Smart water flooding (low salinity water flooding) was mainly invested in a sandstone reservoir. The main reasons for using low salinity water flooding are; to improve oil recovery and to give a support for the reservoir pressure.

In this study, two core plugs of sandstone were used with different permeability from south of Iraq to explain the effect of water injection with different ions concentration on the oil recovery. Water types that have been used are formation water, seawater, modified low salinity water, and deionized water.

The effects of water salinity, the flow rate of water injected, and the permeability of core plugs have been studied in order to summarize the best conditions of low salinity

In this paper, an algorithm is suggested to train a single layer feedforward neural network to function as a heteroassociative memory. This algorithm enhances the ability of the memory to recall the stored patterns when partially described noisy inputs patterns are presented. The algorithm relies on adapting the standard delta rule by introducing new terms, first order term and second order term to it. Results show that the heteroassociative neural network trained with this algorithm perfectly recalls the desired stored pattern when 1.6% and 3.2% special partially described noisy inputs patterns are presented.

The analysis and efficiency of phenol extraction from the industrial water using different solvents, were investigated. To our knowledge, the experimental information available in the literature for liquid-liquid equilibria of ternary mixtures containing the pair phenol-water is limited. Therefore the purpose of the present investigation is to generate the data for the water-phenol with different solvents to aid the correlation of liquid-liquid equilibria, including phase diagrams, distribution coefficients of phenol, tie-lines data and selectivity of the solvents for the aqueous phenol system.

                The ternary equilibrium diagrams and tie-lines

Introduction: The introduction of analytics tools in sports indicates that artificial neural networks can be one of the intelligent approaches to process complex data and identify patterns that help players move according to their most suitable positions. Objective: The purpose of this research is to investigate the possibility of using artificial neural networks to determine the physical and motor abilities of football players and determine their suitable playing positions based on exact quantitative indicators. Method: The study sample consists of 45 youth players aged (15–16) years from the Espanyol Football Academy in Baghdad. The results are analyzed using a multilayer perceptron (MLP) artificial neural network model to ident

Modeling forward kinematics with neural networks allows for efficient handling of nonlinear relationships and realistic error correction in time-critical applications by relying on accurate training data. This paper presents a Multi-Layer Feed-Forward Neural Network (MLFFNN) to solve the forward kinematics of a 3-DOF robot. The proposed MLFFNN consists of 50 hidden neurons and was trained using 628319 samples to find only the position (x, y, z) of the end-effector. Data were generated by MATLAB, assuming an incremental motion of joints. The joint variables ( , , and ) are the inputs of the NN, which outputs the positions of the end effector (x, y, z) calculated using the Denavit-Hartenberg (DH) method. The results demonstrate that t

 This study focuses on a comparison of the performance of two similar locally-fabricated solar water heaters. One of the collectors features a new design for accelerated absorber; its risers are made of converging ducts whose exit area is half that of the entrance. The other collector is a conventional absorber, with risers of the same cross sectional area along its length. Each collector is the primary part of an indirect thermosyphon circulation solar hot water system. Both collectors face south with a fixed tilt angle of 33.3

Gundelia, a genus of flowering plants native to the Mediterranean region, particularly in Iraq, holds promise as a sustainable adsorbent for the treatment of dye-polluted water. This study explores the potential of Gundelia seeds (GS) waste as a biobased adsorbent for removing methylene blue dye from synthesized wastewater. Utilizing various analytical techniques, including scanning electron microscopy (SEM), Fourier transform infrared (FTIR) spectroscopy, and X-ray diffraction analysis (XRD), we assessed GS as an active adsorbent with performance comparable to fabricated and expensive composites. Key parameters such as pH (3-11), pH at the point of zero charge, temperature (298-328 K), dose (0.02-0.1 g), dye concentration (10-50 ppm), and

Finger vein recognition and user identification is a relatively recent biometric recognition technology with a broad variety of applications, and biometric authentication is extensively employed in the information age. As one of the most essential authentication technologies available today, finger vein recognition captures our attention owing to its high level of security, dependability, and track record of performance. Embedded convolutional neural networks are based on the early or intermediate fusing of input. In early fusion, pictures are categorized according to their location in the input space. In this study, we employ a highly optimized network and late fusion rather than early fusion to create a Fusion convolutional neural network

Water pollution is one of the global challenges that the society must address in the 21^st century aiming to improve the water quality, reduce human pollutants and ecosystem health impacts. In phytotoxicity test, the plant of Iresine herbstii was exposed to remove nickel from simulated wastewater using two different ratios (mass of plant to the mass of nickel) (,R_p/Ni) for 21 days with sub-surface batch system. During the exposure period, the removal of Ni concentrations (2, 5 and 10 mg/L) for two mass ratio (2,800 and 34,000) were (83.6%, 77.2%, 78.0%) and (86.8%, 97% and 95.6%), respectively. final result of the rate was found that the highest removal occurred, 97%, at a mass ratio of 34,000 and