A hybrid neural approach to model batch fermentation of dairy industry wastes

In this work, the fermentation of “Ricotta cheese whey” for the production of ethanol was simulated by means of a Hybrid Neural Model (HNM), obtained by coupling neural network approach to mass balance equations describing the time evolution of lactose (substrate), ethanol (product) and biomass concentrations. The realized HNM was compared with a pure neural network model (NM) and the advantages gained from the hybrid approach were emphasized. The experimental data, necessary to develop the model, were collected during batch fermentation runs. For all the proposed networks, the inputs were chosen as the operating variables exhibiting the highest influence on the reaction rate. The simulation results showed that the HNM was capable of an accurate representation of system behavior by predicting biomass, lactose and ethanol concentration profiles with an average error percentage lower than 10%. Moreover, especially if compared with the NM, the HNM showed good forecasting capability even with fermentation run never seen during the training phase.