Minimization of sewage network overflow
We are interested in the optimal control of sewage networks. It is of high public interest to minimize the overflow of sewage onto the streets and to the natural environment that may occur during periods of high rain. The assumption of linear flow in a discrete time setting has proven to be adequate...
| Autores: | , , , , |
|---|---|
| Tipo de recurso: | artículo |
| Fecha de publicación: | 2014 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/26471 |
| Acceso en línea: | https://hdl.handle.net/2117/26471 https://dx.doi.org/10.1007/s11269-013-0468-z |
| Access Level: | acceso abierto |
| Palabra clave: | control theory dynamic programming nonlinear programming optimal control optimisation Author keywords: sewer network optimal contol MLD nonlinear programming problems NOTA: Potser s'hauria de corregir el nom de Maria Gabriela Cembrano Gennari. Jo he preferit no duplicar l'autora. Classificació INSPEC::Optimisation Àrees temàtiques de la UPC::Informàtica::Automàtica i control |
| Sumario: | We are interested in the optimal control of sewage networks. It is of high public interest to minimize the overflow of sewage onto the streets and to the natural environment that may occur during periods of high rain. The assumption of linear flow in a discrete time setting has proven to be adequate for the practical control of larger systems. However, the possibility of overflow introduces a nonlinear and nondiferentiable element to the formulation, by means of a maximum of linear terms. This particular challenge can be addressed by smoothing methods that result in a nonlinear program (NLP) or by logical constraints that result in a mixed integer linear program (MILP). We discuss both approaches and present a novel tailored branch-and-bound algorithm that outperforms competing methods from the literature for a set of realistic rain scenarios. |
|---|