Applicability of neural networks for the fermentation of propionic acid by propionibacterium acidipropionici

Abstract

According to our best knowledge, this is the first report applying Artificial neural networks (ANN) for simulation of batch propionic acid (PA) fermentation. Therefore, the main focus of this research was to investigate the applicability of ANN on PA fermentations. To demonstrate this, we used the results of 40 Propionibacterium acidipropionici fermentations (ca 2,000 data points) to build up the ANN, and additional two independent fermentations to demonstrate the prediction capability of the observed ANN. Analyzing the predicted output parameters we observed, that ratio of propionic acid to acetic acid (PA/AA) variables can only be used for ANN after normalization. Finally, the fit of the ANN model to the measured data was fine (average correlation coefficients over 0.9). A special feature was also tested: fermentation time was also used as an input parameter, thus making the ANN suitable to predict time course of PA fermentations as well which was also satisfying.