An algorithm for real-time estimation of Mezcal fermentation parameters based on redox potential measurements

Abstract

We present an algorithm for the continuous monitoring of the biomass and ethanol concentrations and moreover the kinetic rate in the Mezcal fermentation process. This algorithm performs its task having only available the on-line measurements of the redox potential. The procedure includes an artificial neural network (ANN) that relates the redox potential to the ethanol and biomass concentrations. Then a nonlinear-observer-based algorithm uses the biomass estimations to infer the kinetic rate of this fermentation process. The method shows that the redox potential is a valuable indicator of microorganism metabolic activity during the Mezcal fermentation. In addition, the estimated kinetic rate can be considered as a direct evidence of the presence of mixed culture growth in the process. In this work, the detailed design of the software-sensor is presented, as well as its experimental application at the laboratory level