A hybrid recurrent neural network model for yeast production monitoring and control in a wine base medium

Abstract

A dynamic model based on a recurrent neural network was established to follow the growth of yeast in a wine-base medium. It leads to the estimation and prediction of the yeast concentration in batch cultures, based on the on-line measurement of the volume of CO 2 released and the initial yeast concentration. The mean error of the predicted value of the final yeast concentration is lower than 5%. A hybrid model combining this model with a measurement model (based on linear correlations reflecting the reaction scheme) also leads to the estimation and prediction of the sugar and ethanol concentrations in the culture medium with respective mean errors of 1.6 and 1 g l −1. Moreover, this model was used in an open-loop control strategy in order to achieve a final concentration of yeast by setting the culture temperature. Adjusting culture temperature during growth was necessary for only 4% of the cultures, in order to remain within the range of measurement error (3×10 6 cells ml −1) of yeast concentration. The performance of the model and of the control algorithm used could be assessed by controlling six successive cultures.