| Revista: | Brazilian journal of chemical engineering |
| Base de datos: | PERIÓDICA |
| Número de sistema: | 000308774 |
| ISSN: | 0104-6632 |
| Autores: | Cubillos, F1 Callejas, H Lima, E.L2 Vega, M.P |
| Instituciones: | 1Universidad de Santiago de Chile, Departamento de Ingeniería Química, Santiago de Chile. Chile 2Universidade Federal do Rio de Janeiro, Instituto Alberto Luiz Coimbra de Pos-Graduacao e Pesquisa de Engenharia, Rio de Janeiro. Brasil |
| Año: | 2001 |
| Periodo: | Mar |
| Volumen: | 18 |
| Número: | 1 |
| Paginación: | 113-120 |
| País: | Brasil |
| Idioma: | Inglés |
| Tipo de documento: | Artículo |
| Enfoque: | Experimental |
| Resumen en inglés | This work presents the use of a hybrid-neural model for predictive control of a plug flow polymerisation reactor. The hybrid-neural model (HNM) is based on fundamental conservation laws associated with a neural network (NN) used to model the uncertain parameters. By simulations, the performance of this approach was studied for a peroxide-initiated styrene tubular reactor. The HNM was synthesised for a CSTR reactor with a radial basis function neural net (RBFN) used to estimate the reaction rates recursively. The adaptive HNM was incorporated in two model predictive control strategies, a direct synthesis scheme and an optimum steady state scheme. Tests for servo and regulator control showed excellent behaviour following different setpoint variations, and rejecting perturbations. The good generalisation and training capacities of hybrid models, associated with the simplicity and robustness characteristics of the MPC formulations, make an attractive combination for the control of a polymerisation reactor |
| Disciplinas: | Química |
| Palabras clave: | Ingeniería química, Reactores de polimerización, Control predictivo, Modelo neural híbrido, Redes neuronales |
| Keyword: | Chemistry, Chemical engineering, Polymerization reactors, Predictive control, Hybrid-neural model, Neural networks |
| Texto completo: | Texto completo (Ver HTML) |
