors in the present asphalt industries to achieve the required job mix because of sieves' control problems. This study focuses on the effects on the required specification performance of aggregate deviations by using original and modified asphalt binder with AC(40-50) and

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Publication Date
Sat Apr 01 2023
Journal Name
Journal Of Engineering
Moisture Susceptibility of Hot Mix Asphalt Mixtures Modified by Nano Silica and Subjected to Aging Process
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Moisture damage is described as a reduction in stiffness and strength durability in asphalt mixtures due to moisture. This study investigated the influence of adding nano silica (NS) to the Asphalt on the moisture susceptibility of hot-mix-asphalt (HMA) mixtures under different aging conditions. NS was mixed with asphalt binder at concentrations of 2%, 4%, and 6% by weight of the binder. To detect the microstructure changes of modified Asphalt and estimate the dispersion of NS within the Asphalt, the field emission scanning electron microscope (FE-SEM) was used. To examine the performance of Asphalt mixed with NS at different aging stages (short-term and long-term aging), asphalt mixture tests such as Marshall stability,

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Publication Date
Thu Jan 03 2019
Journal Name
International Journal Of Civil Engineering And Technology (ijciet)
Condition Prediction Models of Deteriorated Trunk Sewer Using Multinomial Logistic Regression and Artificial Neural Network
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Sewer systems are used to convey sewage and/or storm water to sewage treatment plants for disposal by a network of buried sewer pipes, gutters, manholes and pits. Unfortunately, the sewer pipe deteriorates with time leading to the collapsing of the pipe with traffic disruption or clogging of the pipe causing flooding and environmental pollution. Thus, the management and maintenance of the buried pipes are important tasks that require information about the changes of the current and future sewer pipes conditions. In this research, the study was carried on in Baghdad, Iraq and two deteriorations model's multinomial logistic regression and neural network deterioration model NNDM are used to predict sewers future conditions. The results of the

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Publication Date
Thu Mar 31 2016
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Permeability Prediction in One of Iraqi Carbonate Reservoir Using Hydraulic Flow Units and Neural Networks
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Permeability determination in Carbonate reservoir is a complex problem, due to their capability to be tight and heterogeneous, also core samples are usually only available for few wells therefore predicting permeability with low cost and reliable accuracy is an important issue, for this reason permeability predictive models become very desirable.

   This paper will try to develop the permeability predictive model for one of  Iraqi carbonate reservoir from core and well log data using the principle of Hydraulic Flow Units (HFUs). HFU is a function of Flow Zone Indicator (FZI) which is a good parameter to determine (HFUs).

   Histogram analysis, probability analysis and Log-Log plot of Reservoir Qua

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Publication Date
Thu Jan 31 2019
Journal Name
Journal Of Engineering
Experimental Study of Solar Still Under Influence of Various Conditions
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In the present work, experimental tests was done to explain the effect of insulation and water level on the yield output. Linear basin, single slope solar still used to do this purpose. The test was done from May to August 2017 in Mosul City-Iraq (Latitude: Longitude: Elevation: 200 m, and  South-East face). Experimental results showed that the yield output of the still increased by 20.785% and 19.864% in case of using thermal insulation at 4cm and 5cm respectively, also the yield output decrease by 15.134% as the water level increase from 4 to 5cm, with the presence of insulation and 14.147% without it. It has been conclude that the insulation and water level play important role in the process of passive

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Crossref
Publication Date
Tue Sep 01 2020
Journal Name
Rawal Medical Journal
HOMA-IR and TyG Index as acute phase reactants in obese and lean subjects
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Objective: To evaluate biomarkers of insulin resistance and acute phase reactants in obese, overweight and lean subjects and investigate the associations among those biomarkers. Methodology: This cross sectional study included 158 subjects who were stratified into three groups. Group1 comprised 53 obese patients (BMI≥ 30 kg/m2); group 2 were 54 overweight subjects, and group 3 had 51 healthy lean (BMI

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Publication Date
Thu Dec 23 2010
Journal Name
Arabian Journal Of Geosciences
Climatic prediction of the terrestrial and coastal areas of Iraq
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Crossref (2)
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Publication Date
Mon Jan 20 2020
Journal Name
Ibn Al-haitham Journal For Pure And Applied Sciences
Effect of Composition and Dielectric Properties for (YBCO) Superconductor Compound in Different Preparation Methods
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     The superconductor compound (YBa2Cu2.8Zn0.2O7+δ) is prepared by solid state reaction (SSR), Sol-gel (SG) and laser Pulse deposition (PLD) methods. We used the X-ray diffraction technique, which shows an orthorhombic crystalline system for all the samples, and increase in the high-phase (Y-123) and decrease in low-phase and vary in proportion according to the method of preparation with the emergence of some impurities. The behavior of the samples in terms of electrical resistance and critical temperature was investigated all samples showed superconducting behavior. The properties of the dielectric (real dielectric constant, imaginary dielectric constant, loss tangent, alternating electrical conductivity) were s

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Crossref (9)
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Publication Date
Tue Jan 30 2024
Journal Name
Iraqi Journal Of Science
Machine Learning Based Crop Yield Prediction Model in Rajasthan Region of India
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     The present study investigates the implementation of machine learning models on crop data to predict crop yield in Rajasthan state, India. The key objective of the study is to identify which machine learning model performs are better to provide the most accurate predictions. For this purpose, two machine learning models (decision tree and random forest regression) were implemented, and gradient boosting regression was used as an optimization algorithm. The result clarifies that using gradient boosting regression can reduce the yield prediction mean square error to 6%. Additionally, for the present data set, random forest regression performed better than other models. We reported the machine learning model's performance using Mea

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Scopus Crossref
Publication Date
Sat Jun 30 2007
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Corrosion Inhibition of Carbon Steel under Two Phase Flow (Water-Petroleum) Simulated by Turbulently Agitated System
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The corrosion of carbon steel in single phase (water with 0.1N NaCl ) and two immiscible phases (kerosene-water) using turbulently agitated system is investigated. The experiments are carried out for Reynolds number (Re) range of 38000 to 95000 corresponding  to rotational velocities from 600 to 1400 rpm using circular disk turbine agitator at 40 0C. In two-phase system test runs are carried out in aqueous phase (water) concentrations of 1 % vol., 5 % vol., 8% vol., and 16% vol. mixed with kerosene at various Re. The effect of Reynolds number (Re), percent of dispersed phase, dispersed drops diameter, and number of drops per unit volume on the corrosion rate is investigated and discussed. Test runs are carried out using two types of

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