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Modeling and Control of Fuel Cell Using Artificial Neural Networks
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This paper includes an experimental study of hydrogen mass flow rate and inlet hydrogen pressure effect on the fuel cell performance. Depending on the experimental results, a model of fuel cell based on artificial neural networks is proposed. A back propagation learning rule with the log-sigmoid activation function is adopted to construct neural networks model. Experimental data resulting from 36 fuel cell tests are used as a learning data. The hydrogen mass flow rate, applied load and inlet hydrogen pressure are inputs to fuel cell model, while the current and voltage are outputs. Proposed model could successfully predict the fuel cell performance in good agreement with actual data. This work is extended to developed fuel cell feedback control system using PID controller to stabilize the fuel cell voltage. Particle swarm optimization technique is used to tune the PID controller gains. The voltage error and hydrogen flow rate are input and the actuator of the PID controller respectively. Simulation results showed that using PID controller with proposed model of fuel cell can successfully improve system performance in tracking output voltage under different operating conditions.

 

 

 

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Publication Date
Wed Jan 31 2024
Journal Name
Iraqi Geological Journal
Estimation of Rock Mechanical Properties of the Hartha Formation and their Relationship to Porosity Using Well-Log Data
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The physical and elastic characteristics of rocks determine rock strengths in general. Rock strength is frequently assessed using porosity well logs such as neutron and sonic logs. The essential criteria for estimating rock mechanic parameters in petroleum engineering research are uniaxial compressive strength and elastic modulus. Indirect estimation using well-log data is necessary to measure these variables. This study attempts to create a single regression model that can accurately forecast rock mechanic characteristics for the Harth Carbonate Formation in the Fauqi oil field. According to the findings of this study, petrophysical parameters are reliable indexes for determining rock mechanical properties having good performance p

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Publication Date
Thu Jul 01 2021
Journal Name
Energy
Experimental investigations of the performance of a flat-plate solar collector using carbon and metal oxides based nanofluids
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Publication Date
Tue Mar 30 2021
Journal Name
Baghdad Science Journal
Detection of Leishmania tropica Using Nested-PCR and Some of Their Virulence Factors in Thi-Qar Province, Iraq
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Cutaneous leishmaniasis is one of endemic diseases in Iraq. It is considered as widely health problem and is an uncontrolled disease. The aim of the study is to identify of Leishmania species that cause skin lesions among patients in Thi-Qar Province, South of Iraq, also to detect some virulence factors of L. tropica. This study includes three local locations, Al-Hussein Teaching, Suq Al-Shyokh General and Al-Shatrah General Hospitals in Province for the period from the beginning of December 2018 to the end of September 2019. The samples were collected from 80 patients suffering from cutaneous leishmaniasis, both genders, different ages, various residence places and single and multiple lesions. Nested-PCR technique was

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Publication Date
Sun Feb 01 2026
Journal Name
Journal Of Molecular Structure
Biosynthesis, characterization, antimicrobial and adsorbent effects of Îł-aluminum oxide-NPs using aqueous extract of boswellia carterii resin
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γ-Al2O3–NPs were synthesized by a green synthesis process based on Boswellia carterii resin extract and aluminum sulphate in an alkaline medium. Boswellia carterii resin extract is a significant reducing and stabilizing agent for synthesizing γ-Al2O3–NPs.Several techniques, including Fourier–transform infrared (FT-IR), UV–visible spectroscopy, x-ray diffraction, electron microscopy (XRD), energy dispersive x-ray (EDX), scanning electron microscopy (SEM), Transmission electron microscopy (TEM), and atomic force microscopy (AFM), were utilized to investigate the final product. XRD and SEM confirmed a plate-like crystalline structure with an average size of 17.5 nm. FT-IR analysis identified aluminum oxide stretching vibrations (655,

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Publication Date
Thu Dec 09 2021
Journal Name
Iraqi Journal Of Pharmaceutical Sciences ( P-issn 1683 - 3597 E-issn 2521 - 3512)
Preparation and In vitro Characterization of Aceclofenac Nanosuspension (ACNS) for Enhancement of Percutaneous Absorption using Hydrogel Dosage Form
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         Aceclofenac (AC) is an orally active phenyl acetic acid derivative, non-steroidal anti-inflammatory drug with exceptional anti-inflammatory, analgesic and antipyretic properties. It has low aqueous solubility, leading to slow dissolution, low permeability and inadequate bioavailability. The aim of the current study was to prepare and characterize AC-NS-based gel to enhance the dissolution rate and then percutaneous permeability. NS.s were prepared using solvent/antisovent precipitation method at different drug to polymer ratios (1:1, 1:2, and 1:3) using different polymers such as poly vinyl pyrrolidone (PVP-K25), hydroxy propyl methyl cellulose (HPMC-E5) and poloxamer® (388) as stabilizer

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Publication Date
Tue Dec 19 2017
Journal Name
Al-khwarizmi Engineering Journal
Prediction of Reaction Kinetic of Al- Doura Heavy Naphtha Reforming Process Using Genetic Algorithm
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In this study, genetic algorithm was used to predict the reaction kinetics of Iraqi heavy naphtha catalytic reforming process located in Al-Doura refinery in Baghdad.  One-dimensional steady state model was derived to describe commercial catalytic reforming unit consisting of four catalytic reforming reactors in series process.

The experimental information (Reformate composition and output temperature) for each four reactors collected at different operating conditions was used to predict the parameters of the proposed kinetic model. The kinetic model involving 24 components, 1 to 11 carbon atoms for paraffins and 6 to 11 carbon atom for naphthenes and aromatics with 71 reactions. The pre-exponential Arrhenius constants and a

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Publication Date
Tue Jan 01 2019
Journal Name
International Journal Of Civil Engineering And Technology
PREDICTION OF BEARING CAPACITY, ANGLE OF INTERNAL FRICTION, COHESION, AND PLASTICITY INDEX USING ANN (CASE STUDY OF BAGHDAD, IRAQ)
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In the last few years, the use of artificial neural network analysis has increased, particularly, in geotechnical engineering problems and has demonstrated some success. In this research, artificial neural network analysis endeavors to predict the relationship between physical and mechanical properties of Baghdad soil by making different trials between standard penetration test, liquid limit, plastic limit, plasticity index, cohesion, angle of internal friction, and bearing capacity. The analysis revealed that the changes in natural water content and plastic limit have a great effect on the cohesion of soil and the angle of internal friction, respectively. . On the other hand, the liquid limit has a great impact on the bearing capacity and

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Publication Date
Tue Jan 01 2019
Journal Name
International Journal Of Civil Engineering And Technology
PREDICTION OF BEARING CAPACITY, ANGLE OF INTERNAL FRICTION, COHESION, AND PLASTICITY INDEX USING ANN (CASE STUDY OF BAGHDAD, IRAQ).
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In the last few years, the use of artificial neural network analysis has increased, particularly, in geotechnical engineering problems and has demonstrated some success. In this research, artificial neural network analysis endeavors to predict the relationship between physical and mechanical properties of Baghdad soil by making different trials between standard penetration test, liquid limit, plastic limit, plasticity index, cohesion, angle of internal friction, and bearing capacity. The analysis revealed that the changes in natural water content and plastic limit have a great effect on the cohesion of soil and the angle of internal friction, respectively. . On the other hand, the liquid limit has a great impact on the bearing capacity and

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Publication Date
Tue Jan 01 2019
Journal Name
International Journal Of Civil Engineering And Technology
Prediction of bearing capacity, angle of internal friction, cohesion, and plasticity index using ANN (case study of Baghdad, Iraq)
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In the last few years, the use of artificial neural network analysis has increased, particularly, in geotechnical engineering problems and has demonstrated some success. In this research, artificial neural network analysis endeavors to predict the relationship between physical and mechanical properties of Baghdad soil by making different trials between standard penetration test, liquid limit, plastic limit, plasticity index, cohesion, angle of internal friction, and bearing capacity. The analysis revealed that the changes in natural water content and plastic limit have a great effect on the cohesion of soil and the angle of internal friction, respectively. . On the other hand, the liquid limit has a great impact on the bearing capacity and

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Publication Date
Wed Jan 01 2025
Journal Name
Aip Conference Proceedings
Biofilm formation rate measurement in water and biomedical systems using photometric smartphones applications
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