Using hybrid neural models to describe supercritical fluid extraction processes

This work presents the results of a hybrid neural model (HNM) technique as applied to modeling supercritical fluid extraction (SCFE) curves obtained f rom two Brazilian vegetable matrices. The serial HNM employed uses a neural network to estimate parameters of a phenomenological model. A small set of SCFE data for each vegetable was used to generate a semi-empirical extende d data set, large enough for efficient network training, using three differ ent approaches. Afterwards, other sets of experimental data, not used durin g the training procedure, were used to validate each approach. The HNM corr elates well with the experimental data, and it is shown that the prediction s accomplished with this technique may be promising for SCFE purposes.