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A Neural Networks based Predictive Voltage-Tracking Controller Design for Proton Exchange Membrane Fuel Cell Model
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In this work, a new development of predictive voltage-tracking control algorithm for Proton Exchange Membrane Fuel Cell (PEMFCs) model, using a neural network technique based on-line auto-tuning intelligent algorithm was proposed. The aim of proposed robust feedback nonlinear neural predictive voltage controller is to find precisely and quickly the optimal hydrogen partial pressure action to control the stack terminal voltage of the (PEMFC) model for N-step ahead prediction. The Chaotic Particle Swarm Optimization (CPSO) implemented as a stable and robust on-line auto-tune algorithm to find the optimal weights for the proposed predictive neural network controller to improve system performance in terms of fast-tracking desired voltage and less energy consumption through investigating and comparing under random current variations with the minimum number of fitness evaluation less than 20 iterations.

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Publication Date
Tue Jun 30 2020
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Process Optimization Study of Pb(II) Removal by Bulk Liquid Membrane (BLM)
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   Box-Wilson experimental design method was employed to optimized lead ions removal efficiency by bulk liquid membrane (BLM) method. The optimization procedure was primarily based on four impartial relevant parameters: pH of feed phase (4-6), pH of stripping phase (9-11), carrier concentration TBP (5-10) %, and initial metal concentration (60-120 ppm). maximum recovery efficiency of lead ions is 83.852% was virtually done following thirty one-of-a-kind experimental runs, as exact through 24-Central Composite Design (CCD). The best values for the aforementioned four parameters, corresponding to the most restoration efficiency were: 5, 10, 7.5% (v/v), and 90 mg/l, respectively. The obtained experimental data had been

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Publication Date
Mon Sep 30 2019
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Removal of-Copper Ions-from Aqueous Solution Using Liquid-Surfactant-Membrane Technique
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Extraction of copper (Cu) from aqueous solution utilizing Liquid Membrane technology (LM) is more effective than precipitation method that forms sludge and must be disposed of in landfills. In this work, we have formulated a liquid surfactant membrane (LSM) that uses kerosene oil as the main diluent of LSM to remove copper ions from the aqueous waste solution through di- (2-ethylhexyl) phosphoric acid - D2EHPA- as a carrier. This technique displays several advantages including one-stage extraction and stripping process, simple operation, low energy requirement, and. In this study, the LSM process was used to transport Cu (II) ions from the feed phase to the stripping phase, which was prepared, using H2SO4. For LSM p

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Publication Date
Mon Apr 09 2018
Journal Name
Al-khwarizmi Engineering Journal
Neural Network Modeling of Cutting Force and Chip Thickness Ratio for Turning Aluminum Alloy 7075-T6
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The turning process has various factors, which affecting machinability and should be investigated. These are surface roughness, tool life, power consumption, cutting temperature, machining force components, tool wear, and chip thickness ratio. These factors made the process nonlinear and complicated. This work aims to build neural network models to correlate the cutting parameters, namely cutting speed, depth of cut and feed rate, to the machining force and chip thickness ratio. The turning process was performed on high strength aluminum alloy 7075-T6. Three radial basis neural networks are constructed for cutting force, passive force, and feed force. In addition, a radial basis network is constructed to model the chip thickness ratio. T

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Publication Date
Thu Oct 31 2024
Journal Name
Iraqi Geological Journal
Artificial Neural Network Application to Permeability Prediction from Nuclear Magnetic Resonance Log
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Reservoir permeability plays a crucial role in characterizing reservoirs and predicting the present and future production of hydrocarbon reservoirs. Data logging is a good tool for assessing the entire oil well section's continuous permeability curve. Nuclear magnetic resonance logging measurements are minimally influenced by lithology and offer significant benefits in interpreting permeability. The Schlumberger-Doll-Research model utilizes nuclear magnetic resonance logging, which accurately estimates permeability values. The approach of this investigation is to apply artificial neural networks and core data to predict permeability in wells without a nuclear magnetic resonance log. The Schlumberger-Doll-Research permeability is use

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Publication Date
Mon Jan 01 2018
Journal Name
Journal Of Engineering And Applied Sciences
Effect of silver doping for performance of CdS solar cell prepared by thermal vacuum evaporation
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Publication Date
Thu Nov 15 2018
Journal Name
Journal Of Mathematical Imaging And Vision
A New Hybrid form of Krawtchouk and Tchebichef Polynomials: Design and Application
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Publication Date
Mon Jan 01 2018
Journal Name
Journal Of Pharmacy Research
Design and in vitro characterization of bisacodyl as a hollow-type suppositories
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Publication Date
Tue Sep 01 2015
Journal Name
Journal Of Biomechanics
In vitro wear testing of a contemporary design of reverse shoulder prosthesis
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Reverse shoulder arthroplasty is an increasingly common surgical intervention. However there are concerns and known limitations in relation to such joint replacement, while novel designs of reverse shoulder prostheses continue to appear on the market. Many claim to offer improvements over older designs but such assertions are difficult to validate when there is no consensus as to how such implants should be tested in vitro or even if such testing is necessary. In order to permit appropriate in vitro testing of reverse shoulder prostheses a unique, multi-station test rig was designed which was capable of applying motion in three axes to test prostheses. The shoulder simulator can apply up to 110° of motion in the flexion–extension and abd

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Publication Date
Tue Oct 23 2018
Journal Name
Journal Of Economics And Administrative Sciences
Compare some wavelet estimators for parameters in the linear regression model with errors follows ARFIMA model.
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The aim of this research is to estimate the parameters of the linear regression model with errors following ARFIMA model by using wavelet method depending on maximum likelihood and approaching general least square as well as ordinary least square. We use the estimators in practical application on real data, which were the monthly data of Inflation and Dollar exchange rate obtained from the (CSO) Central Statistical organization for the period from 1/2005 to 12/2015. The results proved that (WML) was the most reliable and efficient from the other estimators, also the results provide that the changing of fractional difference parameter (d) doesn’t effect on the results.

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Publication Date
Sat Oct 01 2022
Journal Name
Colloids And Surfaces A: Physicochemical And Engineering Aspects
Application of natural deep eutectic solvents in bulk liquid membrane system for removal of free glycerol from crude fatty acid methyl ester
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Fatty Acid Methyl Ester (FAME) produced from biomass offers several advantages such as renewability and sustainability. The typical production process of FAME is accompanied by various impurities such as alcohol, soap, glycerol, and the spent catalyst. Therefore, the most challenging part of the FAME production is the purification process. In this work, a novel application of bulk liquid membrane (BLM) developed from conventional solvent extraction methods was investigated for the removal of glycerol from FAME. The extraction and stripping processes are combined into a single system, allowing for simultaneous solvent recovery whereby low-cost quaternary ammonium salt-glycerol-based deep eutectic solvent (DES) is used as the membrane phase.

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