Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/3235
Title: Improved 1,3-Propanediol Production with Maintained Physical Conditions and Optimized Media Composition: Validation with Statistical and Neural Approach
Authors: Vivek, N
Astray, G
Gullón, B
Castro, E
Binod, P
Pandey, A
Keywords: 1,3-Propanediol
Modelling
Response surface methodology
Artificial neural networks
Issue Date: 15-Oct-2017
Publisher: Elsevier
Citation: Biochemical Engineering Journal, 126:109-117
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.
URI: http://10.10.100.66:8080/xmlui/handle/123456789/3235
Appears in Collections:2017

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