Dynamic Control of Industrial Wine Fermentation Using Cognitive System and Acoustic Emission

The alcoholic fermentation of wine is a complex, multivariable chemical process. This study proposes a cognitive system for the dynamic control of the industrial wine fermentation process based on acoustic emission signals. The core of the system uses machine learning algorithms to perform perception tasks and predict density as a relevant chemical parameter for control and decision-making during the process. A hydrophone submerged in the fermentation tank is used to monitor the process. At the TRL4 stage we are currently at, measurements were taken at a winery in the Rioja Designation of Origin and were acquired and stored during the alcoholic fermentation process to be used as input data. Manual measurements collected by the winemaker throughout the fermentation process were used to train and validate the results. The performance of the machine learning model was measured using statistical metrics. The results of the experiments show a high correlation between the density calculated using the model and the densities measured by the winemaker. The proposed system is a valid and innovative tool for controlling a process as multivariable as alcoholic fermentation. The anticipatory nature of the acoustic signal with regard to the evolution of temperature in the process is used as the starting point for the new proposal. Its application helps to ensure stable fermentation by reducing the thermal stress on yeasts caused by the thermal shocks of current temperature control systems, improving process control in wineries and serving as a cognitive system for control and decision-making.