Sodium adsorption ratio (SAR) is considered as a measure of the water suitability for irrigation usage. This study examines the effect of the physicochemical parameters on water quality and SAR, which included Calcium(Ca+2), Magnesium(Mg+2), Sodium (Na+), Potassium (K), Chloride (Cl-), Sulfate(SO4-2), Carbonate (CO3-2), Bicarbonate (HCO3-), Nitrate (NO3-), Total Hardness (TH), Total Dissolved Salts (TDS), Electrical Conductivity (EC), degree of reaction (DR), Boron (B) and the monthly and annually flow discharge (Q). The water samples were collected from three stations across the Tigris River in Iraq, which flows through Samarra city (upstream), Baghdad city (central) and the end of Kut city (downstream) for the periods of 2016-2018. Results showed that the water quality of the Tigris River water is within the world health organization (WHO) specifications for drinking water except for Sulfate concentration. An artificial neural network (ANN) was used to develop the model for the three locations to predict SAR. The sum of the squared error function and the coefficient of determination (R2) were used to evaluate the amount of error in predicting values of SAR and performance evaluation of the model. The results showed that the highest value of the coefficient of determination was 0.992, 0.986, and 0.955 for Samarra, Baghdad, and Kut, respectively and the ANN analysis indicated that the prediction of SAR was effected by Sodium for three stations. Thus, the ANN model has been found to provide SAR prediction tool that can be used effectively to describe the suitability of river water quality for irrigation purposes.
Removal of Congo red, Rhodamine B, and Dispers Blue dyes from water solution have been achieved using Flint Clay as an adsorbent. The adsorption was studied as a function of contact time, adsorbent dose, pH, and temperature under batch adsorption technique. The equilibrium data fit with Langmuir, Freundlich and Toth models of adsorption and the linear regression coefficient R2 was used to elucidate the best fitting isotherm model. Different thermodynamic parameters, namely Gibb’s free energy, enthalpy and entropy of the on-going adsorption process have also been evaluated. Batch technique has been employed for the kinetic measurements and the adsorption of the three dyes follows a second order rate kinetics. The kinetic investigations al
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A charge transfer complex formed by interaction between nitron as electron donor with curcumin(1 ) as electron acceptor in ethanol at the temperature of theroom to form a colored complex. The optimum conditions of complex formation were investigated by Univariate method. The linearity range of complex was (3.124– 53.11) μg.mL-1 at 442 nm with molar absorptivity (1858.33) L.mol-1.cm-1, Sandell's sensitivity (0.1681μg.cm-2), and with a correlation coefficient (0.9935). Both modified attapulgite and modified attapulgite – complex have been characterized by using , FTIR, SEM, AFM, and XRD. Theadsorption behaviourof complex onto the modified attapulgite has been researchedthrough the variation of the parameters like the adsorbent weight, p
... Show MoreProcess capability provides a quantitative measure for gasoline production conformance to specifications.It was measured throughout four consecutive months of the last quarter of 2011. Results revealed high percentages (up to 44%) of non conforming gasoline blends to Iraqi marketing specifications for petroleum products (2000) by inspecting 122 different samples of Iraqi regular gasoline (RON 85).
Quality cost analysis as an important financial control tool was carried out to evaluate Cost of Quality (COQ) which was large due to non conforming gasoline reached up to (722.8 M.ID) in October. In this research COQ was investigated in order to identify the opportunities of gasoline quality improvements through production process. Also cus
This paper deals with prediction the effect of soil re-moulding (smear) on the ultimate bearing capacity of driven piles. The proposed method based on detecting the decrease in ultimate bearing capacity of the pile shaft (excluding the share of pile tip) after sliding downward. This was done via conducting an experimental study on three installed R.C piles in a sandy clayey silt soil. The piles were installed so that a gap space is left between its tip and the base of borehole. The piles were tested for ultimate bearing capacity according to ASTM D1143 in three stages. Between each two stages the pile was jacked inside the borehole until a sliding of about 200mm is achieved to simulate the soil re-moulding due to actual pile driving. The re
... Show MoreThis paper deals with prediction the effect of soil remoulding (smear) on the ultimate bearing capacity of driven piles. The proposed method based on detecting the decrease in ultimate bearing capacity of the pile shaft (excluding the share of pile tip) after sliding downward. This was done via conducting an experimental study on three installed R.C piles in a sandy clayey silt soil. The piles were installed so that a gap space is left between its tip and the base of borehole. The piles were tested for ultimate bearing capacity
according to ASTM D1143 in three stages. Between each two stages the pile was jacked inside the borehole until a sliding of about 200mm is achieved to simulate the soil remoulding due to actual pile driving. T
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Feed Forward Back Propagation artificial neural network (ANN) model utilizing the MATLAB Neural Network Toolbox is designed for the prediction of surface roughness of Duplex Stainless Steel during orthogonal turning with uncoated carbide insert tool. Turning experiments were performed at various process conditions (feed rate, cutting speed, and cutting depth). Utilizing the Taguchi experimental design method, an optimum ANN architecture with the Levenberg-Marquardt training algorithm was obtained. Parametric research was performed with the optimized ANN architecture to report the impact of every turning parameter on the roughness of the surface. The results suggested that machining at a cutting speed of 355 rpm with a feed rate of 0.07 m
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