A study in the treatment and reuse of oily wastewater generated from the process of fuel oil treatment of gas turbine power plant was performed. The feasibility of using hollow fiber ultrafiltration (UF) membrane and nanofiltration (NF) membrane type polyamide thin-film composite in a pilot plant was investigated. Three different variables: pressure (0.5, 1, 1.5 and 2 bars), oil content (10, 20, 30 and 40 ppm), and temperature (15, 20, 30 and 40 ᵒC) were employed in the UF process while TDS was kept constant at 150 ppm. Four different variables: pressure (2, 3, 4 and 5 bar), oil content (2.5, 5, 7.5 and 10 ppm), total dissolved solids (TDS) (100, 200,300 and 400 ppm), and temperature (15, 20, 30 and 40 ᵒC) were manipulated with the help of statistical method of Taguchi in the RO process. Analysis of variable (ANOVA) and optimum condition was investigated. The study shows that pressure has the greatest impact on the flux of UF process, while temperature and pressure have similar contribution on flux of NF process. The temperature seems to have the greatest effect on TDS rejection. It was noticed that more than 96% oil removal can be achieved with flux of 624 L/m2.hr by UF process and that the fouling mechanism of UF process follows the cake/gel layer filtration model. It was observed that 100% removal of oil content can be achieved along with 79% for the TDS rejection and flux of 65 L/m2.hr by NF process. The result shows fouling in NF process follows the cake filtration model. It was concluded that the observed values are within ±5% of that the predicted which reflects a strong representative model. The treated wastewater has the characteristics that it can be reused in the process to reduce the operating cost.
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Temperature predicting is the utilization to forecast the condition of the temperature for an upcoming date for a given area. Temperature predictions are done by gathering quantitative data in regard to the current state of the atmosphere. In this study, a proposed hybrid method to predication the daily maximum and minimum air temperature of Baghdad city which combines standard backpropagation with simulated annealing (SA). Simulated Annealing Algorithm are used for weights optimization for recurrent multi-layer neural network system. Experimental tests had been implemented using the data of maximum and minimum air temperature for month of July of Baghdad city that got from local records of Iraqi Meteorological O
... Show MoreIndustrial wastewater containing nickel, lead, and copper can be produced by many industries. The reverse osmosis (RO) membrane technologies are very efficient for the treatment of industrial wastewater containing nickel, lead, and copper ions to reduce water consumption and preserving the environment. Synthetic industrial wastewater samples containing Ni(II), Pb(II), and Cu(II) ions at various concentrations (50 to 200 ppm), pressures (1 to 4 bar), temperatures (10 to 40 oC), pH (2 to 5.5), and flow rates (10 to 40 L/hr), were prepared and subjected to treatment by RO system in the laboratory. The results showed that high removal efficiency of the heavy metals could be achieved by RO process (98.5%, 97.5% and 96% for Ni(II),
... Show MoreThere are still areas around the world suffer from severe shortage of freshwater supplies. Desalination technologies are not widely used due to their high energy usage, cost, and environmental damaging effects. In this study, a mathematical model of single-bed adsorption desalination system using silica gel-water as working pair is developed and validated via earlier experiments. A very good match between the model predictions and the experimental results is recorded. The objective is to reveal the factors affecting the productivity of fresh water and cooling effect in the solar adsorption system. The proposed model is setup for solving within the commercially-available software (Engineering Equation Solver). It is implemented to so
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In the present work advanced oxidation process, photo-Fenton (UV/H2O2/Fe+2) system, for the treatment of wastewater contaminated with oil was investigated. The reaction was influenced by the input concentration of hydrogen peroxide H2O2, the initial amount of the iron catalyst Fe+2, pH, temperature and the concentration of oil in the wastewater. The removal efficiency for the system UV/ H2O2/Fe+2 at the optimal conditions and dosage (H2O2 = 400mg/L, Fe+2 = 40mg/L, pH=3, temperature =30o C) for 1000mg/L load was found to be 72%.
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This study focused on treating wastewater to remove phosphorus by adsorption onto naturaland local materials. Burned kaolin, porcelinite, bauxite and limestone were selected to be testedas adsorption materials.The adsorption isotherms were evaluated by batch experiments, studyingthe effects of pH, temperature and initial phosphorus concentration. The results showed that at pH6, temperature 20°C and 300 mg/l initial phosphorus concentration; the sorption capacity was0.61, 9, 10 and 13 mg/g at 10 h contact time, for burned kaolin, porcelanite, limestone and bauxiterespectively. As the pH increased from 2 to 10 the removal efficiency for the materials differs inbehaviour. The removal efficiency increased from 40 to 90 % for limestone, and dec
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Water quality planning relies on Biochemical Oxygen Demand BOD. BOD testing takes five days. The Particle Swarm Optimization (PSO) is increasingly used for water resource forecasting. This work designed a PSO technique for estimating everyday BOD at Al-Rustumiya wastewater treatment facility inlet. Al-Rustumiya wastewater treatment plant provided 702 plant-scale data sets during 2012-2022. The PSO model uses the daily data of the water quality parameters, including chemical oxygen demand (COD), chloride (Cl-), suspended solid (SS), total dissolved solids (TDS), and pH, to determine how each variable affects the daily incoming BOD. PSO and multiple linear regression (MLR) findings are compared, and their perfor
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Water quality planning relies on Biochemical Oxygen Demand BOD. BOD testing takes five days. The Particle Swarm Optimization (PSO) is increasingly used for water resource forecasting. This work designed a PSO technique for estimating everyday BOD at Al-Rustumiya wastewater treatment facility inlet. Al-Rustumiya wastewater treatment plant provided 702 plant-scale data sets during 2012-2022. The PSO model uses the daily data of the water quality parameters, including chemical oxygen demand (COD), chloride (Cl-), suspended solid (SS), total dissolved solids (TDS), and pH, to determine how each variable affects the daily incoming BOD. PSO and multiple linear regression (MLR) findings are compared, and their performance is evaluated usin
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