The current study deals with the performance of constructed wetland (CW) incorporating a microbial fuel cell (MFC) for wastewater treatment and electricity generation. The whole unit is referred to as CW-MFC. This technique involves two treatments; the first is an aerobic treatment which occurs in the upper layer of the system (cathode section) and the second is anaerobic biological treatment in the lower layer of the system (anode section). Two types of electrode material were tested; stainless steel and graphite. Three configurations for electrodes arrangement CW-MFC were used. In the first unit of CW-MFC, the anode was graphite plate (GPa) and cathode was also graphite plate (GPc), in the second CW-MFC unit, the anode was stainless steel

The current study deals with the performance of constructed wetland (CW) incorporating a microbial fuel cell (MFC) for wastewater treatment and electricity generation. The whole unit is referred to as CW-MFC. This technique involves two treatments; the first is an aerobic treatment which occurs in the upper layer of the system (cathode section) and the second is anaerobic biological treatment in the lower layer of the system (anode section). Two types of electrode material were tested; stainless steel and graphite. Three configurations for electrodes arrangement CW-MFC were used. In the first unit of CW-MFC, the anode was graphite plate (GPa) and cathode was also graphite plate (GPc), in the second CW-MFC unit, the anode was stainless st

The present article delves into the examination of groundwater quality, based on WQI, for drinking purposes in Baghdad City. Further, for carrying out the investigation, the data was collected from the Ministry of Water Resources of Baghdad, which represents water samples drawn from 114 wells in Al-Karkh and Al-Rusafa sides of Baghdad city. With the aim of further determining WQI, four water parameters such as (i) pH, (ii) Chloride (Cl), (iii) Sulfate (SO4), and (iv) Total dissolved solids (TDS), were taken into consideration. According to the computed WQI, the distribution of the groundwater samples, with respect to their quality classes such as excellent, good, poor, very poor and unfit for human drinking purpose, was found to be

   The time spent in drilling ahead is usually a significant portion of total well cost. Drilling is an expensive operation including the cost of equipment and material used during the penetration of rock plus crew efforts in order to finish the well without serious problems. Knowing the rate of penetration should help in speculation of the cost and lead to optimize drilling outgoings. Ten wells in the Nasiriya oil field have been selected based on the availability of the data. Dynamic elastic properties of Mishrif formation in the selected wells were determined by using Interactive Petrophysics (IP V3.5) software based on the las files and log record provided. The average rate of penetration and average dynamic elastic propert

Adverse drug reactions (ADR) are important information for verifying the view of the patient on a particular drug. Regular user comments and reviews have been considered during the data collection process to extract ADR mentions, when the user reported a side effect after taking a specific medication. In the literature, most researchers focused on machine learning techniques to detect ADR. These methods train the classification model using annotated medical review data. Yet, there are still many challenging issues that face ADR extraction, especially the accuracy of detection. The main aim of this study is to propose LSA with ANN classifiers for ADR detection. The findings show the effectiveness of utilizing LSA with ANN in extracting AD

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

The present study develops an artificial neural network (ANN) to model an analysis and a simulation of the correlation between the average corrosion rate carbon steel and the effective parameter Reynolds number (Re), water concentration (Wc) % temperature (T o) with constant of PH 7 . The water, produced fom oil in Kirkuk oil field in Iraq from well no. k184-Depth2200ft., has been used as a corrosive media and specimen area (400 mm2) for the materials that were used as low carbon steel pipe. The pipes are supplied by Doura Refinery . The used flow system is all made of Q.V.F glass, and the circulation of the two –phase (liquid – liquid ) is affected using a Q.V.F pump .The input parameters of the model consists of Reynolds number , w

There are large numbers of weakness in the generated keys of security algorithms. This paper includes a new algorithm to generate key of 5120 bits for a new proposed cryptography algorithm for 10 rounds that combine neural networks and chaos theory (1D logistic map). Two methods of neural networks (NN) are employed as Adaline and Hopfield and the results are combined through several sequential operation. Carefully integrating high quality random number generators from neural networks and chaos theory to obtain suitable key for randomness and complexity.

Prediction of penetration rate (ROP) is important process in optimization of drilling due to its crucial role in lowering drilling operation costs. This process has complex nature due to too many interrelated factors that affected the rate of penetration, which make difficult predicting process. This paper shows a new technique of rate of penetration prediction by using artificial neural network technique. A three layers model composed of two hidden layers and output layer has built by using drilling parameters data extracted from mud logging and wire line log for Alhalfaya oil field. These drilling parameters includes mechanical (WOB, RPM), hydraulic (HIS), and travel transit time (DT). Five data set represented five formations gathered