| Revista: | Brazilian journal of chemical engineering |
| Base de datos: | PERIÓDICA |
| Número de sistema: | 000308697 |
| ISSN: | 0104-6632 |
| Autores: | Meleiro, L.A.C1 Maciel-Filho, R |
| Instituciones: | 1Universidade Estadual de Campinas, Faculdade de Engenharia Quimica, Campinas, Sao Paulo. Brasil |
| Año: | 2000 |
| Periodo: | Dic |
| Volumen: | 17 |
| Número: | 4-7 |
| Paginación: | 991-1002 |
| País: | Brasil |
| Idioma: | Inglés |
| Tipo de documento: | Artículo |
| Enfoque: | Experimental, aplicado |
| Resumen en inglés | Most advanced computer-aided control applications rely on good dynamics process models. The performance of the control system depends on the accuracy of the model used. Typically, such models are developed by conducting off-line identification experiments on the process. These experiments for identification often result in input-output data with small output signal-to-noise ratio, and using these data results in inaccurate model parameter estimates [1]. In this work, a multivariable adaptive self-tuning controller (STC) was developed for a biotechnological process application. Due to the difficulties involving the measurements or the excessive amount of variables normally found in industrial process, it is proposed to develop "soft-sensors" which are based fundamentally on artificial neural networks (ANN). A second approach proposed was set in hybrid models, results of the association of deterministic models (which incorporates the available prior knowledge about the process being modeled) with artificial neural networks. In this case, kinetic parameters - which are very hard to be accurately determined in real time industrial plants operation - were obtained using ANN predictions. These methods are especially suitable for the identification of time-varying and nonlinear models. This advanced control strategy was applied to a fermentation process to produce ethyl alcohol (ethanol) in industrial scale. The reaction rate considered for substratum consumption, cells and ethanol productions are validated with industrial data |
| Disciplinas: | Química |
| Palabras clave: | Fermentaciones, Producción de etanol, Modelos híbridos, Control adaptativo, Redes neuronales artificiales |
| Keyword: | Chemistry, Fermentation, Ethanol production, Hybrid models, Adaptive control, Artificial neural networks |
| Texto completo: | Texto completo (Ver HTML) |
