Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/3235
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dc.contributor.authorVivek, N-
dc.contributor.authorAstray, G-
dc.contributor.authorGullón, B-
dc.contributor.authorCastro, E-
dc.contributor.authorBinod, P-
dc.contributor.authorPandey, A-
dc.date.accessioned2018-07-31T09:16:37Z-
dc.date.available2018-07-31T09:16:37Z-
dc.date.issued2017-10-15-
dc.identifier.citationBiochemical Engineering Journal, 126:109-117en_US
dc.identifier.urihttp://10.10.100.66:8080/xmlui/handle/123456789/3235-
dc.description.abstractThis 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.isoenen_US
dc.publisherElsevieren_US
dc.subject1,3-Propanediolen_US
dc.subjectModellingen_US
dc.subjectResponse surface methodologyen_US
dc.subjectArtificial neural networksen_US
dc.titleImproved 1,3-Propanediol Production with Maintained Physical Conditions and Optimized Media Composition: Validation with Statistical and Neural Approachen_US
dc.typeArticleen_US
Appears in Collections:2017

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