dc.contributor.author |
Vivek, N |
|
dc.contributor.author |
Astray, G |
|
dc.contributor.author |
Gullón, B |
|
dc.contributor.author |
Castro, E |
|
dc.contributor.author |
Binod, P |
|
dc.contributor.author |
Pandey, A |
|
dc.date.accessioned |
2018-07-31T09:16:37Z |
|
dc.date.available |
2018-07-31T09:16:37Z |
|
dc.date.issued |
2017-10-15 |
|
dc.identifier.citation |
Biochemical Engineering Journal, 126:109-117 |
en_US |
dc.identifier.uri |
http://10.10.100.66:8080/xmlui/handle/123456789/3235 |
|
dc.description.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. |
en_US |
dc.language.iso |
en |
en_US |
dc.publisher |
Elsevier |
en_US |
dc.subject |
1,3-Propanediol |
en_US |
dc.subject |
Modelling |
en_US |
dc.subject |
Response surface methodology |
en_US |
dc.subject |
Artificial neural networks |
en_US |
dc.title |
Improved 1,3-Propanediol Production with Maintained Physical Conditions and Optimized Media Composition: Validation with Statistical and Neural Approach |
en_US |
dc.type |
Article |
en_US |