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Prediction and Correlations of Residual Entropy of Superheated Vapor for Pure Compounds
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Prediction of accurate values of residual entropy (SR) is necessary step for the
calculation of the entropy. In this paper, different equations of state were tested for the
available 2791 experimental data points of 20 pure superheated vapor compounds (14
pure nonpolar compounds + 6 pure polar compounds). The Average Absolute
Deviation (AAD) for SR of 2791 experimental data points of the all 20 pure
compounds (nonpolar and polar) when using equations of Lee-Kesler, Peng-
Robinson, Virial truncated to second and to third terms, and Soave-Redlich-Kwong
were 4.0591, 4.5849, 4.9686, 5.0350, and 4.3084 J/mol.K respectively. It was found
from these results that the Lee-Kesler equation was the best (more accurate) one
compared with the others, but this equation is sometimes not very preferable. It was
noted that SRK equation was the closest one in its accuracy to that of the Lee-Kesler
equation in calculating the residual entropy SR of superheated vapor, but it was
developed primarily for calculating vapor-liquid equilibrium and to overcome this
problem, efforts were directed toward the possibility of modifying SRK equation to
increase its accuracy in predicting the residual entropy as much as possible. The
modification was made by redefining the parameter α in SRK equation to be a
function of reduced pressure, acentric factor, and polarity factor for polar compounds
in addition to be originally function of reduced temperature and n parameter –which is
also function of acentric factor– by using statistical methods. This correlation is as
follows:

α =[1+n(γ)]2  , γ=-0.920338Pr-0.34091 +0.064049Tr4 ω +0.370002ω-Pr0.996932 Tr-4x
This new modified correlation decreases the deviations in the results obtained by
using SRK equation in calculating SR when comparing with the experimental data.
The AAD for 2791 experimental data points of 20 pure compounds is 4.3084 J/mol.K
while it becomes 2.4621 J/mol.K after modification. Thus SRK equation after this
modification gives more accurate results for residual entropy of superheated vapor of
pure 20 compounds than the rest of the equations mentioned above.

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Publication Date
Thu Jan 22 2015
Journal Name
مجلة التربية الاساسية
Synthesis and Characterization of Some New Heterocyclic Compounds Via Unsaturated Ketone with Evaluating of Their biological Activity
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Publication Date
Tue Aug 01 2017
Journal Name
Catalysis Science & Technology
Decomposition of selected chlorinated volatile organic compounds by ceria (CeO 2)
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Chlorinated volatile organic compounds (CVOCs) are toxic chemical entities emitted invariably from stationary thermal operations when a trace of chlorine is present. Replacing the high-temperature destruction operations of these compounds with catalytic oxidation has led to the formulation of various potent metal oxides catalysts; among them are ceria-based materials. Guided by recent experimental measurements, this study theoretically investigates the initial steps operating in the interactions of ceria surface CeO2(111) with three CVOC model compounds, namely chloroethene (CE), chloroethane (CA) and chlorobenzene (CB). We find that, the CeO2(111) surface mediates fission of the carbon–chlorine bonds in the CE, CA and CB molecules via mo

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Publication Date
Tue Mar 31 2015
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Reduction of Sulfur Compounds from Petroleum Fraction Using Oxidation-Adsorption Technique
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Oxidation of sulfur compounds in fuel followed by an adsorption process were studied using two modes of operation, batch mode and continuous mode (fixed bed). In batch experiment oxidation process of kerosene with sulfur content 2360 ppm was achieved to study the effect of amount of hydrogen peroxide(2.5, 4, 6 and 10) ml at different temperature(40, 60 and 70)°C. Also the effect of amount acetic acid was studied  at the optimal conditions of the oxidation step(4ml H2O2 and 60 °C).Besides, the role of acetic acid different temperatures(40, 60, 70) °C and 4ml H2O2, effect of reaction time(5, 30, 60, 120, 300) minutes at temperatures(40,60) °C, 4ml H2O2 and 1 mlHAC)&

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Publication Date
Sun Mar 07 2010
Journal Name
Baghdad Science Journal
Studying the biological activity of some Antimony Compounds with amino acids
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In this work four complexes of antimony were prepared ,Na[SbO(gly)2],Na[SbO(Asp)2],Na[SbO(Tyrosin)2], Na [SbO(phen alanin)2]. by reaction SbOCl with salts amino acids identifiefid these complexes by FTIR ,their conductivity was measured and also their biological activity against two types of bacteria was studied ,they were biologically active.

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Publication Date
Sun Jan 01 2023
Journal Name
8th Engineering And 2nd International Conference For College Of Engineering – University Of Baghdad: Coec8-2021 Proceedings
Prediction of consolidation due to dewatering by using MATLAB software
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Publication Date
Sat Oct 01 2022
Journal Name
Baghdad Science Journal
A Crime Data Analysis of Prediction Based on Classification Approaches
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Crime is considered as an unlawful activity of all kinds and it is punished by law. Crimes have an impact on a society's quality of life and economic development. With a large rise in crime globally, there is a necessity to analyze crime data to bring down the rate of crime. This encourages the police and people to occupy the required measures and more effectively restricting the crimes. The purpose of this research is to develop predictive models that can aid in crime pattern analysis and thus support the Boston department's crime prevention efforts. The geographical location factor has been adopted in our model, and this is due to its being an influential factor in several situations, whether it is traveling to a specific area or livin

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Publication Date
Sat May 01 2021
Journal Name
Journal Of Physics: Conference Series
The Prediction of COVID 19 Disease Using Feature Selection Techniques
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Abstract<p>COVID 19 has spread rapidly around the world due to the lack of a suitable vaccine; therefore the early prediction of those infected with this virus is extremely important attempting to control it by quarantining the infected people and giving them possible medical attention to limit its spread. This work suggests a model for predicting the COVID 19 virus using feature selection techniques. The proposed model consists of three stages which include the preprocessing stage, the features selection stage, and the classification stage. This work uses a data set consists of 8571 records, with forty features for patients from different countries. Two feature selection techniques are used in </p> ... Show More
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Publication Date
Wed Mar 01 2017
Journal Name
Neural Computing And Applications
The potential of nonparametric model in foundation bearing capacity prediction
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Publication Date
Sat Dec 30 2023
Journal Name
Iraqi Journal Of Science
Machine Learning Prediction of Brain Stroke at an Early Stage
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     The healthcare sector has traditionally been an early adopter of technological progress, gaining significant advantages, particularly in machine learning applications such as disease prediction. One of the most important diseases is stroke. Early detection of a brain stroke is exceptionally critical to saving human lives. A brain stroke is a condition that happens when the blood flow to the brain is disturbed or reduced, leading brain cells to die and resulting in impairment or death. Furthermore, the World Health Organization (WHO) classifies brain stroke as the world's second-deadliest disease. Brain stroke is still an essential factor in the healthcare sector. Controlling the risk of a brain stroke is important for the surviv

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Publication Date
Sat Dec 30 2023
Journal Name
Iraqi Journal Of Science
Energy Consumption Prediction of Smart Buildings by Using Machine Learning Techniques
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     This paper presents an IoT smart building platform with fog and cloud computing capable of performing near real-time predictive analytics in fog nodes. The researchers explained thoroughly the internet of things in smart buildings, the big data analytics, and the fog and cloud computing technologies. They then presented the smart platform, its requirements, and its components. The datasets on which the analytics will be run will be displayed. The linear regression and the support vector regression data mining techniques are presented. Those two machine learning models are implemented with the appropriate techniques, starting by cleaning and preparing the data visualization and uncovering hidden information about the behavior of

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