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Artificial neural network and response surface methodology for modeling oil content in produced water from an Iraqi oil field
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ABSTRACT<p>The majority of the environmental outputs from gas refineries are oily wastewater. This research reveals a novel combination of response surface methodology and artificial neural network to optimize and model oil content concentration in the oily wastewater. Response surface methodology based on central composite design shows a highly significant linear model with P value &lt;0.0001 and determination coefficient R2 equal to 0.747, R adjusted was 0.706, and R predicted 0.643. In addition from analysis of variance flow highly effective parameters from other and optimization results verification revealed minimum oily content with 8.5 ± 0.7 ppm when initial oil content 991 ppm, temperature 46.4 °C, pressure 21 Mpa, and flowrate 27,000 m3/day which is nearly closed to suggested oily content 8.5 ppm. An artificial neural network (ANN) technique was employed in this study to estimate the oil content in the treatment process. An artificial neural network model was remarkably accurate at simulating the process under investigation. A low mean squared error (MSE) and relative error (RE) equal to 1.55 × 10−7 and 2.5, respectively, were obtained during the training phase, whilst the testing results demonstrated a high coefficient of determination (R2) equal to 0.99.</p>
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Publication Date
Mon Jun 03 2024
Journal Name
Functional Foods In Health And Disease
A comparative study of the effect of extracts extracted from Ocimum basilicum leaves using organic extract and essential oil
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Background: Medicinal plants that possess antimicrobial and antioxidant properties have garnered significant attention for their role in maintaining food quality, improving safety, and impeding spoilage. They also can aid in controlling food contamination risks and augmenting the nutritional value of foods. Objective: The study aimed to obtain botanical extracts possessing antimicrobial capabilities and use them to inhibit the growth of molds and yeasts. Additionally, these extracts are aimed at prolonging product shelf life by harnessing their antioxidant attributes. Methods: Several microorganisms, including E. coli and Pseudomonas, were subjected to testing. Ethanolic alcohol, chloroform, and essential oil extracts were prepared;

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Publication Date
Sat Jul 01 2023
Journal Name
Journal Of Water Process Engineering
Removal kinetics of organic carbon from palm oil mill effluent by native duckweeds and its potential as a biofertilizer
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Publication Date
Fri Jul 01 2022
Journal Name
Advanced Powder Technology
Modification of FAU zeolite as an active heterogeneous catalyst for biodiesel production and theoretical considerations for kinetic modeling
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Publication Date
Tue May 01 2018
Journal Name
Journal Of Physics: Conference Series
Estimation of Heavy Metals Contamination in the Soil of Zaafaraniya City Using the Neural Network
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Publication Date
Thu Oct 31 2019
Journal Name
Journal Of Engineering And Applied Sciences
Feasibility Study of Applying a Pattern Waterflood Without and with Peripheral Injectors for Maximizing Oil Recovery Under Limited Water Sources Availability A Case Study of Heterogeneous Giant Carbonate Reservoir
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Publication Date
Sat Dec 01 2018
Journal Name
Journal Of Accounting And Financial Studies ( Jafs )
The role of artificial intelligence in achieving Customer satisfaction and its reflection on cost accounting: An applied research in the Iraqi electronic industries company
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Based economic units to technology to add innovations that lead to contribute to customer satisfaction, under intense competition and rapid development in customer taste, the economic units tend to apply the concepts that contribute to customer satisfaction led by the introduction of artificial intelligence techniques. In the production prominent role in the contributing and responding to the rapid changes in customer tastes, and consequent impact this in achieving customer satisfaction. Search gained importance of relying on artificial intelligence techniques to achieve customer satisfaction through speed of response to changes in the tastes of customers and thus be able to increase its market share، and sales growth، and to achieve a

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Publication Date
Sun Apr 02 2023
Journal Name
Mathematical Modelling Of Engineering Problems
Traffic Classification of IoT Devices by Utilizing Spike Neural Network Learning Approach
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Whenever, the Internet of Things (IoT) applications and devices increased, the capability of the its access frequently stressed. That can lead a significant bottleneck problem for network performance in different layers of an end point to end point (P2P) communication route. So, an appropriate characteristic (i.e., classification) of the time changing traffic prediction has been used to solve this issue. Nevertheless, stills remain at great an open defy. Due to of the most of the presenting solutions depend on machine learning (ML) methods, that though give high calculation cost, where they are not taking into account the fine-accurately flow classification of the IoT devices is needed. Therefore, this paper presents a new model bas

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Publication Date
Thu May 05 2016
Journal Name
Global Journal Of Engineering Science And Researches
EVALUATE THE RATE OF CONTAMINATION SOILS BY COPPER USING NEURAL NETWORK TECHNIQUE
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The aim of this paper is to design suitable neural network (ANN) as an alternative accurate tool to evaluate concentration of Copper in contaminated soils. First, sixteen (4x4) soil samples were harvested from a phytoremediated contaminated site located in Baghdad city in Iraq. Second, a series of measurements were performed on the soil samples. Third, design an ANN and its performance was evaluated using a test data set and then applied to estimate the concentration of Copper. The performance of the ANN technique was compared with the traditional laboratory inspecting using the training and test data sets. The results of this study show that the ANN technique trained on experimental measurements can be successfully applied to the rapid est

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Publication Date
Sun Dec 31 2017
Journal Name
Al-khwarizmi Engineering Journal
Solving the Inverse Kinematic Equations of Elastic Robot Arm Utilizing Neural Network
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The inverse kinematic equation for a robot is very important to the control robot’s motion and position. The solving of this equation is complex for the rigid robot due to the dependency of this equation on the joint configuration and structure of robot link. In light robot arms, where the flexibility exists, the solving of this problem is more complicated than the rigid link robot because the deformation variables (elongation and bending) are present in the forward kinematic equation. The finding of an inverse kinematic equation needs to obtain the relation between the joint angles and both of the end-effector position and deformations variables. In this work, a neural network has been proposed to solve the problem of inverse kinemati

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Publication Date
Thu Mar 06 2025
Journal Name
Aip Conference Proceedings
Solving 5th order nonlinear 4D-PDEs using efficient design of neural network
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