Model-based automatic tuning of a filtration control system for submerged anaerobic membrane bioreactors (AnMBR)
This paper describes a model-based method to optimise filtration in submerged AnMBRs. The method is applied to an advanced knowledge-based control system and considers three statistical methods: (1) sensitivity analysis (Morris screening method) to identify an input subset for the advanced controlle...
| Autores: | , , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2014 |
| País: | España |
| Institución: | Universitat Politècnica de València (UPV) |
| Repositorio: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Idioma: | inglés |
| OAI Identifier: | oai:riunet.upv.es:10251/52668 |
| Acceso en línea: | https://riunet.upv.es/handle/10251/52668 |
| Access Level: | acceso abierto |
| Palabra clave: | Model filtration Model-based automatic tuning Control system Submerged anaerobic membrane bioreactors INGENIERIA HIDRAULICA TECNOLOGIA DEL MEDIO AMBIENTE |
| Sumario: | This paper describes a model-based method to optimise filtration in submerged AnMBRs. The method is applied to an advanced knowledge-based control system and considers three statistical methods: (1) sensitivity analysis (Morris screening method) to identify an input subset for the advanced controller; (2) Monte Carlo method (trajectory-based random sampling) to find suitable initial values for the control inputs; and (3) optimisation algorithm (performing as a supervisory controller) to re-calibrate these control inputs in order to minimise plant operating costs. The model-based supervisory controller proposed allowed filtration to be optimised with low computational demands (about 5min). Energy savings of up to 25% were achieved when using gas sparging to scour membranes. Downtime for physical cleaning was about 2.4% of operating time. The operating cost of the AnMBR system after implementing the proposed supervisory controller was about 0.045/m3, 53.3% of which were energy costs. |
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