In this study, the feasibility of Forward–Reverse osmosis processes was investigated for treating the oily wastewater. The first stage was applied forward osmosis process to recover pure water from oily wastewater. Sodium chloride (NaCl) and magnesium chloride (MgCl2) salts were used as draw solutions and the membrane that was used in forward osmosis (FO) process was cellulose triacetate (CTA) membrane. The operating parameters studied were: draw solution concentrations (0.25 – 0.75 M), oil concentration in feed solution (FS) (100-1000 ppm), the temperature of FS and draw solution (DS) (30 - 45 °C), pH of FS (4-10) and the flow rate of both DS and FS (20 - 60 l/h). It was found that the water flux and oil concentration in FS increas

Background: Symptoms related to the upper gastro-intestinal tract are very common. Attribution of these symptoms to upper G. I. T.diseases are usually done on clinical bases, which could be confirmed by Esophago Gastro Duodenoscopy (EGD). The use of such tools might increase the diagnosis accuracy for such complaints. The indications for upper G I endoscopy might decrease the negative results of endoscopies.Objective: To follow strict indications for Esophago Gastro Duodenoscopy in order to decrease the negative endoscopy results. Methods: One thousand eight hundred and ninety cases were subjected to EGD from Feb. 1999 to Feb 2009 at Alkindy Teaching Hospital and Abd-Al-Majeed private hospital in Baghdad, Iraq. A special endoscopy unit f

Viscosity is one of the most important governing parameters of the fluid flow, either in the porous media or in pipelines. So it is important to use an accurate method to calculate the oil viscosity at various operating conditions. In the literature, several empirical correlations have been proposed for predicting crude oil viscosity. However, these correlations are limited to predict the oil viscosity at specified conditions. In the present work, an extensive experimental data of oil viscosities collected from different samples of Iraqi oil reservoirs was applied to develop a new correlation to calculate the oil viscosity at various operating conditions either for dead, satura

This paper presents the thermophysical properties of zinc oxide nanofluid that have been measured for experimental investigation. The main contribution of this study is to define the heat transfer characteristics of nanofluids. The measuring of these properties was carried out within a range of temperatures from 25 °C to 45 °C, volume fraction from 1 to 2 %, and the average nanoparticle diameter size is 25 nm, and the base fluid is water. The thermophysical properties, including viscosity and thermal conductivity, were measured by using Brookfield rotational Viscometer and Thermal Properties Analyzer, respectively. The result indicates that the thermophysical properties of zinc oxide nanofluid increasing with nanoparticle volume f

 In this paper, we propose a method using continuous wavelets to study the multivariate fractional Brownian motion through the deviations of the transformed random process to find an efficient estimate of Hurst exponent using eigenvalue regression of the covariance matrix. The results of simulations experiments shown that the performance of the proposed estimator was efficient in bias but the variance get increase as signal change from short to long memory the MASE increase relatively. The estimation process was made by calculating the eigenvalues for the variance-covariance matrix of Meyer’s continuous wavelet details coefficients.

Alongside the development of high-speed rail, rail flaw detection is of great importance to ensure railway safety, especially for improving the speed and load of the train. Several conventional inspection methods such as visual, acoustic, and electromagnetic inspection have been introduced in the past. However, these methods have several challenges in terms of detection speed and accuracy. Combined inspection methods have emerged as a promising approach to overcome these limitations. Nondestructive testing (NDT) techniques in conjunction with artificial intelligence approaches have tremendous potential and viability because it is highly possible to improve the detection accuracy which has been proven in various conventional nondestr

Monaural source separation is a challenging issue due to the fact that there is only a single channel available; however, there is an unlimited range of possible solutions. In this paper, a monaural source separation model based hybrid deep learning model, which consists of convolution neural network (CNN), dense neural network (DNN) and recurrent neural network (RNN), will be presented. A trial and error method will be used to optimize the number of layers in the proposed model. Moreover, the effects of the learning rate, optimization algorithms, and the number of epochs on the separation performance will be explored. Our model was evaluated using the MIR-1K dataset for singing voice separation. Moreover, the proposed approach achi