After harvesting, Alfalfa plant was washed, dried and ground to get fine powder used in treatment water. We used alfalfa plant with ethanol to made alcoholic extract and characterized it applying (GC-Mass, FTIR, UV) spectroscopy to determine active compounds. Alcoholic extract was used to prepare zinc nanoparticle. We characterized Zinc nanoparticles by using FTIR, UV, SEM, EDX Zeta potential and AFM. Zinc nanoparticle with Alfalfa extract and alfalfa powder was used to treat pollutant water with pesticides and negative ions by two methods, namely Batch and continuous processing. Batch process was used two times firstly, with Alfalfa plant to treat water affected by pesticides and negative ions, after 1h pesticides (glyphosate 44.76%, sulfon 49.21%) Negative ions (NO3 33.8%, NO2 46.8%, 17.2%) and when left it 5h to get treated off pesticides (glyphosate 64.52%, sulfon 69.38%), Negative ions (NO3 71%, NO2 80%, SO4 70%). Secondly, we used with Zinc nanoparticles to treat water after 1h pesticides (glyphosate 71.45%, sulfon 52.6%) Negative ions (NO3 72.13%, NO2 14.50% SO4 78.30%) and when left 5h (glyphosate 81.26% sulfon 60.11%) Negative ions (NO3 79.55%, NO2 32.45%, SO4 86.80%), followed by continuous processing flowrate pertaining to pesticides (glyphosate 57.44%, sulfon 59.50%), Negative ions (NO3 32.24%, NO2 6.28%, SO4 65.57%). Zinc nanoparticles were treated in continuous process at concentration 10 ppm only for pesticides for 1h to get treated (glyphosate 77.22%, sulfon 100%) and concentration 50 ppm for treating pesticides (glyphosate 64.52%, sulfon 69.38%), Negative ions (75.41%, 18.69%, 90.70%). Comparing the two process, we found the continuous one more efficient than batch process. Further, comparing alfalfa powder and zinc nanoparticle, we found treatment with zinc nanoparticle more efficient and most removal for organic and inorganic pollutant.
A nonlinear filter for smoothing color and gray images
corrupted by Gaussian noise is presented in this paper. The proposed
filter designed to reduce the noise in the R,G, and B bands of the
color images and preserving the edges. This filter applied in order to
prepare images for further processing such as edge detection and
image segmentation.
The results of computer simulations show that the proposed
filter gave satisfactory results when compared with the results of
conventional filters such as Gaussian low pass filter and median filter
by using Cross Correlation Coefficient (ccc) criteria.
Mass transfer correlations for iron rotating cylinder electrode in chloride/sulphate solution, under isothermal and
controlled heat transfer conditions, were derived. Limiting current density values for the oxygen reduction reaction from
potentiostatic experiments at different bulk temperatures and various turbulent flow rates, under isothermal and heat
transfer conditions, were used for such derivation. The corelations were analogous to that obtained by Eisenberg et all
and other workers.
The present study aims to establish an empirical correlation between biochemical oxygen demand (BOD5) and chemical oxygen demand (COD) of the sewage flowing in Al-Diwaniyah wastewater treatment plant. The strength of the wastewater entering the plant varied from medium to high. High concentrations of BOD5 and COD in the effluent were obtained due to the poor performance of the plant. This was observed from the BOD5 /COD ratios that did not confirm with the typical ratios for the treated sewage. Regression equations for BOD5 and COD removal percentages were suggested which can be used to evaluate rapid effluent assessment after the treatment processes or optimal process control to improve the performance of wastewater treatment plants.
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The performance of sewage pumps stations affected by many factors through its work time which produce undesired transportation efficiency. This paper is focus on the use of artificial neural network and multiple linear regression (MLR) models for prediction the major sewage pump station in Baghdad city. The data used in this work were obtained from Al-Habibia sewage pump station during specified records- three years in Al-Karkh district, Baghdad. Pumping capability of the stations was recognized by considering the influent input importance of discharge, total suspended solids (TSS) and biological oxygen demand (BOD). In addition, the chemical oxygen demands (COD), pH and chloride (Cl). The proposed model performanc
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The open hole well log data (Resistivity, Sonic, and Gamma Ray) of well X in Euphrates subzone within the Mesopotamian basin are applied to detect the total organic carbon (TOC) of Zubair Formation in the south part of Iraq. The mathematical interpretation of the logs parameters helped in detecting the TOC and source rock productivity. As well, the quantitative interpretation of the logs data leads to assigning to the organic content and source rock intervals identification. The reactions of logs in relation to the increasing of TOC can be detected through logs parameters. By this way, the TOC can be predicted with an increase in gamma-ray, sonic, neutron, and resistivity, as well as a decrease in the density log
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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
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Alzheimer's disease (AD) increasingly affects the elderly and is a major killer of those 65 and over. Different deep-learning methods are used for automatic diagnosis, yet they have some limitations. Deep Learning is one of the modern methods that were used to detect and classify a medical image because of the ability of deep Learning to extract the features of images automatically. However, there are still limitations to using deep learning to accurately classify medical images because extracting the fine edges of medical images is sometimes considered difficult, and some distortion in the images. Therefore, this research aims to develop A Computer-Aided Brain Diagnosis (CABD) system that can tell if a brain scan exhibits indications of
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