| Revista: | Brazilian journal of chemical engineering |
| Base de datos: | PERIÓDICA |
| Número de sistema: | 000308678 |
| ISSN: | 0104-6632 |
| Autores: | Zyngier, D1 Araujo, O.Q.F2 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 Federal do Rio de Janeiro, Escola de Quimica, Rio de Janeiro. Brasil |
| Año: | 2000 |
| Periodo: | Dic |
| Volumen: | 17 |
| Número: | 4-7 |
| Paginación: | 433-440 |
| País: | Brasil |
| Idioma: | Inglés |
| Tipo de documento: | Artículo |
| Enfoque: | Experimental, aplicado |
| Resumen en inglés | The increasing degradation of water resources makes it necessary to monitor and control process variables that may disturb the environment, but which may be very difficult to measure directly, either because there are no physical sensors available, or because these are too expensive. In this work, two soft sensors are proposed for monitoring concentrations of nitrate (NO) and ammonium (NH) ions, and of carbonaceous matter (CM) during nitrification of wastewater. One of them is based on reintegration of a process model to estimate NO and NH and on a feedforward neural network to estimate CM. The other estimator is based on Stacked Neural Networks (SNN), an approach that provides the predictor with robustness. After simulation, both soft sensors were implemented in an experimental unit using FIX MMI (Intellution, Inc) automation software as an interface between the process and MATLAB 5.1 (The Mathworks Inc.) software |
| Disciplinas: | Ingeniería, Química |
| Palabras clave: | Ingeniería ambiental, Ingeniería química, Tratamiento de aguas residuales, Sensores virtuales, Redes neuronales |
| Keyword: | Engineering, Chemistry, Chemical engineering, Environmental engineering, Waste water treatment, Soft sensors, Neural networks |
| Texto completo: | Texto completo (Ver HTML) |
