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Artificial neural network and response surface methodology for modeling oil content in produced water from an Iraqi oil field
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ABSTRACT<p>The majority of the environmental outputs from gas refineries are oily wastewater. This research reveals a novel combination of response surface methodology and artificial neural network to optimize and model oil content concentration in the oily wastewater. Response surface methodology based on central composite design shows a highly significant linear model with P value &lt;0.0001 and determination coefficient R2 equal to 0.747, R adjusted was 0.706, and R predicted 0.643. In addition from analysis of variance flow highly effective parameters from other and optimization results verification revealed minimum oily content with 8.5 ± 0.7 ppm when initial oil content 991 ppm, temperature 46.4 °C, pressure 21 Mpa, and flowrate 27,000 m3/day which is nearly closed to suggested oily content 8.5 ppm. An artificial neural network (ANN) technique was employed in this study to estimate the oil content in the treatment process. An artificial neural network model was remarkably accurate at simulating the process under investigation. A low mean squared error (MSE) and relative error (RE) equal to 1.55 × 10−7 and 2.5, respectively, were obtained during the training phase, whilst the testing results demonstrated a high coefficient of determination (R2) equal to 0.99.</p>
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Publication Date
Sun Jul 01 1990
Journal Name
Bulletin Of The Iraq Natural History Museum (p-issn: 1017-8678 , E-issn: 2311-9799)
MEDIUM OPTIMIZATION FOR BIOMASS PRODUCTION AND PROTEIN CONTENT OF CANDIDA UTILES K50
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Molasse medium containing different concentrations of (NH4)2 SO4, (NH4)3 PO4, urea, KCI, and P2O5 were compared with the medium used for commercial production of C. utilis in a factory south of Iraq. An efficient medium, which produced 19. 16% dry wt. and 5. 78% protein, was developed. The effect of adding various concentrations of micronutrients (FeSO4, 7T20, MnSO4. 7H20, ZnSO4. 7E20) was also studied. Results showed that FeSo4. 7H20 caused a noticeable increase in both dry wt. and protein content of the yeast.

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Publication Date
Tue May 20 2008
Journal Name
Journal Of Planner And Development
Estimating Water Quality from Satellite Image and Reflectance Data
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The useful of remote sensing techniques in Environmental Engineering and another science is to save time, Coast and efforts, also to collect more accurate information under monitoring mechanism. In this research a number of statistical models were used for determining the best relationships between each water quality parameter and the mean reflectance values generated for different channels of radiometer operate simulated to the thematic Mappar satellite image. Among these models are the regression models which enable us to as certain and utilize a relation between a variable of interest. Called a dependent variable; and one or more independent variables

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Publication Date
Sat Oct 01 2022
Journal Name
Baghdad Science Journal
Modeling Jar Test Results Using Gene Expression to Determine the Optimal Alum Dose in Drinking Water Treatment Plants
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Coagulation is the most important process in drinking water treatment. Alum coagulant increases the aluminum residuals, which have been linked in many studies to Alzheimer's disease. Therefore, it is very important to use it with the very optimal dose. In this paper, four sets of experiments were done to determine the relationship between raw water characteristics: turbidity, pH, alkalinity, temperature, and optimum doses of alum [   .14 O] to form a mathematical equation that could replace the need for jar test experiments. The experiments were performed under different conditions and under different seasonal circumstances. The optimal dose in every set was determined, and used to build a gene expression model (GEP). The models were co

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Publication Date
Tue Jan 01 2019
Journal Name
International Journal Of Machine Learning And Computing
Facial Emotion Recognition from Videos Using Deep Convolutional Neural Networks
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Its well known that understanding human facial expressions is a key component in understanding emotions and finds broad applications in the field of human-computer interaction (HCI), has been a long-standing issue. In this paper, we shed light on the utilisation of a deep convolutional neural network (DCNN) for facial emotion recognition from videos using the TensorFlow machine-learning library from Google. This work was applied to ten emotions from the Amsterdam Dynamic Facial Expression Set-Bath Intensity Variations (ADFES-BIV) dataset and tested using two datasets.

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Publication Date
Mon May 25 2020
Journal Name
Journal Of Xi'an University Of Architecture & Technology
Iraqi Virtual Science Library (IVSL)): A field study of Iraqi researcher’s viewpoint
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The aim of the research is to shed light on the stages of developing the Iraqi virtual science Library(IVSL) project, and to define its distinctive role in providing all kinds of electronic resources to researchers from professors and graduate students, to identify its contents , the entry interfaces and applications of use, and provide them through electronic portals to publishing houses, research institutions and international universities, The research sample included the teaching staff and researchers participating in educational qualification courses at the Continuing Education Center at the University of Baghdad, The research population and its sample consisted of the category of (IVSL) users, and its sample (387) users. Analysis meth

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Publication Date
Tue Aug 27 2024
Journal Name
Tem Journal
Preparing the Electrical Signal Data of the Heart by Performing Segmentation Based on the Neural Network U-Net
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Research on the automated extraction of essential data from an electrocardiography (ECG) recording has been a significant topic for a long time. The main focus of digital processing processes is to measure fiducial points that determine the beginning and end of the P, QRS, and T waves based on their waveform properties. The presence of unavoidable noise during ECG data collection and inherent physiological differences among individuals make it challenging to accurately identify these reference points, resulting in suboptimal performance. This is done through several primary stages that rely on the idea of preliminary processing of the ECG electrical signal through a set of steps (preparing raw data and converting them into files tha

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Publication Date
Wed Sep 01 2021
Journal Name
Iop Conference Series: Earth And Environmental Science
Time Dependent Behavior of Engineered Cementitious Composite Concrete Produced from Portland Limestone Cement
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Publication Date
Tue Nov 15 2022
Journal Name
Association Of Arab Universities Journal Of Engineering Sciences
A-mechanical properties of engineered cementitious composite concrete produced from Portland limestone cement
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Conventional concretes are almost unbending, and even a small amount of strain potential leaves them brittle. This lack of bendability is a major source of strain loss, and it has been the main goal behind the development of bendable concrete, often known with engineered ce ment composites, or ECC. This form of concrete has a lot more flexibility than regular concrete. Micromechanical polymer fibers are used to strengthen ECC. In most cases, ECC uses a 2% amount of thin, separated fibers. As a result, bendable concrete deforms but unlike traditional concrete, it does not crack. This study aims to include this kind of concrete, bendable concrete, which can be used to solve concrete problems. Karasta (CK) and Tasluja (CT) Portland Lime

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Publication Date
Wed Jan 20 2021
Journal Name
Earth And Environmental Science
Time Dependent Behavior of Engineered Cementitious Composite Concrete Produced from Portland Limestone Cement
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Conventional concretes are nearly unbendable, and just 0.1 percent of strain potential makes them incredibly brittle and stiff. This absence of bendability is a significant cause of strain failure and has been a guiding force in the production of an elegant substance, bendable concrete, also known as engineered cement composites, abbreviated as ECC. This type of concrete is capable of displaying dramatically increased flexibility. ECC is reinforced with micromechanical polymer fibers. ECC usually uses a 2 percent volume of small, disconnected fibers. Thus, bendable concrete deforms but without breaking any further than conventional concrete. This research aims to involve this type of concrete, bendable concrete, that will give solut

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Publication Date
Sun Mar 01 2020
Journal Name
Baghdad Science Journal
The Biofilm Inhibitory Potential of Compound Produced from Chlamydomonas reinhardtii Against Pathogenic Microorganisms
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Microalgae present much usefulness for antimicrobial research because of its enormous biodiversity and rapid growth rate. From this study results it is reaveled that Chlamydomonas reinhardtii were isolated from a pond of water in the province of Diwaniyah. The culture supernatants were obtained when extracted with methanol solvent. Antimicrobial activity of extracts was tested for pathogens, and the best inhibition zone obtained was against Candida albicans (32mm), S.aureus (15mm), and to E.coli (9mm). While it showed no effect against both S.epidermidis and Klebsiella spp. Biofilm was formed by all tested isolates with differences in its strength formation. The C. reinhardtii

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