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Modeling BOD of the Effluent from Abu-Ghraib Diary Factory using Artificial Neural Network October 2018
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The proper operation, and control of wastewater treatment plants, is receiving an increasing attention, because of the rising concern about environmental issues. In this research a mathematical model was developed to predict biochemical oxygen demand in the waste water discharged from Abu-Ghraib diary factory in Baghdad using Artificial Neural Network (ANN).In this study the best selection of the input data were selected from the recorded parameters of the wastewater from the factory. The ANN model developed was built up with the following parameters: Chemical oxygen demand, Dissolved oxygen, pH, Total dissolved solids, Total suspended solids, Sulphate, Phosphate, Chloride and Influent flow rate. The results indicated that the constructed ANN model to predict BOD of the effluent gave a high degree of correlation reaching 93.5% with seven input parameters. This model can be queried to determine the best combination of operating conditions needed in the wastewater treatment plant to achieve the desired effluent disposal limits.

Publication Date
Thu Aug 31 2023
Journal Name
Iraqi Geological Journal
Geochemistry of Sabkhas in Abu Ghraib, Western Baghdad, Iraq
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The study aims to detail the chemistry of Sabkha located in Abu-Graib, western Baghdad to know the content of the rare elements distributed. Sabkhas are found in dry areas with significant evaporation rates. The quantity of dissolved salts rises as water evaporates from the shallow basins, eventually causing salt to crystallize. The creation of the distinctive salt pans and crusts on the top is greatly influenced by this process. The trace elements (Ti, V, Cr, Mn, Ni, Cu, Zn, Ga, As, Se, Rb, Sr, Y, Zr, Nb, Mo, Sn, Ba, Ta, Tl, Pb, Th, U, I, and Br) beside the major oxides were analyzed using XRF technique. There are no clear significant differences in the distribution of the elements between the two Sabkhas in the village of Kadim Al

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Publication Date
Tue Sep 01 2020
Journal Name
Baghdad Science Journal
Developing Arabic License Plate Recognition System Using Artificial Neural Network and Canny Edge Detection
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In recent years, there has been expanding development in the vehicular part and the number of vehicles moving on the roads in all the sections of the country. Arabic vehicle number plate identification based on image processing is a dynamic area of this work; this technique is used for security purposes such as tracking of stolen cars and access control to restricted areas. The License Plate Recognition System (LPRS) exploits a digital camera to capture vehicle plate numbers is used as input to the proposed recognition system. Basically, the proposed system consists of three phases, vehicle license plate localization, character segmentation, and character recognition, the License Plate (LP) detection is presented using canny edge detection

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Publication Date
Tue Sep 01 2020
Journal Name
Baghdad Science Journal
Developing Arabic License Plate Recognition System Using Artificial Neural Network and Canny Edge Detection
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            In recent years, there has been expanding development in the vehicular part and the number of vehicles moving on the roads in all the sections of the country. Arabic vehicle number plate identification based on image processing is a dynamic area of this work; this technique is used for security purposes such as tracking of stolen cars and access control to restricted areas. The License Plate Recognition System (LPRS) exploits a digital camera to capture vehicle plate numbers is used as input to the proposed recognition system. Basically, the proposed system consists of three phases, vehicle license plate localization, character segmentation, and character recognition, the

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Publication Date
Wed Jan 01 2025
Journal Name
Aip Conference Proceedings
Predicting biochemical oxygen demand at Al-Rustumiya wastewater treatment plant inlet using the artificial neural network
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Publication Date
Tue Feb 28 2023
Journal Name
Iraqi Geological Journal
Salt Crystallization and Mineralogy of Sabkhas in Abu Ghraib, Western Baghdad, Abu Graib, Iraq
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The research aims to study Sabkha mineralogy to determine the mineral types, the nature of the precipitation, and the patterns of salt crystallization. Two Sabkhas in Abu Ghraib, west of Baghdad, were studied. It was found that the Sabkhas were formed in flat ponds from saturated solutions in a semi-arid to arid climate. Halite predominates, followed by anhydrite and gypsum as evaporite minerals. As for the minerals of the Sabkha soil, it consisted of feldspar, calcite, quartz, and dolomite, in addition to the clay minerals represented by kaolinite, illite, and chlorite. Needle forms, hopper shapes, dendritic crystals, and polygon shapes are the main crystallization patterns dominantly found in the Sabkhas. All these types of crysta

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Publication Date
Sun May 01 2022
Journal Name
International Journal Of Multiphase Flow
Application of artificial neural network to predict slug liquid holdup
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Publication Date
Thu Dec 19 2024
Journal Name
Baghdad Science Journal
Solution of Wave Equation by Linear Regression Artificial Neural Network
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تعتبر المعادلات التفاضلية الموجية من اهم المواضيع التي تمثل على سبيل المثال الحركة الموجية للاهتزازات الأرضية . ومن هنا فان ايجاد  حلول تقريبيه لمثل هذه المعادلات بدقة وسرعه عالية وبشكل اسرع من الحلول التحليلية والمعقدة , اصبح ممكنا من خلال استخدام الذكاء الاصطناعي واساليب  التعلم  الالي. في هذا البحث هناك ثلاثة أهداف الأول هو تحويل مشكلة القيمة الأولية للمعادلة الموجية إلى شكلها القانوني وإيجاد حلها ا

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Publication Date
Sun Jan 01 2023
Journal Name
Journal Of Engineering
Artificial Neural Network Models to Predict the Cost and Time of Wastewater Projects
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Infrastructure, especially wastewater projects, plays an important role in the life of residential communities. Due to the increasing population growth, there is also a significant increase in residential and commercial facilities. This research aims to develop two models for predicting the cost and time of wastewater projects according to independent variables affecting them. These variables have been determined through a questionnaire distributed to 20 projects under construction in Al-Kut City/ Wasit Governorate/Iraq. The researcher used artificial neural network technology to develop the models. The results showed that the coefficient of correlation R between actual and predicted values were 99.4% and 99 %, MAPE was

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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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