Use of neural networks for liquid–liquid extraction column modelling: an experimental study

This paper presents a new application of neural networks to the modelling of a chemical pilot plant: a pulsed liquid–liquid extraction column. This separation process presents a highly non-linear behaviour and time-varying dynamics. Usually, physical simulation models of chemical plants describing some aspects of hydrodynamics and mass transfer are static or very complex and need excessive computer time. It is proposed that improved predictions can be obtained using a multilayer artificial neural network instead of the physical model of the process. The results obtained illustrate the successful application of such a neural network modelling approach.