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Estimating Pitting Corrosion Depth and Density on Carbon Steel (C-4130) using Artificial Neural Networks
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The purpose of this research is to investigate the impact of corrosive environment (corrosive ferric chloride of 1, 2, 5, 6% wt. at room temperature), immersion period of (48, 72, 96, 120, 144 hours), and surface roughness on pitting corrosion characteristics and use the data to build an artificial neural network and test its ability to predict the depth and intensity of pitting corrosion in a variety of conditions. Pit density and depth were calculated using a pitting corrosion test on carbon steel (C-4130). Pitting corrosion experimental tests were used to develop artificial neural network (ANN) models for predicting pitting corrosion characteristics. It was found that artificial neural network models were shown to be quite effective; the results were validated by the experimental agreement with those acquired from laboratory tests. Specifically, the correlation coefficient, R = 0.9944.

 

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Publication Date
Wed Dec 30 2009
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Prediction of the Point Efficiency of Sieve Tray Using Artificial Neural Network
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An application of neural network technique was introduced in modeling the point efficiency of sieve tray, based on a
data bank of around 33l data points collected from the open literature.Two models proposed,using back-propagation
algorithm, the first model network consists: volumetric liquid flow rate (QL), F foctor for gas (FS), liquid density (pL),
gas density (pg), liquid viscosity (pL), gas viscosity (pg), hole diameter (dH), weir height (hw), pressure (P) and surface
tension between liquid phase and gas phase (o). In the second network, there are six parameters as dimensionless
group: Flowfactor (F), Reynolds number for liquid (ReL), Reynolds number for gas through hole (Reg), ratio of weir
height to hole diqmeter

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Publication Date
Tue Apr 30 2024
Journal Name
Iraqi Journal Of Science
Crescent Moon Visibility: A New Criterion using Deep learned Artificial Neural-Network
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     Many authors investigated the problem of the early visibility of the new crescent moon after the conjunction and proposed many criteria addressing this issue in the literature. This article presented a proposed criterion for early crescent moon sighting based on a deep-learned pattern recognizer artificial neural network (ANN) performance. Moon sight datasets were collected from various sources and used to learn the ANN. The new criterion relied on the crescent width and the arc of vision from the edge of the crescent bright limb. The result of that criterion was a control value indicating the moon's visibility condition, which separated the datasets into four regions: invisible, telescope only, probably visible, and certai

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Publication Date
Tue Dec 05 2023
Journal Name
Baghdad Science Journal
Recognizing Different Foot Deformities Using FSR Sensors by Static Classification of Neural Networks
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Sensing insole systems are a promising technology for various applications in healthcare and sports. They can provide valuable information about the foot pressure distribution and gait patterns of different individuals. However, designing and implementing such systems poses several challenges, such as sensor selection, calibration, data processing, and interpretation. This paper proposes a sensing insole system that uses force-sensitive resistors (FSRs) to measure the pressure exerted by the foot on different regions of the insole. This system classifies four types of foot deformities: normal, flat, over-pronation, and excessive supination. The classification stage uses the differential values of pressure points as input for a feedforwar

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Publication Date
Sat Dec 01 2018
Journal Name
Indian Journal Of Ecology
Classification of al-hammar marshes satellite images in Iraq using artificial neural network based on coding representation
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Publication Date
Wed Mar 01 2023
Journal Name
Iraqi Journal Of Physics
Theoretical Computation of Electron Density in Laser-Induced Carbon Plasma using Anisimov Model
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In this work, electron number density calculated using Matlab program code with the writing algorithm of the program. Electron density was calculated using Anisimov model in a vacuum environment. The effect of spatial coordinates on the electron density was investigated in this study. It was found that the Z axis distance direction affects the electron number density (ne). There are many processes such as excitation; ionization and recombination within the plasma that possible affect the density of electrons. The results show that as Z axis distance increases electron number density decreases because of the recombination of electrons and ions at large distances from the target and the loss of thermal energy of the electrons in high distance

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Publication Date
Tue Feb 01 2022
Journal Name
Baghdad Science Journal
Theoretical and Experimental Study of Corrosion Behavior of Carbon Steel Surface in 3.5% NaCl and 0.5 M HCl with Different Concentrations of Quinolin-2-One Derivative
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A theoretical and protection study was conducted of the corrosion behavior of carbon steel surface with different concentrations of the derivative (Quinolin-2-one), namely 7-Ethyl-4-methyl-1-[(4-nitro-benzylidene)-amino]-1H-quinolin-2-one (EMNQ2O). Theoretically, Density Functional Theory (DFT) of B3LYP/ 6-311++G/ 2d, 2p level was carried out to calculate the geometrical structure, physical properties and chemical inhibition chemical parameters, with the local reactivity in order to predict both the reactive centers and to know the possible sites of nucleophilic and electrophilic attacks, in vacuum and two solvents (DMSO and H2O), all at the equilibrium geometry. Experimentally, the inhibition efficiencies (%IE) in (3.5% NaCl)

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Publication Date
Sat Dec 02 2017
Journal Name
Al-khwarizmi Engineering Journal
Improvement of Microhardness and Corrosion Resistance of Stainless Steel by Nanocomposite Coating
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Abstract

 

Stainless steel (AISI 304) has good electrical and thermal conductivities, good corrosion resistance at ambient temperature, apart from these it is cheap and abundantly available; but has good mechanical properties such as hardness. To improve the  hardness and corrosion resistance of stainless steel its surface can be modified by developing nanocomposite coatings applied on its surface. The main objective of this paper is to study effect of electroco-deposition method on microhardness and corrosion resistance of stainless steel, and to analyze effect of nanoparticles (Al2O3, ZrO2 , and SiC)  on properties of composite coatings. I

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Publication Date
Sat Jul 01 2023
Journal Name
Journal Of Engineering
Effect of Heat Treatments and Carbon Content on the Damping Properties of Structural Steel
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Low- and medium-carbon structural steel components face random vibration and dynamic loads (like earthquakes) in many applications. Thus a modification to improve their mechanical properties, essentially damping properties, is required. The present study focuses on improving and developing these properties, significantly dampening properties, without losing the other mechanical properties. The specimens used in the present study are structural steel ribbed bar ISO 6935 subjected to heating temperatures of (850, 950, and 1050) ˚C, and cooling schemes of annealing, normalizing, sand, and quenching was selected. The damping properties of the specimens were measured experimentally with the area under the curve for the loadi

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Publication Date
Thu Oct 01 2020
Journal Name
Journal Of Engineering Science And Technology
Water quality assessment and sodium adsorption ratio prediction of Tigris River using artificial neural network
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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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