• Membrane and Water Treatment
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• 제9권2호
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• pp.115-121
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• 2018

Artificial neural network (ANN) simulation is used to predict the dynamic change of permeate flux during wheat starch industry wastewater microfiltration with and without static turbulence promoter. The experimental program spans range of a sedimentation times from 2 to 4 h, for feed flow rates 50 to 150 L/h, at transmembrane pressures covering the range of $1{\times}10^5$ to $3{\times}10^5Pa$. ANN predictions of the wastewater microfiltration are compared with experimental results obtained using two different set of microfiltration experiments, with and without static turbulence promoter. The effects of the training algorithm, neural network architectures on the ANN performance are discussed. For the most of the cases considered, the ANN proved to be an adequate interpolation tool, where an excellent prediction was obtained using automated Bayesian regularization as training algorithm. The optimal ANN architecture was determined as 4-10-1 with hyperbolic tangent sigmoid transfer function transfer function for hidden and output layers. The error distributions of data revealed that experimental results are in very good agreement with computed ones with only 2% data points had absolute relative error greater than 20% for the microfiltration without static turbulence promoter whereas for the microfiltration with static turbulence promoter it was 1%. The contribution of filtration time variable to flux values provided by ANNs was determined in an important level at the range of 52-66% due to increased membrane fouling by the time. In the case of microfiltration with static turbulence promoter, relative importance of transmembrane pressure and feed flow rate increased for about 30%.

• Membrane and Water Treatment
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• 제7권3호
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• pp.175-191
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• 2016

In this work, oil-in-water emulsions (O/W) were prepared successfully by membrane emulsification with $0.5{\mu}m$ pore size membrane. Sunflower oil was emulsified in aqueous Tween80 solution with a simple crossflow apparatus equipped with ceramic tube membrane. In order to increase the shear-stress near the membrane wall, a helical-shaped reducer was installed within the lumen side of the tube membrane. This method allows the reduction of continuous phase flow and the increase of dispersed phase flux, for cost effective production. Results were compared with the conventional cross-flow membrane emulsification method. Monodisperse O/W emulsions were obtained using tubular membrane with droplet size in the range $3.3-4.6{\mu}m$ corresponded to the membrane pore diameter of $0.5{\mu}m$. The final aim of this study is to obtain O/W emulsions by simple membrane emulsification method without reducer and compare the results obtained by membrane equipped with helix shaped reducer. To indicate the results statistical methods, $3^p$ type full factorial experimental designs were evaluated, using software called STATISTICA. For prediction of the flux, droplet size and PDI a mathematical model was set up which can describe well the dependent variables in the studied range, namely the run of the flux and the mean droplet diameter and the effects of operating parameters. The results suggested that polynomial model is adequate for representation of selected responses.