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In this paper the bioethanol production in batch culture by free Saccharomyces cerevisiae cells from thick juice as intermediate product of sugar beet processing was examined. The obtained results suggest that it is possible to decrease fermentation time for the cultivation medium based on thick juice with starting sugar content of 5-15 g kg-1. For the fermentation of cultivation medium based on thick juice with starting sugar content of 20 and 25 g kg-1 significant increase in ethanol content was attained during the whole fermentation process, resulting in 12.51 and 10.95 dm3 m-3 ethanol contents after 48 h, respectively. Other goals of this work were to investigate the possibilities for experimental results prediction using artificial neural networks (ANNs) and to find its optimal topology. A feed-forward back-propagation artificial neural network was used to test the hypothesis. As input variables fermentation time and starting sugar content were used. Neural networks had one output value, ethanol content, yeast cell number or sugar content. There was one hidden layer and the optimal number of neurons was found to be nine for all selected network outputs. In this study transfer function was tansig and the selected learning rule was Levenberg-Marquardt. Results suggest that artificial neural networks are good prediction tool for selected network outputs. It was found that experimental results are in very good agreement with computed ones. The coefficient of determination (the R-squared) was found to be 0.9997, 0.9997 and 0.9999 for ethanol content, yeast cell number and sugar content, respectively.

Investigations include the analysis of the impact of commercial products: complex additive (0.1, 0.3, and 0.5%), L-ascorbic acid (0.002, 0.004 and 0.012%), diacetyl ester of tartaric acid with monoglycerides (DATEM E472e, 0.1, 0.3, and 0.5%), α-amylase (0.002, 0.006 and 0.012%), xylanase (0.004, 0.012 and 0.024%), alcohol extract of rosemary, thyme or sage (0.5, 1.0 and 2.0%), as well as the combination of complex additive and rosemary, thyme and sage extract on rheological characteristics of dough. The study includes amylograph, farinograph and extensograph analysis of dough with and without additives (control sample). The volume of lost CO2 gas (mL) is the lowest in dough samples with an added combination of complex additive and thyme extract (0.05 and 0.5%) and rosemary extract (2.0%). In the samples with thyme extract (1.0%) added, the volume of lost gas is at a level of samples with added complex additive, DATEM, and L-ascorbic acid.