Abstract

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Abstract

Molasses, a by-product of the sugar manufacturing process, generally comprises approximately 50% (w/w) of total sugars, but it is currently used primarily [1] as an animal feed and as a raw material in alcohol production. Currently, the sugar production is more than 160 million tones worldwide. Its byproduct molasses contain heavy metals which have growthinhibitory effect. The main sugar content in molasses is sucrose which often need to be hydrolyzed to glucose and fructose especially for utilization by Lactobacillus species. Lactobacillus species can convert sugar content to lactic acid with great efficiency, which is a valuable chemical. Lactic acid production from sugar molasses using batch fermentations with Lactobacillus casei and Lactobacillus sp. MKT878 were investigated in this study. Results showed, that both examined Lactobacillus species could grow on molasses despite the heavy metals inhibitory effects. The conversion of sugar content to lactic acid was successful with yield between 55-80 g/g.

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.