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Wine industry generates large volumes of wastewaters resulting from numerous cleaning operations that occur during the different stages of winemaking. Disposal of these effluents in the environment causes huge problems due to their high organic and inorganic load and seasonal variability. The bioconversion of winery wastewaters in valuable product, such as xanthan, is an important alternative to overcome environmental problems. In this research, the possibility of xanthan production using Xanthomonas campestris on blended wastewaters from different stages of white and rose wine production with initial sugar content of 50 g/L was investigated. In addition to the media parameters (content of sugars, total and assimilable nitrogen, phosphorus, total dissolved salts and apparent viscosity), raw xanthan yield and degree of sugar conversion into product were determined in order to examine the success of xanthan biosynthesis. In applied experimental conditions, xanthan yield of 20.92 and 30.64 g/L and sugar conversion into product of 40.23 and 60.73% were achieved on wastewaters from white and rose wine production, respectively. The results of these experiments suggest that winery wastewaters, after additional optimization of the process in terms of the substrate composition and the cultivation conditions, may be a suitable raw material for industrial xanthan production.

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.