Performance of Artificial Neural Network for Predicting Fermentation Characteristics in Biosurfactant Production by Bacillus subtilis ATCC 6633 using Sugar Cane Molasses
2011
Rahimi Kashkouli, Yousef | Mogharei, Azadeh | Mousavian, Saman | Vahabzadeh, Farzaneh
Artificial neural network (ANN) was successfully applied to model fermentation parameters for biosurfactant production by Bacillus subtilis ATCC 6633 using sugar cane molasses. Cell growth and biosurfactant production were monitored along the surface activity of the cell-free broth. Response surface methodology (RSM) as a formal statistical model building system was used for the ANN development. The network predicted biosurfactant concentration was 0.381 g/l which showed almost no differences with the relevant experimental value which obtained according to the RSM arrangement. Furthermore, the ANN surface tension reduction was 30.48 mN/m, which was within 3.24% of the experimental value. Comparisons between RSM and the ANN showed preference of using ANN as complementary to RSM and not as a replacement to it.
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