Modeling the Biosurfactant Fermentation by Geobacillus stearothermophilus DSM2313

Abstract

Biosurfactants are emerging molecules in the 21^st century. However, their production intensification is still required for the development of feasible bioprocesses. Therefore, this paper studies a new biosurfactant-producer, namely Geobacillus stearothermophilus DSM2313 during statistical optimization via response surface methodology. After the statistical analysis the optimal pH = 7, glucose = 50 g/L and NH₄NO₃ = 2 g/L concentrations were determined. The biosurfactant production of the bacteria was predicted by our developed artificial neural network. The optimal harvesting time of the broth and the emulsification index values can be predicted simultaneously with the constructed artificial neural network. The best experiment was also kinetically described, and kinetic constants observed. Surface tension and emulsification activity were measured to characterize the formed products' efficiency. Based on these results, biosurfactants from Geobacillus stearothermophilus DSM2313 can act as bioemulsifier and can be applied for example in microbial enhanced oil recovery.