Modeling the Biosurfactant Fermentation by Geobacillus stearothermophilus DSM2313

Authors

  • Réka Czinkóczky
    Affiliation
    Department of Applied Biotechnology and Food Science, Faculty of Chemical Technology and Biotechnology, Budapest University of Technology and Economics, Műegyetem rkp. 3., H-1111 Budapest, Hungary
  • Áron Németh
    Affiliation
    Department of Applied Biotechnology and Food Science, Faculty of Chemical Technology and Biotechnology, Budapest University of Technology and Economics, Műegyetem rkp. 3., H-1111 Budapest, Hungary
https://doi.org/10.3311/PPch.20797

Abstract

Biosurfactants are emerging molecules in the 21st 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.

Keywords:

biosurfactant, bioemulsifier, response surface methodology, kinetic modeling, artificial neural network

Citation data from Crossref and Scopus

Published Online

2023-02-01

How to Cite

Czinkóczky, R., Németh, Áron “Modeling the Biosurfactant Fermentation by Geobacillus stearothermophilus DSM2313”, Periodica Polytechnica Chemical Engineering, 67(1), pp. 104–115, 2023. https://doi.org/10.3311/PPch.20797

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Articles