Abstract:
This work is aimed at assessing the use of response surface methodology (RSM) and artificial neural networks
(ANNs) for modelling, and predicting, the optimum parameters for 1,3-Propanediol production by Lactobacillus brevis N1E9.3.3 from glycerol and glucose co-fermentation. A preliminary study of physical parameters was conducted using Plackett-Burman design to reduce the number of input variables up to seven; i) beef extract, ii) yeast extract, iii) MgSO4·7H2O, iv) MnSO4·H2O, v) vitamin B12, vi) glycerol and vii) glucose. The traditional RSM models were improved by ANN models between a 54.08% and 12.19% in terms of root mean square error (RMSE). This study suggested that RSM and ANN can be considered as effective tools to model and predict optimum parameters for 1,3-Propanediol production by L. brevis N1E9.3.3.
