Modeling of an integrated bioreactor/pervaporation system, for bioethanol production, based on a hybrid neural approach

The use of immobilized cells permits to enhance fermentation productivities, and it is suitable for possible integration with continuous ethanol stripping technologies such as pervaporation. The fermentation of glucose by Saccharomyces cerevisiae immobilized in alginate beads and the simultaneous pervaporation of the produced ethanol was modeled by means of a hybrid model in order to estimate the kinetic parameters on the basis of experimental data collected during batch fermentation. Hybrid model predictions are given as a combination of both a theoretical and a “pure” neural network approach, together concurring to the obtainment of system responses. The hybrid modeling permits to describe processes by means of a fundamental theoretical approach, based on the equations of mass conservation coupled with a simple “cause-effect” models, based on Artificial Neural Networks (ANNs). By this model, performances of an integrated fermentation-separation system, finalized to the production of advanced bio-ethanol from different kind of substrates both real and synthetic hydrolyzates, have been evaluated too.