Simulation of liquid-liquid extraction data with artificial neural networks

Liquid-liquid extraction is not understood well enough to allow acceptably accurate design calculations to be made. Modelling and simulation can be difficult and time-consuming and is usually heavily dependent on empirical correlations of restricted range of applicability. The use of artificial neural networks to achieve more precise simulation has therefore been examined. Application to multicomponent equilibrium and diffusion coefficient data, extraction column hydrodynamic data (drop sizes and hold-up), mass transfer stage efficiency and performance prediction of an industrial extraction column has been carried out with widely varying degrees of success. The lack of data for building a neural network is the largest problem faced.