Application of radial basis function neural network for removal of copper using an emulsion liquid membrane process assisted by ultrasound

In this present work, artificial neural network was applied for the prediction of the breakage percentage and the extraction efficiency for the removal of copper using emulsion liquid membrane process. The effect of operational parameters such as emulsification time, ultrasonic power, stirring speed, sulfuric acid concentration, extractant concentration, surfactant concentration, internal phase/organic phase volume ratio, emulsion/external phase volume ratio, and copper concentration in the external phase were studied to optimize the condition for maximum copper removal. The performance of the proposed model (radial basis functionRBF) for predicting copper removal efficiency was found to be very impressive. The RBF model perfectly represents the experimental data.