Application of experimental design approach and artificial neural network (ANN) for the determination of potential micellar-enhanced ultrafiltration process.

In this study, micellar-enhanced ultrafiltration (MEUF) was applied to remove zinc ions from wastewater efficiently. Frequently, experimental design and artificial neural networks (ANNs) have been successfully used in membrane filtration process in recent years. In the present work, prediction of the permeate flux and rejection of metal ions by MEUF was tested, using design of experiment (DOE) and ANN models. In order to reach the goal of determining all the influential factors and their mutual effect on the overall performance the fractional factorial design has been used. The results show that due to the complexity in generalization of the MEUF process by any mathematical model, the neural network proves to be a very promising method in compared with fractional factorial design for the purpose of process simulation. These mathematical models are found to be reliable and predictive tools with an excellent accuracy, because their AARE was ±0.229%, ±0.017%, in comparison with experimental values for permeate flux and rejection, respectively.