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The pollution caused by the use of fossil fuels for the production of mechanical or electrical energy is one of the most important environmental issues nowa­days. In this respect, biofuels represent a viable source of energy. Bioethanol as a renewable energy source is derived from organic material of plant origin, so-called biomass, thus reducing environmental pollution. The aim of this study was to analyze the potential of bioethanol in meeting future energy demands in the Republic of Serbia. [Projekat Ministarstva nauke Republike SRbije, br. TR31002: The improvement of bioethanol production from sugar beet processing products]

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.

Methods that can provide adequate accuracy in the estimation of variables from incomplete information are desirable for the prediction of fermentation processes. A feed-forward back-propagation artificial neural network was used for modelling of thick juice fermentation. Fermentation time and starting sugar content were usedas input variables, i.e. nodes. Neural network had one output node (ethanol content, yeast cell number or sugar content). The hidden layer had nine neurons. Garson's algorithm and connection weights were used for interpreting neural network. The inadequacy of Garson's algorithm can be seen by comparing with the results of regression analysis, which indicates that the influence of the fermentation time is higher. A better agreement of the results was obtained using network connection weights, a method that can be used to determine the relative importance of input variables.

Abstract: Due to the reduction of the economic support for refined sugar efforts have been made to find new ways of using sugar beet outside food industry. This paper investigates the possibilities of introducing bioethanol co-production in Serbian sugar plants. Research shows current state of Serbian sugar industry and technical aspects of bioethanol co-production in sugar plants. These results represent important step toward mass production of bioethanol in Serbian factories. The main goals of introducing the concept of sugar and bioethanol coproduction would be efficient use of available resources for the production of energy, reduction of greenhouse gases emission, decreased dependence on import and creation of new jobs. Besides that, it would provide flexibility in terms of variation of produced quantities of sugar and ethanol, depending on the conditions prevailing on the market. [Copyright &y& Elsevier]