| Revista: | Brazilian journal of chemical engineering |
| Base de datos: | PERIÓDICA |
| Número de sistema: | 000308639 |
| ISSN: | 0104-6632 |
| Autores: | Costa, A.C1 Henriques, A.S.W Alves, T.L.M Maciel-Filho, R2 Lima, E.L |
| Instituciones: | 1Universidade Federal do Rio de Janeiro, Instituto Alberto Luiz Coimbra de Pos-Graduacao e Pesquisa de Engenharia, Rio de Janeiro. Brasil 2Universidade Estadual de Campinas, Faculdade de Engenharia Quimica, Campinas, Sao Paulo. Brasil |
| Año: | 1999 |
| Periodo: | Mar |
| Volumen: | 16 |
| Número: | 1 |
| Paginación: | 53-63 |
| País: | Brasil |
| Idioma: | Inglés |
| Tipo de documento: | Artículo |
| Enfoque: | Experimental, aplicado |
| Resumen en inglés | In this work a hybrid neural modelling methodology, which combines mass balance equations with functional link networks (FLNs), used to represent kinetic rates, is developed for bioprocesses. The simple structure of the FLNs allows the easy and rapid estimation of network weights and, consequently, the use of the hybrid model in an adaptive form. As the proposed model is able to adjust to kinetic and environmental changes, it is suitable for use in the development of optimization strategies for fed-batch bioreactors. The proposed methodology is used to model the processes for penicillin and ethanol production, and the development of an adaptive optimal control scheme is discussed using ethanol fermentation as an example |
| Disciplinas: | Química, Ingeniería |
| Palabras clave: | Fermentaciones, Ingeniería química, Fermentación por lotes, Control óptimo, Modelación neural híbrida |
| Keyword: | Chemistry, Engineering, Fermentation, Chemical engineering, Fed-batch fermentation, Optimal control, Hybrid neural modelling |
| Texto completo: | Texto completo (Ver HTML) |
