Estimating diffusion coefficients of a micellar system using an artificial neural network

Abstract

A three-layer feedforward artificial neural network (ANN) model, trained using the error-back-propagation algorithm has been developed to estimate the diffusion coefficient of sodium dodecyl sulfate (SDS) micellar system over a wide range of operating parameters such as temperature and concentrations of SDS and NaCl. The network model validates the experimentally observed qualitative and quantitative trends. The optimal model parameters in terms of network weights have been estimated and can be used for computing diffusion coefficients over wide-ranging experimental conditions.