Abstract
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.
Highlights
Cilj ovog rada je odabir optimalne arhitekture neuronske mreže i njena primena u modelovanju procesa alkoholne fermentacije podloga na bazi gustog soka kao međuproizvoda tehnologije prerade šećerne repe
Sterilizacija pripremljenih hranljivih podloga je izvedena u autoklavu pri temperaturi od 121 °C i pritisku od 2,2 bar u trajanju od 30 min
Fermentacija hranljive podloge u trajanju od 48 h, uključujući i vreme potrebno za suspendovanje inokuluma, je izvedena šaržnim postupkom pod anaerobnim uslovima, pri temperaturi od 30 °C i brzini mešanja 200 obrt/min, koja je obezbeđena postavljanjem bioreaktora na mikrobiološku laboratorijsku tresilicu u termostatu
Summary
Interesovanje za etanol kao zamenu za fosilne izvore energije je u porastu zbog težnje da se smanji negativan uticaj na životnu sredinu i postigne energetska sigurnost. U literaturi najčešće se navode koncept neuronskih mreža i metoda odzivnih površina. Metoda odzivnih površina korišćena je za modelovanje mikrofiltracije suspenzija kvasca [7], optimizaciju alkoholne fermentacije ekstrakcionog soka šećerne repe [2], optimizaciju proizvodnje biodizela [8]. Iako metoda odzivnih površina ima svoje prednosti teško je reći da ona može da se primeni na sve biotehnološke procese i operacije [9,10,11]. Koncept neuronskih mreža nije nov, ali se interesovanje za njega povećalo tokom poslednjih godina [12] i najpopularniji je za primene u biotehnologiji [6]. Cilj ovog rada je odabir optimalne arhitekture neuronske mreže i njena primena u modelovanju procesa alkoholne fermentacije podloga na bazi gustog soka kao međuproizvoda tehnologije prerade šećerne repe
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