No abstract

In the present study, the removal of zinc from synthetic waste water using emulsion liquid membrane extraction technique was investigated. Synthetic surfactant solution is used as the emulsifying agent. Diphenylthiocarbazon (dithizone) was used as the extracting agent dissolved in carbon tetrachloride as the organic solvent and sulfuric acid is used as the stripping agent. The parameters that influence the extraction percentage of Zn+2 were studied. These are the ratio of volume of organic solvent to volume of aqueous feed (0.5-4), ratio of volume of surfactant solution to volume of aqueous feed (0.2-1.6), pH of the aqueous feed solution (5-10), mixing intensity (100-1000) rpm, concentration of extracting agent (20-400) ppm, surfactant concentration (0.2-2) wt.%, contact time (3-30) min, and concentration of strip phase (0.25-2) M . It was found that 87.4 percent of Zn+2 can be removed from the aqueous feed solution at the optimum operating conditions.

An artificial neural network (ANN) was applied for the prediction of the heat transfer coefficient in bubble columns, in order to obtain a general model and to facilitate the scale up of these multiphase contactors, covering a wide range of operating conditions, physical properties, and column dimensions, obtained from literature. A large number of data was collected (more than 1000) via a comprehensive literature survey. Selected parameters affecting the heat transfer coefficient were organized in six groups to serve as the input parameters. These were: gas superficial velocity, gas density, liquid density, diameter of the column, liquid viscosity, and gas hold-up. Four Back-Propagation Networks (BPNNS) were built. Two were trained using a different number of input parameters. The first ANN was trained with six inputs, which were the aforementioned parameters. The second was trained with three inputs only. These were gas velocity, liquid viscosity and gas hold-up. Each ANN was examined for two structures i.e., one hidden layer and two hidden layers. Comparison between these networks was made to find the optimal ANN structure with minimum %AARE and the maximum correlation coefficient (%R). It was found that the ANN structure of [6-13-1] with a %AARE of 16.2 and a %R of 94 was the best.

A particular solution of the two and three dimensional unsteady state thermal or mass diffusion equation is obtained by introducing a combination of variables of the form,η = (x+y) / √ct , and η = (x+y+z) / √ct, for two and three dimensional equationsrespectively. And the corresponding solutions are,θ (t,x,y) = θ0 erfc (x+y)/√8ct and θ( t,x,y,z) =θ0 erfc (x+y+z/√12ct)

In this work, the possibility of utilizing osmosis phenomenon to produce energy as a type of the renewable energy using Thin Film Composite Ultra Low Pressure membrane TFC-ULP was studied. Where by forward osmosis water passes through the membrane toward the concentrated brine solution, this will lead to raise the head of the high brine solution. This developed static head may be used to produce energy. The aim of the present work is to study the static head developed and the flux on the high brine water solution side when using forward and reverse osmosis membranes for an initial concentration range from 35-300 g/l for each type of membrane used at room temperature and pressure conditions, and finally calculating the maximum possible power generated from developed static head.