Hybrid modeling and receding horizon control of sewer networks

In this work, a control-oriented sewer network model is presented based on a hybrid linear modeling framework. The model equations are described independently for each network element, thus allowing the model to be applied to a broad class of networks. A parameter calibration procedure using data ob...

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Detalhes bibliográficos
Autores: Joseph Duran, Bernat, Ocampo-Martínez, Carlos|||0000-0001-9251-6044, Cembrano Gennari, Gabriela|||0000-0003-1436-6022
Formato: artículo
Fecha de publicación:2014
País:España
Recursos:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/28327
Acesso em linha:https://hdl.handle.net/2117/28327
https://dx.doi.org/10.1002/2013WR015119
Access Level:acceso abierto
Palavra-chave:REAL-TIME CONTROL
URBAN DRAINAGE SYSTEMS
WASTE-WATER SYSTEMS
PREDICTIVE CONTROL
SURROGATE MODELS
SIMULATION
OVERFLOW
Classificació INSPEC::Control theory
Àrees temàtiques de la UPC::Informàtica::Automàtica i control
Descrição
Resumo:In this work, a control-oriented sewer network model is presented based on a hybrid linear modeling framework. The model equations are described independently for each network element, thus allowing the model to be applied to a broad class of networks. A parameter calibration procedure using data obtained from simulation software that solves the physically based model equations is described and validation results are given for a case study. Using the control model equations, an optimal control problem to minimize flooding and pollution is formulated to be solved by means of mixed-integer linear or quadratic programming. A receding horizon control strategy based on this optimal control problem is applied to the case study using the simulation software as a virtual reality. Results of this closed-loop simulation tests show the effectiveness of the proposed approach in fulfilling the control objectives while complying with physical and operational constraints.