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http://hdl.handle.net/1843/47828| Tipo: | Artigo de Periódico |
| Título: | Comparison of response surface methodology and artificial neural network for modeling xylose-to-xylitol bioconversion |
| Autor(es): | Fábio Coelho Sampaio Janaína Teles de Faria Gabriel Dumond de Lima Silva Ricardo Melo Gonçalves Cristiano Grijó Pitangui Alessandro Alberto Casazza Saleh al Arni Attilio Converti |
| Resumen: | Previous experimental data of xylose-to-xylitol bioconversion by Debaryomyces hansenii carried out according to a 33 full factorial design were used to model this process by two different artificial neural network (ANN) training methods. Models obtained for four responses were compared with those of response surface methodology (RSM). ANN models were shown to be superior to RSM in the predictive capacity, whereas the latter showed better performance in the generalization capability step. RSM with optimization using a genetic algorithm was revealed as a whole to be the best modeling option, which suggests that the comparative performances of RSM and ANN may be a highly problem-specific issue. |
| Asunto: | Redes neurais (Computação) Levedos |
| Idioma: | eng |
| País: | Brasil |
| Editor: | Universidade Federal de Minas Gerais |
| Sigla da Institución: | UFMG |
| Departamento: | ICA - INSTITUTO DE CIÊNCIAS AGRÁRIAS |
| Tipo de acceso: | Acesso Restrito |
| Identificador DOI: | https://doi.org/10.1002/ceat.201600066 |
| URI: | http://hdl.handle.net/1843/47828 |
| Fecha del documento: | 2017 |
| metadata.dc.url.externa: | https://onlinelibrary.wiley.com/doi/full/10.1002/ceat.201600066 |
| metadata.dc.relation.ispartof: | Chemical Engineering & Technology |
| Aparece en las colecciones: | Artigo de Periódico |
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