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Prediction of Monthly Fluoride Content in Tigris River using SARIMA Model in R Software
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The need to create the optimal water quality management process has motivated researchers to pursue prediction modeling development. One of the widely important forecasting models is the sessional autoregressive integrated moving average (SARIMA) model. In the present study, a SARIMA model was developed in R software to fit a time series data of monthly fluoride content collected from six stations on Tigris River for the period from 2004 to 2014. The adequate SARIMA model that has the least Akaike's information criterion (AIC) and mean squared error (MSE) was found to be SARIMA (2, 0, 0) (0,1,1). The model parameters were identified and diagnosed to derive the forecasting equations at each selected location. The correlation coefficient between the actual and predicted values for fluoride concentration at the six locations, Al-Karakh, East Tigris, Al-Wathbah, AL-Karamah, Al-Rashid and Al-Wahda WTP intakes, was 0.93, 0.82, 0.86, 0.90, 0.83 and 0.89, respectively. Model verification results indicated that the model forecasting outputs rationally estimated the actual monthly fluoride content in the selected locations.

 

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Publication Date
Wed Sep 01 2010
Journal Name
Al-khwarizmi Engineering Journal
Prediction of the Scale Removal Rate in Heat Exchanger Piping System Using the Analogies between Mass and Momentum Transfer
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The possibility of predicting the mass transfer controlled CaCO3 scale removal   rate has been investigated.

Experiments were carried out using chelating agents as a cleaning solution at different time and Reynolds’s number. The results of CaCO3 scale removal or (mass transfer rate) (as it is the controlling process) are compared with proposed model of prandtl’s and Taylor particularly based on the concept of analogy among momentum and mass transfer.

Correlation for the variation of Sherwood number ( or mass transfer rate ) with Reynolds’s number have been obtained .

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Publication Date
Sun Dec 17 2017
Journal Name
Al-khwarizmi Engineering Journal
Experimental and Prediction Using Artificial Neural Network of Bed Porosity and Solid Holdup in Viscous 3-Phase Inverse Fluidization
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In the present investigation, bed porosity and solid holdup in viscous three-phase inverse fluidized bed (TPIFB) are determined for aqueous solutions of carboxy methyl cellulose (CMC) system using polyethylene and polypropylene as  a particles with low-density and diameter (5 mm) in a (9.2 cm) inner diameter with height (200 cm) of vertical perspex column. The effectiveness of gas velocity Ug , liquid velocity UL, liquid viscosity μL, and particle density ρs on bed porosity BP and solid holdups εg were determined. The bed porosity increases with "increasing gas velocity", "liquid velocity", and "liquid viscosity". Solid holdup decreases with increasing gas, liquid

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Publication Date
Fri Apr 01 2022
Journal Name
Journal Of Engineering
Prediction of Shear Strength Parameters of Gypseous Soil using Artificial Neural Networks
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The shear strength of soil is one of the most important soil properties that should be identified before any foundation design. The presence of gypseous soil exacerbates foundation problems. In this research, an approach to forecasting shear strength parameters of gypseous soils based on basic soil properties was created using Artificial Neural Networks. Two models were built to forecast the cohesion and the angle of internal friction. Nine basic soil properties were used as inputs to both models for they were considered to have the most significant impact on soil shear strength, namely: depth, gypsum content, passing sieve no.200, liquid limit, plastic limit, plasticity index, water content, dry unit weight, and initial

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Publication Date
Wed Dec 30 2009
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Prediction of the Point Efficiency of Sieve Tray Using Artificial Neural Network
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An application of neural network technique was introduced in modeling the point efficiency of sieve tray, based on a
data bank of around 33l data points collected from the open literature.Two models proposed,using back-propagation
algorithm, the first model network consists: volumetric liquid flow rate (QL), F foctor for gas (FS), liquid density (pL),
gas density (pg), liquid viscosity (pL), gas viscosity (pg), hole diameter (dH), weir height (hw), pressure (P) and surface
tension between liquid phase and gas phase (o). In the second network, there are six parameters as dimensionless
group: Flowfactor (F), Reynolds number for liquid (ReL), Reynolds number for gas through hole (Reg), ratio of weir
height to hole diqmeter

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Publication Date
Sun Sep 02 2012
Journal Name
Baghdad Science Journal
Influence of Fluoride Addition on Hydroxyapatite Prepared for Medical Applications
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In this study, hydroxyapatite (HAP, Ca10(PO4)6(OH)2) has been prepared as bioceramic material with biological specifications useful to used for orthopedic and dental implant applications. Wet chemical processing seems to form the fine grain size and uniform characteristic nanocrystalline materials by the interstice factors controlling which affected the grain size and crystallinity in order to give good mechanical and/or constituent properties similar as natural bone. Fluorinated hydroxyapatite [4-6 wt% F, (FHA, Ca10(PO4)6(OH)2–Fx] was developed in new method for its posses to increased strength and to give higher corrosion resistance in biofluids than pure HAP moreover reduces the risk of dental caries. The phase's and functional groups

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Publication Date
Thu Oct 29 2020
Journal Name
Complexity
Training and Testing Data Division Influence on Hybrid Machine Learning Model Process: Application of River Flow Forecasting
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The hydrological process has a dynamic nature characterised by randomness and complex phenomena. The application of machine learning (ML) models in forecasting river flow has grown rapidly. This is owing to their capacity to simulate the complex phenomena associated with hydrological and environmental processes. Four different ML models were developed for river flow forecasting located in semiarid region, Iraq. The effectiveness of data division influence on the ML models process was investigated. Three data division modeling scenarios were inspected including 70%–30%, 80%–20, and 90%–10%. Several statistical indicators are computed to verify the performance of the models. The results revealed the potential of the hybridized s

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Publication Date
Mon Apr 24 2017
Journal Name
Ibn Al-haitham Journal For Pure And Applied Sciences
Calculation of the Concentrations of Depleted Uranium in The Diyala River Sediment Samples Using The Nuclear Track Detector CR-39
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   In this study,  depleted uranium concentrations were calculated  in  sediments  Diyala River samples , where 14 samples of sediment along the Diyala River were collected , starting from the Rustumiya area and ending with the Tuwaitha area after its confluence with the River Tigris , using the solid - state nuclear track detectors technique (SSNTDs).        We found that the highest concentration of depleted uranium was in the sample (A-13), and was taken from the Tuwaitha area with the specific activity of the amount (78.1 ± 8.5 Bq/kg ) and the lowest concentration of the depleted uranium was in the sample  (A-7), and was taken from the Azwaip area with  the

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Publication Date
Mon Jan 01 2024
Journal Name
Aip Conference Proceedings
Assessment of water quality in the shatt Al-Hillah river
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Publication Date
Wed Aug 28 2019
Journal Name
Journal Of Engineering
Evaluation of Heavy Metals Content in Simulated Solid Waste Food Compost
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Composting is one of the solid waste management (SWM) methods where the organic component decomposed biologically under controlled conditions. In this study, a 0.166 m3 bioreactor tank was designed to compose 59.2Kg of simulated common municipal solid food waste having a bulk density, organic matter, organic carbon, pH, nitrogen content, C/N and nitrification index (NH4-N/ NO3-N) of 536.62 kg/m3, 62.34%, 34.76%, 6.53, 1.86%, 23 and 0.34 respectively. The bioreactor operated aerobically for 30 days, and anaerobically for 70 days, until the end of the composting process. Results proved that the composting process could reduce the mass of the waste by 69%.  Nitrogen content,

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Publication Date
Sat Feb 02 2019
Journal Name
Journal Of The College Of Education For Women
content Analysis for Some Type of Pillows used in Iraqi houses
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content Analysis for Some Type of Pillows used in Iraqi houses

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