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Prediction of Equilibrium Mixing Index and Optimum Mixing Time for Three solid materials in Fluidized Column

     Equilibrium and rate of mixing of free flowing solid materials are found using gas fluidized bed. The solid materials were sand (size 0.7 mm), sugar (size0.7 mm) and 15% cast iron used as a tracer. The fluidizing gas was air with velocity ranged from 0.45-0.65 m/s while the mixing time was up to 10 minutes. The mixing index for each experiment was calculated by averaging the results of 10 samples taken from different radial and axial positions in fluidized QVF column 150 mm ID and 900 mm height.

     The experimental results were used in solving a mathematical model of mixing rate and mixing index at an equilibrium proposed by Rose. The results show that mixing index increases with increasing air velocity and mixing time until it reaches an optimum value then decrease to an equilibrium value. The results also show the dependency of the mixing index on the particle size of the tracer component.

      The statistical analysis of the obtained theoretical mixing index versus the experimental mixing index shows that the standard error is 0.92 % (about 1 %) with the correlation coefficient of 0.9857 and the total residual of 0.002 for the ninth observation of the mixing index (theoretical versus experimental) over 95% confidence level.

 

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Publication Date
Sun Sep 01 2019
Journal Name
Baghdad Science Journal
Investigation of Superhydrophobic/Hydrophobic Materials Properties Using Electrospinning Technique

     The aim of this research is to study the surface alteration characteristics and surface morphology of the superhydrophobic/hydrophobic nanocomposite coatings prepared by an electrospinning method to coat various materials such as glass and metal. This is considered as a low cost method of fabrication for polymer solutions of Polystyrene (PS), Polymethylmethacrylate (PMMA) and Silicone Rubber (RTV). Si were prepared in various wt% of composition for each solutions. Contact angle measurement, surface tension, viscosity, roughness tests were calculated for all specimens. SEM showed the morphology of the surfaces after coated. PS and PMMA showed superhydrophobic properties for metal substrate, while Si showed hydroph

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Publication Date
Mon Dec 18 2017
Journal Name
Al-khwarizmi Engineering Journal
Prediction of Surface Roughness and Material Removal Rate in Electrochemical Machining Using Taguchi Method

Electrochemical machining is one of the widely used non-conventional machining processes to machine complex and difficult shapes for electrically conducting materials, such as super alloys, Ti-alloys, alloy steel, tool steel and stainless steel.  Use of optimal ECM process conditions can significantly reduce the ECM operating, tooling, and maintenance cost and can produce components with higher accuracy. This paper studies the effect of process parameters on surface roughness (Ra) and material removal rate (MRR), and the optimization of process conditions in ECM. Experiments were conducted based on Taguchi’s L9 orthogonal array (OA) with three process parameters viz. current, electrolyte concentration, and inter-electrode gap. Sig

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Publication Date
Mon Jun 01 2020
Journal Name
Al-khwarizmi Engineering Journal
Prediction of Cutting Force in Turning Process by Using Artificial Neural Network

       

Cutting forces are important factors for determining machine serviceability and product quality. Factors such as speed feed, depth of cut and tool noise radius affect on surface roughness and cutting forces in turning operation. The artificial neural network model was used to predict cutting forces with related to inputs including cutting speed (m/min), feed rate (mm/rev), depth of cut (mm) and work piece hardness (Map). The outputs of the ANN model are the machined cutting force parameters, the neural network showed that all (outputs) of all components of the processing force cutting force FT (N), feed force FA (N) and radial force FR (N) perfect accordance with the experimental data. Twenty-five samp

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Publication Date
Thu Oct 01 2015
Journal Name
Journal Of Economics And Administrative Sciences
Forecasting The Wet and Dry Rainy Seasons in Mosul Using Standardized Precipitation Index (SPI)

Iraq suffers the continuing lack of water resources in generdwether it is surface or underearth water or rain. The study of rain has got the utmost importance in order to the rain direction in Iraq and in Mosul in particular and what it will be in future. It also shows the wet as well as the dry seasons and the possibility of expecting them and expecting their quantities in order to invest them and to keep this vital resource The research deals with predict the wet and dry rainy seasons in Mosul using (SPI) Standardized precipitation index extracted from conversion of Gamma distribution to standardized normal distribution , depending on data of monthly rain amounts for 1940-2013 . Results showed existence of 31 w

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Publication Date
Wed Aug 01 2018
Journal Name
Journal Of Economics And Administrative Sciences
Analysis of the rebound effect and interactive between fiscal and monetary policy on the General economic equilibrium (IS-LM)

      Will address this research interaction and coordination between fiscal and monetary policies and the impact of this interaction and coordination on economic stability and growth، and how the financial implications of monetary policy may stimulate action monetary policy and treatment side effects and the nature of responsiveness and bounce between procedures both two policies and their impact on the balance of overall economic and explained in the folds of searchjustifications coordination and the extent necessary in order to address the imbalances in economic activity through twinning actions of monetary and fiscal، has embodied this coordination and interaction between policies and their impact m

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Publication Date
Fri Feb 01 2019
Journal Name
Physica B: Condensed Matter
Effect of gas mixing ratio on structural characteristics of titanium dioxide nanostructures synthesized by DC reactive magnetron sputtering

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Publication Date
Tue Apr 24 2018
Journal Name
Ibn Al-haitham Journal For Pure And Applied Sciences
Three Locally Clays as A Surfaces for Adsorption of Cephalexin Monohydrate From Aqueous Solution: Thermodynamic and Desorption Equilibrium

    The  adsorption of cephalexin.H2O from aqueous solution on attapulgite, bentonite and kaolin has been studied at the human body temperature (37.5ËšC) and at 5, 27, 47ËšC in 0.1M hydrochloric acid (pH 1.2). The value of pH 1.2 has been chosen to simulate the pH of stomach fluid. The clays show the following order: Bentonite > attapulgite > kaolin, for their activity to adsorb cephalexin.H2O. The charged clay particles can attract molecules either by electrostatic forces, for the molecules of oppositely charged, or by inducing dipole formation in the neutral molecule. The L-shaped adsorption isotherm indicated that drug molecules arrangement in a flat geometry on the clay surface. The

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Publication Date
Mon Oct 03 2022
Journal Name
Egyptian Journal Of Chemistry
Improving Biogas Production from Rice Husk Waste by Mixing with Pomegranate peels Waste by using Anaerobic Digestion

Biogas is one of the most important sources of renewable energy and is considered as an environment friendly energy source. The major goal of this research is to see if rice husk (Rh) waste and pomegranate peels (PP) waste are suitable for anaerobic digestion and what effect NaOH pre-treatment has on biogas generation. Rice husk and pomegranate peels were tested in anaerobic digestion under patch anaerobic conditions as separate wastes as well as blended together in equal proportions. The cumulative biogas output for the blank test (no pretreatment) was 1923 and 2526 ml, respectively using a single rice husk (Rh) and pomegranate peel (PP) substrates. The 50% rice husk digestion and 50% of pomegranate peels for blank test gave the result 224

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Publication Date
Tue Dec 05 2023
Journal Name
Baghdad Science Journal
An Observation and Analysis the role of Convolutional Neural Network towards Lung Cancer Prediction

Lung cancer is one of the most serious and prevalent diseases, causing many deaths each year. Though CT scan images are mostly used in the diagnosis of cancer, the assessment of scans is an error-prone and time-consuming task. Machine learning and AI-based models can identify and classify types of lung cancer quite accurately, which helps in the early-stage detection of lung cancer that can increase the survival rate. In this paper, Convolutional Neural Network is used to classify Adenocarcinoma, squamous cell carcinoma and normal case CT scan images from the Chest CT Scan Images Dataset using different combinations of hidden layers and parameters in CNN models. The proposed model was trained on 1000 CT Scan Images of cancerous and non-c

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Publication Date
Mon Jan 01 2018
Journal Name
Rehabend
Prediction of impact force-time history in sandy soils

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