Tigris River is the lifeline that supplies a great part of Iraq with water from north to south. Throughout its entire length, the river is battered by various types of pollutants such as wastewater effluents from municipal, industrial, agricultural activities, and others. Hence, the water quality assessment of the Tigris River is crucial in ensuring that appropriate and adequate measures are taken to save the river from as much pollution as possible. In this study, six water treatment plants (WTPs) situated on the two-banks of the Tigris within Baghdad City were Al Karkh; Sharq Dijla; Al Wathba; Al Karama; Al Doura, and Al Wahda from northern Baghdad to its south, that selected to determine the removal efficiency of turbidity and the water quality index used to assess the quality of water for drinking purposes, in addition to finding the model based on past information to predict the quality of treated wastewater produced in each WTP using an artificial neural network (ANN) approach. The selected parameters for this study were turbidity, total hardness, total solids, suspended solids, and alkalinity. The results showed that all the WTPs possessed a high rate of efficiency in the removal of turbidity from raw water. Also, the results of the water quality index for all WTPs were classified over a study period of three years from 2015 to 2017 as being a good water quality and based on these results, the water treatment plants can be considered to be doing efficient water treatment process. The ANN model has been found at all WTPs to have a coefficient of determination (R2) for expected models was more than 0.7 to provide a WQI prediction tool that can be used with a moderate level of predictive acceptance to describe the suitability of WTP water quality for drinking purposes.
This paper proposes improving the structure of the neural controller based on the identification model for nonlinear systems. The goal of this work is to employ the structure of the Modified Elman Neural Network (MENN) model into the NARMA-L2 structure instead of Multi-Layer Perceptron (MLP) model in order to construct a new hybrid neural structure that can be used as an identifier model and a nonlinear controller for the SISO linear or nonlinear systems. Two learning algorithms are used to adjust the parameters weight of the hybrid neural structure with its serial-parallel configuration; the first one is supervised learning algorithm based Back Propagation Algorithm (BPA) and the second one is an intelligent algorithm n
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A field experiment was conducted in Al-Yusufiya district - Al-Mahmoudiya district, Baghdad province during the winter season 2021, to study improving the efficiency and management of water use and the productivity of lettuce under different irrigation systems. The Nested-Factorial Experiments design was used, where the main plots include the first factor, irrigation levels (I1) 50%, (I2) 75%, (I3) 100, (I4) 125%, (I5) 150% ETpan. After depleting 35% of the available water and in terms of climatic data from the American Evaporative Basin, Class A. Then the main factor is divided into three replicates, and the coefficients of the second factor are distributed randomly within each replicate, which includes the irrigation system: surface drip i
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This research consists of two parts, the first part concern with analyzing the collected data of BOD and COD values in discharge waste water from Al-Dora refinery during 2010 to find the relationship between these two variables The results indicates that there
is a high correlation between BOD and COD when using a natural logarithm model (0.86 ln(COD)) with correlation coefficient of 0.98. This relationship is useful in predicting the BOD value using the COD value. The second part includes analyzing collected data from the same site in order to find a relationsip between BOD and other parameters COD, Phenol(phe), Temperature(T), Oil, Sulphat(SO4),pH and Total dissolved solids( TDS) discharged from the refinery. The results indicated
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This research consists of two parts, the first part concern with analyzing the collected data of BOD and COD values in discharge waste water from Al-Dora refinery during 2010 to find the relationship between these two variables The results indicates that there is a high correlation between BOD and COD when using a natural logarithm model (0.86 ln(COD)) with correlation coefficient of 0.98. This relationship is useful in predicting the BOD value using the COD value. The second part includes analyzing collected data from the same site in order to find a relationsip between BOD and other parameters COD, Phenol(phe), Temperature(T), Oil, Sulphat(SO4),pH and Total dissolved solids( TDS) discharged from the refinery. The results indicated that th
... Show MoreThis study was conducted to examine the discharge capacity of the reach of the Tigris River between Kut and Amarah Barrages of 250km in length. The examination includes simulation the current capacity of the reach by using HEC-RAS model. 247cross sections surveyed in 2012 were used in the simulation. The model was calibrated using observed discharges of 533, 800, 1025 and 3000m3/s discharged at Kut Barrage during 2013, 1995, 1995 and 1988, respectively, and its related water level at three gauge stations located along the reach. The result of calibration process indicated that the lowest Root Mean Square Error of 0.095 can be obtained when using Manning’s n coefficient of 0.026, 0.03 for th
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Failure in asphalt mixture and distress in pavement are major issues to roads infrastructure. Selecting an appropriate chemical composition of asphalt cement is a key component in avoiding these issues. This work aimed to investigate the effect of the chemical composition of different polar fractions on the rheological and physical properties of asphalt cement. Four types of asphalt cement with penetration grades of 20/30, 40/50, 60/70 and 85/100 were divided into four fractions. Complex shear modules, rutting resistance and rotational viscosity of the asphalt cement were determined by using a Dynamic Shear Rheometer and a Rotation Viscometer, respectively. The results show that an increase in the asphaltene content and Gastel index resulte
... Show MoreFive sites were chosen to the north of Babil Governorate in order to identify the limnological features and the impact of the Hindiya Dam during 2019. Site2 was located near the dam to reflect the ecological features of this site, whereas other sites, S1 was located at the upstream of the dam as a control site. Moreover, the two other sites S3 and S4 were located down the dam. The results of the study showed a close correlation between air and water temperature at all sites. Also there were significant differences in average of thirteen out of eighteen water parameters.Water temperature, total alkalinity, bicarbonate, DO, POS, TH and Mg+2 ions decreased from 22.76˚C, 203.33 mg/L,
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