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Solar Powered Air-Conditioning Using Absorption Refrigeration Technique
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The present work includes design, construction and operates of a prototype solar absorption refrigeration system, using methanol as a refrigerant to avoid any refrigerant that cause global warming and greenhouse effect. Flat plate collector was used because it’s easy, ninexpensive and efficient. Many test runs (more than 50) were carried out on the system from May to October, 2013; the main results were taken between the period of July 15, 2013 to August 15, 2013 to find the maximum C.O.P, cooling, temperature and pressure of the system. The system demonstrates a maximum generator temperature of 93.5 oC, on July 18, 2013 at 2:30 pm, and the average mean generator temperature Tgavr was 74.7 °C, for this period. The maximum pressure Pg obtained was 2.25 bar on July 19, 2013 at 2:00 pm. The current system shows cooling capacity of 0.15 ton with coefficient of performance of 0.48, and minimum evaporator temperature obtained was 14.2oC. A comparison of the present with previous works, showed that most of the previous work used ammonia as the main refrigerant, and even that used methanol it was as aqua methanol, or to be part of pair refrigerant, while the present work use the methanol as the main and the only refrigerant in the system. The results and the factors that provided by the current work, give a good understanding for using the methanol as a refrigerant with the solar absorption system. And the system can work in continuous operation cycle. This work gave fundamental understanding for designing solar refrigeration system, by using the results of present study to design air-conditioning unit, with one ton capacity, using the solar energy, and the methanol as a refrigerant.

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Publication Date
Thu Jun 01 2023
Journal Name
Heliyon
Heterogeneously catalyzed transesterification reaction using waste snail shell for biodiesel production
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Biodiesel as an attractive energy source; a low-cost and green synthesis technique was utilized for biodiesel preparation via waste cooking oil methanolysis using waste snail shell derived catalyst. The present work aimed to investigate the production of biodiesel fuel from waste materials. The catalyst was greenly synthesized from waste snail shells throughout a calcination process at different calcination time of 2–4 h and temperature of 750–950 ◦C. The catalyst samples were characterized using X-Ray Diffraction (XRD), Brunauer-Emmett-Teller (BET), Energy Dispersive X-ray (EDX), and Fourier Transform Infrared (FT-IR). The reaction variables varying in the range of 10:1–30:1 M ratio of MeOH: oil, 3–11 wt% catalyst loading, 50–

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Publication Date
Thu Jun 29 2023
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Drilling Optimization by Using Advanced Drilling Techniques in Buzurgan Oil Field
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Efficient and cost-effective drilling of directional wells necessitates the implementation of best drilling practices and advanced techniques to optimize drilling operations. Failure to adequately consider drilling risks can result in inefficient drilling operations and non-productive time (NPT). Although advanced drilling techniques may be expensive, they offer promising technical solutions for mitigating drilling risks. This paper aims to demonstrate the effectiveness of advanced drilling techniques in mitigating risks and improving drilling operations when compared to conventional drilling techniques. Specifically, the advanced drilling techniques employed in Buzurgan Oil Field, including vertical drilling with mud motor, managed pres

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Publication Date
Wed Jan 13 2021
Journal Name
Iraqi Journal Of Science
Implementable Self-Learning PID Controller Using Least Mean Square Adaptive Algorithm
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More than 95% of the industrial controllers in use today are PID or modified PID controllers. However, the PID is manually tuning to be responsive so that the Process Variable is rapidly and steady moved to track the set point with minimize overshoot and stable output. The paper presents generic teal-time PID controller architecture. The developed architecture is based on the adaption of each of the three controller parameters (PID) to be self- learning using individual least mean square algorithm (LMS). The adaptive PID is verified and compared with the classical PID. The rapid realization of the adaptive PID architecture allows the readily fabrication into a hardware version either ASIC or reconfigurable.

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Publication Date
Wed Mar 29 2017
Journal Name
Iraqi Journal Of Pharmaceutical Sciences ( P-issn 1683 - 3597 E-issn 2521 - 3512)
Acquired Error in using Antibiotic for Surgery Patients in Iraqi Hospitals
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The use of antibiotics (AB) in surgery focused in either treating established infection or to prevent suspected post-operative infection. Inappropriate use of antibiotic for treatment of patients with common infections is a major problem worldwide, with great implications with regards to cost of treatment and development of resistance to the antimicrobial agent. Moreover, antibiotics may often be dispensed without a clear clinical indication. This study was conducted to estimate the medication errors in using antibiotic for surgery patients which may effect their wound healing. A 260 patients with clean-contaminated and contaminated surgery were included from two teaching hospitals, 160 patient from Medical city hospital and 100 from Al-

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Publication Date
Fri Sep 30 2022
Journal Name
Iraqi Journal Of Science
Educational Data Mining For Predicting Academic Student Performance Using Active Classification
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     The increasing amount of educational data has rapidly in the latest few years. The Educational Data Mining (EDM) techniques are utilized to detect the valuable pattern so that improves the educational process and to obtain high performance of all educational elements. The proposed work contains three stages: preprocessing, features selection, and an active classification stage. The dataset was collected using EDM that had a lack in the label data, it contained 2050 records collected by using questionnaires and by using the students’ academic records. There are twenty-five features that were combined from the following five factors: (curriculum, teacher, student, the environment of education, and the family). Active learning ha

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Publication Date
Sat Jan 01 2022
Journal Name
Journal Of Ecological Engineering
Biosynthetic of Titanium Dioxide Nanoparticles Using Zizyphus Spina-Christi Leaves Extract
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Publication Date
Wed Sep 01 2021
Journal Name
Iop Conference Series: Earth And Environmental Science
Improving the Properties of Asphalt Concrete Mixtures Using Iron Filling Wastes
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Publication Date
Wed Feb 01 2023
Journal Name
Baghdad Science Journal
Retrieving Encrypted Images Using Convolution Neural Network and Fully Homomorphic Encryption
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A content-based image retrieval (CBIR) is a technique used to retrieve images from an image database. However, the CBIR process suffers from less accuracy to retrieve images from an extensive image database and ensure the privacy of images. This paper aims to address the issues of accuracy utilizing deep learning techniques as the CNN method. Also, it provides the necessary privacy for images using fully homomorphic encryption methods by Cheon, Kim, Kim, and Song (CKKS). To achieve these aims, a system has been proposed, namely RCNN_CKKS, that includes two parts. The first part (offline processing) extracts automated high-level features based on a flatting layer in a convolutional neural network (CNN) and then stores these features in a

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Publication Date
Mon Apr 01 2019
Journal Name
2019 4th Scientific International Conference Najaf (sicn)
Pneumatic Control System of Automatic Production Line Using SCADA Implement PLC
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Publication Date
Sun Feb 25 2024
Journal Name
Baghdad Science Journal
Oil spill classification based on satellite image using deep learning techniques
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 An oil spill is a leakage of pipelines, vessels, oil rigs, or tankers that leads to the release of petroleum products into the marine environment or on land that happened naturally or due to human action, which resulted in severe damages and financial loss. Satellite imagery is one of the powerful tools currently utilized for capturing and getting vital information from the Earth's surface. But the complexity and the vast amount of data make it challenging and time-consuming for humans to process. However, with the advancement of deep learning techniques, the processes are now computerized for finding vital information using real-time satellite images. This paper applied three deep-learning algorithms for satellite image classification

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