Diyala river is the most important tributaries in Iraq, this river suffering from pollution, therefore, this research aimed to predict organic pollutants that represented by biological oxygen demand BOD, and inorganic pollutants that represented by total dissolved solids TDS for Diyala river in Iraq, the data used in this research were collected for the period from 2011-2016 for the last station in the river known as D17, before the river meeting Tigris river in Baghdad city. Analysis Neural Network ANN was used in order to find the mathematical models, the parameters used to predict BOD were seven parameters EC, Alk, Cl, K, TH, NO3, DO, after removing the less importance parameters. While the parameters that used to predict TDS were fourteen parameters pH, DO, BOD, PO4, NO3,Ca, Mg, TH, K, Na, SO4,Cl, EC, Alk. The results indicated that the best correlation coefficient is 86.5% for BOD, and the most important parameter is Chloride Cl, and the best correlation coefficient is 95.4% for TDS and the most important parameters are total hardness TH and electrical conductivity EC, according to direct relation between these parameters and TDS.
Although many technological improvements are occurring in power production worldwide, power plants in third world countries are still using old technologies that are causing thermal pollution to the water bodies. Power facilities that dump hot water into water bodies are damaging aquatic life. In the study, the impact of the Al Dora thermal power plant on a nearby stretch of Tigris River in Baghdad city was assessed by measuring the temperature of the disposed of hot water in various cross-sections of the selected stretch of Tigris River, including measuring the thermal mixing length. The measurements were conducted in winter, spring, and summer. For field measurements, it was found that the impact of recovery distances
... Show MoreThe 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
... Show MoreIn this paper,we focus on the investigated and studied of transition rate in metal/organic semiconductor interface due to quantum postulate and continuum transition theory. A theoretical model has been used to estimate the transition rate cross the interface through estimation many parameters such that ;transition energy ,driving electronic energy U(eV) ,Potential barrier ,electronic coupling ,semiconductor volume ,density ,metal work function ,electronic affinity and temperature T. The transition energy is critical facter of charge transfer through the interfaces of metal organic films device and itscontrol of charge injection and transport cross interface. However,the potential at interfa
... Show MoreIron–phthalocyanine (FePc) organic photoconductive detector was fabricated using pulsed laser deposition (PLD) technique to work in ultraviolet (UV) and visible regions. The organic semiconductor material (iron phthalocyanine) was deposited on n-type silicon wafer (Si) substrates at different thicknesses (100, 200 and 300) nm. FePc organic photoconductive detector has been improved by two methods: the first is to manufacture the detector on PSi substrates, and the second is by coating the detector with polyamide–nylon polymer to enhance the photoconductivity of the FePc detector. The current–voltage (I–V) characteristics, responsivity, photocurrent gain, response time and the quantum efficiency of the fabricated photoconduc
... Show MoreThis work was conducted to study the extraction of eucalyptus oil from natural plants (Eucalyptus camadulensis leaves) by organic solvents. the effects of the main operating parameters were studied; type of solvent (n-hexane and ethanol), time to reach equilibrium, the temperature (45°C to 65°C) for n-hexane and (45°C to 75°C) for ethanol, solvent to solid ratio (5:1 to 8:1 (v/w)), agitation speed (0 to 900 rpm) and the particle size (0.5 to 2.5 cm) of fresh leaves to find the best processing conditions for the achieving maximum oil yield. The concentration of eucalyptus oil in solvent was measured by using UV-spectrophotometer. The results (for n-hexane) showed that the agitation speed of 900 rpm, temperature 65°C with solvent to soli
... Show MoreThe limitations of wireless sensor nodes are power, computational capabilities, and memory. This paper suggests a method to reduce the power consumption by a sensor node. This work is based on the analogy of the routing problem to distribute an electrical field in a physical media with a given density of charges. From this analogy a set of partial differential equations (Poisson's equation) is obtained. A finite difference method is utilized to solve this set numerically. Then a parallel implementation is presented. The parallel implementation is based on domain decomposition, where the original calculation domain is decomposed into several blocks, each of which given to a processing element. All nodes then execute computations in parall
... Show MoreIn this paper, precision agriculture system is introduced based on Wireless Sensor Network (WSN). Soil moisture considered one of environment factors that effect on crop. The period of irrigation must be monitored. Neural network capable of learning the behavior of the agricultural soil in absence of mathematical model. This paper introduced modified type of neural network that is known as Spiking Neural Network (SNN). In this work, the precision agriculture system is modeled, contains two SNNs which have been identified off-line based on logged data, one of these SNNs represents the monitor that located at sink where the period of irrigation is calculated and the other represents the soil. In addition, to reduce p
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